estimate_fedfunds_rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate by modelization the evolution of FED FUNDS Rate in USA\n",
    "\n",
    "Using : \n",
    "- SPX\n",
    "- Inflation \n",
    "- Unemployement \n",
    "- and past history of FEDFUNDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import re\n",
    "import time\n",
    "import math\n",
    "from datetime import timedelta\n",
    "from datetime import timezone\n",
    "from datetime import datetime\n",
    "import json\n",
    "\n",
    "# Third Party\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "\n",
    "# FINANCE\n",
    "import yfinance as yf\n",
    "import ccxt\n",
    "#from openbb_terminal.sdk import openbb\n",
    "\n",
    "# CHARTS\n",
    "import ta\n",
    "from ta.trend import IchimokuIndicator\n",
    "from ta.momentum import RSIIndicator\n",
    "from ta.volatility import AverageTrueRange\n",
    "from ta.volume import VolumeWeightedAveragePrice\n",
    "# PLOT\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "sns.set_theme(context='paper', style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "load_dotenv(find_dotenv())\n",
    "API_GLASSNODE_KEY = os.getenv(\"API_GLASSNODE_KEY\")\n",
    "#openbb.keys.glassnode(key=os.getenv(\"API_GLASSNODE_KEY\"))\n",
    "#openbb.keys.messari(key=os.getenv(\"API_MESSARI_KEY\"))\n",
    "\n",
    "# unemployment\n",
    "path_unemployment = \"data/UNRATE.csv\"\n",
    "# federal funds rate\n",
    "path_rate = \"data/FEDFUNDS.csv\"\n",
    "# inflation\n",
    "path_inflation = \"data/CPIAUCSL_08_2024.csv\"\n",
    "\n",
    "ratio_test = 0.017\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data\n",
    "\n",
    "- unemployment data :  https://www.bls.gov/charts/employment-situation/civilian-unemployment-rate.htm# \n",
    "    - less data :  https://fred.stlouisfed.org/series/UNRATE\n",
    "- Interest rate :  https://fred.stlouisfed.org/series/FEDFUNDS\n",
    "- INFLATION : https://www.usinflationcalculator.com/inflation/current-inflation-rates/\n",
    "   - calculated from CPI : https://fred.stlouisfed.org/series/CPIAUCSL  \n",
    "   Inflation = 100 * [ CPI(month, year) - CPI(month, year-1) ] / CPI(month, year-1)\n",
    "- SPX SP500 : Yahoo Finance Ticker ^GSPC but history limited to 1985\n",
    "  - So Use date copy paste table from  https://www.multpl.com/s-p-500-historical-prices/table/by-month if no update needed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load local data\n",
    "# load unemployment\n",
    "df_unemployment = pd.read_csv(path_unemployment)\n",
    "df_unemployment[\"DATE\"] = pd.to_datetime(df_unemployment[\"DATE\"])\n",
    "df_unemployment.rename(columns={\"UNRATE\": \"Total\"}, inplace=True)\n",
    "# load federal funds rate\n",
    "df_rate = pd.read_csv(path_rate)\n",
    "df_rate[\"DATE\"] = pd.to_datetime(df_rate[\"DATE\"])\n",
    "# load inflation\n",
    "df_inflation = pd.read_csv(path_inflation)\n",
    "df_inflation[\"DATE\"] = pd.to_datetime(df_inflation[\"DATE\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-02-01</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-03-01</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-04-01</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE\n",
       "0   1948-01-01     3.4\n",
       "1   1948-02-01     3.8\n",
       "2   1948-03-01     4.0\n",
       "3   1948-04-01     3.9\n",
       "4   1948-05-01     3.5\n",
       "..         ...     ...\n",
       "915 2024-04-01     3.9\n",
       "916 2024-05-01     4.0\n",
       "917 2024-06-01     4.1\n",
       "918 2024-07-01     4.3\n",
       "919 2024-08-01     4.2\n",
       "\n",
       "[920 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unemployment.rename(columns={\"Total\": \"UNRATE\"}, inplace=True)\n",
    "df_unemployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check unemployment nan into df_unemployment \n",
    "# assert that nothing is nan\n",
    "assert df_unemployment.isna().sum().sum() == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>CPIAUCSL</th>\n",
       "      <th>CPI-12</th>\n",
       "      <th>Inflation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>300.356</td>\n",
       "      <td>282.390</td>\n",
       "      <td>6.362123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>301.509</td>\n",
       "      <td>284.535</td>\n",
       "      <td>5.965523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>301.744</td>\n",
       "      <td>287.553</td>\n",
       "      <td>4.935090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>303.032</td>\n",
       "      <td>288.764</td>\n",
       "      <td>4.941059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>303.365</td>\n",
       "      <td>291.359</td>\n",
       "      <td>4.120690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>304.003</td>\n",
       "      <td>294.996</td>\n",
       "      <td>3.053262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>304.628</td>\n",
       "      <td>294.977</td>\n",
       "      <td>3.271781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>306.187</td>\n",
       "      <td>295.209</td>\n",
       "      <td>3.718721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>307.288</td>\n",
       "      <td>296.341</td>\n",
       "      <td>3.694055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>307.531</td>\n",
       "      <td>297.863</td>\n",
       "      <td>3.245787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>308.024</td>\n",
       "      <td>298.648</td>\n",
       "      <td>3.139482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>308.742</td>\n",
       "      <td>298.812</td>\n",
       "      <td>3.323160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>309.685</td>\n",
       "      <td>300.356</td>\n",
       "      <td>3.105981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>311.054</td>\n",
       "      <td>301.509</td>\n",
       "      <td>3.165743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>312.230</td>\n",
       "      <td>301.744</td>\n",
       "      <td>3.475131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>313.207</td>\n",
       "      <td>303.032</td>\n",
       "      <td>3.357731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>313.225</td>\n",
       "      <td>303.365</td>\n",
       "      <td>3.250210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>929</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>313.049</td>\n",
       "      <td>304.003</td>\n",
       "      <td>2.975629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>313.534</td>\n",
       "      <td>304.628</td>\n",
       "      <td>2.923566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>314.121</td>\n",
       "      <td>306.187</td>\n",
       "      <td>2.591227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  CPIAUCSL   CPI-12  Inflation\n",
       "912 2023-01-01   300.356  282.390   6.362123\n",
       "913 2023-02-01   301.509  284.535   5.965523\n",
       "914 2023-03-01   301.744  287.553   4.935090\n",
       "915 2023-04-01   303.032  288.764   4.941059\n",
       "916 2023-05-01   303.365  291.359   4.120690\n",
       "917 2023-06-01   304.003  294.996   3.053262\n",
       "918 2023-07-01   304.628  294.977   3.271781\n",
       "919 2023-08-01   306.187  295.209   3.718721\n",
       "920 2023-09-01   307.288  296.341   3.694055\n",
       "921 2023-10-01   307.531  297.863   3.245787\n",
       "922 2023-11-01   308.024  298.648   3.139482\n",
       "923 2023-12-01   308.742  298.812   3.323160\n",
       "924 2024-01-01   309.685  300.356   3.105981\n",
       "925 2024-02-01   311.054  301.509   3.165743\n",
       "926 2024-03-01   312.230  301.744   3.475131\n",
       "927 2024-04-01   313.207  303.032   3.357731\n",
       "928 2024-05-01   313.225  303.365   3.250210\n",
       "929 2024-06-01   313.049  304.003   2.975629\n",
       "930 2024-07-01   313.534  304.628   2.923566\n",
       "931 2024-08-01   314.121  306.187   2.591227"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculatate inflation\n",
    "df_inflation[\"CPI-12\"]= df_inflation[\"CPIAUCSL\"].shift(12)\n",
    "df_inflation[\"Inflation\"] = 100 * (df_inflation[\"CPIAUCSL\"] - df_inflation[\"CPI-12\"]) / df_inflation[\"CPI-12\"]\n",
    "df_inflation.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.694055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.245787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.139482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.323160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.105981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.165743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.475131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation\n",
       "908 2023-09-01     3.8   3.694055\n",
       "909 2023-10-01     3.8   3.245787\n",
       "910 2023-11-01     3.7   3.139482\n",
       "911 2023-12-01     3.7   3.323160\n",
       "912 2024-01-01     3.7   3.105981\n",
       "913 2024-02-01     3.9   3.165743\n",
       "914 2024-03-01     3.8   3.475131\n",
       "915 2024-04-01     3.9   3.357731\n",
       "916 2024-05-01     4.0   3.250210\n",
       "917 2024-06-01     4.1   2.975629\n",
       "918 2024-07-01     4.3   2.923566\n",
       "919 2024-08-01     4.2   2.591227"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_unemployment.merge(df_inflation.filter([\"DATE\", \"Inflation\"]), on=\"DATE\", how=\"left\")\n",
    "\n",
    "df.tail(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num_month\"] = df[\"DATE\"].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>FEDFUNDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1954-07-01</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1954-08-01</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1954-09-01</td>\n",
       "      <td>1.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1954-10-01</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954-11-01</td>\n",
       "      <td>0.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>837</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>842 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  FEDFUNDS\n",
       "0   1954-07-01      0.80\n",
       "1   1954-08-01      1.22\n",
       "2   1954-09-01      1.07\n",
       "3   1954-10-01      0.85\n",
       "4   1954-11-01      0.83\n",
       "..         ...       ...\n",
       "837 2024-04-01      5.33\n",
       "838 2024-05-01      5.33\n",
       "839 2024-06-01      5.33\n",
       "840 2024-07-01      5.33\n",
       "841 2024-08-01      5.33\n",
       "\n",
       "[842 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3269b0a60>]"
      ]
     },
     "execution_count": 10,
     "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(df_rate[(df_rate[\"DATE\"] >= \"2004-07-01\") & (df_rate[\"DATE\"] <= \"2024-07-01\")][\"FEDFUNDS\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.242086</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-02-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.481961</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-03-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.384966</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS\n",
       "0   1948-01-01     3.4  10.242086          1       NaN\n",
       "1   1948-02-01     3.8   9.481961          2       NaN\n",
       "2   1948-03-01     4.0   6.818182          3       NaN\n",
       "3   1948-04-01     3.9   8.272727          4       NaN\n",
       "4   1948-05-01     3.5   9.384966          5       NaN\n",
       "..         ...     ...        ...        ...       ...\n",
       "915 2024-04-01     3.9   3.357731          4      5.33\n",
       "916 2024-05-01     4.0   3.250210          5      5.33\n",
       "917 2024-06-01     4.1   2.975629          6      5.33\n",
       "918 2024-07-01     4.3   2.923566          7      5.33\n",
       "919 2024-08-01     4.2   2.591227          8      5.33\n",
       "\n",
       "[920 rows x 5 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add funds rate to df\n",
    "df = df.merge(df_rate, on=\"DATE\", how=\"left\")\n",
    "#df[\"FEDFUNDS\"] = df_rate[(df_rate[\"DATE\"] >= \"2004-07-01\") & (df_rate[\"DATE\"] <= \"2024-07-01\")][\"FEDFUNDS\"].values\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>1871-01-01</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>1871-02-01</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>1871-03-01</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>1871-04-01</td>\n",
       "      <td>4.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>1871-05-01</td>\n",
       "      <td>4.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>5235.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>5415.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>5538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>5478.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-09-01</td>\n",
       "      <td>5528.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1845 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE    Close\n",
       "1844 1871-01-01     4.44\n",
       "1843 1871-02-01     4.50\n",
       "1842 1871-03-01     4.61\n",
       "1841 1871-04-01     4.74\n",
       "1840 1871-05-01     4.86\n",
       "...         ...      ...\n",
       "4    2024-05-01  5235.22\n",
       "3    2024-06-01  5415.14\n",
       "2    2024-07-01  5538.00\n",
       "1    2024-08-01  5478.21\n",
       "0    2024-09-01  5528.93\n",
       "\n",
       "[1845 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the indice SPX from yahoo finance\n",
    "date_start = df[\"DATE\"].min().strftime(\"%Y-%m-%d\")\n",
    "date_end = df[\"DATE\"].max().strftime(\"%Y-%m-%d\")\n",
    "\n",
    "updating = False\n",
    "if updating:\n",
    "    # check if data/SPX.csv exists and if last date is the last date of df\n",
    "    if os.path.exists(\"data/SPX.csv\"):\n",
    "        df_spx = pd.read_csv(\"data/SPX.csv\")\n",
    "        last_date = df_spx[\"DATE\"].max()\n",
    "        if last_date >= date_end:\n",
    "            print(\"SPX data is up to date\")\n",
    "            updating = False\n",
    "        else:\n",
    "            print(\"SPX data is not up to date\")\n",
    "            updating = True\n",
    "    else:\n",
    "        print(\"SPX data is not up to date\")\n",
    "        updating = True\n",
    "\n",
    "    if updating:\n",
    "        print(\"Updating...\")\n",
    "        df_spx = yf.Ticker(\"^GSPC\").history(\n",
    "                start=date_start, interval=\"1mo\")\n",
    "        df_spx = df_spx[[\"Close\"]]\n",
    "        df_spx[\"DATE\"] = df_spx.index\n",
    "        df_spx[\"DATE\"] = df_spx[\"DATE\"].dt.strftime(\"%Y-%m-%d\")\n",
    "        df_spx[\"DATE\"] = pd.to_datetime(df_spx[\"DATE\"])\n",
    "        df_spx.to_csv(\"data/SPX.csv\", index=False)\n",
    "        print(\"SPX data is updated\")\n",
    "    else:\n",
    "        df_spx[\"DATE\"] = pd.to_datetime(df_spx[\"DATE\"])\n",
    "else:\n",
    "    # read historical data\n",
    "    df_spx = pd.read_csv(\"data/SP500_history.csv\")\n",
    "    # Rename the columns\n",
    "    df_spx.rename(columns={\"Date\": \"DATE\", \"Value\": \"Close\"}, inplace=True)\n",
    "\n",
    "    # Convert the date format and clean the 'Close' values by removing commas\n",
    "    df_spx[\"DATE\"] = pd.to_datetime(df_spx[\"DATE\"], format=\"%b %d, %Y\").dt.strftime(\"%Y-%m-%d\")\n",
    "    df_spx[\"DATE\"] = pd.to_datetime(df_spx[\"DATE\"])\n",
    "    # sort by \"DATE\"\n",
    "    df_spx.sort_values(by=\"DATE\", inplace=True)\n",
    "    df_spx[\"Close\"] = df_spx[\"Close\"].str.replace(\",\", \"\").astype(float)\n",
    "\n",
    "# Display \n",
    "df_spx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Close</th>\n",
       "      <th>Close-12</th>\n",
       "      <th>SPX_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1844</th>\n",
       "      <td>1871-01-01</td>\n",
       "      <td>4.44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1843</th>\n",
       "      <td>1871-02-01</td>\n",
       "      <td>4.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>1871-03-01</td>\n",
       "      <td>4.61</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1841</th>\n",
       "      <td>1871-04-01</td>\n",
       "      <td>4.74</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1840</th>\n",
       "      <td>1871-05-01</td>\n",
       "      <td>4.86</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>5235.22</td>\n",
       "      <td>4146.17</td>\n",
       "      <td>0.262664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>5415.14</td>\n",
       "      <td>4345.37</td>\n",
       "      <td>0.246186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>5538.00</td>\n",
       "      <td>4508.08</td>\n",
       "      <td>0.228461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>5478.21</td>\n",
       "      <td>4426.24</td>\n",
       "      <td>0.237667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-09-01</td>\n",
       "      <td>5528.93</td>\n",
       "      <td>4409.10</td>\n",
       "      <td>0.253982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1845 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE    Close  Close-12  SPX_diff\n",
       "1844 1871-01-01     4.44       NaN       NaN\n",
       "1843 1871-02-01     4.50       NaN       NaN\n",
       "1842 1871-03-01     4.61       NaN       NaN\n",
       "1841 1871-04-01     4.74       NaN       NaN\n",
       "1840 1871-05-01     4.86       NaN       NaN\n",
       "...         ...      ...       ...       ...\n",
       "4    2024-05-01  5235.22   4146.17  0.262664\n",
       "3    2024-06-01  5415.14   4345.37  0.246186\n",
       "2    2024-07-01  5538.00   4508.08  0.228461\n",
       "1    2024-08-01  5478.21   4426.24  0.237667\n",
       "0    2024-09-01  5528.93   4409.10  0.253982\n",
       "\n",
       "[1845 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp = df_spx.copy()\n",
    "df_tmp[\"Close-12\"] = df_tmp[\"Close\"].shift(12)\n",
    "df_tmp[\"SPX_diff\"] = (df_tmp[\"Close\"] - df_tmp[\"Close-12\"]) / df_tmp[\"Close-12\"]\n",
    "df_spx = df_tmp\n",
    "df_spx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Close</th>\n",
       "      <th>Close-12</th>\n",
       "      <th>SPX_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>1872-01-01</td>\n",
       "      <td>4.86</td>\n",
       "      <td>4.44</td>\n",
       "      <td>0.094595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>1872-02-01</td>\n",
       "      <td>4.88</td>\n",
       "      <td>4.50</td>\n",
       "      <td>0.084444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>1872-03-01</td>\n",
       "      <td>5.04</td>\n",
       "      <td>4.61</td>\n",
       "      <td>0.093275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>1872-04-01</td>\n",
       "      <td>5.18</td>\n",
       "      <td>4.74</td>\n",
       "      <td>0.092827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>1872-05-01</td>\n",
       "      <td>5.18</td>\n",
       "      <td>4.86</td>\n",
       "      <td>0.065844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>5235.22</td>\n",
       "      <td>4146.17</td>\n",
       "      <td>0.262664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>5415.14</td>\n",
       "      <td>4345.37</td>\n",
       "      <td>0.246186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>5538.00</td>\n",
       "      <td>4508.08</td>\n",
       "      <td>0.228461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>5478.21</td>\n",
       "      <td>4426.24</td>\n",
       "      <td>0.237667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-09-01</td>\n",
       "      <td>5528.93</td>\n",
       "      <td>4409.10</td>\n",
       "      <td>0.253982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1833 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE    Close  Close-12  SPX_diff\n",
       "1832 1872-01-01     4.86      4.44  0.094595\n",
       "1831 1872-02-01     4.88      4.50  0.084444\n",
       "1830 1872-03-01     5.04      4.61  0.093275\n",
       "1829 1872-04-01     5.18      4.74  0.092827\n",
       "1828 1872-05-01     5.18      4.86  0.065844\n",
       "...         ...      ...       ...       ...\n",
       "4    2024-05-01  5235.22   4146.17  0.262664\n",
       "3    2024-06-01  5415.14   4345.37  0.246186\n",
       "2    2024-07-01  5538.00   4508.08  0.228461\n",
       "1    2024-08-01  5478.21   4426.24  0.237667\n",
       "0    2024-09-01  5528.93   4409.10  0.253982\n",
       "\n",
       "[1833 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_spx.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x326a6bee0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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12VOWe7N3dQAAYEgFu25b7t6b0t+Tl+2EsAIAQBoJxMJK92Ege8cBe1cHAACGVDAUWb22Z8+KveOAvasDAABDKjoM1P1uoAwm2AIAALsI9DNnxWXzW5eP+6nLAAAgefQ3wVaKrGI7aqT9ltqXCCsAAKSVw8NAPXtTpp82OhHlDIi9+30AAMCQ6m+Crd0RVgAASCPBcP/DQHZGWAEAII30dzeQ3RFWAABII4GuYSC7r63SXfJUCgAAjtuR7gayM8IKAABphGEgAABga/09G8jukqdSAABw3Lh1GQAA2BrDQAAAwNaYYAsAAGytvwcZ2h1hBQCANBIKm3I6JLfLkehSBoywAgBAGgmEDHk9LjkchBUAAGBDwa6wkkwIKwAApJFgyJA3iZbalwgrAACklWDIpGcFAADYV0cgLJ/Xnegy4kJYAQAgTRxo8Kuuwa/2zlCiS4kLYQUAgDTx5Is7JEl1Df4EVxIfwgoAAGmiMM+X6BIGhbACAECayB8RCSu3XX1OgiuJD2EFAIA0EQ5Hnrg8MicjwZXEh7ACAECaCHWFFQ/rrAAAADsKG5Gw4nYl19d/ct1oDQAA4maYlm7+yWbtrGmWRM8KAACwmfaOUCyoSJKbsAIAAOwkEDR6vPa4WG4fAADYSCAU7vHa7XYkqJLBIawAAJDi+vSsuOlZAQAANtLZK6y4XfSsAAAAGwmE6FkBAAA21n0YyOmQXE56VgAAgI0Egocn2LqTrFdFIqwAAJDyug8DJduCcBJhBQCAlNd9GMiTZEvtS4QVAABSXve7gZJt9VqJsAIAQMrrMQxEzwoAALCbQLr1rKxbt04VFRWqqKjQ1Vdfrd27d0uSVq1apXnz5qm8vFz33nuvQqGQJMk0TS1btkyXXnqpysrKtGLFClmWJUnq7OzU7bffHjtuzZo1Q9g0AAAgSZ3d7gZK+Z6VXbt26Yc//KF++ctfauPGjSovL9f3vvc9bd68WU899ZTWrl2r559/Xg0NDVq9erUkac2aNdq+fbs2btyoTZs26ZVXXtGzzz4rSXr00UcVDof13HPP6Xe/+51Wr16trVu3DnkjAQBIZ2l1N9App5yizZs3q7CwUOFwWPv27VN+fr6qq6s1f/585ebmyuVyqbKyUuvXr5ckVVdXa+HChfJ6vfL5fFq0aFGPfVdeeaUcDofy8/M1f/782D4AADA0kn0YyB3vAR6PR6+//rq+9a1vqaOjQ6tWrdKKFSs0Y8aM2HuKiopUW1srSaqtrVVRUdGA923fvn1QDTEMQ4ZhHPuNcZ6z+5/pgDanB9qcHmhzejhWm9/ZVa+/bKuNvXa7HLb4fOKpIe6wIknnnHOOXnnlFb344ou6/vrrNW3aNDkcPZfujb62LKvPPqfTecx98dqxY8egjhuIbdu2Ddu57Yo2pwfanB5oc3o4UpvveaKmx2t/e1vSTbmIK6zU1NTok08+0axZsyRJZWVl+t73vifTNFVXVxd7X11dnUpKSiRJpaWlffYVFxf32Ddu3Lg+++I1efJkZWVlDerYIzEMQ9u2bdPUqVPlciXf8sSDQZtpc6qizbQ5VR2zzb3CSmHBSJ199tknprij8Pv9A+5oiCusNDc369vf/raeeeYZFRUVafPmzXI6nfrnf/5nPfTQQ1q8eLFycnK0Zs0azZ07V5JUXl6up59+WvPmzZNpmlq3bp0WLFgQ2/fkk09qxowZamlp0aZNm3TPPffE19ouLpdr2P5iDue57Yo2pwfanB5oc3oYaJs9bnt8NvHUEFdYOfPMM3Xbbbfpa1/7mpxOp0aMGKH/+q//0umnn65du3apsrJS4XBYM2fO1I033ihJuuKKK1RTU6MFCxYoFAqprKxMCxculCQtWbJE999/vyoqKhQKhVRZWRnrtQEAAEMvGe8GinvOyuWXX67LL7+8z/aqqipVVVX12e5yubR06VItXbq0z77MzEw98MAD8ZYAAACOwTAtOR19t7v622hzg5pgCwAA7O2ypRvk7CeYJOOty8lXMQAAOKpQOHJbsGlasW0FIzIkJecwUPJVDAAAjqqlPdhnW/4In6Q0WG4fAADYX3Nb37ASDpuSJDdhBQAAJFpzW6DH65v/YabCRldYYRgIAAAkWnOvYaCiwmyFjMj8FXpWAABAQu2vb9dHtS09tmV4XbFhoGScYMutywAApJCvPfhin225Wd7Dw0BJuM5K8sUrAAAQl7wcL3NWAACAfXncyT0MlHwVAwCAfhldvSf9ifWsMMEWAAAkSih85LASXcyWsAIAABImEDKO+R7mrAAAgIQ5Ws9KFD0rAAAgYYLhY/es8GwgAACQMJ2BY4cVX4brBFQytAgrAACkiFZ/ZJn9klHZuvZLZ/bY9+PvXKxFnztNp44bmYDKjg8r2AIAkCLa/CFJ0lfnnS63u+dKtZPGjdSkJAwqEj0rAACkjGjPSk6WR64knJtyJKnTEgAA0lw0rORmedUZCCe4mqFDWAEAIEVEh4Fysjzyd0bCysicjESWNCSYswIAQIro3rNy8cxx+uu7+3X1F85IcFXHj7ACAECKaOsIyel0KMvnlsPh0PeuvSDRJQ0JhoEAAEgRrf6gcjI9cjgcx35zEiGsAACQItr8IeVmeRJdxpAjrAAAkCJa2gPKyfImuowhR1gBACAFhA1Tja0BFeb5El3KkCOsAACQAt7b0yjLkkaNzEx0KUOOsAIAQAp476NGSdKMyWMSXMnQI6wAAJACDjT6JSkpH1R4LIQVAABSQF2DXxlel/JymGALAABs6GBjh8bkZ6XcGisSYQUAgKTX1mFo36F2ZWWk5sL0hBUAAJLc/37QJklqbg8kuJLhQVgBACDJOZ2RoZ+vL5iW4EqGB2EFAIAk195pSpImloxIcCXDg7ACAECSa+80JEkjsjMSXMnwIKwAAJDk2jtN5WR65HGn5td6arYKAIA00tZpaGRuavaqSIQVAACSmmlaamo3NCY/9Z4JFEVYAQAgibW0BxU2LI0pyEp0KcOGsAIAQBLzd4YkSdk+T4IrGT6EFQAAklhHIHInUGaKrl4rEVYAAEhqHYGwJMmX4UpwJcOHsAIAQJIyTEsPr3lTEj0rAADAhmrqWlXf3CmJOSsAAMCG9h5si/180ticBFYyvAgrAAAkkc5gWG0dkTuAon9OLvWpdDRhBQAA2MDdK/+iyjuflWGYau8KK585IzfBVQ2v1J2NAwBAimnrCOnd3Q2SpDXVO2RaliTJ503tvgfCCgAASWLTll2xn9dUvx/7OSsjtcNKarcOAIAU8vjz7/W7PduXumusSHGGld/+9reqqKjQl770JV155ZV6++23JUmrVq3SvHnzVF5ernvvvVehUGQMzTRNLVu2TJdeeqnKysq0YsUKWV1dVp2dnbr99ttjx61Zs2aImwYAQOqoPdTe7/a5548/wZWceAMOK3/729+0cuVKPfbYY9qwYYNuuOEG3XTTTdq8ebOeeuoprV27Vs8//7waGhq0evVqSdKaNWu0fft2bdy4UZs2bdIrr7yiZ599VpL06KOPKhwO67nnntPvfvc7rV69Wlu3bh2WRgIAkOwaWjr73X5y8YgTXMmJN+A5K3l5ebrvvvtUUFAgSZo2bZrq6+tVXV2t+fPnKzc3MhO5srJS9957r66//npVV1dr4cKF8nq9kqRFixZp/fr1mj9/vqqrq/XAAw/I4XAoPz9f8+fP1/r163X22WcPqiGGYcgwjEEde7Rzdv8zHdDm9ECb0wNtTi0dgcioxTmfGqPX3zsQ2x6dXJtsbY6n3gGHlUmTJmnSpEmSIsM7Dz74oD772c+qtrZWM2bMiL2vqKhItbW1kqTa2loVFRUNeN/27dsHXHhvO3bsGPSxx7Jt27ZhO7dd0eb0QJvTA21ODSs27pckjc8P6/Vu2/fvq1HB+KyUbHNU3HcDtbW16dZbb1VDQ4NWrlyp73znO3I4HD3eE31tWVaffU6n85j7BmPy5MnKysoa9PH9MQxD27Zt09SpU+VypfbkpSjaTJtTFW2mzcksEDJU/0SNJOmUiSdLf2mI7Tt10kQpVJd0bfb7/QPuaIgrrOzevVs33nijpk2bpocfflgZGRkqKSlRXV1d7D11dXUqKSmRJJWWlvbZV1xc3GPfuHHj+uwbDJfLNWwXaTjPbVe0OT3Q5vRAm5PfuzsOxX4Om1aPfb4Mj8xQ8rU5nloH3JWxb98+LV68WF/5yle0fPlyZWRkSJLKy8v1+9//Xi0tLTJNU2vWrNHcuXNj+55++mkFg0F1dnZq3bp1PfY9+eSTMk1TTU1N2rRpU2wfAAA47GCjP/ZzMNRzrofXkzwBZbAG3LOyatUqtbS0aMOGDdqwYUNs+8qVK7Vw4UJVVlYqHA5r5syZuvHGGyVJV1xxhWpqarRgwQKFQiGVlZVp4cKFkqQlS5bo/vvvV0VFhUKhkCorKzVr1qwhbh4AAMmvqS0Y+/nsyWP079fP0vdW/kWS5PU41f99QqljwGHlrrvu0l133dXvvqqqKlVVVfXZ7nK5tHTpUi1durTPvszMTD3wwANxlAoAQHpq80fCyuq75mrUyEwVj8qO7XM5HUc6LGWwgi0AADbX2hVWcrI8sW3/MHeKHA5p9MjMRJV1wvBsIAAAbK72ULuyfG75vIe/tq+aO0VXzZ0i0zQTWNmJQc8KAAA21tjSqfc+alRJt6EfKbJMSO8lQFIVYQUAABvb1/VMoFNKRya2kAQirAAAYGN/fSeycu1ZkwoTXEniEFYAALCxp//0oSRpTP7QrtKeTAgrAADYlGVZsUXfzphYkOBqEoewAgCATR1s7FAwZOiLcyamzWTa/hBWAACwqeb2gCSpYIQvwZUkFmEFAACbau8ISZJyMj3HeGdqI6wAAGBTbV1hJZuwAgAA7Ki1vWuZ/UxvgitJLMIKAAA2daCxQ5I0Oj/1n/9zNIQVAABsasfHjXI6CCuEFQAAbKixtVNvf3hI44tG9HiAYToirAAAYEPbP6yXJH36U2MSXEniEVYAALCh5b95PdEl2AZhBQAAGysZnZPoEhKOsAIAgA2deUrkKctl545PcCWJR1gBAMCGOoNhjcnPlNOZvs8EiiKsAABgQ81tQeVkpfdicFGEFQAAhsE7u+q1v759UMe2+YM61NShk4tHDHFVySm9b9wGAGAYNLZ26vb/3CJJ2vjDL8d9fHTl2qLC7CGtK1nRswIAwBAKhAxdfc8Lx3WO+uZIWCnM8w1FSUmPsAIASHv+zpDChjkk59rxceNxn6OxNSBJKhhBWJEIKwCANOfvDOnKf3tW/+ept4bkfC1dT0qOCoXjD0FNXWFlZE7GkNSU7AgrAIC0duujL0uSqv/68ZCcr3dY+fCTprjP0dTWFVZyCSsSYQUAkMYaWjr10f7WIT3nb577e4/XjjiXSflof4s2vrxLkpRHz4okwgoAII19/7H/7fHasqzjOl9HIBzrWZk6aZQkKRA04jrHqme2x372uPmalggrAIA0FQwZ+vuehp7bBjG/pLu/vXcg9vNnzi6RJNW3dMYVgt4fggm6qYawAgBISw0tnX22dXSGj+uc3e8EGpEdGcJ5+Ld/0//7308GdPzufc3yH2cNqYiwAgBIO52BcGxirSTNnDJGknSg0X9c563tWrF21Z3lyvC6Ytv/+Maxw0owZOhffvin2Ot//Pzpx1VLKiGsAADSzv9s3Rtby6Ts3PH60kWnSJI+rGk6rvMebPRr1MhMjcnP0ojsw8/18XpcRzkqInoHkCTNmlqsK8omH1ctqYSwAgBIO6Z5eA7J9MmjY4uvRdc3GaxDTZ0aPTJTknTS2NzY9oyjhBXTtFTX4FdL2+FbnrN8PA2nO8IKACDt/Ge3BeB8Xlds8bXf/uH9QZ8zGDLU1BbQqK6wkplxOHB4PUf+ur175V903QPV2vLW3ti2cPj47kpKNYQVAEBa6d6rIkXCSvf1THrvH6hDXc/ziYaV7sJG/+f8eH+Ltn5wUJK07o8fxrZPHj9yUDWkKsIKACCt+DtDPV4Hw6acTofmXzhRUs+5IwP113f36+v/8f8kSWMLsmLbl3710/3+zqjd+1r6bLv2S2fpi3NOibuGVEZYAQCklRZ/ZG7IuWeM1efOOUkzJo+WdHieyJGCxZGYpqX7Vr0We108Kjv280UzxkmS3njvgIx+HpToD/S9Tfm8M8fK6Yxz2dsUR1gBAKSVxpZIz8m0U0fpO5Uz5XFHJr9G/7xx2Utxna/3U5ZLuoWV7vbU9u1FaWjuu9ZLXjZL7PdGWAEApJVn/7xbkjQ6P6vH9oxuk2DjWXF2977mHq9H9zNnRZLC/fSsRBemi/bquF0O7gTqB2EFAJBWorcnTxmf32N7tGdFkprbej45+UhCYVPP/nmPJOkfLv2U7vnaBXK5en61XlU+RZLU3tF3yKe+uUMet1P5uZFbpwtG+OSI98mHaYD4BgBIK53BsArzfH3u2um+cNv+hnaNzD32cMxvnvt7bHhnwcWT5Mvo+7U6oTiy3kp7R9+5MA0tnSoY4ZPZ1ZNz2kn5fd4DwgoAIM20tod6rC4b5XYd7tHYX+/XpyYUHPEcTa0B/eM9z/fY1l9QkQ4vCBcIHX768ju76vVhTZPqmzs1bkyOdnzcJEkaX5Tb3ynSHmEFAJBWWtoDGlMwss92d7fhm7qG9qOeY+OWXQP+fS5n5LxGt/Vbbv/PLbGfC/MyFTYiT38uHZ0z4POmE+asAADSRmNLp9o7w8rN6tuzMmPKGM2aWixJqu/nLp3uuq9IOyLbq8fuvvSI73V13YZsmn0n2EpSYZ5Pp58c6cUZN4aw0h/CCgAgLRxo8Ovqe1+QpH6HgUZke/Xtq2ZIkp778x5V3PxMnzt99te3a9feZrW2H55/cvd1F8SeLdQfZ9fwknGElXEL83y652sX6O7rLtCkcSPjalO6YBgIAJAW9uw/vM5Jfz0rUt+nIz/75z1asmh67PXXHnyxx/513//iMZ+oHO1ZOVJYKRjhU5bPo3NOH3vU86QzelYAAGnhjb/XxX7O8PYfMFy9Vo7t/pygYLcJslLkmULHCirdz2l0PR/o96/s7rG/MK//dVlwGGEFAJAWouuhSIfv0Omt9xon3Rdya2nvufZKZ7BneDmS6LorRteclcd+/26P/YV5Rx5CQgRhBQCQ8nqvHjuQHhFJ6p5deoeVuedPGNA5eg8DdfR6HtDR5rsggjkrAICUF13WPmqgYaX7SraNrZFzfH3BVAVDhubNOnlA5+g+DNQZCMvpkKKjS+eeMXbAtaSzQYWVH//4xzp48KAeeOABSdIzzzyjlStXKhwO6/TTT9f999+vnJzI7VerVq3S2rVrZRiG5syZo+9+97vyeDwyTVMPPfSQXnrpJRmGocsuu0xLlixhmWEAwJBr7dUrcqRhoO5G5fnU3BaIvd5f75cUWQtlxpQxA/7d3YeBag60ybSkxfM+pUWfO0184w1MXMNANTU1WrJkiVavXh3b9sEHH2jZsmX67//+b73wwgsqKirS8uXLJUmbN2/WU089pbVr1+r5559XQ0ND7Ng1a9Zo+/bt2rhxozZt2qRXXnlFzz777BA2DQCAiDZ/z6XujzTBtruRuRk9wsr2nYfkcEgnF4+I63cfXmfFUm19ZLG54sJsuV3OPs8RQv/i+pSefPJJzZ49W1VVVbFtL774oi6++GKNHRu55Wrx4sXauHGjTNNUdXW15s+fr9zcXLlcLlVWVmr9+vWSpOrqai1cuFBer1c+n0+LFi2K7QMAYCi1dvTsWTnaPJHH//3zeuK+zysvJ0NNbcHYE5h37m3WSWNzlR/nHJPuK9jWHuoKK6Oy4zpHuotrGOjmm2+WJD366KOxbbW1tSouLo69Lioqkt/vV1NTk2prazVjxowe+2pra2PHFRUV9btvMAzDkGEMbGZ2POfs/mc6oM3pgTanB9p8WEtXD8nln50kl8up8WOzj/i5ZPsivS45WR4FQ4Y6AiFZViRozJ5WPIjPMzK5Nxw2tL+rZ2X0SN+QXZdkvc7x1DskE2z7m2cS3dZ7X/S1ZVl99jmdg+8O27Fjx6CPPZZt27YN27ntijanB9qcHmiztGNnZEG4Ub5WlRR49dZbbx3zHG0tTZKkN/62Vc+9EVnJ1m22aevWrXHV0t4Z+VKuO3BQ7YFIcPnw/XfkdA7tjJVUvs7HHVZKSkpUU1MTe11XV6fs7Gzl5eWppKREdXV1PfaVlJRIkkpLS/vs695DE6/JkycrKytr0Mf3xzAMbdu2TVOnTpXLlR6ztWkzbU5VtDm92/z8269LatGnzz5LYwsG9l3x+sfvSB/u1uQpZ+iZ1/8mya9rL79AeTkZcdXU5g9KT9cqv6BQ4cYO+bxBzZw549gHDlCyXme/3z/gjobjDiuXXHKJrrnmGtXV1Wns2LF6/PHHVVZWJqfTqfLyci1fvlyLFy9WTk6O1qxZo7lz50qSysvL9fTTT2vevHkyTVPr1q3TggULBl2Hy+Uatos0nOe2K9qcHmhzekj3Ntc3d+jV7fslSQV5mQP+LHzeyFekYUbuJjq5eIQK8uL/j2KfL7K0fyhsyh8IK8vnGZbrkWzXOZ5ajzusnHbaabr11lt13XXXKRQKaeLEifr+978vSbrooou0c+dOVVZWKhwOa+bMmbrxxhslSVdccYVqamq0YMEChUIhlZWVaeHChcdbDgAAPfx8/eHhkWgAGYjoHUOBkKHmtoBOPWnkoH6/1+2U0xFZ8ba9I6QsH0ucxWtQn9g3v/nNHq8rKipUUVHR73urqqp63D0U5XK5tHTpUi1dunQwJQAAcEydwbBeeyfSq/KDf/lMXMd63ZGw8u7uBrV3huMe/olyOBzyZbhVV+/X3oNtuuCswU95SFfEOwBAympo6ZRpWpp7/gRNmVAQ17H7DrVJkn729NuSNOiwIkV6dHbti0zSPWtS4aDPk65YjQYAkLJaupbLLx2dE/ex5eeN7/E6L8c76DoyMw7PzygqYI2VeBFWAAApK7oC7WCCxuTx+T1ejzyOnpXiUYfDEnNW4kdYAQCkrNauZfZHZMcfVnqvBXY8w0Cf+/RJsZ+zfJ5BnyddEVYAACmr1R8ZBsrNGtwQzm1XnxP7uTAvvmX2uyvodiw9K/HjEwMApKxoWMnJGlxvxpzppfJfEdbbHxzSpNKRg64jt9vvz8zgqzdefGIAgJRkGKY2bdklafA9K5I09/wJmnv+hOOqJbfbMBTDQPFjGAgAkJJe3b5fHYHIc3lyMhMbELqHJY+br9548YkBAFJSdJ0USXK5Evt1507w7092fHoAgJRUe6g90SVgiDBnBQCQkj6saZIk/fJ7cxNbSJfvVM6Q5Djm+9AXYQUAkHLa/EHt3teiz5xdqsK8zESXI0n63Dnjj/0m9IthIABAyjnQ1CFJOmlsboIrwVAgrAAAUs6+A5H5KmMLshJcCYYCw0AAgJQSClv6we/+Jkk67aSRiS0GQ4KeFQBASvnFHw7Efh7M05ZhP4QVAEDK2PFxo+qaQrHXTid336QChoEAACnjvY8aJUmjRvp0Z9X5Ca4GQ4WeFQBAyvjTGzWSpDurztOkcSMTWwyGDGEFAJASwoap/Q1+ZXgcOrl4RKLLwRAirAAAUsKf3vhE/s6wZp/O2iqphrACAEgJz7/6kSRp5qTsBFeCoUZYAQAkvabWgN7/qFFnnlKg3ExXosvBECOsAACS3ju76iVJn50xLsGVYDgQVgAASW/7zkOSpDNPKUhwJRgOhBUAQNJ7c8dBjcrzqXgU81VSEYvCAQCSzu59zXr6jx9q/oUT9Uldq/YebNPc8yfI4WDF2lREWAEAJI1DTR3667v79fP122Salv70t5rYvk9/akwCK8NwIqwAAJJCfXOHqu77Q7/7Thqbq0+fPvYEV4QThbACALC1Aw1++QNh3fro/8S23XXt+SoZla2/bKvV3PMnaES2Vw6HQ4ZhJLBSDBfCCgDAtj6pa9VNy1/qse2Zh74Ue5ryVy5htdp0QFgBANjSgQa/HvzlX2OvR+Zm6LZ/PCcWVJA+CCsAANt5dXutHlh9OKis+rdyjSnISmBFSCTCCgAg4QIhQx2dYT354vuqPdSuxtaAJOmyiyfpsosnqTAvM8EVIpEIKwCAhPqwpknfeXhzn+3nnD5W137prARUBLthBVsAwAnR1BrQ1x6s1v+8WdNj++a/1fT7/hHZ3hNRFpIAYQUAcEK88tZe7a/366HfvBHbFgobeun1T1Q8Klu3XX2OLrt4Umzfp07mOT+IYBgIADDsDjT49bP12yT17DH58W/fVEt7UIs+d5rmTC/VnOml2lPboiyfW5+fdXKCqoXdEFYAAMNu0yu7Yz93BML6qLZFP3ziDe3e1yJJKjtvfGz/fV+ffcLrg70xDAQAGHZbdxyQx+1U2bnjFQqb+sYP/hgLKvd/fbZys5ifgiOjZwUAMGx++8J7euIP70uSLp4xTqPze96CfP8NszX9tNGJKA1JhLACABgWu/Y2x4KKJC383KkyTEvP/nm3vjrvdJWfP0EuVqPFABBWAABDzrIs/eb5v0uSbr/6XE0sHaGSUTmSpF/fM08OByEFA0dYAQAMqYONHfqPx/6qDz5p0uTxI3Xh9JIe+wkqiBdhBQAwaG3+oNo7wyrM88npcKj6rx9pxdq3YvtvvHx6AqtDqiCsAAAGZd1LH+iXv39XkuR0SKZ1eN+lF0zQuaeP1aknjUxMcUgphBUAwBHVN3fI6XAoO9Mjr8cV2/77Lbv6DSrZPrduXvxpnXtGUSLKRYoirABAkmvrCCnT65LLNbRLZ334SZNu/z9bFAgakqQsn1u3/eO5MkxTP1u/TaPzM/XQNz+jwrxM7a9v11sfHNKc6SXKzvQMaR0AYQUAbCQUNvTK27VySDq5eIQmFI/osf9QU4d8GW61tAf0wl8+0ts7D+nDT5o0piBL550+VuOLR/S7TH0gZGj3vmbtPdCmLJ9HUycVKqdrIbZQ2NT7HzVo0riR2vFRo/7w14+0ZeveWG9JZoZbHYGw/J1h3f1ff4md896vzVJhXmTdlKLCbBUVZg/LZwIQVgBgmDW2durj/a2qOdCmxtZO7dnXok+dXKAvfeYUbXlrr17/+wG9s+uQGloCfY49pTRPF5xZpMs+e6o2vLxTv3nuvX5/R2t7ILak/eqN2/Vv/3y+zjwlX6++16rfv/m/+t+/1/U5pmBEhsaNydX2nYd6zDfp7rarz9Gc6aVqaOnUuj9+oD+8+pE6g4YuOKtIJ43NHfyHAsSBsAIAg2SalvYebFPNgVadUjpSYwuyZJqW3vuoQTUH2vTOrnq99s5+tXeE+hz72jv79VjXnI/uRuX5NO200QqFTW15a6927W3usbhaYZ5PY/KztKe2WTctnK4ZU8ZoRLZXnUFDL7y6R6s2vKOOgKE7f/5n5WV71dwelNQsSfr87JM1emSmmloDavUHtWtvs97+8JAcDsnhkEaNzNSZpxRqyvh8FRVmy+txatqpkdVlC0b49LUvT9XXvjxVpmmJu49xIhFWAOAIDMPU1g8O6sOaJjU0dyozw628nAy5XA7t2deiP77xicLG4S6JKePzVXOgVe2d4di20tE5mnbqKE0sHiHTkk47aaR8GS5V//VjbX3/oOZML9GC/+9UjcrL1L5DbSouzI7NPbn1H89RY2un/v0Xr+rDmmZ95uxSfX3BVOXlZMiyrB7rlWRmuHXZxafqsotP1f/dvFOrNmxXc3tQY0Z69LlzJ2rSuJG64KziPm38qLZFudleFYzwDfhzcbLqLE6whIeVl19+WT/4wQ8UCARUXFysZcuWacyYMYkuC0CKamkPquZAu17/e53e3nlIwZChlragCvJ88ridam0P6rwzi7S/vl3v7GpQqz94xHNNLBmhU8eNVHamRxte3qX3P25UYZ5P82adrMnj83VKad4R53FEeyy6Gzem77BKfq5PP/r2xeoMGsrMOPyv7KMtrPbli06RJH1S16LppSFdeMFkuVyuft/be04MYEcJDSsNDQ265ZZb9Ktf/UpTpkzRr371K91xxx1atWpVIssCbM00LbX6g2psDaiptVO79rboYJNf23fW66SxuTprUqGKCrN1xsQCeVzO47pDxDAttfmDysnynpBnuITChixLPW6RlRQbduj+BR0MGWrrCKnVH1SbP6Q2f1Ct/pDaO0MKhgyFw6aCYVOhsKlQ2JC/M6TtH9bpUEtNj3OXjs7W2IIsNbR2KhA01BEI6/9u3imnIzJfZP6FE3XWpEKNyPbKNC01tQXk7whrdEGmpozPj9U09/wJamoNaNK4PGX5hvZuGIfD0SOoDOT9l108SYZhaOvWrUNaC5AICQ0rW7Zs0ZQpUzRlyhRJ0lVXXaXly5fr4MGDGj06sU/htCxLH9Y0aWdtp5R1UE6nU33mn3Xf4Di8zZIlyzp8Hiu6vetnq2uDZenwvugxlmSYpoyu2W4Oh0NOp0NOR6Tr1eFwyOlwyOVyyOV0yCFH7FjDiBwXPdbpcEiR/+v2L/lutfVqhyVLpmlq90d+tWmfHE5Htxr7qb1XO/q2q9sx3X62Ig2WpciXUNgwFTYsGaYpy4qMnWd4XPJ53XK7nYfb1fVn9L2GETlWknKyPPK6XXK7nAqEIl84nYGwwoYZa6ej6zOJfo7RLz+HLB082Kx36t6X2+Xs+sy73utwyLQshcOmAiEj9uXncjoiNWa4leGJHBM2zK72ROqLfMF2/z1d19MhGV3ntCzJtCyZVtfn1nX9om3oCIRldl3PVn9QTa0BNbcHY9t621Pbope37o29zvC6NH5srs48pVATinLV0h6S1+PUvoNt2v1JvTa/v1WOrjU0DMNURyCs+uZO1TX41eYPRn5/1zUpHZ2jU0ryFDJMNbUG5Oz6DKLDEQUjfHK5uv7OWJYyvC5l+TyyLEv+zrD8nSH5O8MKhU0ZZuSaB4Lhrn1htXeGFApHrueIbK+cDocM01KGxxlrc/QLuzMY7jH8MlB5WS595uwSTR6fr4kleZo8Pr9PCAiEDH1S16qSUdlxhY6TxuYy4RQYJgkNK/v371dx8eExVK/Xq/z8fNXW1sYdVgzDkGEYQ1bbJ3WtuuWRLZEXfzw0ZOdNGq80JLqCE++d1kRX0IPDIfm87ljYycn0aExBliaPH6n8XJ/ycrwamZuhklE5KhmVLcM09c7uBuVmevTR/lbt3NuslvbIJMoPPmnq/5d8VNNnU2aGW2MLMjW2YKSyfG7lZnlU39ypHR836X+6glBeduSW10DIkMPhkGGaCobMY7bJ5XTI44709rhdDnndLmX53Coela1sn1tZPrda/CGFwoZMU3I6pWDI1LixufK6nfJ3huVwSj6PW5kZLuVkeZWb5VF2pke5WV5lZ3qUnelWhtslt9spj9spr9sV+Z1OSzs/eE9Tp07tMSTS+98bbqc0sTi3333JJlp/srcjHrQ5ecRTb0LDSu8JYlFOZ/zd1jt27BiKkmJM09KiCwvU2etfwL2rdTgcsZ6KSF+BIzZL3tH1P47Ye6PbD7+hzz5H9L/oI/ujPRiWZcm0Dr82LUtGt9Iix0W+DKIz+w/3fkTef7Tfq35eO7o29K2/v3M4em4/Urt7bY/WHO09itYdMiwFw5YMw5LT2fWert6l3sdIUmfQVNi0ZBiSx+1Qhschjzva+9R1daK9Odbhnp0+n6/6ft6S5HY55HE55HY55HJGtgcNS6GwpVA40nvj7OqRcTkdsevQt0ctstJn97ZEr5ej6++O0xFpg8flOMq8BENSh6QOWe1N2tse2VrolhSSTiuUTit0S3LLMDPV0BrWoZaw3C7J43LK53Vo1AiPDDNSWzBkdfVo9fd7LUkZsmaOiX0mblfPuizLUiBk9ejFCoYtBUJmJHR5nMrwOOV2DeQhdhlH2de7pyPc9f8dkZdBKRSU+t57c9i2bduO8ftTD21OD6nc5oSGlZKSEr366qux18FgUI2NjSopKTnKUf2bPHmysrKyhrI8TZ9uaNu2bX3+SyyVGQZtTgfRNs84e1ratTkdrzNtTm3J2ma/3z/gjoaEhpULL7xQ999/v3bs2KHJkydr7dq1mj59ugoKCuI+l8vlGraLNJzntivanB5oc3qgzekh2docT60JDSsFBQV6+OGHddtttykQCKiwsFDLly9PZEkAAMBmEr7OyuzZs7V+/fpElwEAAGxqaB/RCQAAMMQIKwAAwNYIKwAAwNYIKwAAwNYIKwAAwNYIKwAAwNYIKwAAwNYIKwAAwNYIKwAAwNYIKwAAwNYSvtz+8TJNU5LU0dEx5Oc2DENS5MmQyfRwqONBm2lzqqLNtDlVJWubo9/b0e/xo3FYlmUNd0HDqb6+Xnv27El0GQAAYBBOPvlkFRYWHvU9SR9WwuGwmpublZGRIaeTUS0AAJKBaZoKBALKy8uT2330gZ6kDysAACC10RUBAABsjbACAABsjbACAABsjbACAABsjbACAABsjbACAABsjbACAABsjbACAABsjbACAABsLekfZDhcXn75Zf3gBz9QIBBQcXGxli1bpjFjxiS6rOP229/+Vk888YQcDocyMzP1b//2b5o2bZouueQSZWZmxh6CNX/+fF1//fXq7OzUPffco61bt8owDF177bW66qqrEtyK+Nxxxx167bXXlJubK0maMGGCHnnkEa1atUpr166VYRiaM2eOvvvd78rj8cg0TT300EN66aWXZBiGLrvsMi1ZskQOhyPBLRmYtWvX6je/+U3sdXt7u2pqavTCCy/ommuuScnr/OMf/1gHDx7UAw88IEl65plntHLlSoXDYZ1++um6//77lZOTI0kpc917t3nFihV67rnn5HQ6VVhYqLvvvlsTJ05UMBjUeeedpwkTJsSOraqq0mWXXabGxkbdcccd+vjjj2UYhpYuXaqysrJENemYerf56quvVl1dnXw+nyTp3HPP1Z133nnUa5lsf9e7t3nFihWqrq6O7WtqalJjY6NeffVVud3ulLnO/bLQR319vXXeeedZ7733nmVZlvXYY49Z11xzTYKrOn5vvPGG9dnPftaqr6+3LMuyXnrpJevCCy+09u/fb11wwQWWaZp9jlm+fLl18803W6ZpWg0NDdbcuXOtN9988wRXfnzKy8ut999/v8e2P/3pT9a8efOslpYWKxwOW//yL/9i/fznP7csy7Ief/xx66tf/aoVCASsjo4O66qrrrI2bdqUiNKPWzgctr761a9av/jFL1LyOn/yySfWTTfdZE2bNs367ne/a1mWZe3YscOaNWuWtX//fsuyLOs//uM/rLvuusuyrNS47v21ecOGDdbll19utbe3W5ZlWb/+9a+tRYsWWZZlWa+//rq1YMGCfs/1rW99y/rhD38YO+/s2bOtffv2nYBWxKe/NgeDQWvGjBlWc3Nzn/cf7Vomy9/1/trcXWtrq/WFL3zBevbZZy3LSo3rfDQMA/Vjy5YtmjJliqZMmSJJuuqqq/Taa6/p4MGDCa7s+OTl5em+++5TQUGBJGnatGmqr6/Xn//8Z2VlZamqqkoVFRV64IEHYo/urq6u1pVXXimHw6H8/HzNnz9f69evT2Qz4nLo0CHt27dPjzzyiCoqKvTNb35Te/fuVXV1tebPn6/c3Fy5XC5VVlbG2lVdXa2FCxfK6/XK5/Np0aJFSdXm7n75y1/K5XLpmmuu0RtvvJFy1/nJJ5/U7NmzVVVVFdv24osv6uKLL9bYsWMlSYsXL9bGjRtlmmZKXPf+2jxhwgTdeeedysrKkhT5Z3vv3r2SpDfeeEOhUEhf/epXVVFRoRUrVsgwDIXDYf3xj3+M9SqMGzdOc+bM0caNG098o46hvza/++678nq9uuWWW1RRUaE77rhDjY2Nko5+LZPl73p/be7uRz/6kaZPn67Pf/7zklLjOh8NYaUf+/fvV3Fxcey11+tVfn6+amtrE1jV8Zs0aZLmzJkjKfK0ywcffFCf/exnJUmzZ8/WT3/6U61du1a1tbX6/ve/L0mqra1VUVFR7BxFRUVJ9TkcOHBAc+bM0R133KENGzZo2rRpuuGGG7R3794e17h7u5K9zVEtLS36+c9/rjvvvFMOh0OBQCDlrvPNN9+sxYsXx4a1pEhbel9bv9+vpqamfvcl23Xvr83Tpk3TjBkzJEmBQEAPPfSQvvCFL8T2X3zxxVq9erV+85vfaMuWLVq1apUaGxvV2dmZtG1ubm7WBRdcoGXLlmn9+vXKysrS0qVLJR39WibzdY7as2ePNm7cqFtuuaXH9mS/zkdDWOmHZVn9jlM7nanxcbW1tekb3/iG9u7dq2XLlmnBggW67777lJmZKZ/PpxtuuCE2LtrfZ5FMn8MZZ5yhn/3sZyotLZXD4dB1112nvXv3yul09mlX9HWytznqd7/7nWbPnq1TTz1VklL6OvfW3z+/0W2pfN0PHDigf/qnf1Jubq5uvfVWSdL111+vW265RR6PR3l5eaqqqlJ1dbUsy5LU9/NIljZfdNFF+vGPf6z8/Hy53W594xvf0JYtW+T3+496LVPhOj/22GO64oorYr3kUupe56jkqvYEKSkpUV1dXex1MBhUY2OjSkpKEljV0Ni9e7cWLVqknJwcPfbYYxoxYoQ2bdqkt99+O/Yey7LkdkfmXpeWlvb4LOrq6nr8l6ndvfnmm3r++ed7bLMsS6FQqE+7otc32dsc9eyzz2rhwoWx16l8nbvr/c9vXV2dsrOzlZeX1+++VLnub731lhYuXKhzzjlHK1askNfrlSQ98cQT+uijj2Lvi173wsJCZWRk6MCBA7F9ydTmzZs365VXXom9tixLTqdTbrf7qNcy2a+zaZp67rnnevyzLaXudY4irPTjwgsv1LvvvqsdO3ZIitxdMX369B4pNhnt27dPixcv1le+8hUtX75cGRkZkiJdij/60Y8UDAYVDoe1atUqzZ8/X5JUXl6uJ598UqZpqqmpSZs2bdLcuXMT2Yy4BINB3XfffbF/UH/9619r0qRJuvbaa/X73/9eLS0tMk1Ta9asibWrvLxcTz/9tILBoDo7O7Vu3bqkarMktba2aseOHTr33HNj21L5Ond3ySWXaPPmzbEvpMcff1xlZWVyOp0qLy9Pyev+zjvv6JprrtHtt9+uW265pcd/NW/btk0//elPZZqm/H6/fvWrX+mLX/yiXC6XLrnkEj3xxBOSpL179+rll19OmrtEGhoa9OCDD6qtrU2StHLlSpWVlcnr9R71Wib73/UdO3bI6/XqlFNO6bE9Va9zFLcu96OgoEAPP/ywbrvtNgUCARUWFmr58uWJLuu4rVq1Si0tLdqwYYM2bNgQ2/7Tn/5Uhw4d0pe//GWFw2HNmjVL3/72tyVJS5Ys0f3336+KigqFQiFVVlZq1qxZCWpB/M4//3zddNNNqqqqkmEYKikp0U9+8hOVlpZq165dqqysVDgc1syZM3XjjTdKkq644grV1NRowYIFCoVCKisr6/NfMXa3Z88eFRQUxG7plCLdxKl6nbs77bTTdOutt+q6665TKBTSxIkTY3NzLrroIu3cuTPlrvujjz4q0zS1cuVKrVy5Mrb9mWee0e233657771XFRUVCgaD+sIXvqDKykpJ0l133aXvfe97+uIXv6hwOKzbb79dJ598coJaEZ8FCxbo448/1le+8hWZpqkpU6bo3//93yUd/Vom+9/1PXv2qLS0tM/2VL3OUQ4rOqAFAABgQwwDAQAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAWyOsAAAAW/v/AfDQscrp/vAoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_spx[\"Close\"].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.242086</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-02-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.481961</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.107595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-03-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.384966</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126220</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.237667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff\n",
       "0   1948-01-01     3.4  10.242086          1       NaN -0.024984\n",
       "1   1948-02-01     3.8   9.481961          2       NaN -0.107595\n",
       "2   1948-03-01     4.0   6.818182          3       NaN -0.056728\n",
       "3   1948-04-01     3.9   8.272727          4       NaN  0.054795\n",
       "4   1948-05-01     3.5   9.384966          5       NaN  0.126220\n",
       "..         ...     ...        ...        ...       ...       ...\n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453\n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664\n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186\n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461\n",
       "919 2024-08-01     4.2   2.591227          8      5.33  0.237667\n",
       "\n",
       "[920 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.merge(df_spx.filter([\"DATE\",\"SPX_diff\"]), how=\"left\", on=\"DATE\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x326b55cf0>]"
      ]
     },
     "execution_count": 17,
     "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(np.diff(df[\"FEDFUNDS\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.242086</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024984</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-02-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.481961</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.107595</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-03-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056728</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.384966</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126220</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.237667</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "0   1948-01-01     3.4  10.242086          1       NaN -0.024984   \n",
       "1   1948-02-01     3.8   9.481961          2       NaN -0.107595   \n",
       "2   1948-03-01     4.0   6.818182          3       NaN -0.056728   \n",
       "3   1948-04-01     3.9   8.272727          4       NaN  0.054795   \n",
       "4   1948-05-01     3.5   9.384966          5       NaN  0.126220   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "919 2024-08-01     4.2   2.591227          8      5.33  0.237667   \n",
       "\n",
       "     FEDFUNDS_diff  \n",
       "0              NaN  \n",
       "1              NaN  \n",
       "2              NaN  \n",
       "3              NaN  \n",
       "4              NaN  \n",
       "..             ...  \n",
       "915            0.0  \n",
       "916            0.0  \n",
       "917            0.0  \n",
       "918            0.0  \n",
       "919            NaN  \n",
       "\n",
       "[920 rows x 7 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate next month diff in unemployment rate \n",
    "df[\"FEDFUNDS_diff\"] = np.diff(df[\"FEDFUNDS\"], append=np.nan)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "        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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>ff_higher_50</th>\n",
       "      <th>ff_stable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.242086</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1948-02-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.481961</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.107595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1948-03-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1948-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.384966</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.237667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "0   1948-01-01     3.4  10.242086          1       NaN -0.024984   \n",
       "1   1948-02-01     3.8   9.481961          2       NaN -0.107595   \n",
       "2   1948-03-01     4.0   6.818182          3       NaN -0.056728   \n",
       "3   1948-04-01     3.9   8.272727          4       NaN  0.054795   \n",
       "4   1948-05-01     3.5   9.384966          5       NaN  0.126220   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "919 2024-08-01     4.2   2.591227          8      5.33  0.237667   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ff_higher_50  \\\n",
       "0              NaN        False        False         False         False   \n",
       "1              NaN        False        False         False         False   \n",
       "2              NaN        False        False         False         False   \n",
       "3              NaN        False        False         False         False   \n",
       "4              NaN        False        False         False         False   \n",
       "..             ...          ...          ...           ...           ...   \n",
       "915            0.0        False        False         False         False   \n",
       "916            0.0        False        False         False         False   \n",
       "917            0.0        False        False         False         False   \n",
       "918            0.0        False        False         False         False   \n",
       "919            NaN        False        False         False         False   \n",
       "\n",
       "     ff_stable  \n",
       "0        False  \n",
       "1        False  \n",
       "2        False  \n",
       "3        False  \n",
       "4        False  \n",
       "..         ...  \n",
       "915       True  \n",
       "916       True  \n",
       "917       True  \n",
       "918       True  \n",
       "919      False  \n",
       "\n",
       "[920 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ff_lower_25 = (df[\"FEDFUNDS_diff\"] < -0.05) & (df[\"FEDFUNDS_diff\"] >= -0.25)\n",
    "ff_lower_50 = df[\"FEDFUNDS_diff\"] < -0.25 \n",
    "ff_higher_25 = (df[\"FEDFUNDS_diff\"] > 0.05) & (df[\"FEDFUNDS_diff\"] <= 0.25)\n",
    "ff_higher_50 = df[\"FEDFUNDS_diff\"] > 0.25\n",
    "ff_stable = df[\"FEDFUNDS_diff\"].abs() <= 0.05\n",
    "# add ur to df\n",
    "df[\"ff_lower_25\"] = ff_lower_25\n",
    "df[\"ff_lower_50\"] = ff_lower_50\n",
    "df[\"ff_higher_25\"] = ff_higher_25\n",
    "df[\"ff_higher_50\"] = ff_higher_50\n",
    "df[\"ff_stable\"] = ff_stable\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>spx-3</th>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
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       "      <th>fedfunds-3_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1948-01-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>10.242086</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.024984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <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>1948-02-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>9.481961</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.107595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.760125</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>1948-03-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.818182</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.056728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.663779</td>\n",
       "      <td>-0.760125</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>1948-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>8.272727</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024984</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>-2.663779</td>\n",
       "      <td>-0.760125</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1948-05-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.384966</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.126220</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.107595</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.112239</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>-2.663779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.213053</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>0.059762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228518</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.302883</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.237667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.332339</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>920 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "0   1948-01-01     3.4  10.242086          1       NaN -0.024984   \n",
       "1   1948-02-01     3.8   9.481961          2       NaN -0.107595   \n",
       "2   1948-03-01     4.0   6.818182          3       NaN -0.056728   \n",
       "3   1948-04-01     3.9   8.272727          4       NaN  0.054795   \n",
       "4   1948-05-01     3.5   9.384966          5       NaN  0.126220   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "919 2024-08-01     4.2   2.591227          8      5.33  0.237667   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...     spx-3  \\\n",
       "0              NaN        False        False         False  ...       NaN   \n",
       "1              NaN        False        False         False  ...       NaN   \n",
       "2              NaN        False        False         False  ...       NaN   \n",
       "3              NaN        False        False         False  ... -0.024984   \n",
       "4              NaN        False        False         False  ... -0.107595   \n",
       "..             ...          ...          ...           ...  ...       ...   \n",
       "915            0.0        False        False         False  ...  0.213053   \n",
       "916            0.0        False        False         False  ...  0.228518   \n",
       "917            0.0        False        False         False  ...  0.302883   \n",
       "918            0.0        False        False         False  ...  0.240453   \n",
       "919            NaN        False        False         False  ...  0.262664   \n",
       "\n",
       "     UNRATE-1_diff  UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  \\\n",
       "0              NaN            NaN            NaN               NaN   \n",
       "1              0.4            NaN            NaN         -0.760125   \n",
       "2              0.2            0.4            NaN         -2.663779   \n",
       "3             -0.1            0.2            0.4          1.454545   \n",
       "4             -0.4           -0.1            0.2          1.112239   \n",
       "..             ...            ...            ...               ...   \n",
       "915            0.1           -0.1            0.2         -0.117400   \n",
       "916            0.1            0.1           -0.1         -0.107521   \n",
       "917            0.1            0.1            0.1         -0.274582   \n",
       "918            0.2            0.1            0.1         -0.052063   \n",
       "919           -0.1            0.2            0.1         -0.332339   \n",
       "\n",
       "     Inflation-2_diff  Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  \\\n",
       "0                 NaN               NaN              NaN              NaN   \n",
       "1                 NaN               NaN              NaN              NaN   \n",
       "2           -0.760125               NaN              NaN              NaN   \n",
       "3           -2.663779         -0.760125              NaN              NaN   \n",
       "4            1.454545         -2.663779              NaN              NaN   \n",
       "..                ...               ...              ...              ...   \n",
       "915          0.309388          0.059762              0.0              0.0   \n",
       "916         -0.117400          0.309388              0.0              0.0   \n",
       "917         -0.107521         -0.117400              0.0              0.0   \n",
       "918         -0.274582         -0.107521              0.0              0.0   \n",
       "919         -0.052063         -0.274582              0.0              0.0   \n",
       "\n",
       "     fedfunds-3_diff  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "..               ...  \n",
       "915              0.0  \n",
       "916              0.0  \n",
       "917              0.0  \n",
       "918              0.0  \n",
       "919              0.0  \n",
       "\n",
       "[920 rows x 33 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add last 3 month data about unemployment rate, inflation , fedfunds, SPX to df \n",
    "# as new columns (i.e: \"Total-1\", \"Total-2\", \"Total-3\")\n",
    "\n",
    "df[\"UNRATE-1\"] = df[\"UNRATE\"].shift(1)\n",
    "df[\"UNRATE-2\"] = df[\"UNRATE\"].shift(2)\n",
    "df[\"UNRATE-3\"] = df[\"UNRATE\"].shift(3)\n",
    "\n",
    "df[\"Inflation-1\"] = df[\"Inflation\"].shift(1)\n",
    "df[\"Inflation-2\"] = df[\"Inflation\"].shift(2)\n",
    "df[\"Inflation-3\"] = df[\"Inflation\"].shift(3)\n",
    "\n",
    "df[\"fedfunds-1\"] = df[\"FEDFUNDS\"].shift(1)\n",
    "df[\"fedfunds-2\"] = df[\"FEDFUNDS\"].shift(2)\n",
    "df[\"fedfunds-3\"] = df[\"FEDFUNDS\"].shift(3)\n",
    "\n",
    "df[\"spx-1\"] = df[\"SPX_diff\"].shift(1)\n",
    "df[\"spx-2\"] = df[\"SPX_diff\"].shift(2)\n",
    "df[\"spx-3\"] = df[\"SPX_diff\"].shift(3)\n",
    "\n",
    "# diff : the difference between the current value and the previous value\n",
    "df[\"UNRATE-1_diff\"] = df[\"UNRATE\"] - df[\"UNRATE-1\"]\n",
    "df[\"UNRATE-2_diff\"] = df[\"UNRATE-1\"] - df[\"UNRATE-2\"]\n",
    "df[\"UNRATE-3_diff\"] = df[\"UNRATE-2\"] - df[\"UNRATE-3\"] \n",
    "\n",
    "df[\"Inflation-1_diff\"] = df[\"Inflation\"] - df[\"Inflation-1\"]\n",
    "df[\"Inflation-2_diff\"] = df[\"Inflation-1\"] - df[\"Inflation-2\"]\n",
    "df[\"Inflation-3_diff\"] = df[\"Inflation-2\"] - df[\"Inflation-3\"]\n",
    "\n",
    "df[\"fedfunds-1_diff\"] = df[\"FEDFUNDS\"] - df[\"fedfunds-1\"]\n",
    "df[\"fedfunds-2_diff\"] = df[\"fedfunds-1\"] - df[\"fedfunds-2\"]\n",
    "df[\"fedfunds-3_diff\"] = df[\"fedfunds-2\"] - df[\"fedfunds-3\"]\n",
    "# 1954-10-01\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['DATE', 'UNRATE', 'Inflation', 'num_month', 'FEDFUNDS', 'SPX_diff',\n",
       "       'FEDFUNDS_diff', 'ff_lower_25', 'ff_lower_50', 'ff_higher_25',\n",
       "       'ff_higher_50', 'ff_stable', 'UNRATE-1', 'UNRATE-2', 'UNRATE-3',\n",
       "       'Inflation-1', 'Inflation-2', 'Inflation-3', 'fedfunds-1', 'fedfunds-2',\n",
       "       'fedfunds-3', 'spx-1', 'spx-2', 'spx-3', 'UNRATE-1_diff',\n",
       "       'UNRATE-2_diff', 'UNRATE-3_diff', 'Inflation-1_diff',\n",
       "       'Inflation-2_diff', 'Inflation-3_diff', 'fedfunds-1_diff',\n",
       "       'fedfunds-2_diff', 'fedfunds-3_diff'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"list_feat = ['Total', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'num_month', 'Total-1',\\n       'Total-2', 'Total-3', 'Inflation-1', 'Inflation-2', 'Inflation-3',\\n       'fedfunds-1', 'fedfunds-2', 'fedfunds-3', 'spx-1', 'spx-2', 'spx-3']\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"list_feat = ['Total', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'num_month', 'Total-1',\n",
    "       'Total-2', 'Total-3', 'Inflation-1', 'Inflation-2', 'Inflation-3',\n",
    "       'fedfunds-1', 'fedfunds-2', 'fedfunds-3', 'spx-1', 'spx-2', 'spx-3']\"\"\"\n",
    "\n",
    "\n",
    "#df_x = df.filter(list_feat)\n",
    "#df_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>spx-3</th>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>2024-08-01</td>\n",
       "      <td>4.2</td>\n",
       "      <td>2.591227</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.237667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.332339</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "919 2024-08-01     4.2   2.591227          8      5.33  0.237667   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...     spx-3  \\\n",
       "919            NaN        False        False         False  ...  0.262664   \n",
       "\n",
       "     UNRATE-1_diff  UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  \\\n",
       "919           -0.1            0.2            0.1         -0.332339   \n",
       "\n",
       "     Inflation-2_diff  Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  \\\n",
       "919         -0.052063         -0.274582              0.0              0.0   \n",
       "\n",
       "     fedfunds-3_diff  \n",
       "919              0.0  \n",
       "\n",
       "[1 rows x 33 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_for_pred = df.iloc[-1:]\n",
    "df_for_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>spx-3</th>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1954-10-01</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-0.853432</td>\n",
       "      <td>10</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.342511</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240428</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.261292</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1954-11-01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-0.260708</td>\n",
       "      <td>11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.364898</td>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.259943</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1954-12-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.372162</td>\n",
       "      <td>12</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.408377</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.351526</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-0.631032</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.398272</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.342511</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1955-02-01</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.629863</td>\n",
       "      <td>2</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.413912</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.364898</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</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>914</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.475131</td>\n",
       "      <td>3</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197494</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213053</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.228518</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "81  1954-10-01     5.7  -0.853432         10      0.85  0.342511   \n",
       "82  1954-11-01     5.3  -0.260708         11      0.83  0.364898   \n",
       "83  1954-12-01     5.0  -0.372162         12      1.28  0.408377   \n",
       "84  1955-01-01     4.9  -0.631032          1      1.39  0.398272   \n",
       "85  1955-02-01     4.7  -0.629863          2      1.29  0.413912   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "914 2024-03-01     3.8   3.475131          3      5.33  0.302883   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...     spx-3  \\\n",
       "81           -0.02        False        False         False  ...  0.240428   \n",
       "82            0.45        False        False         False  ...  0.259943   \n",
       "83            0.11        False        False          True  ...  0.351526   \n",
       "84           -0.10         True        False         False  ...  0.342511   \n",
       "85            0.06        False        False          True  ...  0.364898   \n",
       "..             ...          ...          ...           ...  ...       ...   \n",
       "914           0.00        False        False         False  ...  0.197494   \n",
       "915           0.00        False        False         False  ...  0.213053   \n",
       "916           0.00        False        False         False  ...  0.228518   \n",
       "917           0.00        False        False         False  ...  0.302883   \n",
       "918           0.00        False        False         False  ...  0.240453   \n",
       "\n",
       "     UNRATE-1_diff  UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  \\\n",
       "81            -0.4            0.1            0.2         -0.555924   \n",
       "82            -0.4           -0.4            0.1          0.592725   \n",
       "83            -0.3           -0.4           -0.4         -0.111455   \n",
       "84            -0.1           -0.3           -0.4         -0.258870   \n",
       "85            -0.2           -0.1           -0.3          0.001169   \n",
       "..             ...            ...            ...               ...   \n",
       "914           -0.1            0.2            0.0          0.309388   \n",
       "915            0.1           -0.1            0.2         -0.117400   \n",
       "916            0.1            0.1           -0.1         -0.107521   \n",
       "917            0.1            0.1            0.1         -0.274582   \n",
       "918            0.2            0.1            0.1         -0.052063   \n",
       "\n",
       "     Inflation-2_diff  Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  \\\n",
       "81          -0.297508         -0.261292            -0.22            -0.15   \n",
       "82          -0.555924         -0.297508            -0.02            -0.22   \n",
       "83           0.592725         -0.555924             0.45            -0.02   \n",
       "84          -0.111455          0.592725             0.11             0.45   \n",
       "85          -0.258870         -0.111455            -0.10             0.11   \n",
       "..                ...               ...              ...              ...   \n",
       "914          0.059762         -0.217179             0.00             0.00   \n",
       "915          0.309388          0.059762             0.00             0.00   \n",
       "916         -0.117400          0.309388             0.00             0.00   \n",
       "917         -0.107521         -0.117400             0.00             0.00   \n",
       "918         -0.274582         -0.107521             0.00             0.00   \n",
       "\n",
       "     fedfunds-3_diff  \n",
       "81              0.42  \n",
       "82             -0.15  \n",
       "83             -0.22  \n",
       "84             -0.02  \n",
       "85              0.45  \n",
       "..               ...  \n",
       "914             0.00  \n",
       "915             0.00  \n",
       "916             0.00  \n",
       "917             0.00  \n",
       "918             0.00  \n",
       "\n",
       "[838 rows x 33 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creation of a multi-class target : df_y[\"class\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1954-10-01</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-0.853432</td>\n",
       "      <td>10</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.342511</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.261292</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1954-11-01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-0.260708</td>\n",
       "      <td>11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.364898</td>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1954-12-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.372162</td>\n",
       "      <td>12</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.408377</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-0.631032</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.398272</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1955-02-01</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.629863</td>\n",
       "      <td>2</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.413912</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3</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>914</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.475131</td>\n",
       "      <td>3</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "81  1954-10-01     5.7  -0.853432         10      0.85  0.342511   \n",
       "82  1954-11-01     5.3  -0.260708         11      0.83  0.364898   \n",
       "83  1954-12-01     5.0  -0.372162         12      1.28  0.408377   \n",
       "84  1955-01-01     4.9  -0.631032          1      1.39  0.398272   \n",
       "85  1955-02-01     4.7  -0.629863          2      1.29  0.413912   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "914 2024-03-01     3.8   3.475131          3      5.33  0.302883   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...  \\\n",
       "81           -0.02        False        False         False  ...   \n",
       "82            0.45        False        False         False  ...   \n",
       "83            0.11        False        False          True  ...   \n",
       "84           -0.10         True        False         False  ...   \n",
       "85            0.06        False        False          True  ...   \n",
       "..             ...          ...          ...           ...  ...   \n",
       "914           0.00        False        False         False  ...   \n",
       "915           0.00        False        False         False  ...   \n",
       "916           0.00        False        False         False  ...   \n",
       "917           0.00        False        False         False  ...   \n",
       "918           0.00        False        False         False  ...   \n",
       "\n",
       "     UNRATE-1_diff  UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  \\\n",
       "81            -0.4            0.1            0.2         -0.555924   \n",
       "82            -0.4           -0.4            0.1          0.592725   \n",
       "83            -0.3           -0.4           -0.4         -0.111455   \n",
       "84            -0.1           -0.3           -0.4         -0.258870   \n",
       "85            -0.2           -0.1           -0.3          0.001169   \n",
       "..             ...            ...            ...               ...   \n",
       "914           -0.1            0.2            0.0          0.309388   \n",
       "915            0.1           -0.1            0.2         -0.117400   \n",
       "916            0.1            0.1           -0.1         -0.107521   \n",
       "917            0.1            0.1            0.1         -0.274582   \n",
       "918            0.2            0.1            0.1         -0.052063   \n",
       "\n",
       "     Inflation-2_diff  Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  \\\n",
       "81          -0.297508         -0.261292            -0.22            -0.15   \n",
       "82          -0.555924         -0.297508            -0.02            -0.22   \n",
       "83           0.592725         -0.555924             0.45            -0.02   \n",
       "84          -0.111455          0.592725             0.11             0.45   \n",
       "85          -0.258870         -0.111455            -0.10             0.11   \n",
       "..                ...               ...              ...              ...   \n",
       "914          0.059762         -0.217179             0.00             0.00   \n",
       "915          0.309388          0.059762             0.00             0.00   \n",
       "916         -0.117400          0.309388             0.00             0.00   \n",
       "917         -0.107521         -0.117400             0.00             0.00   \n",
       "918         -0.274582         -0.107521             0.00             0.00   \n",
       "\n",
       "     fedfunds-3_diff  class  \n",
       "81              0.42      2  \n",
       "82             -0.15      4  \n",
       "83             -0.22      3  \n",
       "84             -0.02      1  \n",
       "85              0.45      3  \n",
       "..               ...    ...  \n",
       "914             0.00      2  \n",
       "915             0.00      2  \n",
       "916             0.00      2  \n",
       "917             0.00      2  \n",
       "918             0.00      2  \n",
       "\n",
       "[838 rows x 34 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"class\"] = 0*df[\"ff_lower_50\"] + 1*df[\"ff_lower_25\"] + 2*df[\"ff_stable\"] + 3*df[\"ff_higher_25\"] + 4*df[\"ff_higher_50\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_stable</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>ff_higher_50</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FEDFUNDS_diff  ff_lower_50  ff_lower_25  ff_stable  ff_higher_25  \\\n",
       "81           -0.02        False        False       True         False   \n",
       "82            0.45        False        False      False         False   \n",
       "83            0.11        False        False      False          True   \n",
       "84           -0.10        False         True      False         False   \n",
       "85            0.06        False        False      False          True   \n",
       "..             ...          ...          ...        ...           ...   \n",
       "914           0.00        False        False       True         False   \n",
       "915           0.00        False        False       True         False   \n",
       "916           0.00        False        False       True         False   \n",
       "917           0.00        False        False       True         False   \n",
       "918           0.00        False        False       True         False   \n",
       "\n",
       "     ff_higher_50  class  \n",
       "81          False      2  \n",
       "82           True      4  \n",
       "83          False      3  \n",
       "84          False      1  \n",
       "85          False      3  \n",
       "..            ...    ...  \n",
       "914         False      2  \n",
       "915         False      2  \n",
       "916         False      2  \n",
       "917         False      2  \n",
       "918         False      2  \n",
       "\n",
       "[838 rows x 7 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_y = df.filter(['FEDFUNDS_diff',\n",
    "     'ff_lower_50', 'ff_lower_25', 'ff_stable',\"ff_higher_25\", \"ff_higher_50\" ,\"class\"])\n",
    "df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_targets = ['ff_lower_50', 'ff_lower_25', 'ff_stable',\"ff_higher_25\", \"ff_higher_50\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split Train / Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nb_test :  823\n"
     ]
    }
   ],
   "source": [
    "\n",
    "nb_test = int(df.shape[0] * (1 - ratio_test))\n",
    "print(\"nb_test : \", nb_test)\n",
    "#from sklearn.model_selection import train_test_split\n",
    "#xtrain, xtest, ytrain, ytest = train_test_split(data, target, train_size=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1954-10-01</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-0.853432</td>\n",
       "      <td>10</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.342511</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.261292</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1954-11-01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-0.260708</td>\n",
       "      <td>11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.364898</td>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1954-12-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.372162</td>\n",
       "      <td>12</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.408377</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-0.631032</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.398272</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1955-02-01</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.629863</td>\n",
       "      <td>2</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.413912</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3</td>\n",
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       "    <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>899</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>6.411498</td>\n",
       "      <td>12</td>\n",
       "      <td>4.10</td>\n",
       "      <td>-0.163086</td>\n",
       "      <td>0.23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.707968</td>\n",
       "      <td>-0.632475</td>\n",
       "      <td>-0.446331</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.52</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6.362123</td>\n",
       "      <td>1</td>\n",
       "      <td>4.33</td>\n",
       "      <td>-0.134059</td>\n",
       "      <td>0.24</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.049375</td>\n",
       "      <td>-0.707968</td>\n",
       "      <td>-0.632475</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.70</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>3.6</td>\n",
       "      <td>5.965523</td>\n",
       "      <td>2</td>\n",
       "      <td>4.57</td>\n",
       "      <td>-0.080320</td>\n",
       "      <td>0.08</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.396601</td>\n",
       "      <td>-0.049375</td>\n",
       "      <td>-0.707968</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.935090</td>\n",
       "      <td>3</td>\n",
       "      <td>4.65</td>\n",
       "      <td>-0.096261</td>\n",
       "      <td>0.18</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.030432</td>\n",
       "      <td>-0.396601</td>\n",
       "      <td>-0.049375</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.941059</td>\n",
       "      <td>4</td>\n",
       "      <td>4.83</td>\n",
       "      <td>-0.061446</td>\n",
       "      <td>0.23</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.005969</td>\n",
       "      <td>-1.030432</td>\n",
       "      <td>-0.396601</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>823 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "81  1954-10-01     5.7  -0.853432         10      0.85  0.342511   \n",
       "82  1954-11-01     5.3  -0.260708         11      0.83  0.364898   \n",
       "83  1954-12-01     5.0  -0.372162         12      1.28  0.408377   \n",
       "84  1955-01-01     4.9  -0.631032          1      1.39  0.398272   \n",
       "85  1955-02-01     4.7  -0.629863          2      1.29  0.413912   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "899 2022-12-01     3.5   6.411498         12      4.10 -0.163086   \n",
       "900 2023-01-01     3.4   6.362123          1      4.33 -0.134059   \n",
       "901 2023-02-01     3.6   5.965523          2      4.57 -0.080320   \n",
       "902 2023-03-01     3.5   4.935090          3      4.65 -0.096261   \n",
       "903 2023-04-01     3.4   4.941059          4      4.83 -0.061446   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...  \\\n",
       "81           -0.02        False        False         False  ...   \n",
       "82            0.45        False        False         False  ...   \n",
       "83            0.11        False        False          True  ...   \n",
       "84           -0.10         True        False         False  ...   \n",
       "85            0.06        False        False          True  ...   \n",
       "..             ...          ...          ...           ...  ...   \n",
       "899           0.23        False        False          True  ...   \n",
       "900           0.24        False        False          True  ...   \n",
       "901           0.08        False        False          True  ...   \n",
       "902           0.18        False        False          True  ...   \n",
       "903           0.23        False        False          True  ...   \n",
       "\n",
       "     UNRATE-1_diff  UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  \\\n",
       "81            -0.4            0.1            0.2         -0.555924   \n",
       "82            -0.4           -0.4            0.1          0.592725   \n",
       "83            -0.3           -0.4           -0.4         -0.111455   \n",
       "84            -0.1           -0.3           -0.4         -0.258870   \n",
       "85            -0.2           -0.1           -0.3          0.001169   \n",
       "..             ...            ...            ...               ...   \n",
       "899           -0.1            0.0            0.1         -0.707968   \n",
       "900           -0.1           -0.1            0.0         -0.049375   \n",
       "901            0.2           -0.1           -0.1         -0.396601   \n",
       "902           -0.1            0.2           -0.1         -1.030432   \n",
       "903           -0.1           -0.1            0.2          0.005969   \n",
       "\n",
       "     Inflation-2_diff  Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  \\\n",
       "81          -0.297508         -0.261292            -0.22            -0.15   \n",
       "82          -0.555924         -0.297508            -0.02            -0.22   \n",
       "83           0.592725         -0.555924             0.45            -0.02   \n",
       "84          -0.111455          0.592725             0.11             0.45   \n",
       "85          -0.258870         -0.111455            -0.10             0.11   \n",
       "..                ...               ...              ...              ...   \n",
       "899         -0.632475         -0.446331             0.32             0.70   \n",
       "900         -0.707968         -0.632475             0.23             0.32   \n",
       "901         -0.049375         -0.707968             0.24             0.23   \n",
       "902         -0.396601         -0.049375             0.08             0.24   \n",
       "903         -1.030432         -0.396601             0.18             0.08   \n",
       "\n",
       "     fedfunds-3_diff  class  \n",
       "81              0.42      2  \n",
       "82             -0.15      4  \n",
       "83             -0.22      3  \n",
       "84             -0.02      1  \n",
       "85              0.45      3  \n",
       "..               ...    ...  \n",
       "899             0.52      3  \n",
       "900             0.70      3  \n",
       "901             0.32      3  \n",
       "902             0.23      3  \n",
       "903             0.24      3  \n",
       "\n",
       "[823 rows x 34 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df.iloc[:nb_test].copy()\n",
    "df_test = df.iloc[nb_test:].copy()\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.120690</td>\n",
       "      <td>5</td>\n",
       "      <td>5.06</td>\n",
       "      <td>0.026188</td>\n",
       "      <td>0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.820370</td>\n",
       "      <td>0.005969</td>\n",
       "      <td>-1.030432</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.08</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.053262</td>\n",
       "      <td>6</td>\n",
       "      <td>5.08</td>\n",
       "      <td>0.114497</td>\n",
       "      <td>0.04</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-1.067428</td>\n",
       "      <td>-0.820370</td>\n",
       "      <td>0.005969</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.271781</td>\n",
       "      <td>7</td>\n",
       "      <td>5.12</td>\n",
       "      <td>0.152452</td>\n",
       "      <td>0.21</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.218519</td>\n",
       "      <td>-1.067428</td>\n",
       "      <td>-0.820370</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.718721</td>\n",
       "      <td>8</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.064368</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>0.218519</td>\n",
       "      <td>-1.067428</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.694055</td>\n",
       "      <td>9</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.145066</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>0.218519</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>909</th>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.245787</td>\n",
       "      <td>10</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.143028</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>-0.448268</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910</th>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.139482</td>\n",
       "      <td>11</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.138499</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.106306</td>\n",
       "      <td>-0.448268</td>\n",
       "      <td>-0.024666</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>911</th>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.323160</td>\n",
       "      <td>12</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.197494</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.183678</td>\n",
       "      <td>-0.106306</td>\n",
       "      <td>-0.448268</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>912</th>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>3.7</td>\n",
       "      <td>3.105981</td>\n",
       "      <td>1</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.213053</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.183678</td>\n",
       "      <td>-0.106306</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>2024-02-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.165743</td>\n",
       "      <td>2</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228518</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.183678</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.475131</td>\n",
       "      <td>3</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "904 2023-05-01     3.7   4.120690          5      5.06  0.026188   \n",
       "905 2023-06-01     3.6   3.053262          6      5.08  0.114497   \n",
       "906 2023-07-01     3.5   3.271781          7      5.12  0.152452   \n",
       "907 2023-08-01     3.8   3.718721          8      5.33  0.064368   \n",
       "908 2023-09-01     3.8   3.694055          9      5.33  0.145066   \n",
       "909 2023-10-01     3.8   3.245787         10      5.33  0.143028   \n",
       "910 2023-11-01     3.7   3.139482         11      5.33  0.138499   \n",
       "911 2023-12-01     3.7   3.323160         12      5.33  0.197494   \n",
       "912 2024-01-01     3.7   3.105981          1      5.33  0.213053   \n",
       "913 2024-02-01     3.9   3.165743          2      5.33  0.228518   \n",
       "914 2024-03-01     3.8   3.475131          3      5.33  0.302883   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...  \\\n",
       "904           0.02        False        False         False  ...   \n",
       "905           0.04        False        False         False  ...   \n",
       "906           0.21        False        False          True  ...   \n",
       "907           0.00        False        False         False  ...   \n",
       "908           0.00        False        False         False  ...   \n",
       "909           0.00        False        False         False  ...   \n",
       "910           0.00        False        False         False  ...   \n",
       "911           0.00        False        False         False  ...   \n",
       "912           0.00        False        False         False  ...   \n",
       "913           0.00        False        False         False  ...   \n",
       "914           0.00        False        False         False  ...   \n",
       "915           0.00        False        False         False  ...   \n",
       "916           0.00        False        False         False  ...   \n",
       "917           0.00        False        False         False  ...   \n",
       "918           0.00        False        False         False  ...   \n",
       "\n",
       "     UNRATE-1_diff  UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  \\\n",
       "904            0.3           -0.1           -0.1         -0.820370   \n",
       "905           -0.1            0.3           -0.1         -1.067428   \n",
       "906           -0.1           -0.1            0.3          0.218519   \n",
       "907            0.3           -0.1           -0.1          0.446941   \n",
       "908            0.0            0.3           -0.1         -0.024666   \n",
       "909            0.0            0.0            0.3         -0.448268   \n",
       "910           -0.1            0.0            0.0         -0.106306   \n",
       "911            0.0           -0.1            0.0          0.183678   \n",
       "912            0.0            0.0           -0.1         -0.217179   \n",
       "913            0.2            0.0            0.0          0.059762   \n",
       "914           -0.1            0.2            0.0          0.309388   \n",
       "915            0.1           -0.1            0.2         -0.117400   \n",
       "916            0.1            0.1           -0.1         -0.107521   \n",
       "917            0.1            0.1            0.1         -0.274582   \n",
       "918            0.2            0.1            0.1         -0.052063   \n",
       "\n",
       "     Inflation-2_diff  Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  \\\n",
       "904          0.005969         -1.030432             0.23             0.18   \n",
       "905         -0.820370          0.005969             0.02             0.23   \n",
       "906         -1.067428         -0.820370             0.04             0.02   \n",
       "907          0.218519         -1.067428             0.21             0.04   \n",
       "908          0.446941          0.218519             0.00             0.21   \n",
       "909         -0.024666          0.446941             0.00             0.00   \n",
       "910         -0.448268         -0.024666             0.00             0.00   \n",
       "911         -0.106306         -0.448268             0.00             0.00   \n",
       "912          0.183678         -0.106306             0.00             0.00   \n",
       "913         -0.217179          0.183678             0.00             0.00   \n",
       "914          0.059762         -0.217179             0.00             0.00   \n",
       "915          0.309388          0.059762             0.00             0.00   \n",
       "916         -0.117400          0.309388             0.00             0.00   \n",
       "917         -0.107521         -0.117400             0.00             0.00   \n",
       "918         -0.274582         -0.107521             0.00             0.00   \n",
       "\n",
       "     fedfunds-3_diff  class  \n",
       "904             0.08      2  \n",
       "905             0.18      2  \n",
       "906             0.23      3  \n",
       "907             0.02      2  \n",
       "908             0.04      2  \n",
       "909             0.21      2  \n",
       "910             0.00      2  \n",
       "911             0.00      2  \n",
       "912             0.00      2  \n",
       "913             0.00      2  \n",
       "914             0.00      2  \n",
       "915             0.00      2  \n",
       "916             0.00      2  \n",
       "917             0.00      2  \n",
       "918             0.00      2  \n",
       "\n",
       "[15 rows x 34 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <th>class</th>\n",
       "      <th>TRAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1954-10-01</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-0.853432</td>\n",
       "      <td>10</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.342511</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.261292</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1954-11-01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-0.260708</td>\n",
       "      <td>11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.364898</td>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1954-12-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.372162</td>\n",
       "      <td>12</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.408377</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-0.631032</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.398272</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1955-02-01</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.629863</td>\n",
       "      <td>2</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.413912</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <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>914</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.475131</td>\n",
       "      <td>3</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "81  1954-10-01     5.7  -0.853432         10      0.85  0.342511   \n",
       "82  1954-11-01     5.3  -0.260708         11      0.83  0.364898   \n",
       "83  1954-12-01     5.0  -0.372162         12      1.28  0.408377   \n",
       "84  1955-01-01     4.9  -0.631032          1      1.39  0.398272   \n",
       "85  1955-02-01     4.7  -0.629863          2      1.29  0.413912   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "914 2024-03-01     3.8   3.475131          3      5.33  0.302883   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...  \\\n",
       "81           -0.02        False        False         False  ...   \n",
       "82            0.45        False        False         False  ...   \n",
       "83            0.11        False        False          True  ...   \n",
       "84           -0.10         True        False         False  ...   \n",
       "85            0.06        False        False          True  ...   \n",
       "..             ...          ...          ...           ...  ...   \n",
       "914           0.00        False        False         False  ...   \n",
       "915           0.00        False        False         False  ...   \n",
       "916           0.00        False        False         False  ...   \n",
       "917           0.00        False        False         False  ...   \n",
       "918           0.00        False        False         False  ...   \n",
       "\n",
       "     UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  Inflation-2_diff  \\\n",
       "81             0.1            0.2         -0.555924         -0.297508   \n",
       "82            -0.4            0.1          0.592725         -0.555924   \n",
       "83            -0.4           -0.4         -0.111455          0.592725   \n",
       "84            -0.3           -0.4         -0.258870         -0.111455   \n",
       "85            -0.1           -0.3          0.001169         -0.258870   \n",
       "..             ...            ...               ...               ...   \n",
       "914            0.2            0.0          0.309388          0.059762   \n",
       "915           -0.1            0.2         -0.117400          0.309388   \n",
       "916            0.1           -0.1         -0.107521         -0.117400   \n",
       "917            0.1            0.1         -0.274582         -0.107521   \n",
       "918            0.1            0.1         -0.052063         -0.274582   \n",
       "\n",
       "     Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  fedfunds-3_diff  \\\n",
       "81          -0.261292            -0.22            -0.15             0.42   \n",
       "82          -0.297508            -0.02            -0.22            -0.15   \n",
       "83          -0.555924             0.45            -0.02            -0.22   \n",
       "84           0.592725             0.11             0.45            -0.02   \n",
       "85          -0.111455            -0.10             0.11             0.45   \n",
       "..                ...              ...              ...              ...   \n",
       "914         -0.217179             0.00             0.00             0.00   \n",
       "915          0.059762             0.00             0.00             0.00   \n",
       "916          0.309388             0.00             0.00             0.00   \n",
       "917         -0.117400             0.00             0.00             0.00   \n",
       "918         -0.107521             0.00             0.00             0.00   \n",
       "\n",
       "     class  TRAIN  \n",
       "81       2      1  \n",
       "82       4      1  \n",
       "83       3      1  \n",
       "84       1      1  \n",
       "85       3      1  \n",
       "..     ...    ...  \n",
       "914      2      0  \n",
       "915      2      0  \n",
       "916      2      0  \n",
       "917      2      0  \n",
       "918      2      0  \n",
       "\n",
       "[838 rows x 35 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"TRAIN\"] = 1\n",
    "df_test[\"TRAIN\"] = 0\n",
    "df = pd.concat([df_train, df_test], axis=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_feat = ['UNRATE', 'Inflation', 'FEDFUNDS', 'SPX_diff', 'num_month', \n",
    "       'UNRATE-1_diff', 'UNRATE-2_diff', 'UNRATE-3_diff',\n",
    "       'Inflation-1_diff', 'Inflation-2_diff', 'Inflation-3_diff',\n",
    "       'fedfunds-1_diff', 'fedfunds-2_diff', 'fedfunds-3_diff', \n",
    "       'spx-1', 'spx-2', 'spx-3']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check features repart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check Out of range\n",
    "check if Test out of Train range for each features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>min TEST</th>\n",
       "      <th>min TRAIN</th>\n",
       "      <th>pc_out_min</th>\n",
       "      <th>max TEST</th>\n",
       "      <th>max TRAIN</th>\n",
       "      <th>pc_out_max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Features</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UNRATE</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>14.800000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation</th>\n",
       "      <td>2.923566</td>\n",
       "      <td>-1.958761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.120690</td>\n",
       "      <td>14.592275</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <td>5.060000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.330000</td>\n",
       "      <td>19.100000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPX_diff</th>\n",
       "      <td>0.026188</td>\n",
       "      <td>-0.425084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.526508</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_month</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-2.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-2.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <td>-0.100000</td>\n",
       "      <td>-2.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <td>-1.067428</td>\n",
       "      <td>-2.631140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>2.138555</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <td>-1.067428</td>\n",
       "      <td>-2.631140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>2.138555</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <td>-1.067428</td>\n",
       "      <td>-2.631140</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.446941</td>\n",
       "      <td>2.138555</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.630000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.630000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6.630000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>3.060000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-1</th>\n",
       "      <td>-0.061446</td>\n",
       "      <td>-0.425084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.526508</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-2</th>\n",
       "      <td>-0.096261</td>\n",
       "      <td>-0.425084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.526508</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-3</th>\n",
       "      <td>-0.096261</td>\n",
       "      <td>-0.425084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.526508</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  min TEST  min TRAIN  pc_out_min   max TEST  max TRAIN  \\\n",
       "Features                                                                  \n",
       "UNRATE            3.500000   3.400000         0.0   4.300000  14.800000   \n",
       "Inflation         2.923566  -1.958761         0.0   4.120690  14.592275   \n",
       "FEDFUNDS          5.060000   0.050000         0.0   5.330000  19.100000   \n",
       "SPX_diff          0.026188  -0.425084         0.0   0.302883   0.526508   \n",
       "num_month         1.000000   1.000000         0.0  12.000000  12.000000   \n",
       "UNRATE-1_diff    -0.100000  -2.200000         0.0   0.300000  10.400000   \n",
       "UNRATE-2_diff    -0.100000  -2.200000         0.0   0.300000  10.400000   \n",
       "UNRATE-3_diff    -0.100000  -2.200000         0.0   0.300000  10.400000   \n",
       "Inflation-1_diff -1.067428  -2.631140         0.0   0.446941   2.138555   \n",
       "Inflation-2_diff -1.067428  -2.631140         0.0   0.446941   2.138555   \n",
       "Inflation-3_diff -1.067428  -2.631140         0.0   0.446941   2.138555   \n",
       "fedfunds-1_diff   0.000000  -6.630000         0.0   0.230000   3.060000   \n",
       "fedfunds-2_diff   0.000000  -6.630000         0.0   0.230000   3.060000   \n",
       "fedfunds-3_diff   0.000000  -6.630000         0.0   0.230000   3.060000   \n",
       "spx-1            -0.061446  -0.425084         0.0   0.302883   0.526508   \n",
       "spx-2            -0.096261  -0.425084         0.0   0.302883   0.526508   \n",
       "spx-3            -0.096261  -0.425084         0.0   0.302883   0.526508   \n",
       "\n",
       "                  pc_out_max  \n",
       "Features                      \n",
       "UNRATE                   0.0  \n",
       "Inflation                0.0  \n",
       "FEDFUNDS                 0.0  \n",
       "SPX_diff                 0.0  \n",
       "num_month                0.0  \n",
       "UNRATE-1_diff            0.0  \n",
       "UNRATE-2_diff            0.0  \n",
       "UNRATE-3_diff            0.0  \n",
       "Inflation-1_diff         0.0  \n",
       "Inflation-2_diff         0.0  \n",
       "Inflation-3_diff         0.0  \n",
       "fedfunds-1_diff          0.0  \n",
       "fedfunds-2_diff          0.0  \n",
       "fedfunds-3_diff          0.0  \n",
       "spx-1                    0.0  \n",
       "spx-2                    0.0  \n",
       "spx-3                    0.0  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_min = df.filter(list_feat).groupby(df[\"TRAIN\"]).min().transpose()\n",
    "df_min[\"feat\"] = df_min.index\n",
    "df_min[\"out_min\"] = df_min[0] < df_min[1]\n",
    "df_min[\"pc_out_min\"] = df_min[\"feat\"].apply(\n",
    "    lambda x: 100*sum(df[df[\"TRAIN\"] == 0 ][x] <  df[df[\"TRAIN\"] == 1][x].min()) / df[df[\"TRAIN\"] == 0 ].shape[0]\n",
    "    )\n",
    "assert df_min[\"pc_out_min\"].max() < 5, \"Some TEST features are not in Train !\"\n",
    "df_max = df.filter(list_feat).groupby(df[\"TRAIN\"]).max().transpose()\n",
    "df_max[\"feat\"] = df_max.index\n",
    "df_max[\"out_max\"] = df_max[0] > df_max[1]\n",
    "df_max[\"pc_out_max\"] = df_max[\"feat\"].apply(\n",
    "    lambda x: 100*sum(df[df[\"TRAIN\"] == 0 ][x] >  df[df[\"TRAIN\"] == 1][x].max()) / df[df[\"TRAIN\"] == 0 ].shape[0]\n",
    "    )\n",
    "assert df_max[\"pc_out_max\"].max() < 5 , \"Some TEST features are not in Train !\"\n",
    "df_range = df_min.copy()\n",
    "df_range.drop(columns=[\"feat\", \"out_min\"], inplace=True)\n",
    "df_range.rename_axis(columns=None, inplace=True)\n",
    "df_range.rename_axis(index=\"Features\", inplace=True)\n",
    "df_range.rename(columns={0: \"min TEST\", 1: \"min TRAIN\"}, inplace=True)\n",
    "df_range_max = df_max.copy()\n",
    "df_range_max.drop(columns=[\"feat\", \"out_max\"], inplace=True)\n",
    "df_range_max.rename_axis(columns=None, inplace=True)\n",
    "df_range_max.rename_axis(index=\"Features\", inplace=True)\n",
    "df_range_max.rename(columns={0: \"max TEST\", 1: \"max TRAIN\"}, inplace=True)\n",
    "df_range = df_range.join(df_range_max)\n",
    "df_range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check repart target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(\n",
    "    df,\n",
    "    x=\"class\",\n",
    "    hue=\"TRAIN\",\n",
    "    stat=\"probability\",\n",
    "    common_norm=False,\n",
    "    discrete=True,\n",
    ")\n",
    "\n",
    "plt.gca().set_title(\"Distribution of Target classes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Repart for  ff_lower_50 :\n",
      "- on TRAIN :\n",
      "   - proba:\n",
      " False    0.859052\n",
      "True     0.140948\n",
      "Name: ff_lower_50, dtype: float64 \n",
      "  - nb:  [707. 116.]\n",
      "- on TEST :\n",
      "   - proba:\n",
      " False    1.0\n",
      "Name: ff_lower_50, dtype: float64 \n",
      "  - nb:  [15.]\n",
      "\n",
      "Repart for  ff_lower_25 :\n",
      "- on TRAIN :\n",
      "   - proba:\n",
      " False    0.879708\n",
      "True     0.120292\n",
      "Name: ff_lower_25, dtype: float64 \n",
      "  - nb:  [724.  99.]\n",
      "- on TEST :\n",
      "   - proba:\n",
      " False    1.0\n",
      "Name: ff_lower_25, dtype: float64 \n",
      "  - nb:  [15.]\n",
      "\n",
      "Repart for  ff_stable :\n",
      "- on TRAIN :\n",
      "   - proba:\n",
      " False    0.609964\n",
      "True     0.390036\n",
      "Name: ff_stable, dtype: float64 \n",
      "  - nb:  [502. 321.]\n",
      "- on TEST :\n",
      "   - proba:\n",
      " True     0.933333\n",
      "False    0.066667\n",
      "Name: ff_stable, dtype: float64 \n",
      "  - nb:  [14.  1.]\n",
      "\n",
      "Repart for  ff_higher_25 :\n",
      "- on TRAIN :\n",
      "   - proba:\n",
      " False    0.782503\n",
      "True     0.217497\n",
      "Name: ff_higher_25, dtype: float64 \n",
      "  - nb:  [644. 179.]\n",
      "- on TEST :\n",
      "   - proba:\n",
      " False    0.933333\n",
      "True     0.066667\n",
      "Name: ff_higher_25, dtype: float64 \n",
      "  - nb:  [14.  1.]\n",
      "\n",
      "Repart for  ff_higher_50 :\n",
      "- on TRAIN :\n",
      "   - proba:\n",
      " False    0.868773\n",
      "True     0.131227\n",
      "Name: ff_higher_50, dtype: float64 \n",
      "  - nb:  [715. 108.]\n",
      "- on TEST :\n",
      "   - proba:\n",
      " False    1.0\n",
      "Name: ff_higher_50, dtype: float64 \n",
      "  - nb:  [15.]\n",
      "end\n"
     ]
    }
   ],
   "source": [
    "def print_repart(df_train, df_test, str_target):\n",
    "    print(\"\\nRepart for \", str_target, \":\")\n",
    "    print(\"- on TRAIN :\\n\",\n",
    "        \"  - proba:\\n\",\n",
    "        df_train[str_target].value_counts() / df_train.shape[0],\n",
    "        \"\\n  - nb: \", \n",
    "        df_train[str_target].shape[0]*(df_train[str_target].value_counts() / df_train.shape[0]).values,\n",
    "    )\n",
    "    print(\"- on TEST :\\n\", \n",
    "        \"  - proba:\\n\",\n",
    "        df_test[str_target].value_counts() / df_test.shape[0],\n",
    "        \"\\n  - nb: \", \n",
    "        df_test[str_target].shape[0]*(df_test[str_target].value_counts() / df_test.shape[0]).values,)\n",
    "\n",
    "for str_target in list_targets:\n",
    "    print_repart(df_train, df_test, str_target)\n",
    "\n",
    "print(\"end\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE', ylabel='FEDFUNDS'>"
      ]
     },
     "execution_count": 43,
     "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": [
    "sns.lineplot(data=df.dropna(), x=\"DATE\", y=\"FEDFUNDS\", hue=\"TRAIN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE', ylabel='FEDFUNDS_diff'>"
      ]
     },
     "execution_count": 44,
     "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": [
    "sns.lineplot(data=df.dropna(), x=\"DATE\", y=\"FEDFUNDS_diff\", hue=\"TRAIN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='DATE', ylabel='class'>"
      ]
     },
     "execution_count": 45,
     "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": [
    "sns.lineplot(data=df.dropna(), x=\"DATE\", y=\"class\", hue=\"TRAIN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>UNRATE</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>num_month</th>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <th>SPX_diff</th>\n",
       "      <th>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>...</th>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <th>class</th>\n",
       "      <th>TRAIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1954-10-01</td>\n",
       "      <td>5.7</td>\n",
       "      <td>-0.853432</td>\n",
       "      <td>10</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.342511</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.261292</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>0.42</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1954-11-01</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-0.260708</td>\n",
       "      <td>11</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.364898</td>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>-0.297508</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1954-12-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.372162</td>\n",
       "      <td>12</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.408377</td>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>-0.555924</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1955-01-01</td>\n",
       "      <td>4.9</td>\n",
       "      <td>-0.631032</td>\n",
       "      <td>1</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.398272</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.4</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>0.592725</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1955-02-01</td>\n",
       "      <td>4.7</td>\n",
       "      <td>-0.629863</td>\n",
       "      <td>2</td>\n",
       "      <td>1.29</td>\n",
       "      <td>0.413912</td>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>0.001169</td>\n",
       "      <td>-0.258870</td>\n",
       "      <td>-0.111455</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.45</td>\n",
       "      <td>3</td>\n",
       "      <td>1</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>914</th>\n",
       "      <td>2024-03-01</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.475131</td>\n",
       "      <td>3</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.302883</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>-0.217179</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3.357731</td>\n",
       "      <td>4</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.240453</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.059762</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.250210</td>\n",
       "      <td>5</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.262664</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.309388</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>2024-06-01</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2.975629</td>\n",
       "      <td>6</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.246186</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>-0.117400</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>2024-07-01</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.923566</td>\n",
       "      <td>7</td>\n",
       "      <td>5.33</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>-0.052063</td>\n",
       "      <td>-0.274582</td>\n",
       "      <td>-0.107521</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          DATE  UNRATE  Inflation  num_month  FEDFUNDS  SPX_diff  \\\n",
       "81  1954-10-01     5.7  -0.853432         10      0.85  0.342511   \n",
       "82  1954-11-01     5.3  -0.260708         11      0.83  0.364898   \n",
       "83  1954-12-01     5.0  -0.372162         12      1.28  0.408377   \n",
       "84  1955-01-01     4.9  -0.631032          1      1.39  0.398272   \n",
       "85  1955-02-01     4.7  -0.629863          2      1.29  0.413912   \n",
       "..         ...     ...        ...        ...       ...       ...   \n",
       "914 2024-03-01     3.8   3.475131          3      5.33  0.302883   \n",
       "915 2024-04-01     3.9   3.357731          4      5.33  0.240453   \n",
       "916 2024-05-01     4.0   3.250210          5      5.33  0.262664   \n",
       "917 2024-06-01     4.1   2.975629          6      5.33  0.246186   \n",
       "918 2024-07-01     4.3   2.923566          7      5.33  0.228461   \n",
       "\n",
       "     FEDFUNDS_diff  ff_lower_25  ff_lower_50  ff_higher_25  ...  \\\n",
       "81           -0.02        False        False         False  ...   \n",
       "82            0.45        False        False         False  ...   \n",
       "83            0.11        False        False          True  ...   \n",
       "84           -0.10         True        False         False  ...   \n",
       "85            0.06        False        False          True  ...   \n",
       "..             ...          ...          ...           ...  ...   \n",
       "914           0.00        False        False         False  ...   \n",
       "915           0.00        False        False         False  ...   \n",
       "916           0.00        False        False         False  ...   \n",
       "917           0.00        False        False         False  ...   \n",
       "918           0.00        False        False         False  ...   \n",
       "\n",
       "     UNRATE-2_diff  UNRATE-3_diff  Inflation-1_diff  Inflation-2_diff  \\\n",
       "81             0.1            0.2         -0.555924         -0.297508   \n",
       "82            -0.4            0.1          0.592725         -0.555924   \n",
       "83            -0.4           -0.4         -0.111455          0.592725   \n",
       "84            -0.3           -0.4         -0.258870         -0.111455   \n",
       "85            -0.1           -0.3          0.001169         -0.258870   \n",
       "..             ...            ...               ...               ...   \n",
       "914            0.2            0.0          0.309388          0.059762   \n",
       "915           -0.1            0.2         -0.117400          0.309388   \n",
       "916            0.1           -0.1         -0.107521         -0.117400   \n",
       "917            0.1            0.1         -0.274582         -0.107521   \n",
       "918            0.1            0.1         -0.052063         -0.274582   \n",
       "\n",
       "     Inflation-3_diff  fedfunds-1_diff  fedfunds-2_diff  fedfunds-3_diff  \\\n",
       "81          -0.261292            -0.22            -0.15             0.42   \n",
       "82          -0.297508            -0.02            -0.22            -0.15   \n",
       "83          -0.555924             0.45            -0.02            -0.22   \n",
       "84           0.592725             0.11             0.45            -0.02   \n",
       "85          -0.111455            -0.10             0.11             0.45   \n",
       "..                ...              ...              ...              ...   \n",
       "914         -0.217179             0.00             0.00             0.00   \n",
       "915          0.059762             0.00             0.00             0.00   \n",
       "916          0.309388             0.00             0.00             0.00   \n",
       "917         -0.117400             0.00             0.00             0.00   \n",
       "918         -0.107521             0.00             0.00             0.00   \n",
       "\n",
       "     class  TRAIN  \n",
       "81       2      1  \n",
       "82       4      1  \n",
       "83       3      1  \n",
       "84       1      1  \n",
       "85       3      1  \n",
       "..     ...    ...  \n",
       "914      2      0  \n",
       "915      2      0  \n",
       "916      2      0  \n",
       "917      2      0  \n",
       "918      2      0  \n",
       "\n",
       "[838 rows x 35 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feat on train\n",
    "xtrain = df[df[\"TRAIN\"] == 1][list_feat].values\n",
    "# feat on test\n",
    "xtest = df[df[\"TRAIN\"] == 0][list_feat].values \n",
    "# target on train\n",
    "ytrain = df[df[\"TRAIN\"] == 1][\"class\"].values\n",
    "# target on test\n",
    "ytest = df[df[\"TRAIN\"] == 0][\"class\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00290089, -0.35965725,  0.20002009,  0.94215697,  0.43433528,\n",
       "        -0.22650564,  0.47548912,  0.24042241, -0.89257936, -0.15294263,\n",
       "        -0.74473277, -0.00932664, -0.00850823, -0.00934766,  0.88019467,\n",
       "         0.98878507,  1.08986795]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X = scaler.fit_transform(xtrain)\n",
    "X_test = scaler.transform(xtest)\n",
    "\n",
    "# for last pred : (to predict next month value)\n",
    "x_for_pred = df_for_pred.filter(list_feat).iloc[-1].values.reshape(1, -1)\n",
    "X_for_pred = scaler.transform(x_for_pred)\n",
    "X_for_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaler_y = StandardScaler()\n",
    "\n",
    "#Y = scaler_y.fit_transform(ytrain.reshape(-1, 1))\n",
    "Y = ytrain.reshape(-1, 1)\n",
    "Y_test = ytest.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On Target CLass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_stable</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>ff_higher_50</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FEDFUNDS_diff  ff_lower_50  ff_lower_25  ff_stable  ff_higher_25  \\\n",
       "81           -0.02        False        False       True         False   \n",
       "82            0.45        False        False      False         False   \n",
       "83            0.11        False        False      False          True   \n",
       "84           -0.10        False         True      False         False   \n",
       "85            0.06        False        False      False          True   \n",
       "..             ...          ...          ...        ...           ...   \n",
       "914           0.00        False        False       True         False   \n",
       "915           0.00        False        False       True         False   \n",
       "916           0.00        False        False       True         False   \n",
       "917           0.00        False        False       True         False   \n",
       "918           0.00        False        False       True         False   \n",
       "\n",
       "     ff_higher_50  class  \n",
       "81          False      2  \n",
       "82           True      4  \n",
       "83          False      3  \n",
       "84          False      1  \n",
       "85          False      3  \n",
       "..            ...    ...  \n",
       "914         False      2  \n",
       "915         False      2  \n",
       "916         False      2  \n",
       "917         False      2  \n",
       "918         False      2  \n",
       "\n",
       "[838 rows x 7 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ff_lower_50', 'ff_lower_25', 'ff_stable', 'ff_higher_25', 'ff_higher_50']"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_targets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x2500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_corr(corr_matrix, title='Corrélation with 2 variables', aspect=1, size=25):\n",
    "    \"\"\"\n",
    "    Plot heatmap of correlations of all inputs between them\n",
    "    \"\"\"\n",
    "    fig_size = (size, size*len(corr_matrix.index)/len(corr_matrix.columns))\n",
    "    fig, ax = plt.subplots(figsize=fig_size)\n",
    "    im = ax.matshow(corr_matrix, aspect=aspect)\n",
    "    plt.xticks(range(len(corr_matrix.columns)), corr_matrix.columns, rotation=90)\n",
    "    plt.yticks(range(len(corr_matrix.index)), corr_matrix.index)\n",
    "    plt.title(title)\n",
    "    fig.colorbar(im, aspect=1/aspect, orientation='vertical', location=\"right\")\n",
    "    plt.show()\n",
    "nb_plot = len(list_feat)\n",
    "list_col_targets = [ f\"target_{n_t}\" for n_t in range(len([\"class\"]))]\n",
    "list_col_corr = list_feat[:nb_plot] + list_col_targets\n",
    "\n",
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "\n",
    "\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, Y)), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "\n",
    "fig = plot_corr(corr_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ett+9vb1xdXUtxYhExNEo0RApx6pUqcLhw4dt9zXZtGkTVatWLeWoRMSRaDKoSDl26tQpRowYwenTp/Hy8qJy5cosWbKE++67r7RDExEHoURDpJzLycnh9OnT5OTk0LBhQ1xctLyOiJQcJRoi5di2bdvybPP09MTHx4cGDRrc8XhExPHoq4tIObZ9+3a+/fZb2rZti5OTE1999RX16tXjypUrDB8+nGeffba0QxQRk1OiIVKOWSwWNm3aZJuTcebMGaZPn84//vEP+vfvr0RDTCEzM5Ps7OzSDsPuXFxccHNzK+0wikyJhkg5duHChVwTP+vXr8+5c+eoVKkSTk66KE3KvszMTI4ePUJ5mATg5ORE06ZNTZdsKNEQKcfc3d359NNP6dy5MwCfffYZHh4enDt3rlx8QxTzy87OxjDgD9W8cHd1Lu1w7CYjK4e4S6lkZ2ebLtHQZFCRcuyHH35g+PDhpKenY7VaqVy5MgsXLmTbtm3UrVuX4ODg0g5R5JZSU1OJjY2lcc0KeLk5bqKRmpnDT+eu0aRJE7y8vEo7nCJRRUOkHLty5Qo7d+7k2LFjODs706hRI5ydnXVHVxEpMUo0RMqxKVOmsGPHDpo0aVLaoYjcHsMKhqW0o7Afw1raERSbEg2Rcqxp06Zs2bKFRx55JNc9T2rWrFmKUYmII1GiIVKOffDBB3zwwQe5tlksFmJjY0spIhFxNEo0RMqxH3/8sbRDECkZVgOs5h1eKJDVvNdtKNEQKceysrL4/PPPuXbtGnD9vidnzpxh9OjRpRyZiDgKJRoi5diYMWP46aefuHTpEvfffz9Hjx6ldevWpR2WSDFYHXzRLvNWa7T0n0g5dvToUaKjo+nYsSNvvfUW69atIy0trbTDEhEHooqGSDlWo0YNXF1dadCgAf/973/p0qWLbRhFxFSsVjN/6S+YieefqKIhUo65urqyf/9+GjduzOeff87FixdJSkoq7bBExIEo0RApx1577TX++c9/8vjjj3Pq1Ckee+wxGjduXNphiRSdYfxv0S5HfZh3AoqGTkTKofPnz2MYBhMnTmT58uVcunSJ+fPnk5yczIgRI0o7PBFxIEo0RMqhCRMmsH//fgA6dOiAYRhYLBacnZ1td3IVESkJSjREyqHly5cDEBoayqxZs0o5GpESYOSA1ZHvdWLeoRPN0RApx5RkiIi9qaIhIiLmZxim/tZfIAPAnBUbVTRERETEblTREBHg+pUoGzZs4LnnnqNGjRqlHY5I0Tj8gl0AzqUdRbGooiEiAFy4cIFFixZx4cKF0g5FRByIKhoiImJ6huHYN1W73jdVNERERERyUaIhIiIidqOhExERMT/DAKsjj52Y89JWUKJR5mVnZ3PlyhXc3d1xclIBSuzHarXSoEEDrFYrqamppR2OODCr1UpGRgaVK1fGxUV/hhydxTAcefqM+V26dInTp0+XdhgiIiWuQYMGVKtW7bbaSE1NJTY2lkZe6Xg6O+6fs7QcCydSPWjSpAleXl6lHU6RKJUs49zd3QEInzSLM2fiSzkacWSeHu5EvvM3Br88jrT0jNIORxxY/fp1mTo51Pb5Jo5NiUYZd2O45MyZeI4dO1HK0Ygjq+DlCcB//3uKa6lppRyNlAclOhxstYLFgVfsspp36Ny8kYuIiEiZp0RDRERE7EZDJyIi4gCsYDjw0ImJqaIhIiIidqOKhoiImJ/VwKFv32o174JdqmiIiIiI3aiiISIi5mc4+BwNEy9BroqGiIiI2I0qGiIiYn6G9fqiXY7KxBUNUyYaPj4+7N27l1q1atm2HTx4kLFjx7J7925iYmJ44YUXWLp0Kf7+/rZ9lixZws8//8yMGTPYsmULU6ZMoV69egAYhsGvv/7KAw88wLRp0/D29rYdt23bNsaNG8eiRYvo1KkTOTk59OzZE4CsrCxOnDjBAw88AMDdd9/NihUr8Pf3x8XFBU9Pz1yxDxkyhCeffNJur42IiEhZYspEozBcXV2ZMGEC0dHR1KxZM999WrRowapVq2y/Z2Rk0LdvX1auXMmYMWNs29etW0ePHj1YuXIlnTp1wtnZmejoaADi4+Pp3Lmz7fffmjZtGo8++mjJdkxERMREHDbRqF27Nr6+voSGhrJ69epCral/+fJlrl69mquaceTIEU6ePMny5cvx8/Pju+++o0WLFvYMPV+eHu62e1GI2IPX/95fXnqfiZ15epT8zdQMw4pBTom3W1YYGjopm8aOHUtQUBBvv/02Q4cOzfP8d999R2BgIJmZmSQnJ1O/fn2Cg4Pp37+/bZ9169bRtWtXKleuTNeuXVm5ciXz588v1PknTJiQZ+hkzZo1VKpUqch9iXznb0U+RqQ4Pvzn6tIOQUQciCkTDYslb2ZnGEaeqoWbmxtz5syhd+/etGnTJs8xN4ZODMNg2bJlbNy4kc6dO+Ps7AxAUlISO3bssA2v9OzZk/79+xMfH0/dunULjLMkh04GvzyO//73VIm0JZIfLy9PPvznap56+i+k6u6tYkf33XdvyX95Mqw49IJdJr5015SJhre3N5cvX841GfTixYtUqVIlz76NGjUiLCyM0NBQunTpkm97FouFwYMH89133zF69Gjee+89nJyceP/997FarYSGhtr2dXJyYvXq1UycOLHE+3UraekZunW33BGpqWl6r4ldpaVnlHYIcgeZch0NX19f1qxZQ07O9fG4a9eusX79ejp06JDv/kFBQbRo0YK1a9fest1JkyZx7NgxoqKisFqtrF+/ngkTJrB7927bY/Lkybz//vukpKSUdLdERKS4rMb1y1sd9mGU9itcbKasaEycOJGZM2fSvXt3XFxcyM7OpnPnzrz88ss3PWby5MkcPnz4lu3WrFmTYcOGMXfuXCpUqEBaWhrPPvtsrn0CAgJYunQpGzZsYNCgQbdsL785Gv7+/oSEhBTQQxEREcdgMQzDvGlSOZCamkpsbCz9/zKSY8dOlHY44sAqeHmyZ/dG/Px7a+hE7MrHpxFrVs+jSZMmeHl53VZbNz4jG5KIJ1klFGHZk4YrJ6lVIq/ZnWbKoRMRERExByUaIiIiYjemnKMhIiKSi9UKDrxgFzibtjRg0rBFRETEDFTREBER83P0BbtM3DdVNERERMRuVNEQERHzsxqY+Vt/wQxwLu0YikcVDREREQe1b98+AgMDefLJJxk4cCDnz5/Ps8+pU6fo378/AQEBPP3002zevLlEY1CiISIi4oCSkpIIDQ1l5syZfPzxx/j5+TF+/Pg8+40fP57AwEC2b9/Ou+++y9SpU4mLiyuxODR0YhLPV/8jyb8WfMdYkeJy8XQDYFjtx8hOyyzlaMSReVevYYdWraa+w2mBLNf7duLEiTx3KgeoXr06NWrkfl3379+Pj48PPj4+APTp04eZM2dy4cIFqlevbttv3bp1truinz17FhcXlzy3z7gdSjRERERMIjQ0lNOnT+fZPnz4cEaMGJFrW2JiIrVr17b97ubmhre3N2fPns2VaDg7X5/8ERQUxNGjRxkwYAB33313icWsRENERMzPWj4qGrNmzbppReP3DMOwVSp+K7/jATZt2sSFCxf4y1/+QuPGjfPcVLS4lGiIiIiYRKNGjQp9U7U6depw4MAB2++ZmZkkJydTp04d2zar1cpHH31Ep06dcHNzo3r16vj5+fHDDz+UWKKhyaAiImJ+VuN6VcNhH0W/0fqf//xnfvjhB44fPw5cr1i0aNGCqlWr2vZxcnLinXfeYdOmTQAkJyezZ88e2rRpUzL/LqiiISIi4pCqVq3K3LlzGTduHBkZGVSrVo2ZM2cCEBgYyNSpU3nooYeYN28eb731Fhs2bMAwDIKDg+nUqVOJxaFEQ0RETM8gB8Nw3JuqGZbi9a1du3Zs3bo1z/bo6Gjbzw0bNmT16tXFjq0gGjoRERERu1GiISIiInajoRMRETG/G5NBHVbRJ4OWFapoiIiIiN2ooiEiIuZnOPiCXSbumyoaIiIiYjcOlWj4+/tz8ODBW+6zdetWfH19CQ4OZty4cSxZsqTAdhcvXsyOHTsAiIqKYtGiRSUSr4iIlBCjtBfUsvNDFQ3z2Lp1K8OHD+e9997Ldw34/Bw4cICsrCwA+vbty/Dhw+0ZooiIiMNwyDka/v7+BAYG8vXXX5OQkEDHjh0JDw9n6tSpHDlyhLi4OC5evJjrmCNHjvC3v/2NzMxMzp8/T7NmzZgzZw7vvfceR48eJT4+HovFwpkzZ0hMTCQiIoJz584xefJkzpw5g8ViwdfXl5CQEFxdXW8aQ3E5u7vabuMtYg833l96n4m9Obu7lnyjhmHqb/0FMsx71YlDJhoAKSkprFu3jgsXLtCpUyf69OlDeHg4x44do1evXgQGBhIWFmbbf82aNQwePJjHH3+czMxMAgMD2bNnDwMGDGDXrl306tWLgIAAFi5caDsmNDSUNm3asHjxYtLT0xkyZAjLli1j6NChN42hcePGxerPExF9b+8FESmkZ5a/UtohiIgDcdhEw9/fH7h+69y7776bpKSkW+4fERHBv/71LyIjIzl16hSXLl0iNTX1pvunpqZy8OBBli5dCoCHhwfBwcFERkbaEo2ixnArOydGcfn0hWIfL1IQF083nln+CtteepvstMzSDkccWJUG1fXlqRxx2ETD3d3d9rPFYsEooOzUv39/GjRogK+vLx07diQuLu6WxxiGked5q9VKTs7/r0df1BhuJScjSx/+ckdkp2XqvSZ2lZORVfKN3pg06ags5u1buZsMmp+UlBQOHTrE6NGj6dq1KxkZGRw9etSWNDg7O+dKIAAqVKjAI488YrsRTXp6OuvXr+exxx674/GLiIiUVQ5b0SiKSpUqMWzYMPr06UOVKlWoUKECrVu35syZM8D1IZA5c+aQkZGR67hZs2YxZcoUunfvTmZmJu3bt9cVKSIipUGTQcssi3E79Xyxu9TUVGJjY/kk9B8knzxf2uGIA3PxdKPXuld5v98CDZ2IXXk3rEGXWS/QpEkTvLy8bqutG5+R9yYfxjPnWglFWPakOVfglHfzEnnN7jRVNERExPwMB5+j4WTevmmOhoiIiNiNKhoiImJ+jn7ViYn7poqGiIiI2I0SDREREbEbDZ2IiIj56fLWMksVDREREbEbVTRERMT8HP3yVhNXa1TREBEREbtRRcMkjmYn80u27t4q9uOe7U4v4N/ZF8nIzihwf5HiuifblS4l3ajmaJRZqmiIiIiI3SjREBEREbvR0ImIiJifVgYts1TREBEREbtRRUNERByA1bEng2LevqmiISIiInajioaIiJif1TD1PIYCWXV5q4iIiEgeqmiIiIj56aqTMksVDREREbEbJRoiIiJiNxo6ERERB2CY+n4gBTNv3+54ouHj40Pjxo1xcfn/U9euXZu3336bsLAw9u/fT7Vq1XId0759e0JDQ1m4cCFr166lVq1aAGRlZVGjRg3GjBnDQw89BHDTNmbMmEFKSgrh4eF89tlnuZ5bsmQJP//8MzNmzGDhwoWsWbOGbdu2UadOHds+AwYMICAggJ49e+Y5R0ZGBg0bNmTcuHHUr18fgIsXLzJ9+nSOHTuGxWLB1dWVF154gWeeeaZkXkgRERETKJWKxooVK2zJwu8FBwczdOjQmx77xBNPEBERYfs9OjqagQMHsn37dlticLM2YmJiChVfWloaoaGhrFmzBmdn5wLjNAyDyMhIXnjhBT788EMqVqxIeHg4Dz/8MLNnzwYgISGB3r17U7NmTdq2bVuoOEREpJB0eWuZZfqhk8DAQKKjo3nvvfcIDQ0tkTa7du3KTz/9xKJFiwgJCSlwf4vFwssvv8yWLVv44IMP6Nu3L+fOnSMjI4OcnBycnZ2pU6cOS5Yswdvbu1gxuXm44e7pXqxjRQrjxvtL7zOxNzcPt9IOQe6gUkk0XnzxxVxDJzNnzsTHxweA9957j08++STX/uPGjaNdu3Y3bc/Hx4fjx4/bfv99G08++SRDhgwpdHyurq7MmTOHXr160bZtW/70pz8V6rjfxjFu3DjGjh3L2rVradmyJX/84x/p1q0bf/jDHwodx2+NmFH4+EVux5urJpZ2CCJFp8tbyyzTDZ3kx2Kx4OHhUWAbTk75X2RjtVrzPNegQQPCw8N57bXXiI6OLnIcbdq0Yc+ePRw+fJhvvvmGr776isWLF7No0SJ8fX0L2zWbhWFLSTh1tsjHiRSWu6c7b66ayJsDIshIyyjtcMSB1bm3tr48lSOmHzoBOHLkCK1bty5wP29vb65cuZJn+6VLl6hSpUqe7c888wxffPEFEyZMKLBtwzD4/vvv8fPz49KlS8yfP5/XX3+dli1b0rJlSwYPHszcuXN57733ipVoZKZn6sNf7oiMtAy918SuMtMz7dCq4eA3VTPvHA3Tr6Px/vvvc+zYMYKDgwvct2HDhtx1111s3brVti0+Pp5PP/2UDh065HvMG2+8wU8//cS///3vm7abk5PD4sWLAejWrRuVKlXiwIEDrFixAuv/yl2ZmZnExcXRrFmzIvRORETE3MpcRSO/ORo1atRg2bJlAOzcuZOjR48C14c87rnnHtauXUvVqlULbNvJyYnIyEhmzJjBihUrAHBzc2Ps2LE3nYdRsWJF5syZQ58+fW4aZ3Z2Ng888ABr1qzBze36JKeVK1cye/ZsnnjiCTw9PTEMgy5duhR5WEhERMTMLIbh0CucmF5qaiqxsbHMHjmfX04mlHY44sDcPd2ZvmEy45+bpKETsat7GtZhzLwQmjRpgpeX1221deMz8t4TO/FMv1wyAZZBaR5VONXoiRJ5ze400w+diIiISNlV5oZOREREisxw8CXITdw3VTRERETEblTREBER8zMcfAlyVTRERERE8lKiISIiInajoRMRETE/3eukzFJFQ0REROxGFQ0RETE/w+rY9zoxcd+UaJjEG5WdcKmqApTYj+HhxBVgibcTFk+918R+sis7cbW0g5A7RomGiIiY3vWChnkvAS2IiQsamqMhIiIi9qOKhoiImJ/h4FedmLikoYqGiIiI2I0SDREREbEbDZ2IiIj56fLWMksVDREREbEbVTRERMT8rIADX96KeQsaqmiIiIiI/aiiISIi5qfLW8ssVTRERETEblTREBER89Nt4sssVTRERETEbkydaBw+fJi//OUvdO/ene7du/PCCy/wn//8BwB/f386d+5MYGAgzzzzDE8++SQhISEkJyeTnZ1NcHAwr7/+eq723n33XXr16kVGRkaR4ujUqRMxMTEABAYGcunSJQDefvttOnTowMiRI/niiy/w8/OjZ8+eXL58+bb7LiIiYgamHTrJzMxk0KBBLFmyhEceeQSAL774ghdffJHdu3cDMG3aNB599FEArFYrI0eO5I033mDBggXMmzePnj17snXrVnr06MHevXtZtWoVGzduxN3dvdhxRUdH237etGkT06ZNo127dowfP56AgABGjRpVvIbd3DA8ih+XSEFuvL/0PhO7c3Mr+TYN4/rDUZm4b6ZNNNLS0khJSSEtLc227c9//jNz5szB2dk5z/5OTk60bt2aqKgoAGrUqMG8efN4+eWXueuuu5g0aRJLliyhZs2aBZ770KFDTJ48GavVSuPGjUlPT7c95+Pjw969e5k2bRrnzp3jzTff5Nlnn2XXrl24ubmRkpLCG2+8UeT+Xp34cpGPESmOlHnjSzsEEXEgpk00KleuzLhx4xg+fDje3t60bNmSRx99lKeffpqKFSvm2f/y5cvs2LGDtm3b2rY9+uijDB06lGHDhjFp0iQefvjhAs+blZXFiBEjmDJlCn5+fnz11Vd8+OGHefZbsGAB/v7+tqrKqVOnqFevHkOHDi1WfytGvINz3NliHStSGIaHOynzxlNp5HQs6UUbPhQpipw/1C75L0+GYeoJkwVSRaN0DBgwgN69e/PNN9/w7bffEhUVxZIlS9i4cSMAEyZMwNPTE8MwMAyDtm3b5hq6sFqtxMTEUKNGDfbs2UPfvn1xcrr1tJXjx4+TnZ2Nn58fAG3btuXee++1XydvyMzUh7/cEZb0DL3XxL4yM0s7ArmDTJtoHDx4kG+//ZbBgwfj6+uLr68vI0eOpF+/fnz00UdA7jka+ZkzZw4pKSls376dPn36sGDBAkaOHFnguY3fZZYuLqZ9GUVEHIPVcPAlyM3bN9NedVK1alWWLl3KV199ZduWlJREcnIyzZo1K/D4Dz/8kG3btjF//ny8vb1ZuHAhq1evZs+ePbc8zsfHBw8PDz799FPg+pUvJ06cuL3OiIiIOCjTfhVv2LAh77zzDgsXLmTixIl4enri7u7OsGHDaN269S2P/eGHH5g0aRLvvPOObfLn/fffz1tvvcXYsWPZvHkz9erVy/dYFxcXlixZwhtvvMGiRYto0KDBnRk6ERERMSGL8ftxAClTUlNTiY2NpeIbC3E5k1Da4YgDMzzcufL2m1R+5U3N0RC7yq5fh6tvjaBJkyZ4eXndVls3PiMbfL0Bz6sXSyjCsiet4t2c/tNzJfKa3WmmrWjY07Rp02wLcP3ewIEDeeaZZ+5sQCIiIialRCMfEyZMKO0QRESkKAxMPWGyQCbummkng4qIiEjZp4qGiIiYn9WK4cgLdpm4b6poiIiIiN2ooiEiIuZnOPiCXSa+QFQVDREREbEbJRoiIiJiNxo6MYmXzp3nRPwvpR2GODBPLw82Al0SEkhLTS/tcMSBNfJwYV5JN2oYYJh3wmSBNHQiIiIikpcqGiIiYn66e2uZpYqGiIiI2I0qGiIiYn6G1dSLWhXIxPNPVNEQERERu1FFQ0REzE9zNMosVTRERETEbpRoiIiIiN1o6ERERMxPC3aVWapoiIiIiN2ooiEiIuanu7eWWapoiIiIiN2ooiEiIqZnWK0YDrxgl5n7popGCVu8eDE7duwAICwsjCVLlpRyRCIiIqVHFY0SduDAAerWrVvi7bp7uOPp5VHi7Yrc4Onpkev/Ivbi7uFe8o1acew5GuYtaJReohETE8PMmTO5//77+fHHH0lLSyM8PJxDhw6RmJhIREQEAEuWLOHnn39mxowZ9O/fn2bNmnHgwAEuXbrE008/TZUqVdizZw+JiYlMmDCBTp063fK8hW3j6tWrTJ06lSNHjmCxWHj44YcJCwujYsWK9O/fn4ceeojDhw+TkJBAixYt+Pvf/87atWs5evQo8fHxWCwWAI4cOULfvn1JSkqievXqzJ49m5o1axb59frbvPCiv8gixbB6w4LSDkFEHEipVjS+//573njjDZo3b84//vEP5s2bh6+v7y2POXHiBJs3b+bs2bP4+/szfvx4oqKi2L59OwsXLiww0ShsGxEREbi4uPDBBx9gGAYTJkxg+vTptgTo1KlTrF69moyMDLp168YXX3zBgAED2LVrF7169SIgIIAvv/ySs2fPsnbtWipUqMCQIUOIiopi5MiRRX6txo2cyqmTPxf5OJHC8vT0YPWGBfzluVdJS0sv7XDEgd3bsJ6+PJUjpZpoVK9enebNmwPw4IMPsmrVqgKP6dSpE05OTtxzzz04OzvToUMHAOrVq8fly5cLdd7CtLF3717effddnJyuT2P5y1/+wksvvWRrw8/PD2dnZ7y8vKhfvz5JSUn5nsvf35+KFSva+nj+/PlCxfh7GekZpKXqw1/sLy0tXe81sauM9IySb1SXt5ZZpToZ1MMj71iwxWLB+M0LmpWVlet5Nze3XL+7uBQ9VypMG9bfzfC1Wq3k5OTYfnd3//8xxt/HfLO2b7WfiIiIIypzV514e3tz7NgxrFYraWlp7N+/v1TiePzxx1mzZo0twVizZg2PPfZYgcc5OzvnSkhEROQOMKyO/zCpMpdoBAQEUKVKFTp37sxLL71Ey5YtSyWOiRMnkp2dTUBAAN26dQNg0qRJBR7n7+/PnDlziIqKsneIIiIiZZ7FUC2/TEtNTSU2NpaRr7zOiZ/OlHY44sA8vTzYuD2S3gGDNUdD7KpR4/rMe3sKTZo0wcvL67bauvEZWX/HO3gkJ5ZQhGVPunctznR7uUReszvN4dbRWL58OR988EG+zz311FMMHjz4DkckIiJSfjlcovHSSy/lujpERERESo/DJRoiIlL+GAYYDnx5q5knOZS5yaAiIiLiOFTREBER87M6+IJdJu6bEg0REREHtW/fPmbNmkVGRga1a9fmb3/7GzVq1Mi1z/Hjx5kyZQopKSlkZWXRs2fPEp3rqERDRETMz7CC1byLWhWoGAt2JSUlERoayj/+8Q98fHz4xz/+wfjx41mxYkWu/UaMGEFISAjdunXjypUrBAUFcd999xV477HCUqIhIiJiEidOnLDdg+u3qlevnqdSsX//fnx8fPDx8QGgT58+zJw5kwsXLlC9enXg+m0+XnzxRTp37gxA5cqVqV+/Pr/88kuJxaxEwySuZWfwa2ZaaYchDiznf58GVzPTSdV7TezoWrYdbqpWTuZohIaGcvr06TxPDx8+nBEjRuTalpiYSO3atW2/u7m54e3tzdmzZ22JhqurK71797bts3fvXr799lveeuutEgtdiYaIiIhJzJo166YVjd8zDAOLxZJne37HA2zcuJHZs2ezcOFC6tSpc/vB/o8SDREREZNo1KhRoZcgr1OnDgcOHLD9npmZSXJycp4kIjs7m8mTJ/Pll1/a5nOUJK2jISIi5mfl/4dPHPJR9Jfkz3/+Mz/88APHjx8HYNOmTbRo0YKqVavm2m/s2LH89NNPbN68ucSTDFBFQ0RExCFVrVqVuXPnMm7cODIyMqhWrRozZ84EIDAwkKlTp2KxWPjwww+pV68eL7zwgu3YPn360Ldv3xKJQ4mGiIiYnmEYOPLNyIvbt3bt2rF169Y826Ojo20/Hzt2rNhxFYaGTkRERMRuVNEQERHzMxz88lYTV2tU0RARERG7UUVDRETMr5ws2GVGqmiIiIiI3SjREBEREbvR0ImIiJieYRgYJh5eKIiZL91VRUNERETspkiJho+PD4mJibm2HTx4EH9/fwBiYmLw8fFh9+7dufZZsmQJYWFhAGzZsoWWLVsSGBhIYGAgAQEB+Pn5MWTIEJKTk3Mdt23bNnx8fPjss88AyMnJsR3XrVs3fHx8bL+/+OKLAPj7+9O5c2fb9huPjz/++JZ927dvH08++WRRXo58X4Ndu3YxadIkAK5evUpwcDDdunVj27ZtvPnmm/j7+xMREVGs84iIyE1oCfIyq8SHTlxdXZkwYQLR0dHUrFkz331atGjBqlWrbL9nZGTQt29fVq5cyZgxY2zb161bR48ePVi5ciWdOnXC2dnZtppZfHw8nTt3zrW62Q3Tpk3j0UcfLVS8165dY8mSJaxfvz7P+u/F0bFjRzp27AhAbGwsv/zyC3v37gUgLCyMTz75hPr16xe5XQ8Pd7y8PG87PpGb8fTyyPV/EXvx8HAv7RDkDirxRKN27dr4+voSGhrK6tWrb3o72t+6fPkyV69exdvb27btyJEjnDx5kuXLl+Pn58d3331HixYtSjpc9uzZg9VqZcaMGbY14Atj8eLFbNu2jcqVK3PffffZtm/ZsoXt27cTHh7OhAkTuHTpEoGBgWRmZmIYBkOHDiUsLIz27dsXKc6Fi6YUaX+R4tq8JbK0QxApOium/tZfIBP3zS6TQceOHUtQUBBvv/02Q4cOzfP8d999Z/vjm5ycTP369QkODqZ///62fdatW0fXrl2pXLkyXbt2ZeXKlcyfP79Q558wYQKenrm//a9Zs4ZKlSrl2ffpp5/m6aefJiYmptD9+/zzz4mOjmbz5s3cddddTJgwIc8+jRs3ZurUqYSHh9uqLj4+PqxYsYJatWoV+lw3jBj+Oid+OlPk40QKy9PLg81bInm252DSUtNLOxxxYI0a19eXp3KkSImGxWLJs80wjDxVCzc3N+bMmUPv3r1p06ZNnmNuDJ0YhsGyZcvYuHEjnTt3xtnZGYCkpCR27NhhG17p2bMn/fv3Jz4+nrp16xYYZ1GGTopj//79dOzY0Za49OnTp0iJSnGkp2eQmppm13OIAKSlpuu9JnaVnp5R2iHIHVSkyaDe3t5cvnw517aLFy9SpUqVPPs2atSIsLAwQkND+fXXX/Ntz2KxMHjwYHx8fBg9ejRW6/Xa0Pvvv4/VaiU0NBR/f39ee+01nJycWL16dVHCzWPXrl22yaGDBg26rbZ+y8VFVwmLiJSq/13e6qiPcnOvE19fX9asWUNOTg5wfSLl+vXr6dChQ777BwUF0aJFC9auXXvLdidNmsSxY8eIiorCarWyfv16JkyYwO7du22PyZMn8/7775OSklKUkHPp2LEj0dHRREdHs2zZsmK34+vryyeffGK7Sia/W/CKiIhIEYdOJk6cyMyZM+nevTsuLi5kZ2fTuXNnXn755ZseM3nyZA4fPnzLdmvWrMmwYcOYO3cuFSpUIC0tjWeffTbXPgEBASxdupQNGzYUWI3Ib46Gv78/ISEhBfSwcNq3b0///v157rnnqFixIg8//HCJtCsiIsWke52UWRbDzMuNlQOpqanExsYy6KWx/Pf4qdIORxyYl5cnOz7+B92efEFzNMSu7rv/XpYtn0mTJk3w8vK6rbZufEbW/cdc3M//UkIRlj0ZNe4h/oVRJfKa3WmaXJCPq1ev0q9fv5s+f7MrWEREpJQYmPoS0AKZuCSgRCMfFStWzHchMBERESkaJRoiImJ6tqszHJSZ+6abqomIiIjdKNEQERERu9HQiYiImJ/udVJmqaIhIiIidqOKhoiImJ5hOPhkUBMveaVEwyQqunhQ2c1ci7SIuXi6eQBQyc0T1+y8N1AUKSkVXTxKOwS5g5RoiIiI+WmORpmlORoiIiJiN6poiIiI6RkGGCb+1l8QE0/RUEVDRERE7EeJhoiIiNiNhk5ERMT8NBm0zFJFQ0REROxGFQ0RETE9w+rgk0FN3DdVNERERMRuVNEQERHzMzD1PIYC6fJWERERkbyUaIiIiIjdaOhERETMz8Eng5p5WKhIFQ0fHx8SExNzbTt48CD+/v4AxMTE4OPjw+7du3Pts2TJEsLCwgDYsmULLVu2JDAwkMDAQAICAvDz82PIkCEkJyfnOm7btm34+Pjw2WefAZCTk2M7rlu3bvj4+Nh+f/HFFwHw9/enc+fOtu03Hh9//HG+ffrmm2/o3bs3gYGBdO/enW3bthXlJQEgOjqa/v37AxAVFcWiRYsAOHv2LN27dycgIIB9+/YxdOhQOnXqRGRkZJHPISIiYkYlXtFwdXVlwoQJREdHU7NmzXz3adGiBatWrbL9npGRQd++fVm5ciVjxoyxbV+3bh09evRg5cqVdOrUCWdnZ6KjowGIj4+nc+fOtt9/a9q0aTz66KMFxpqens6wYcN45513aNmyJQkJCfTo0YOmTZty3333FbHn1/Xt29f2c0xMDBUrViQqKoqEhAR2797N4cOHcXNzK3K77h5ueHrp1spiP56eHrn+L2Iv7h5F/wwsiO51UnaVeKJRu3ZtfH19CQ0NZfXq1Tg5FVw0uXz5MlevXsXb29u27ciRI5w8eZLly5fj5+fHd999R4sWLUo01oyMDCZMmEDLli0BqFOnDtWqVSMhIeGWiUZ2djYzZszg888/p2rVqvzhD3+wPbdw4UISExN56qmnmDdvHleuXKFLly44OztjGAZBQUHMnDkTHx+fIsU6Y1548TopUkTvbphf2iGIiAOxyxyNsWPHEhQUxNtvv83QoUPzPP/dd98RGBhIZmYmycnJ1K9fn+DgYNvwA1yvZnTt2pXKlSvTtWtXVq5cyfz5hfsAnDBhAp6enrm2rVmzhkqVKuXaVrlyZZ555hnb7+vXr+fq1asFVkM2bNjADz/8wIcffojFYuGVV17Js0+7du149dVX2b59O6tWrbplBaYwwkZO5fTJuGIdK1IYnp4evLthPgOfCyEtLb20wxEH1qDhH0r8y5MW7Cq7ipRoWCyWPNsMw8hTtXBzc2POnDn07t2bNm3a5DnmxtCJYRgsW7aMjRs30rlzZ5ydnQFISkpix44dtuGVnj170r9/f+Lj46lbt26BcRZ26OS3fVi8eDHr169n+fLlVKhQ4Zb779u3j6eeegp3d3cAevXqRVRUVKHPVxwZ6ZmkperDX+wvLS1d7zWxq4z0zNIOQe6gIk0G9fb25vLly7m2Xbx4kSpVquTZt1GjRoSFhREaGsqvv/6ab3sWi4XBgwfj4+PD6NGjsVqvp2zvv/8+VquV0NBQ/P39ee2113BycmL16tVFCTePXbt22SaHDho0CIBr164xdOhQdu/ezaZNm3jggQcK1ZbxmwEzFxddvCMiUqoMi+M/TKpIiYavry9r1qwhJycHuP5Hev369XTo0CHf/YOCgmjRogVr1669ZbuTJk3i2LFjREVFYbVaWb9+PRMmTGD37t22x+TJk3n//fdJSUkpSsi5dOzYkejoaKKjo1m2bBnZ2dkMGjQIV1dXoqKiqF27dqHa6dChA9u3byctLY3s7OxiD4eIiIg4uiJ9FZ84cSIzZ86ke/fuuLi4kJ2dTefOnXn55ZdveszkyZM5fPjwLdutWbMmw4YNY+7cuVSoUIG0tDSeffbZXPsEBASwdOlSNmzYYKtG3Ex+czT8/f0JCQnJtW3Xrl38+9//plGjRvTu3du2/dVXX6Vjx443bb93797ExcXRvXt3qlSpwgMPPHBbCZCIiIijshiGmS+acXypqanExsYy6pVJnPzpTGmHIw7M08uD9dvfoU/Ay5qjIXbVsHF95r49mSZNmuDl5XVbbd34jKw+ew5u8b+UUIRlT2bde7gwZnSJvGZ3miYX5OPkyZOMGjUq3+c8PT1Zv379HY5IRETEnJRo5KNhw4aadyEiYiKG1YJhNe+EyYKYuW+6qZqIiIjYjSoaIiJifg6+YFe5uamaiIiISFGooiEiIqZnGBYMEy9qVRAz900VDREREbEbJRoiIiJiNxo6MYkKLu7c5epZ8I4ixeTp6gHAXa6euLiat0wrZV8FF/cSb9MwHHsyqJmX1lRFQ0REROxGFQ0RETE9LdhVdqmiISIiInajioaIiJieYZh7HkNBzNw3VTRERETEbpRoiIiIiN1o6ERERMzPMPeEyQJp6EREREQkL1U0RETE9HR5a9mlioaIiIjYjSoaIiJierq8texSRUNERETsRhUNERExPcNw8Dkahnn7VqSKho+PD4mJibm2HTx4EH9/fwBiYmLw8fFh9+7dufZZsmQJYWFhAGzZsoWWLVsSGBhIYGAgAQEB+Pn5MWTIEJKTk3Mdt23bNnx8fPjss88AyMnJsR3XrVs3fHx8bL+/+OKLAPj7+9O5c2fb9huPjz/+ON8+/ec//6F3794EBATwzDPP8PnnnxflJQEgOjqa/v37AxAVFcWiRYsAOHv2LN27dycgIIB9+/YxdOhQOnXqRGRkZJHPISIiYkYlXtFwdXVlwoQJREdHU7NmzXz3adGiBatWrbL9npGRQd++fVm5ciVjxoyxbV+3bh09evRg5cqVdOrUCWdnZ6KjowGIj4+nc+fOtt9/a9q0aTz66KMFxmq1Whk5ciTTp0+nbdu2HDt2jOeee46vvvoKT8/i3ZK9b9++tp9jYmKoWLEiUVFRJCQksHv3bg4fPoybm1uR23X3cMfTy6NYMYkUhoene67/i9iLu4feY+VJiScatWvXxtfXl9DQUFavXo2TU8FFk8uXL3P16lW8vb1t244cOcLJkydZvnw5fn5+fPfdd7Ro0aJEY3VycuKzzz7D1dUVgLi4OCpWrIizs/Mtj8vOzmbGjBl8/vnnVK1alT/84Q+25xYuXEhiYiJPPfUU8+bN48qVK3Tp0gVnZ2cMwyAoKIiZM2fi4+NTpFhfnxVa9A6KFMPCtX8r7RBEiswwLKYeXiiImftmlzkaY8eOJSgoiLfffpuhQ4fmef67774jMDCQzMxMkpOTqV+/PsHBwbbhB7hezejatSuVK1ema9eurFy5kvnz5xfq/BMmTMhTkVizZg2VKlXKs6+rqytZWVl06tSJc+fOMXHixAIrDhs2bOCHH37gww8/xGKx8Morr+TZp127drz66qts376dVatW3bICUxhTQmfx86n4Yh0rUhgenu4sXPs3Rjw/jvS0jNIORxxYvXvr6stTOVKkRMNiyZtRGYaRp2rh5ubGnDlz6N27N23atMlzzI2hE8MwWLZsGRs3bqRz5862SkJSUhI7duywDa/07NmT/v37Ex8fT926dQuMs7BDJze4urry+eefc/r0afr160ejRo1o27btTffft28fTz31FO7u18t/vXr1IioqqtDnK46M9AzSUtPteg4RgPQ0vdfEvjLS7ZDIWsGwlnyzZYaJ+1akyaDe3t5cvnw517aLFy9SpUqVPPs2atSIsLAwQkND+fXXX/Ntz2KxMHjwYHx8fBg9ejRW6/VX8v3338dqtRIaGoq/vz+vvfYaTk5OrF69uijh5rFr1y7b5NBBgwZx7do1duzYYXu+QYMGtGrVitjY2ALbMn5zUbOLiy7eERERyU+REg1fX1/WrFlDTk4OANeuXWP9+vV06NAh3/2DgoJo0aIFa9euvWW7kyZN4tixY0RFRWG1Wlm/fj0TJkxg9+7dtsfkyZN5//33SUlJKUrIuXTs2JHo6Giio6NZtmwZrq6uTJkyhb179wLXJ5gePHiQVq1a3bKdDh06sH37dtLS0sjOzi72cIiIiJQMKxashgM/KCdzNCZOnMjMmTPp3r07Li4uZGdn07lzZ15++eWbHjN58mQOHz58y3Zr1qzJsGHDmDt3LhUqVCAtLY1nn3021z4BAQEsXbqUDRs2MGjQoFu2l98cDX9/f0JCQnJtc3Nz4+2332batGnMmTMHi8XChAkTeOihh27Zfu/evYmLi6N79+5UqVKFBx544LYSIBEREUdlMQwzL2zq+FJTU4mNjSV8eASnT8SVdjjiwDy9PFi2eR6Dnh2pORpiVw0a/YGpiybSpEkTvLy8bqutG5+RnhMX43z6bAlFWPbkNKhNWsSwEnnN7jRNLsjHyZMnGTVqVL7PeXp6sn79+jsckYiIiDkp0chHw4YNNe9CRESkBCjREBER0zOsDn6vExP3TXdvFREREbtRRUNERMzPAIe+tMHEfVNFQ0REROxGFQ0RETE9w3DwORomvqmaKhoiIiJiN6pomES4Uy0MJ/NmtGIClut3LV5gqQtOmaUcjDgyi1PNEm/TaljAxN/6C2I1cd9U0RARERG7UaIhIiIidqOhExERMT3DsJh6wmRBzNw3VTRERETEblTREBER89OCXWWWKhoiIiJiN6poiIiI6VkBi4nnMRTExAUNVTRERETEfpRoiIiIiN1o6EREREzPcPCVQXV5q4iIiEg+VNEQERHTMwzMPWOyAGa+dFcVDREREbGbEkk0/P39OXjw4C332bp1K76+vgQHBzNu3DiWLFlSYLuLFy9mx44dAERFRbFo0aKSCDePTZs2MXDgwGIdGx0dTf/+/YHcMZ49e5bu3bsTEBDAvn37GDp0KJ06dSIyMrLE4hYRkesMw4LVgR9mnqNxx4ZOtm7dyvDhwwkKCiIsLKxQxxw4cIC6desC0Ldv3xKPKSkpidmzZ/Phhx/y8MMP33Z7v40xJiaGihUrEhUVRUJCArt37+bw4cO4ubkVr3F3V/As5rEihXHj/aX3mdibu2tpRyB3UIkmGv7+/gQGBvL111+TkJBAx44dCQ8PZ+rUqRw5coS4uDguXryY65gjR47wt7/9jczMTM6fP0+zZs2YM2cO7733HkePHiU+Ph6LxcKZM2dITEwkIiKCc+fOMXnyZM6cOYPFYsHX15eQkBBcXV1vGkN+tm/fTt26dRk7diyffvppofqYnZ3NjBkz+Pzzz6latSp/+MMfbM8tXLiQxMREnnrqKebNm8eVK1fo0qULzs7OGIZBUFAQM2fOxMfHp8ivrcffXyjyMSLF4bnm1dIOQaTIHP2qEwwLZu1diVc0UlJSWLduHRcuXKBTp0706dOH8PBwjh07Rq9evQgMDMxV0VizZg2DBw/m8ccfJzMzk8DAQPbs2cOAAQPYtWsXvXr1IiAggIULF9qOCQ0NpU2bNixevJj09HSGDBnCsmXLGDp06E1jaNy4cZ5YBwwYAMCWLVsK3b8NGzbwww8/8OGHH2KxWHjllVfy7NOuXTteffVVtm/fzqpVq4iPj6dz585ER0cX+jy/l/7aPzBOnS/28SIF8nTDc82rpPVfAGmZpR2NODDLvTX05akcKfFEw9/fH4Dq1atz9913k5SUdMv9IyIi+Ne//kVkZCSnTp3i0qVLpKam3nT/1NRUDh48yNKlSwHw8PAgODiYyMhIW6JR1BiKYt++fTz11FO4u7sD0KtXL6Kiokqs/ZvKyNKHv9wZaZl6r4l9ZWSVdgRyB5V4onHjDzCAxWLBKOCanP79+9OgQQN8fX3p2LEjcXFxtzzGMIw8z1utVnJycm4ZQ1RUFOvXrwegWbNmREREFKlfv4/hBhcXXSEsIlLarDj40AkW014mWqpxp6SkcOjQIUaPHk3Xrl3JyMjg6NGjtqTB2dk5VwIBUKFCBR555BFWr14NQHp6OuvXr+exxx675bn69u1LdHQ00dHRt5VkdOjQge3bt5OWlkZ2dvZtDYeIiIg4ulL9Ol6pUiWGDRtGnz59qFKlChUqVKB169acOXMGuD4EMmfOHDIyMnIdN2vWLKZMmUL37t3JzMykffv2DB8+/I7E3Lt3b+Li4ujevTtVqlThgQceICUl5Y6cW0RE8mfi9awcnsUoaGxDSlVqaiqxsbGkv7oC48S50g5HHJmnG57vh5LWa5bmaIhdWRrVxGPBizRp0gQvL6/bauvGZ2TWyBVwIrGEIiyDGtXCdV7JvGZ3miYY/M7JkycZNWpUvs95enra5nmIiEjZYRjmvvFYQSwmLgko0fidhg0bat6FiIhICVGiISIipmeYfJnuApm4b2a9WkZERERMQImGiIiIg9q3bx+BgYE8+eSTDBw4kPPnb77CdGxsLK1atSrxGJRoiIiI6VnLwaOokpKSCA0NZebMmXz88cf4+fkxfvz4PPtlZWWxbNky/vrXv3Lt2rVinOnWNEdDRETEJE6cOIGTU94aQfXq1alRo0aubfv378fHx8d2I88+ffowc+ZMLly4QPXq1W37ffvtt/z8888sWrSI/v37l3jMSjRERMQBWDBMe3/Tgt24d2toaCinT5/O8/zw4cMZMWJErm2JiYnUrl3b9rubmxve3t6cPXs2V6LRunVrWrduTXx8vF1iV6JhEqedPEhz9iztMMSBOTm70QI47uyJ1dm5tMMRB+bp5MEDpR2ESc2aNeumFY3fMwwDiyVv8pXf8fakRENEREzPalx/OKz/9a1Ro0aFXhm0Tp06HDhwwPZ7ZmYmycnJ1KlTxx4R3pQmg4qIiDigP//5z/zwww8cP34cgE2bNtGiRQuqVq16R+NQoiEiIuKAqlatyty5cxk3bhzdunXj448/ZubMmQAEBgZy5MiROxKHhk5ERMT0rFiwOvBkUIrZt3bt2rF169Y82/O71UbdunX54YcfinWeW1FFQ0REROxGFQ0REXEAjn15a3ErGmWBKhoiIiJiN6poiIiI6RV3mW6zMG89QxUNERERsSNVNERExPQMcOg5GmZei0wVDREREbEbJRoiIiJiNxo6ERER0zNw7MmgZq4KlEjs/v7+HDx48Jb7bN26FV9fX4KDgxk3bhxLliwpsN3FixezY8cOAKKioli0aFFJhGvz6aef0qNHDwIDA+nRowd79+4tchtLliwhLCwMgPnz57Np0yYAfvzxR5544gl69OjBjz/+SHBwMN26dWPbtm0l2QUREZEy7Y5VNLZu3crw4cMJCgqy/WEuyIEDB6hbty4Affv2LdF4fvnlF15//XU2btxI/fr1iY2NpV+/fuzevZsqVaoUq82QkBDbzzt37uThhx9m1qxZfPPNN/zyyy/FSmRucPJwwcnTrdjHixTEydM11/9F7MXJo+T/9Dj65a1m7luJ/mv7+/sTGBjI119/TUJCAh07diQ8PJypU6dy5MgR4uLiuHjxYq5jjhw5wt/+9jcyMzM5f/48zZo1Y86cObz33nscPXqU+Ph4LBYLZ86cITExkYiICM6dO8fkyZM5c+YMFosFX19fQkJCcHV1vWkMv+fk5MSUKVOoX78+APfddx8Wi4VLly7dMtG4du0akyZN4ujRo1StWpUqVapQuXJlAMLCwqhXrx516tQhKiqK7Oxshg0bxvHjx7l06RKBgYGsWrUKb2/vIr+29894rsjHiBTHQ6sGl3YIIuJASjytTElJYd26dVy4cIFOnTrRp08fwsPDOXbsGL169SIwMDBXRWPNmjUMHjyYxx9/nMzMTAIDA9mzZw8DBgxg165d9OrVi4CAABYuXGg7JjQ0lDZt2rB48WLS09MZMmQIy5YtY+jQoTeNoXHjxrnirF27NrVr17b9Pn/+fP7whz/QsGHDW/Zv0aJF5OTk8NFHH5Gamkrfvn1ticYNzzzzDHFxcbbEKCYmhvDw8HxvYlNYx8M2kHbqYsE7ihSTk6crD60azJEBkVjTsko7HHFgnvfeXeJfngwHX4LczH0r8UTD398fgOrVq3P33XeTlJR0y/0jIiL417/+RWRkJKdOneLSpUukpqbedP/U1FQOHjzI0qVLAfDw8CA4OJjIyEhbolGUGDIzM5k6dSoxMTG8++67WCy3/sfcv38/Y8aMwcnJiYoVK9K9e3dOnjx5y2NKgjU9G2tapt3PI2JNy9J7TezKmp5d2iHIHVTiiYa7u7vtZ4vFgmHcepmR/v3706BBA3x9fenYsSNxcXG3PMYwjDzPW61WcnJybhlDVFQU69evB6BZs2ZERERw8eJFhg8fToUKFdi4cWOeysStYrjB1VXj2SIipc2wgNW8X/oLZJi4b6V6xUxKSgqHDh1i9OjRdO3alYyMDI4ePWpLGpydnXMlEAAVKlTgkUceYfXq1QCkp6ezfv16HnvssVueq2/fvkRHRxMdHU1ERARXr17l+eefp1mzZixbtqzQSUaHDh3YvHkz2dnZpKWl8dFHHxWj5yIiIuVDqa6jUalSJYYNG0afPn2oUqUKFSpUoHXr1pw5cwa4PgQyZ84cMjIych03a9YspkyZQvfu3cnMzKR9+/YMHz68SOfeuHEjp06dws3NjR49eti2T506lYceeuimxw0bNozJkyfTtWtXqlatSr169Yp0XhERkfLEYhQ0tiGlKjU1ldjYWH4cuY60k+dLOxxxYE6ebrTYMIzvnlusORpiV54Na/DAvH40adIELy+v22rrxmfkuVfXkHXCcT8jXRvVoOaC/iXymt1pWhn0dw4cOMD06dPzfa5+/fosWLDgDkckIiJiXko0fqdNmza3dRmqiIjceQbmvsNpQczcNzMvny4iIiJlnCoaIiJieo5+UzVVNERERETyoURDRERE7EZDJyIiYnpWiwVrAbeQMDMz900VDREREbEbVTRERMT0dHlr2aVEwyQuuRikuJj5rSZlncv/3l/nXQyy9V4TO6qk91e5okRDRERMT5e3ll2aoyEiIiJ2o4qGiIiYntVy/eGozNw3VTRERETEbpRoiIiIiN1o6EREREzPigUrJh5fKICZ+6aKhoiIiNiNKhoiIuIQzHwJqCNTRUNERETsRhUNERExPSvmvgS0IGZejEwVDREREbEbVTRERMT0rJj7W39BzNy3Eqlo+Pv7c/DgwVvus3XrVnx9fQkODmbcuHEsWbKkwHYXL17Mjh07AIiKimLRokUlEa7N559/zjPPPENAQAC9e/fmP//5T5HbWLJkCWFhYQDMnz+fTZs2AfDjjz/yxBNP0KNHD3788UeCg4Pp1q0b27ZtK8EeiIiIlG13rKKxdetWhg8fTlBQkO0Pc0EOHDhA3bp1Aejbt2+JxpOSkkJoaCgbN26kYcOG7N69m9GjR7N79+5itxkSEmL7eefOnTz88MPMmjWLb775hl9++YW9e/cWu21nd1dcPN2KfbxIQZw9XXP9X8RenN31HitPSjTR8Pf3JzAwkK+//pqEhAQ6duxIeHg4U6dO5ciRI8TFxXHx4sVcxxw5coS//e1vZGZmcv78eZo1a8acOXN47733OHr0KPHx8VgsFs6cOUNiYiIRERGcO3eOyZMnc+bMGSwWC76+voSEhODq6nrTGH6vUqVKfPXVV7i6umIYBvHx8VSpUqXAPl67do1JkyZx9OhRqlatSpUqVahcuTIAYWFh1KtXjzp16hAVFUV2djbDhg3j+PHjXLp0icDAQFatWoW3t3eRX9s2Ec8V+RiR4vBf/nJphyBSLLq8tWwq8YpGSkoK69at48KFC3Tq1Ik+ffoQHh7OsWPH6NWrF4GBgbkqGmvWrGHw4ME8/vjjZGZmEhgYyJ49exgwYAC7du2iV69eBAQEsHDhQtsxoaGhtGnThsWLF5Oens6QIUNYtmwZQ4cOvWkMjRs3zhOrq6sr8fHx9O3bl+TkZObOnVtg/xYtWkROTg4fffQRqamp9O3b15Zo3PDMM88QFxdnS4xiYmIIDw8nOjq6uC8rByZu4NfTF4p9vEhBnD1d8V/+MrtfeoectKzSDkcc2F0NquvLUzlS4omGv78/ANWrV+fuu+8mKSnplvtHRETwr3/9i8jISE6dOsWlS5dITU296f6pqakcPHiQpUuXAuDh4UFwcDCRkZG2RKMoMdStW5d9+/bx3XffMXDgQBo3bsy999570/3379/PmDFjcHJyomLFinTv3p2TJ0/eso8lIScji+y0TLufRyQnTe81sa+cjJJPZHX31rKrxBMNd3d3288WiwXDuHUxq3///jRo0ABfX186duxIXFzcLY8xDCPP81arlZycnFvGEBUVxfr16wFo1qwZo0eP5vDhw/j5+QHQokULGjVqxPHjx2+ZaNyI4QZXV401ioiI3EyprqORkpLCoUOHGD16NF27diUjI4OjR4/akgZnZ+dcCQRAhQoVeOSRR1i9ejUA6enprF+/nscee+yW5+rbty/R0dFER0cTERFBTk4OY8aM4ccffwTg+++/Jy4ujhYtWtyynQ4dOrB582ays7NJS0vjo48+Km73RUSkhFjLwcOsSnUdjUqVKjFs2DD69OlDlSpVqFChAq1bt+bMmTPA9SGQOXPmkJGRkeu4WbNmMWXKFLp3705mZibt27dn+PDhRTp3jRo1mDNnDuPHj8dqteLm5sbcuXOpVavWLY8bNmwYkydPpmvXrlStWpV69eoVrdMiIiLliMUoaGxDSlVqaiqxsbF8EbqWlJPnSzsccWAunm50Wjecz/ot0hwNsatKDWvw51nP06RJE7y8vG6rrRufkT+MWkeqA39GejWswYNz+5XIa3anaWXQ3zlw4ADTp0/P97n69euzYMGCOxyRiIiIeSnR+J02bdrc1mWoIiIi8v+UaIiIiOkZlusPR2XmvunurSIiImI3qmiIiIjpmf0S0IKYuW+qaIiIiIjdqKIhIiKmZ2Dub/0FMfM6FKpoiIiIiN0o0RARERG70dCJSfxl7St4uemfS+wnx2rwffwVRu6diLOTia+lkzIvNTObnxKvlmibBuYeXiiImfumioaIiIjYjb4ii4iI6Vkt1x+Oysx9U0VDRERE7EYVDRERMT1d3lp2qaIhIiIidqOKhoiImJ6WIC+7VNEQERERu1GiISIiInajoRMRETE9LdhVdqmiISIiInajioaIiJie4eALdhkm7psqGiIiImI3xUo03n77bTp06MDIkSMLfUynTp2IiYkBYOvWrfj6+hIcHIxh3P7IU2JiIj4+PsU+ft68eUycOLFYxy5ZsoSwsDAA5s+fz6ZNmwD48ccfeeKJJ+jRowc//vgjwcHBdOvWjW3bthU7ThERyZ+1HDzMqlhDJ5s2bWLatGm0a9euWCfdunUrw4cPJygoqFjHl5T4+HimT5/O/v37efrpp2+7vZCQENvPO3fu5OGHH2bWrFl88803/PLLL+zdu7fYbVut1++uKWIvN95fep+JvVnN/FdTiqzIicarr77KuXPnePPNN+nXrx/Hjx8nNjaWrKwsmjdvzvjx46lYsSKHDh1i8uTJWK1WGjduTHp6OgBTp07lyJEjxMXFcfHiRc6cOUO9evUYOnQoABMnTqRWrVqMGDECf39/AgMD+frrr0lISKBjx46Eh4cDsHHjRlasWEGFChVo1qyZLb74+HjGjx/PtWvXMAyDxx9/nFGjRuXblw0bNtCuXTvuu+8+Lly4UKj+X7t2jUmTJnH06FGqVq1KlSpVqFy5MgBhYWHUq1ePOnXqEBUVRXZ2NsOGDeP48eNcunSJwMBAVq1ahbe3d1Ffdk6eL9lbKovczI8JKaUdgkiR6aqTsqvIicaCBQvw9/dn2rRpbN26lYYNGxIREYFhGMyYMYPZs2czYcIERowYwZQpU/Dz8+Orr77iww8/BCA8PJxjx47Rq1cvAgMDbcMON5OSksK6deu4cOECnTp1ok+fPuTk5DB79myio6OpVasWCxYssO2/du1amjdvzmuvvUZqaioTJkwgOTk53z/uY8aMAWDhwoWF7v+iRYvIycnho48+IjU1lb59+9oSjRueeeYZ4uLiSExMJCIigpiYGMLDw4mOji70eX6vYY2KeLo5F/t4kYLkWA1+TEjhgTqVcHYy8cwzKfPSMnP05akcua2rTnbv3s2hQ4fYvn07AFlZWVSrVo3jx4+TnZ2Nn58fAG3btuXee+8t1jn8/f0BqF69OnfffTdJSUl8//33tG7dmlq1agEQHBzM4sWLAfDz82PEiBGcOHGCNm3aMGbMmGJVEG5m//79jBkzBicnJypWrEj37t05efJkibV/M05O6MNf7ghnJ4vea2JXTroMoVy5rUTDarUya9YsHnzwQQCuXr1KVlYWCQkJeSZ5urjkfyqLxZJr36ysrFzPu7u733Tf/Npu3bo1u3bt4quvviImJoagoCAWLFjAV199xe7du4Hryctv51MU1W9jcHV1LXY7IiJSMgwMrKYeYLg1w8R9u628sn379qxevZqcnBxycnIICwtj7ty5+Pj44OHhwaeffgrA4cOHOXHiRL5teHt7ExsbC0BycjIHDx4s1HkPHDhAXFwcAFu2bLE998Ybb7B69Wo6d+5MeHg49913HydOnCAkJITo6Giio6NvK8no0KEDmzdvJjs7m7S0ND766KNityUiIuLobivRuDExMyAggKeeegpPT0/GjRuHi4sLS5YsITIykoCAAJYvX37ToZPnn3+eixcv0qVLF8aMGUPr1q0LPG/jxo0JDw9n0KBB9OzZk3PnztmeGzRoEDExMXTv3p2ePXtSp04devTocTvdzGXYsGFUqlSJrl27MmDAAOrVq1dibYuISPGU9qWnurz15ixGSSxkIXaTmppKbGwsjWtVxMtNC7mK/eRYDb6Pv0LTupU1R0PsKjUzm58Sr9KkSRO8vLxur63/fUbuD11LysnzJRRh2VOpYQ0em/V8ibxmd5r+cv3OgQMHmD59er7P1a9fP9cVLiIiUjbo8tayS4nG77Rp0+a2LkMVERGR/6eLjERERMRuVNEQERHTMzD3hMmCmHnoRBUNERERsRtVNERExPSsgNWBL5Yyc7VGFQ0RERGxG1U0RETE9KwOvgS5mfumioaIiIjYjSoaJpE+JwIu/FLaYYgDs7q5w5AppE18FafMjNIORxxYevV7ILj495y6GfN+53dsqmiIiIiI3SjREBEREbvR0ImIiJie2e9wWhAz900VDREREbEbVTRERMT0dHlr2aWKhoiIiNiNKhoiIuIQzPud37GpoiEiIiJ2o4qGiIiYnq46KbtU0RARERG7UaIhIiIidqOhExERMT1d3lp2Fbui8fbbb9OhQwdGjhxZ6GM6depETEwMAFu3bsXX15fg4GAM4/ZfwMTERHx8fIp8XFRUFN27dycgIIDnnnuOw4cPF7mNiRMnsnDhQtvPe/fuBeCLL77Az8+Pnj178ssvv9jO8+WXXxb5HCIiImZU7IrGpk2bmDZtGu3atSvW8Vu3bmX48OEEBQUVN4Tb9u233xIZGcnmzZupWrUqe/bsYejQoezbtw+LxVKsNiMiImw///Of/yQgIIBRo0axbds2KlasSFRUVLHaNVxdr99dU8ROrK7uuf4vYi+Gq6t92rVLq3K7ipVovPrqq5w7d44333yTfv36cfz4cWJjY8nKyqJ58+aMHz+eihUrcujQISZPnozVaqVx48akp6cDMHXqVI4cOUJcXBwXL17kzJkz1KtXj6FDhwLXqwK1atVixIgR+Pv7ExgYyNdff01CQgIdO3YkPDwcgI0bN7JixQoqVKhAs2bNbPHFx8czfvx4rl27hmEYPP7444waNSpPPypXrsyUKVOoWrUqAM2bN+fSpUukpaXh5eV10/5fuHCB8ePHk5CQQPXq1bFardSqVQuA/v3706tXL86dO8euXbtwc3Nj586dXLt2jStXrhAUFMSmTZuK/Jr/EjS0yMeIFMfpl8JLOwQRcSDFSjQWLFiAv78/06ZNY+vWrTRs2JCIiAgMw2DGjBnMnj2bCRMmMGLECKZMmYKfnx9fffUVH374IQDh4eEcO3aMXr16ERgYSFhY2C3Pl5KSwrp167hw4QKdOnWiT58+5OTkMHv2bKKjo6lVqxYLFiyw7b927VqaN2/Oa6+9RmpqKhMmTCA5ORlvb+9c7TZq1IhGjRoBYLVamTZtGh06dLhlkgEwZcoU7rvvPpYvX8758+fp2bMnf/rTn3LtM3jwYE6ePGlLoLZs2cL27dtZtWpVYV/mXO7ZtAT3i2eLdaxIYVhd3Tn9UjgNlk/FKSujtMMRB5Zxd+0S//Kky1vLrtueDLp7924OHTrE9u3bAcjKyqJatWocP36c7Oxs/Pz8AGjbti333ntvsc7h7+8PQPXq1bn77rtJSkri+++/p3Xr1rZKQnBwMIsXLwbAz8+PESNGcOLECdq0acOYMWPyJBm/dfXqVcaOHUtSUhKRkZEFxrN//342bNgAQI0aNejYsWOx+lUUlqwsnDL14S/255SVofea2JUlK6u0Q5A76LYTDavVyqxZs3jwwQeB63+0s7KySEhIyDPJ08Ul/9NZLJZc+2b97k3o7u5+033za7t169bs2rWLr776ipiYGIKCgliwYAFfffUVu3fvBq4nLyEhIZw6dYohQ4bQvHlz5s6dm+tct/LbGFztNN4oIiJidre9jkb79u1ZvXo1OTk55OTkEBYWxty5c/Hx8cHDw4NPP/0UgMOHD3PixIl82/D29iY2NhaA5ORkDh48WKjzHjhwgLi4OAC2bNlie+6NN95g9erVdO7cmfDwcO677z5OnDhBSEgI0dHRREdHExISQkJCAv369SMoKIiZM2cWOsno0KEDGzdutMV7I3kREZHSYQCGQ/9nXrdd0QgPD2f69OkEBASQk5PDQw89xLhx43BxcWHJkiW88cYbLFq0iAYNGtx06OT5559n9OjRdOnShXvuuYfWrVsXeN7GjRsTHh7OoEGD8PLyolWrVrbnBg0axPjx4/nkk09wcXHh/vvvp0ePHnnaWLFiBSkpKWzfvt029AMQGRlJzZo1b3ru119/nfDwcJ588knuvvvuYl1WKyIiUh5YjJJYxELsJjU1ldjYWOq+Nx+PC7+UdjjiwKxu7pwcMoWGS1/XHA2xq/Tq9xAfHEKTJk0KnHxfkBufkdtDV3Pp1LkSirDsqXZvTQJm/aVEXrM7TSuD5mP79u2sWLEi3+datWplu7xWREREbk2JRj4CAgIICAgo7TBERKSQtAR52aWbqomIiIjdKNEQERHTM8rBozj27dtHYGAgTz75JAMHDuT8+fN59klOTuaVV16hW7dudOnShZ07dxbzbPlToiEiIuKAkpKSCA0NZebMmXz88cf4+fkxfvz4PPu99dZb3H///ezYsYMVK1bwxhtvcPZsya1ErTkaIiIiJnHixAmcnPLWCKpXr06NGjVybdu/fz8+Pj62JRj69OnDzJkzuXDhAtWrVwcgOzubPXv28NFHHwFQt25dHnvsMT744AMGDx5cIjEr0RAREdMrL5NBQ0NDOX36dJ7nhw8fzogRI3JtS0xMpHbt2rbf3dzc8Pb25uzZs7ZEIzk5mfT0dNvtPABq1aqlioaIiEh5NGvWrJtWNH7PMAwsFkue7b89/sZSWr/fL79zFJcSDRERMT0Dc9/htCA3ajWNGjUq9IJdderU4cCBA7bfMzMzSU5Opk6dOrZt1apVw93dnfPnz9tWxD537hyNGzcusdiVaJjElzHVyDiRU9phiANz8nSj0RD4bHdNrGmZpR2OODD3RtWoF1zaUTi+P//5z0ydOpXjx49z//33s2nTJlq0aEHVqlVt+zg7O9OxY0fee+89Ro0axS+//MK+fft45ZVXSiwOJRoiImJ65r/12K0Vp29Vq1Zl7ty5jBs3joyMDKpVq8bMmTMBCAwMZOrUqTz00EO8/vrrTJo0iaeffprs7GzCwsJo0KBBicWuRENERMRBtWvXjq1bt+bZHh0dbfu5atWqLFq0yG4xKNEQERHTs+LYczTM3Dct2CUiIiJ2o0RDRERE7EZDJyIi4gAcezJo8e92UvpU0RARERG7UUVDRERMT5NByy5VNERERMRuVNEQERHTswJWw7zzGAqiioaIiIhIPlTREBERh+C49QxzK3ZF4+2336ZDhw6MHDmy0Md06tSJmJgYALZu3Yqvry/BwcG229TejsTERHx8fIp83ObNm+nevTvdu3fnhRde4NSpU0VuY+LEiSxcuND28969ewH44osv8PPzo2fPnvzyyy90796dgIAAvvzyyyKfQ0RExIyKXdHYtGkT06ZNo127dsU6fuvWrQwfPpygoKDihnDbTp48yezZs/nggw+oVq0aa9asYdKkSaxZs6bYbUZERNh+/uc//0lAQACjRo1i27ZtVKxYkaioqGK1a3F3xcnTrdhxiRTE4ulq+7/GVMWeLO6upR2C3EHFSjReffVVzp07x5tvvkm/fv04fvw4sbGxZGVl0bx5c8aPH0/FihU5dOgQkydPxmq10rhxY9LT0wGYOnUqR44cIS4ujosXL3LmzBnq1avH0KFDgetVgVq1ajFixAj8/f0JDAzk66+/JiEhgY4dOxIeHg7Axo0bWbFiBRUqVKBZs2a2+OLj4xk/fjzXrl3DMAwef/xxRo0alacfDRs2ZO/evbi6upKdnU1CQgLe3t4F9v/ChQuMHz+ehIQEqlevjtVqpVatWgD079+fXr16ce7cOXbt2oWbmxs7d+7k2rVrXLlyhaCgIDZt2lTk1/wPM58r8jEixdHwHy+XdggiRWbFwOrAgydm7luxEo0FCxbg7+/PtGnT2Lp1Kw0bNiQiIgLDMJgxYwazZ89mwoQJjBgxgilTpuDn58dXX33Fhx9+CEB4eDjHjh2jV69eBAYGEhYWdsvzpaSksG7dOi5cuECnTp3o06cPOTk5zJ49m+joaGrVqsWCBQts+69du5bmzZvz2muvkZqayoQJE0hOTs43iXB1deXgwYOEhISQlpbGihUrCuz/lClTuO+++1i+fDnnz5+nZ8+e/OlPf8q1z+DBgzl58qQtgdqyZQvbt29n1apVhXiF84obu4HMUxeKdaxIYVg8XWn4j5c5+cI7GGlZpR2OODC3e6vry1M5ctuTQXfv3s2hQ4fYvn07AFlZWVSrVo3jx4+TnZ2Nn58fAG3btuXee+8t1jn8/f0BqF69OnfffTdJSUl8//33tG7d2lZJCA4OZvHixQD4+fkxYsQITpw4QZs2bRgzZswtKxWPPvooX3zxBTt37mTw4MHs2rWLSpUq3XT//fv3s2HDBgBq1KhBx44di9WvojAysrCmZdr9PFJ+3RguMdL0XhP7MjJKPpE1HHwJcjP37bYTDavVyqxZs3jwwQcBuHr1KllZWSQkJOSZ5Onikv/pLBZLrn2zsnK/Cd3d3W+6b35tt27dml27dvHVV18RExNDUFAQCxYs4KuvvmL37t3A9eTl2WefJS4ujrZt2wLwxBNPMGnSJH7++edcQzH5+W0Mrq4abxQREcnPbc/5at++PatXryYnJ4ecnBzCwsKYO3cuPj4+eHh48OmnnwJw+PBhTpw4kW8b3t7exMbGApCcnMzBgwcLdd4DBw4QFxcHwJYtW2zPvfHGG6xevZrOnTsTHh7Offfdx4kTJwgJCSE6Opro6GhCQkK4cuUKI0eOJDExEYC9e/fi5OREo0aNbnnuDh06sHHjRlu8N5IXEREpHQb/vwy5Iz7MW88ogYpGeHg406dPJyAggJycHB566CHGjRuHi4sLS5Ys4Y033mDRokU0aNDgpkMnzz//PKNHj6ZLly7cc889tG7dusDzNm7cmPDwcAYNGoSXlxetWrWyPTdo0CDGjx/PJ598gouLC/fffz89evTI00bTpk0ZN24cgwYNwsnJiUqVKrFs2TI8PT1vee7XX3+d8PBwnnzySe6+++5iXVYrIiJSHliMkljEQuwmNTWV2NhYfg5ZS8aJ86UdjjgwJ083Gm0azomgRZqjIXbl3qgG9eY/T5MmTfDy8rqttm58Rq4OXca5k4klFGHZU7NhLf4ya1CJvGZ3mlYGzcf27dtvevVJq1atbJfXiohI2aDLW8suJRr5CAgIICAgoLTDEBERMT0lGiIiYnq6vLXs0krDIiIiYjeqaIiIiOnduLzVUZm3nqGKhoiIiNiRKhoiImJ6hkG+q0Y7CjN3TRUNERERsRslGiIiImI3GjoxiQcrJeNcVbeJF/sxPNxJAVp6X8TimVHa4YgDy6nkytUSblMLdpVdqmiIiIiI3aiiISIipqfLW8suVTRERETEblTREBER09MS5GWXKhoiIiJiN6poiIiI6emqk7JLFQ0RERGxGyUaIiIiYjcaOhEREdMzDMPB73Vi3r6poiEiIiJ2o4qGiIiYnhbsKrtU0bgD5s2bx8SJE0s7DBERkTtOiYYdxcfHM2zYMN59993SDkVExKEZ/P+iXY75n3mV+6GTK1euMG7cOBITE7FYLDRt2pSXX36Zvn374uvry/fff09GRgahoaF07NiRxYsXs3PnTjZu3EhGRgY9evRg+PDhBAYG5ml7w4YNtGvXjvvuu48LF27vzquGuxuGh/tttSFyKzfeX3qfib0Z7m6lHYLcQeU+0di+fTseHh5s27aNnJwcJk2aRGZmJhcuXKBly5ZERETw3XffMXDgQHbu3MmQIUP45ptvWLx4MadOnaJ9+/b5JhkAY8aMAWDhwoW3Hee18Jdvuw2Rwvh1wfjSDkFEHEi5TzTatm3L22+/zYABA2jTpg0DBgzA3d2dChUq0KtXLwBatGhBgwYN+Oabb+jSpQuzZs0iICCAWrVq8fe///2OxFlh6js4/3z2jpxLyifDw51fF4znrlenY0nPKO1wxIHl1Ktd4l+etDJo2VXuE43GjRuza9cuYmJiiImJYeDAgfTv3x8Xl7wvjbOzMwBnz57FycmJ8+fPc+nSJWrXrs38+fPZvXs3AP7+/oSEhJRonJaMTH34yx1hSc/Qe03sypKRWdohyB1U7hONyMhITp8+zbRp0/D19SUpKYkff/yRK1eusHPnTp544gkOHTpEfHw8rVq14urVq4waNYpJkybx888/M2rUKNauXUtISEiJJxciIlJIDr5gFybuW7m/6uS5554jOTmZbt260bNnT1JSUvjrX/+Kk5MTO3fu5JlnnmHy5MksXLiQypUrEx4eTqtWrejcuTN//etfcXV1Zd68eaXdDRERkTKp3Fc0KleuzNKlS3Nti4+Px2KxMGPGjDz7/zapcHJyYs2aNQWeY8SIEbcdp4iI3JzmaJRd5b6iISIiIvZT7isa+albty4//PBDaYchIiKFZP5lrW7NzH1TRUNERETsRomGiIiI2I2GTkRExPQMA6wmvgS0IGbumioaIiIiYjeqaIiIiOkZ/3s4KjP3TRUNERERsRtVNERExPS0YFfZpYqGiIiI2I0qGiaRnuaK5ZpbaYchDsywXn9/paa6YUkz77cnKfuMNNcSb1MVjbJLFQ0RERGxGyUaIiIiYjcaOhEREQdgYJh5VasCmbdvqmiIiIiI3aiiISIipqfJoGWXKhoiIiJiN6poiIiI6V1fgty83/oLYuaeqaIhIiIidqNEQ0REROxGQyciImJ6huHYl7eauW+qaIiIiIjdqKJhR1FRUbz33ntYLBY8PT2ZOHEizZs3L+2wREQcjuHgl7eaeaKrEg07+fbbb4mMjGTz5s1UrVqVPXv2MHToUPbt24fFYint8ERERO6Icp9oXLlyhXHjxpGYmIjFYqFp06a8/PLL9O3bF19fX77//nsyMjIIDQ2lY8eOLF68mJ07d7Jx40YyMjLo0aMHw4cPJzAwMFe7lStXZsqUKVStWhWA5s2bc+nSJdLS0vDy8ipynIaHK3i6l0ifRfJjeLrl+r+IvRgeJX/3Vs3RKLvKfaKxfft2PDw82LZtGzk5OUyaNInMzEwuXLhAy5YtiYiI4LvvvmPgwIHs3LmTIUOG8M0337B48WJOnTpF+/bt8yQZAI0aNaJRo0YAWK1Wpk2bRocOHYqVZADkTH3xtvopUlg5y0JLOwQRcSDlPtFo27Ytb7/9NgMGDKBNmzYMGDAAd3d3KlSoQK9evQBo0aIFDRo04JtvvqFLly7MmjWLgIAAatWqxd///vdbtn/16lXGjh1LUlISkZGRxY7TOXwFltPnin28SEEMTzdyloXiPGgWlrTM0g5HHJjRoGaJf3nSEuRlV7lPNBo3bsyuXbuIiYkhJiaGgQMH0r9/f1xc8r40zs7OAJw9exYnJyfOnz/PpUuXqF27NvPnz2f37t0A+Pv7ExISwqlTpxgyZAjNmzdn7ty5uLsXf+jDkp6FJS2j2MeLFJYlLVPvNbGv9KzSjkDuoHKfaERGRnL69GmmTZuGr68vSUlJ/Pjjj1y5coWdO3fyxBNPcOjQIeLj42nVqhVXr15l1KhRTJo0iZ9//plRo0axdu1aQkJCCAkJsbWbkJBAv379ePHFF3nxRQ17iIhI+VTuE43nnnuOsLAwunXrhoeHB7Vq1WLIkCF8/PHH7Ny5k0WLFmGxWFi4cCGVK1dm5MiRtGrVis6dO2O1Wtm7dy/z5s0jNDT3uPaKFStISUlh+/btbN++3bY9MjKSmjVr3uluiog4OMPUl4AWzLx9K/eJRuXKlVm6dGmubfHx8VgsFmbMmJFn/3nz5tl+dnJyYs2aNfm2+/rrr/P666+XaKwiIiJmU+4TDRERMT+rAVYTXwJaEKuJu6YlyPNRt25dfvjhh9IOQ0RExPRU0RAREdMzHHyOhpn7poqGiIiI2I0qGiIiYnqGYTj0HA0zL0GuioaIiIjYjRINERERsRsNnYiIiOlpMmjZpYqGiIiI2I0qGiIiYnpWHHsyqO7eKnaXnu0Cma6lHYY4MmcXXID0TBfItJZ2NOLIsl30x6cc0b+1iIiYnoG55zEUxMw90xwNERERsRslGiIiImI3GjoRERHT08qgZZcqGiIiImI3qmiIiIjpacGusksVDREREbEbVTRERMT0DMOKYTju+i9m7psqGiIiImI3qmiIiIjpGZh7me6CmLlnqmiIiIiI3aiiYUebN29m1apVAHh7e/PWW29x7733lm5QIiIid5ASDTs5efIks2fP5oMPPqBatWqsWbOGSZMmsWbNmtIOTUTE4RiGYepFrQpi5r6V+0TjypUrjBs3jsTERCwWC02bNuXll1+mb9+++Pr68v3335ORkUFoaCgdO3Zk8eLF7Ny5k40bN5KRkUGPHj0YPnw4gYGBudpt2LAhe/fuxdXVlezsbBISEvD29i5+oO6u4Ol2m70VuYUb7y+9z8Te3HUn6vKk3Cca27dvx8PDg23btpGTk8OkSZPIzMzkwoULtGzZkoiICL777jsGDhzIzp07GTJkCN988w2LFy/m1KlTtG/fPk+ScYOrqysHDx4kJCSEtLQ0VqxYUew4Xf72l2IfK1IULqtHlnYIIkVmxXDoyaBm7lu5TzTatm3L22+/zYABA2jTpg0DBgzA3d2dChUq0KtXLwBatGhBgwYN+Oabb+jSpQuzZs0iICCAWrVq8fe///2W7T/66KN88cUX7Ny5k8GDB7Nr1y4qVapU5Dizx62GU+eK1UeRQvF0w2X1SLL/Mg/SMks7GnFk99bUl6dypNwnGo0bN2bXrl3ExMQQExPDwIED6d+/Py4ueV8aZ2dnAM6ePYuTkxPnz5/n0qVL1K5dm/nz57N7924A/P39efbZZ4mLi6Nt27YAPPHEE0yaNImff/6ZZs2aFT3QjCx9+MudkZap95rYV0ZWiTepORplV7lPNCIjIzl9+jTTpk3D19eXpKQkfvzxR65cucLOnTt54oknOHToEPHx8bRq1YqrV68yatQoW9IwatQo1q5dS0hICCEhIbZ2v//+e0aOHEl0dDS1atVi7969ODk50ahRo1LsrYiIyJ1V7hON5557jrCwMLp164aHhwe1atViyJAhfPzxx+zcuZNFixZhsVhYuHAhlStXZuTIkbRq1YrOnTtjtVrZu3cv8+bNIzQ0NFe7TZs2Zdy4cQwaNAgnJycqVarEsmXL8PT0LKWeiog4LgMHv0285miYV+XKlVm6dGmubfHx8VgsFmbMmJFn/3nz5tl+dnJyuuXlqj179qRnz54lFquIiIjZaGVQERERsZtyX9HIT926dfnhhx9KOwwRESkkA3MPLxTEzD1TRUNERETsRomGiIiY3o3LWx35UdKOHj1KUFAQ3bp1IygoiJMnT95y/4SEBNq3b09iYmKRzqNEQ0REpJzJzMxk+PDhhISEsGPHDl555RWGDx+eb0JjGAabNm2iX79+nD9/vsjn0hwNERExPcPBlyC/Mf/kxIkTODnlrRFUr16dGjVqFLq9I0eO4OzszGOPPQZAx44dmTp1KocPH6ZFixa59v3555/54osvWL58Od26dSty7Eo0RERETCI0NJTTp0/n2T58+HBGjBiRZ/vevXsZMmRInu1Dhw6ldu3aubbVrFmThISEPIlG/fr1cy3tUFRKNERExPTKyxLks2bNumlFIz++vr75XkX5z3/+k5iYmDzb82v7dinREBERMYlGjRrh5eV12+3UqVOHc+dy36jz/PnzeaocJUGJRhlntVqv/1C3WukGIo7P3fX6/++taZebXonY/O/zzPb5Jndc8+bNycjI4Msvv6Rdu3Z8/vnnWCwWmjZtWuLnUqJRxmVkZADgEtqjlCOR8kK375Y7JSMjg4oVK5ZIW1bDse91UtJ9c3FxYenSpbz11ltERETg6enJwoULbXcpHzRoEH369KFjx463f67bbkHsqnLlyjRo0AB3d3e7jJ2J3HDixAlCQ0OZNWuW7jIsdmW1WsnIyKBy5cqlHUq59uCDD7Jhw4Z8n1u2bFm+248dO1bk8yjRKONcXFyoVk3DJmJ/Tk5OnD59GicnpxIZAxa5lZKqZNxgGDj4ZNDSjqD49BVZRERE7EYVDRERMb3ysmCXGamiISIiInajRENERETsRkMnIgJcX1lw+PDhN11hUKQsM3DwlUFNPHSiRENEAKhRo0a+90oQEbkdSjRERMT0tGBX2aU5GiIiImI3qmiIiIgDMEw9j6Fg5u2bKhoiIiJiN6poiIiI6VkNc89jKIjVxF1TRUNERETsRomGiIiI2I2GTkRExPQMw8EX7DJx31TREBEREbtRRUNEREzPcPDLW83cN1U0RERExG5U0RAREdPTHI2ySxUNERERsRtVNERExPwcvKKBifumioaIiIjYjRINERERsRsNnYiIiOkZmPn+pgUzc9+UaIiIiOk1aPCH0g7BrszcP4vh0LNnRETEkWVmZvL9999jtVpLOxS7c3JyomnTpri5uZV2KEWiRENEREwtMzOT7Ozs0g7D7lxcXEyXZIASDREREbEjXXUiIiIidqNEQ0REROxGiYaIiIjYjRINERERsRslGiIiImI3SjRERETEbpRoiIiIiN38H4L2S0ylTcNlAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_corr(\n",
    "    corr_matrix_targets.where(corr_matrix_targets>0.1, 0),\n",
    "    aspect=0.1, \n",
    "    size=5,\n",
    "    title=\"Positive Features Correlation with UNRATE\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_corr(\n",
    "    corr_matrix_targets.where(corr_matrix_targets < -0.1, 0),\n",
    "    aspect=0.1, \n",
    "    size=5,\n",
    "    title=\"Negative Features Correlation with UNRATE\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>target_0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>UNRATE</th>\n",
       "      <td>-0.124944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPX_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_month</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <td>-0.109565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  target_0\n",
       "UNRATE           -0.124944\n",
       "Inflation         0.000000\n",
       "FEDFUNDS          0.000000\n",
       "SPX_diff          0.000000\n",
       "num_month         0.000000\n",
       "UNRATE-1_diff    -0.109565\n",
       "UNRATE-2_diff     0.000000\n",
       "UNRATE-3_diff     0.000000\n",
       "Inflation-1_diff  0.000000\n",
       "Inflation-2_diff  0.000000\n",
       "Inflation-3_diff  0.000000\n",
       "fedfunds-1_diff   0.000000\n",
       "fedfunds-2_diff   0.000000\n",
       "fedfunds-3_diff   0.000000\n",
       "spx-1             0.000000\n",
       "spx-2             0.000000\n",
       "spx-3             0.000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix_targets.where(corr_matrix_targets < -0.1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### on FEDFUNDS_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"FEDFUNDS_diff\"].values.reshape(-1, 1))), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On ff_lower_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ff_lower_50\"].values.reshape(-1, 1))), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On ff_lower_25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ff_lower_25\"].values.reshape(-1, 1))), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On ff_stable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ff_stable\"].values.reshape(-1, 1))), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On ff_higher_25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7797e3ri5uTkwIhEpSpSEiBRhpUuX5sCBA/b7xKxevZoyZco4OCoRKSo0MVWkCDt58iT9+/fn1KlTeHl5UapUKWbPns0jjzzi6NBEpAhQEiJSxGVmZnLq1CkyMzOpUaMGrq5aPkhE/hhKQkSKsPXr12fb5unpia+vL9WrV//D4xGRokVfeUSKsA0bNvD999/TpEkTrFYru3fvpmrVqly5coWQkBA6duzo6BBFxMSUhIgUYRaLhdWrV9vngJw+fZpx48bx4Ycf0rNnTyUh4hTS0tLIyMhwdBiFztXVFXd3d0eHUaCUhIgUYefPn88yCbVatWokJiZSsmRJrFZdPCf3v7S0NA4dOkhRmFhgtVqpW7euqRIRJSEiRZiHhwdffvklbdq0AeCrr76iWLFiJCYmFolvluL8MjIyMAx4qKwXHm4ujg6n0KSmZxJ7MZmMjAxTJSGamCpShB0+fJiQkBBu3LiBzWajVKlSzJgxg/Xr11OlShV69Ojh6BBF7ig5OZmYmBhq+RTHy928SUhyWiY/J16nTp06eHl5OTqcAqNKiEgRduXKFTZv3szRo0dxcXGhZs2auLi46M66IvKHUBIiUoSNHj2aTZs2UadOHUeHInJvDBsYFkdHUXgMm6MjKBRKQkSKsLp167J27VqefPLJLPeQ8fHxcWBUIlJUKAkRKcI++eQTPvnkkyzbLBYLMTExDopIRIoSJSEiRdiRI0ccHYJIwbAZYDPnkAVws38mpCREpAhLT09n+/btXL9+Hbh5H5nTp08zcOBAB0cmIkWBkhCRImzQoEH8/PPPXLx4kUcffZRDhw7RqFEjR4clkg82ky9YZs4qj5ZEFCnCDh06RFRUFAEBAYwcOZLly5eTkpLi6LBEpIhQJUSkCKtQoQJubm5Ur16dn376iWeffdY+NCPiVGw2sxYLbjLpfBdVQkSKMDc3N3bt2kWtWrXYvn07Fy5cICkpydFhiUgRoSREpAh7++23+fTTT3nmmWc4efIkzZo1o1atWo4OSyTvDOO/C5aZ9WHOCS8ajhEpgs6dO4dhGLzzzjssWLCAixcvMm3aNC5dukT//v0dHZ6IFBFKQkSKoOHDh7Nr1y4AWrRogWEYWCwWXFxc7HfUFREpbEpCRIqgBQsWABAWFsbEiRMdHI1IATAywWbme8eYczhGc0JEijAlICLiSKqEiIiI8zMM01YLADAAzFfpUSVEREREHEKVEBEBbl4xs3LlSrp27UqFChUcHY5I3ph+sTIAF0dHUeBUCRERAM6fP8/MmTM5f/68o0MRkSJClRAREXF6hmHuG9jd7JsqISIiIiIFQkmIiIiIOISGY0RExPkZBtjMPB5jvstzQUnIfS8jI4MrV67g4eGB1arClRQem81G9erVsdlsJCcnOzocMTGbzUZqaiqlSpXC1VUfQ0WZxTDMPJXH+V28eJFTp045OgwRkQJXvXp1ypYte09tJCcnExMTQ02vG3i6mPfjLCXTwvHkYtSpUwcvLy9Hh1NglILe5zw8PAAIHzGR06fjHByNmJlnMQ/m/fMDer8+hJQbqY4OR0ysWrUqjBkVZv/7JoVn586dTJw4kdTUVCpVqsQHH3xw23WAYmJi+Otf/8revXvt22JjYxk+fDhJSUlYrVZGjRpFw4YNCyw+JSH3uVtDMKdPx3H06HEHRyNmVtzLE4CffjrJ9eQUB0cjRUGBDjHbbGAx8Wpltry/VklJSYSFhfHhhx/i6+vLhx9+yLBhw1i4cGGW/dLT01m8eDGLFi3i+vXrWZ4bOHAgnTp1omvXrhw6dIg33niDL7/8ssCqMZpkICIiYkK7du3C19cXX19fALp160Z0dHS2BQm///57fvnlF2bOnJlle2JiIkeOHKFDhw4A1KtXj+rVq7N9+/YCi1GVEBERESdx/PjxHCtI5cuXzzbMkpCQQKVKley/u7u74+3tzdmzZylfvrx9e6NGjWjUqBFxcVmH/M+ePUu5cuVwc3Ozb/Px8SE+Pr6guqMkREREzMAGhomHY/4rLCwsx4sVQkJC6N+/f5ZthmFgsWS/tDe3w2D3enxuKAkRERFxEhMnTrxtJeT3KleuzJ49e+y/p6WlcenSJSpXrpyrc1WuXJkLFy6QkZFhv5T63LlztGrVKp/RZ6ckREREnJ/NwNS30bXdrEjUrFkz15NC//KXvzBmzBiOHTvGo48+yurVq2nQoAFlypTJ1fE+Pj7Url2bdevW0blzZw4fPsxPP/1E06ZN892N31MSIiIiYkJlypRhypQpDBkyhNTUVMqWLcuECRMACA4OZsyYMTz++ON3bGPSpEm8++67fPjhhwBMnjyZUqVKFViMSkJERMT5GSafE5LPZdubNm3KunXrsm2PiorKtq1KlSocPnw4y7aHHnqIxYsX5+vcuaFLdEVERMQhVAkRERHnZ9huLlhmVrqB3f3D19eXHTt2ULFiRfu2ffv2MXjwYLZu3Up0dDR//etfmTNnDv7+/vZ9Zs+ezS+//ML48eNZu3Yto0ePpmrVqsDNS5F+/fVXateuzdixY/H29rYft379eoYMGcLMmTNp3bo1mZmZ9sVb0tPTOX78OLVr1wagXLlyLFy4EH9/f1xdXfH09MwSe58+fXjuuecK7bURERFxFk6ZhOSGm5sbw4cPJyoqCh8fnxz3adCgQZaxrtTUVLp3786iRYsYNGiQffvy5ctp3749ixYtonXr1ri4uNjH0+Li4mjTpk2O42tjx47lqaeeKtiOiYiImIRpk5BKlSrh5+dHWFgYS5YsydXiKpcvX+batWtZqiAHDx7kxIkTLFiwgJYtW/LDDz/QoEGDwgw9R57FPOz39hApDF7/fX956X0mhcyzWMHfuM4wbBhkFni79wtDwzHOZ/DgwXTu3Jm5c+fSt2/fbM//8MMPBAcH2xdwqVatGj169KBnz572fZYvX07btm0pVaoUbdu2ZdGiRUybNi1X5x8+fHi24ZilS5dSsmTJPPdl3j8/yPMxIvmx8dMljg5BRIoIp0xCclpG1jCMbNUOd3d3Jk+eTJcuXWjcuHG2Y24NxxiGwfz581m1ahVt2rTBxcUFuHkHwk2bNtmHbDp06EDPnj2Ji4ujSpUqd42zIIdjer8+hJ9+OlkgbYnkxMvLk42fLuH5F/5Gsu6iK4XokUceLvgvVoYNUy9WZtLLj50yCfH29uby5ctZJqZeuHCB0qVLZ9u3Zs2aDB06lLCwMJ599tkc27NYLPTu3ZsffviBgQMH8tFHH2G1Wvn444+x2WyEhYXZ97VarSxZsoR33nmnwPt1Jyk3UnV7dflDJCen6L0mhSrlRqqjQ5D7hFOuE+Ln58fSpUvJzLw5/nf9+nVWrFhBixYtcty/c+fONGjQgGXLlt2x3REjRnD06FEiIyOx2WysWLGC4cOHs3XrVvtj1KhRfPzxx1y9erWguyUiIvllM25eomvah+HoV7hQOGUl5J133mHChAkEBgbi6upKRkYGbdq04fXXX7/tMaNGjeLAgQN3bNfHx4d+/foxZcoUihcvTkpKCh07dsyyT1BQEHPmzGHlypX06tXrju3lNCfE39+f0NDQu/RQRETE/CyGYZgzvTKJ5ORkYmJi6Pm3tzh69LijwxETK+7lybatq2jp30XDMVKofH1rsnTJVOrUqZPrm7Hdzq2/kTVIwJP0Aorw/pOCGyeoWCCv2f3EKYdjRERExPkpCRERERGHcMo5ISIiIlnYbGDixcrAxZRlAxN2SURERJyBKiEiIuL8zL5YmUn7pkqIiIiIOIQqISIi4vxsBmatFtxkgIujYyh4qoSIiIiIQygJEREREYfQcIyTmFjyT6SXufude0Xyy+LpDsAy72YYxdIcHI2YmVvJCoXQqs20d5oFwGLOvqkSIiIiIg6hSoiIiDg/myohzkiVEBEREXEIVUJERMT52QyTV0LMecN7VUJERETEIVQJERERp2eQiWGY9wZ2hsWcfVMlRERERBxCSYiIiIg4hIZjRETE+dmMm5fpmpYmpoqIiIgUGFVCRETE+RkmX6zMpH1TJUREREQcwlRJiL+/P/v27bvjPuvWrcPPz48ePXowZMgQZs+efdd2Z82axaZNmwCIjIxk5syZBRKviIgUEMN2c06IWR+qhJjDunXrCAkJ4aOPPsJiseTqmD179pCeng5A9+7dCQkJKcwQRUREigRTzgnx9/cnODiYb7/9lvj4eAICAggPD2fMmDEcPHiQ2NhYLly4kOWYgwcP8sEHH5CWlsa5c+eoV68ekydP5qOPPuLQoUPExcVhsVg4ffo0CQkJREREkJiYyKhRozh9+jQWiwU/Pz9CQ0Nxc3O7bQz5ZfFws99qXaQw3Hp/6X0mhc3i4VbwjRomX7bdMOfVMaZMQgCuXr3K8uXLOX/+PK1bt6Zbt26Eh4dz9OhROnXqRHBwMEOHDrXvv3TpUnr37s0zzzxDWloawcHBbNu2jVdeeYUtW7bQqVMngoKCmDFjhv2YsLAwGjduzKxZs7hx4wZ9+vRh/vz59O3b97Yx1KpVK1/9qTCx+729ICK5VHnZG44OQUSKCNMmIf7+/gCUL1+ecuXKkZSUdMf9IyIi+Pe//828efM4efIkFy9eJDk5+bb7Jycns2/fPubMmQNAsWLF6NGjB/PmzbMnIXmN4U7OhUWSfvJ8vo8XuRuLpzuVl71B/MtzMVLSHB2OmJjbw+X1xUoAEychHh4e9p8tFgvGXUpZPXv2pHr16vj5+REQEEBsbOwdjzEMI9vzNpuNzMz/re+f1xjuxEhN1weD/CGMlDS916RQGanpBd/orQmcZmUxZ9+K3MTUnFy9epX9+/czcOBA2rZtS2pqKocOHbInFC4uLlmSC4DixYvz5JNPsmTJEgBu3LjBihUraNas2R8ev4iIiDMybSUkL0qWLEm/fv3o1q0bpUuXpnjx4jRq1IjTp08DN4dVJk+eTGpqapbjJk6cyOjRowkMDCQtLY3mzZvryhkREUfQxFSnZDHuZYxACl1ycjIxMTEk9v+Q9OPnHB2OmJjF050H17zJmY7TNRwjhcqtZgV8ZvyVOnXq4OXldU9t3fob+fClA3hmXi+gCO8/KS7FOeldv0Bes/uJKiEiIuL8DJPPCbGas2+aEyIiIiIOoUqIiIg4P7NfHWPSvqkSIiIiIg6hJEREREQcQsMxIiLi/HSJrlNSJUREREQcQpUQERFxfma/RNekVR5VQkRERMQhVAlxEo89eBE3W4KjwxATsxXz4BzQsHoi1hupd91fJL/SH3ThYkE3qjkhTkmVEBEREXEIJSEiIiLiEBqOERER56cVU52SKiEiIiLiEKqEiIiICdjMPTEVc/ZNlRARERFxCFVCRETE+dkM086bAG72z4RUCRERERGHUCVEREScn66OcUqqhIiIiIhDKAkRERERh9BwjIiImIBh2vur3GTOvv3hSYivry+1atXC1fV/p65UqRJz585l6NCh7Nq1i7Jly2Y5pnnz5oSFhTFjxgyWLVtGxYoVAUhPT6dChQoMGjSIxx9/HOC2bYwfP56rV68SHh7OV199leW52bNn88svvzB+/HhmzJjB0qVLWb9+PZUrV7bv88orrxAUFESHDh2ynSM1NZUaNWowZMgQqlWrBsCFCxcYN24cR48exWKx4Obmxl//+ldefPHFgnkhRUREnJxDKiELFy60JxK/16NHD/r27XvbY1u1akVERIT996ioKF599VU2bNhgTxpu10Z0dHSu4ktJSSEsLIylS5fi4uJy1zgNw2DevHn89a9/ZePGjZQoUYLw8HCeeOIJJk2aBEB8fDxdunTBx8eHJk2a5CoOERHJJV2i65ScfjgmODiYqKgoPvroI8LCwgqkzbZt2/Lzzz8zc+ZMQkND77q/xWLh9ddfZ+3atXzyySd0796dxMREUlNTyczMxMXFhcqVKzN79my8vb3zFZPN3R1bMY98HSuSGzYPjyz/FSksNnd3R4cg9wmHJCGvvfZaluGYCRMm4OvrC8BHH33EF198kWX/IUOG0LRp09u25+vry7Fjx+y//76N5557jj59+uQ6Pjc3NyZPnkynTp1o0qQJf/7zn3N13G/jGDJkCIMHD2bZsmU0bNiQP/3pT7Rr146HHnoo13H81qWB/fJ1nEheXRg7wtEhiOSdLtF1Sk43HJMTi8VCsWLF7tqG1ZrzxUA2my3bc9WrVyc8PJy3336bqKioPMfRuHFjtm3bxoEDB9i7dy+7d+9m1qxZzJw5Ez8/v9x2zc578izczsTn+TiR3LJ5eHBh7AjKDR+FNTXV0eGIiaU/WFlfrAQwwXAMwMGDB2nUqNFd9/P29ubKlSvZtl+8eJHSpUtn2/7iiy/y9ddfM3z48Lu2bRgGP/74Iy1btuTixYtMmzaNd999l4YNG9KwYUN69+7NlClT+Oijj/KVhFjT0rDe0AeDFD5raqrea1KorGlphdCqYfIb2JlzTojTrxPy8ccfc/ToUXr06HHXfWvUqMEDDzzAunXr7Nvi4uL48ssvadGiRY7HvPfee/z888989913t203MzOTWbNmAdCuXTtKlizJnj17WLhwIbb/ltDS0tKIjY2lXr16eeidiIiIed13lZCc5oRUqFCB+fPnA7B582YOHToE3BxGefDBB1m2bBllypS5a9tWq5V58+Yxfvx4Fi5cCIC7uzuDBw++7byPEiVKMHnyZLp163bbODMyMqhduzZLly7F/b8TrhYtWsSkSZNo1aoVnp6eGIbBs88+m+ehJhEREbOyGIapV3dxesnJycTExFB2/BTcYs84OhwxMVsxD85NiqDCoHc0HCOFKv2hB7k4dAB16tTBy8vrntq69Tfy4eOb8bxxuWACvA+lFCvNyZqtCuQ1u584/XCMiIiIOKf7bjhGREQkzwyTL9tu0r6pEiIiIiIOoUqIiIg4P8Pky7arEiIiIiJScJSEiIiIiENoOEZERJyf7h2To507dzJx4kRSU1OpVKkSH3zwARUqVMiyz6VLlxg2bBi//PILmZmZvP3227Rq1QqA9evXM27cuCy3Wlm6dCklS5bMf19+Q0mIiIiICSUlJREWFsaHH36Ir68vH374IcOGDbMv1nnLyJEjefTRR5k7dy5xcXF07dqVunXrUqlSJfbt20f//v15+eWXCyVGDceIiIjzM2zmf+TRrl278PX1td+lvlu3bkRHR3P+/Hn7PhkZGWzbts2+KniVKlVo1qwZn3zyCQDfffcd27Zto0OHDvTo0YNvv/22AP6x/keVECfxQNCjeCaXc3QYYmKZVjfOAaVfehwXW7qjwxETS/Eqw0VHB+Gkjh8/nuMd4cuXL59tmCUhIYFKlSrZf3d3d8fb25uzZ89Svnx54OZQzI0bN7IMt1SsWJGzZ8+SlpZG5cqVee2112jatCl79+6lb9++rF27loceeqhA+qMkREREnN7NYoE5L2OF/xVCwsLCOHXqVLbnQ0JC6N+/f9ZjDAOLxZJt398mMbfu3PL7/axWK+7u7lmGbp5++mmefPJJdu7cmaubxuaGkhAREREnMXHixNtWQn6vcuXK7Nmzx/57Wloaly5donLlyvZtZcuWxcPDg3PnzuHj4wNAYmIitWrVIjExkY0bN/L3v//dvr9hGLi5uRVYfzQnREREnJ9h+98VMmZ8/LcUUrNmTerWrZvt8fuhGIC//OUvHD58mGPHjgGwevVqGjRokOWu8y4uLgQEBPDRRx8BcObMGXbu3EmrVq3w8vJi9uzZ7N69G4CDBw/yn//8h5YtWxbYP5sqISIiIiZUpkwZpkyZwpAhQ0hNTaVs2bJMmDABgODgYMaMGcPjjz/Ou+++y4gRI3jhhRfIyMhg6NChVK9eHYBZs2YxYcIEUlNTcXV1ZerUqZQrV3DzE5WEiIiImFTTpk1Zt25dtu1RUVH2n8uUKcPMmTNzPL5Ro0asWbOm0OJTEiIiIs4vn5exOg2T9k1zQkRERMQhVAkRERHnZwNMfIku5iyEqBIiIiIijqFKiIiIOL9bl+ialeaEiIiIiBQcVUJERMT52UxeCTFp31QJEREREYdw6iTkwIED/O1vfyMwMJDAwED++te/8p///AcAf39/2rRpQ3BwMC+++CLPPfccoaGhXLp0iYyMDHr06MG7776bpb1//etfdOrUidTU1DzF0bp1a6Kjo4Gbq9BdvHjz/pBz586lRYsWvPXWW3z99de0bNmSDh06cPny5Xvuu4iIiLNz2uGYtLQ0evXqxezZs3nyyScB+Prrr3nttdfYunUrAGPHjuWpp54CwGaz8dZbb/Hee+8xffp0pk6dSocOHVi3bh3t27dnx44dLF68mFWrVuHh4ZHvuH67Ct3q1asZO3YsTZs2ZdiwYQQFBTFgwIB8tWuzuJJpLbibBon8XqbVNct/RQqLzVII7zHDuPkwK5P2zWn/2qSkpHD16lVSUlLs2/7yl78wefJkXFxcsu1vtVpp1KgRkZGRAFSoUIGpU6fy+uuv88ADDzBixAhmz55tv4vgnezfv59Ro0Zhs9moVasWN27csD/n6+vLjh07GDt2LImJibz//vt07NiRLVu24O7uztWrV3nvvffy3N9f6jyb52NE8uP4E50cHYKIFBFOm4SUKlWKIUOGEBISgre3Nw0bNuSpp57ihRdeoESJEtn2v3z5Mps2baJJkyb2bU899RR9+/alX79+jBgxgieeeOKu501PT6d///6MHj2ali1bsnv3bjZu3Jhtv+nTp+Pv72+vxpw8eZKqVavSt2/ffPW3aswXFEu5lK9jRXIj0+rK8Sc6UfM/H+Niy3B0OGJiNzy9C/6LlWGYdvImoErI/eiVV16hS5cu7N27l++//57IyEhmz57NqlWrABg+fDienp4YhoFhGDRp0iTLcIjNZiM6OpoKFSqwbds2unfvjtV652kyx44dIyMjw34r4yZNmvDwww8XXif/y2pk4GJLL/TziLjY9F6TwmU1lOTKTU6bhOzbt4/vv/+e3r174+fnh5+fH2+99RYvvfQSn332GZB1TkhOJk+ezNWrV9mwYQPdunVj+vTpvPXWW3c9t/G7jNTV1WlfRhERc7AZJl+23Zx9c9qrY8qUKcOcOXPYvXu3fVtSUhKXLl2iXr16dz1+48aNrF+/nmnTpuHt7c2MGTNYsmQJ27Ztu+Nxvr6+FCtWjC+//BK4eYXO8ePH760zIiIiRZDTfoWvUaMG//znP5kxYwbvvPMOnp6eeHh40K9fPxo1anTHYw8fPsyIESP45z//aZ+I+uijjzJy5EgGDx7MmjVrqFq1ao7Hurq6Mnv2bN577z1mzpxJ9erV/5DhGBEREbOxGL8fW5D7SnJyMjExMVQ/vBHP5CRHhyMmlml149ifuvHo9ys0J0QKVYpXGU499jx16tTBy8vrntqy/438diWe1y4UUIT3n5QS5Tj1564F8prdT5y2ElKYxo4da1987PdeffVVXnzxxT82IBERERNSEpKD4cOHOzoEERHJCwPTTt4EbvbPhJx2YqqIiIg4N1VCRETE+dlsGGZerMykfVMlRERERBxClRAREXF+hskXKzPphayqhIiIiIhDKAkRERERh9BwjJNI//Yo1sQzjg5DTMzm7gF/6kbajgNY01IdHY6YWLrPg/DY8wXbqGGAYc7Jm4CGY0REREQKkiohIiLi/HQXXaekSoiIiIg4hCohIiLi/AybaRf0Akw730WVEBEREXEIVUJERMT5aU6IU1IlRERERBxCSYiIiIg4hIZjRETE+WmxMqekSoiIiIg4hCohIiLi/HQXXaekSoiIiIg4hCohIiLi9AybDcPEi5WZtW+qhBSwWbNmsWnTJgCGDh3K7NmzHRyRiIjI/UmVkAK2Z88eqlSpUuDt2tzcb95qXaSQ3Hp/6X0mhc3m5l4IjWLuOSHmLIQ4LgmJjo5mwoQJPProoxw5coSUlBTCw8PZv38/CQkJREREADB79mx++eUXxo8fT8+ePalXrx579uzh4sWLvPDCC5QuXZpt27aRkJDA8OHDad269R3Pm9s2rl27xpgxYzh48CAWi4UnnniCoUOHUqJECXr27Mnjjz/OgQMHiI+Pp0GDBvzf//0fy5Yt49ChQ8TFxWGxWAA4ePAg3bt3JykpifLlyzNp0iR8fHzy/HqdfSkk7y+ySD780u89R4cgIkWEQyshP/74I++99x7169fnww8/ZOrUqfj5+d3xmOPHj7NmzRrOnj2Lv78/w4YNIzIykg0bNjBjxoy7JiG5bSMiIgJXV1c++eQTDMNg+PDhjBs3zp4cnTx5kiVLlpCamkq7du34+uuveeWVV9iyZQudOnUiKCiIb775hrNnz7Js2TKKFy9Onz59iIyM5K233srza1Vp+Uw8zsXn+TiR3LK5e/BLv/eoOmsk1rRUR4cjJpZaobK+WAng4CSkfPny1K9fH4DHHnuMxYsX3/WY1q1bY7VaefDBB3FxcaFFixYAVK1alcuXL+fqvLlpY8eOHfzrX//Car05beZvf/sb//jHP+xttGzZEhcXF7y8vKhWrRpJSUk5nsvf358SJUrY+3ju3Llcxfh71vQ0fTDIH8Kalqr3mhQqa3pawTeqS3SdkkMnphYrVizbNovFgvGbFzs9PT3L8+7uWccSXV3znkflpg3b72Yi22w2MjMz7b97ePxv3Pz3Md+u7TvtJyIiUtTcd1fHeHt7c/ToUWw2GykpKezatcshcTzzzDMsXbrUnnwsXbqUZs2a3fU4FxeXLMmKiIj8AQyb+R8mdN8lIUFBQZQuXZo2bdrwj3/8g4YNGzokjnfeeYeMjAyCgoJo164dACNGjLjrcf7+/kyePJnIyMjCDlFERMSpWQyND9zXkpOTiYmJ4cHFk/FIPOPocMTEbO4enBowlupThmtOiBSqVJ8HOfPKQOrUqYOXl9c9tXXrb2S1Tf+k2KWEAorw/nPDuyKn271eIK/Z/cR064QsWLCATz75JMfnnn/+eXr37v0HRyQiIiI5MV0S8o9//CPLVSwiIiJyfzJdEiIiIkWPYYBh4kt0zTpx4r6bmCoiIiJFgyohIiLi/GwmX6zMpH1TJUREREQcQpUQERFxfoYNbOZc0AvQYmUiIiIiBUmVECdx4LuyZBzXcvBSeCye7lQYAN/u8sFIKYQbjIn8l2vNspR9pYAb1ZwQp6RKiIiIiDiEkhARERFxCA3HiIiI87Nh2iEL4Gb/TEiVEBEREXEIVUJERMTpGYaBmW8Kb9a+qRIiIiIiDqFKiIiIOD/D5JfoqhIiIiIiUnBUCREREeenxcqckiohIiIi4hBKQkRERMQhNBwjIiJOzzAMDJMOWYAu0RUREREpUHlKQnx9fUlISMiybd++ffj7+wMQHR2Nr68vW7duzbLP7NmzGTp0KABr166lYcOGBAcHExwcTFBQEC1btqRPnz5cunQpy3Hr16/H19eXr776CoDMzEz7ce3atcPX19f++2uvvQaAv78/bdq0sW+/9fj888/v2LedO3fy3HPP5eXlyPE12LJlCyNGjADg2rVr9OjRg3bt2rF+/Xref/99/P39iYiIyNd5RETkNm4t227ah6Nf4MJR4MMxbm5uDB8+nKioKHx8fHLcp0GDBixevNj+e2pqKt27d2fRokUMGjTIvn358uW0b9+eRYsW0bp1a1xcXIiKigIgLi6ONm3a2H//rbFjx/LUU0/lKt7r168ze/ZsVqxYQZkyZfLQ05wFBAQQEBAAQExMDGfOnGHHjh0ADB06lC+++IJq1arluV2LhxsWT/d7jk/kdm69v/Q+k8Jm8XBzdAhynyjwJKRSpUr4+fkRFhbGkiVLsFrvXmy5fPky165dw9vb277t4MGDnDhxggULFtCyZUt++OEHGjRoUNDhsm3bNmw2G+PHj2fChAm5Pm7WrFmsX7+eUqVK8cgjj9i3r127lg0bNhAeHs7w4cO5ePEiwcHBpKWlYRgGffv2ZejQoTRv3jxPcZaZ2CNP+4vkV/llfRwdgkje2TBttQAwbd8KZWLq4MGD6dy5M3PnzqVv377Znv/hhx/sH8yXLl2iWrVq9OjRg549e9r3Wb58OW3btqVUqVK0bduWRYsWMW3atFydf/jw4Xh6embZtnTpUkqWLJlt3xdeeIEXXniB6OjoXPdv+/btREVFsWbNGh544AGGDx+ebZ9atWoxZswYwsPD7dUaX19fFi5cSMWKFXN9rluSwj4i4+T5PB8nklsWT3fKL+vD+ZfnYKSkOTocMTHXh8vri5UAeUxCLBZLtm2GYWSrdri7uzN58mS6dOlC48aNsx1zazjGMAzmz5/PqlWraNOmDS4uLgAkJSWxadMm+5BNhw4d6NmzJ3FxcVSpUuWuceZlOCY/du3aRUBAgD2p6datW56SmPwwUtP1wSB/CCMlTe81KVRGarqjQ5D7RJ4mpnp7e3P58uUs2y5cuEDp0qWz7VuzZk2GDh1KWFgYv/76a47tWSwWevfuja+vLwMHDsRmu1lv+vjjj7HZbISFheHv78/bb7+N1WplyZIleQk3my1bttgnqvbq1eue2votV1dd6Swi4lD/vUTXrA/dOwbw8/Nj6dKlZGZmAjcnda5YsYIWLVrkuH/nzp1p0KABy5Ytu2O7I0aM4OjRo0RGRmKz2VixYgXDhw9n69at9seoUaP4+OOPuXr1al5CziIgIICoqCiioqKYP39+vtvx8/Pjiy++sF/Ns27duny3JSIiUlTl6Sv8O++8w4QJEwgMDMTV1ZWMjAzatGnD66+/fttjRo0axYEDB+7Yro+PD/369WPKlCkUL16clJQUOnbsmGWfoKAg5syZw8qVK+9axchpToi/vz+hoaF36WHuNG/enJ49e9K1a1dKlCjBE088USDtiohIPuneMU7JYph1GTaTSE5OJiYmhov9l5Bx/JyjwxETs3i6U2FNKOc6TtOcEClUrjUrUHbG36hTpw5eXl731Natv5FVPpyCx7kzBRTh/Se1woPE/XVAgbxm9xNNZsjBtWvXeOmll277/O2utBEREQcxMO1lrMDN/pmQkpAclChRIsdF0ERERJzJzp07mThxIqmpqVSqVIkPPviAChUqZNnn0qVLDBs2jF9++YXMzEzefvttWrVqBUBsbCzDhw8nKSkJq9XKqFGjaNiwYYHFpyREREScnv0qEpPKT9+SkpIICwvjww8/xNfXlw8//JBhw4axcOHCLPuNHDmSRx99lLlz5xIXF0fXrl2pW7culSpVYuDAgXTq1ImuXbty6NAh3njjDb788ssCGxLSDexERERMaNeuXfj6+uLr6wv8b02r8+f/t/BlRkYG27Zto1u3bgBUqVKFZs2a8cknn5CYmMiRI0fo0KEDAPXq1aN69eps3769wGJUJURERMRJHD9+PMfboZQvXz7bMEtCQgKVKlWy/+7u7o63tzdnz56lfPnywM2hmBs3bmRZybtixYqcPXuWs2fPUq5cOdzc/nevHx8fH+Lj4wusP0pCRETE+RWRe8eEhYVx6tSpbE+HhITQv3//LNsMw8hxpfPfJjG3LpD9/X5WqzVXx98rJSEiIiJOYuLEibethPxe5cqV2bNnj/33W/drq1y5sn1b2bJl8fDw4Ny5c/j4+ACQmJhIrVq1qFy5MhcuXCAjI8O+Mvi5c+fsk1YLguaEiIiI0zNMvmz7rYpFzZo1qVu3brbH74diAP7yl79w+PBhjh07BsDq1atp0KABZcqUse/j4uJCQEAAH330EQBnzpxh586dtGrVCh8fH2rXrm1fFfzw4cP89NNPNG3atMD+3VQJcRI+HilYPK85OgwxMcPTgwygsud1LKQ6OhwxMcPjATIcHUQRUKZMGaZMmcKQIUNITU2lbNmyTJgwAYDg4GDGjBnD448/zrvvvsuIESN44YUXyMjIYOjQoVSvXh2ASZMm8e677/Lhhx8CMHnyZEqVKlVgMSoJERER51dE5oTkVdOmTXO8v9lv18IqU6YMM2fOzPH4hx56yH5H+8Kg4RgRERFxCFVCRETE6RkGGCauhJj1Lm+qhIiIiIhDKAkRERERh9BwjIiIOD9NTHVKqoSIiIiIQ6gSIiIiTs+wmXxiqkn7pkqIiIiIOIQqISIi4vwMTDtvArjZPxNSJUREREQcQkmIiIiIOISGY0RExPmZfGKqWYea8lQJ8fX1JSEhIcu2ffv24e/vD0B0dDS+vr5s3bo1yz6zZ89m6NChAKxdu5aGDRsSHBxMcHAwQUFBtGzZkj59+nDp0qUsx61fvx5fX1+++uorADIzM+3HtWvXDl9fX/vvr732GgD+/v60adPGvv3W4/PPP8+xT3v37qVLly4EBwcTGBjI+vXr8/KSADdvBNSzZ08AIiMj7TcCOnv2LIGBgQQFBbFz50769u1L69atmTdvXp7PISIiYjYFXglxc3Nj+PDhREVF4ePjk+M+DRo0yHJXvtTUVLp3786iRYsYNGiQffvy5ctp3749ixYtonXr1ri4uNjv/BcXF0ebNm2y3AnwlrFjx/LUU0/dNdYbN27Qr18//vnPf9KwYUPi4+Np3749devW5ZFHHsljz2/q3r27/efo6GhKlChBZGQk8fHxbN26lQMHDuDu7p7ndo1ibuDpka+YRHLD8HTP8l+RwmIUcyv4NnXvGKdU4ElIpUqV8PPzIywsjCVLlmC13r3YcvnyZa5du4a3t7d928GDBzlx4gQLFiygZcuW/PDDDzRo0KBAY01NTWX48OE0bNgQgMqVK1O2bFni4+PvmIRkZGQwfvx4tm/fTpkyZXjooYfsz82YMYOEhASef/55pk6dypUrV3j22WdxcXHBMAw6d+7MhAkT8PX1zVOsmWNey18nRfIoc36Yo0MQkSKiUOaEDB48mM6dOzN37lz69u2b7fkffviB4OBg0tLSuHTpEtWqVaNHjx72IQ24WQVp27YtpUqVom3btixatIhp06bl6vzDhw/H09Mzy7alS5dSsmTJLNtKlSrFiy++aP99xYoVXLt27a5VlJUrV3L48GE2btyIxWLhjTfeyLZP06ZNefPNN9mwYQOLFy++Y+UmN1zCF2I5lZivY0Vyw/B0J3N+GC69JmJJSXN0OGJiRnWfAv9ipcXKnFOekhCLxZJtm2EY2aod7u7uTJ48mS5dutC4ceNsx9wajjEMg/nz57Nq1SratGmDi4sLAElJSWzatMk+ZNOhQwd69uxJXFwcVapUuWucuR2O+W0fZs2axYoVK1iwYAHFixe/4/47d+7k+eefx8Pj5vBIp06diIyMzPX58sNyIx1LSmqhnkMEwJKSpveaFK4b6Y6OQO4TeZqY6u3tzeXLl7Nsu3DhAqVLl862b82aNRk6dChhYWH8+uuvObZnsVjo3bs3vr6+DBw4EJvtZqr38ccfY7PZCAsLw9/fn7fffhur1cqSJUvyEm42W7ZssU9U7dWrFwDXr1+nb9++bN26ldWrV1O7du1ctWX8ZoDO1VUXGYmIOJRhMf/DhPKUhPj5+bF06VIyMzOBmx/gK1asoEWLFjnu37lzZxo0aMCyZcvu2O6IESM4evQokZGR2Gw2VqxYwfDhw9m6dav9MWrUKD7++GOuXr2al5CzCAgIICoqiqioKObPn09GRga9evXCzc2NyMhIKlWqlKt2WrRowYYNG0hJSSEjIyPfQywiIiJFWZ6+wr/zzjtMmDCBwMBAXF1dycjIoE2bNrz++uu3PWbUqFEcOHDgju36+PjQr18/pkyZQvHixUlJSaFjx45Z9gkKCmLOnDmsXLnSXsW4nZzmhPj7+xMaGppl25YtW/juu++oWbMmXbp0sW9/8803CQgIuG37Xbp0ITY2lsDAQEqXLk3t2rXvKTkSEREpiiyGYdYLf8whOTmZmJgYXMPmYjl51tHhiIkZnh5kLBuO68tjNSdECpXxcCUyJr5BnTp18PLyuqe2bv2NLD9pMu5xZwoowvtPWpUHOT9oYIG8ZvcTTWbIwYkTJxgwYECOz3l6erJixYo/OCIRERHzURKSgxo1amieh4iIEzFsFgybOSdvAqbtm25gJyIiIg6hSoiIiDg/ky9WphvYiYiIiBQgVUJERMTpGYYFw6QLegGm7ZsqISIiIuIQSkJERETEITQc4ySupHpgS/G8+44i+ebOA8CllGKQou8nUnisqR7c+TaheWcY5p6YatZlRfWXRkRERBxClRAREXF6WqzMOakSIiIiIg6hSoiIiDg9wzDvvAkwb99UCRERERGHUBIiIiIiDqHhGBERcX6GeSdvAqDhGBEREZGCo0qIiIg4PV2i65xUCRERERGHUCVEREScni7RdU6qhIiIiIhDqBIiIiJOzzBMPifEMGff8lQJ8fX1JSEhIcu2ffv24e/vD0B0dDS+vr5s3bo1yz6zZ89m6NChAKxdu5aGDRsSHBxMcHAwQUFBtGzZkj59+nDp0qUsx61fvx5fX1+++uorADIzM+3HtWvXDl9fX/vvr732GgD+/v60adPGvv3W4/PPP8+xT//5z3/o0qULQUFBvPjii2zfvj0vLwkAUVFR9OzZE4DIyEhmzpwJwNmzZwkMDCQoKIidO3fSt29fWrduzbx58/J8DhEREbMp8EqIm5sbw4cPJyoqCh8fnxz3adCgAYsXL7b/npqaSvfu3Vm0aBGDBg2yb1++fDnt27dn0aJFtG7dGhcXF6KiogCIi4ujTZs29t9/a+zYsTz11FN3jdVms/HWW28xbtw4mjRpwtGjR+natSu7d+/G09Mzjz2/qXv37vafo6OjKVGiBJGRkcTHx7N161YOHDiAu7t73hv2cAPPfBwnklu33l96n0lh83BzdARynyjwJKRSpUr4+fkRFhbGkiVLsFrvXmy5fPky165dw9vb277t4MGDnDhxggULFtCyZUt++OEHGjRoUKCxWq1WvvrqK9zcbv4fIjY2lhIlSuDi4nLH4zIyMhg/fjzbt2+nTJkyPPTQQ/bnZsyYQUJCAs8//zxTp07lypUrPPvss7i4uGAYBp07d2bChAn4+vrmKdbiE1/OewdF8uGBZSGODkEkzwzDYtohCzDvcEyhzAkZPHgwnTt3Zu7cufTt2zfb8z/88APBwcGkpaVx6dIlqlWrRo8ePexDGnCzCtK2bVtKlSpF27ZtWbRoEdOmTcvV+YcPH56tkrF06VJKliyZbV83NzfS09Np3bo1iYmJvPPOO3etVKxcuZLDhw+zceNGLBYLb7zxRrZ9mjZtyptvvsmGDRtYvHjxHSs3uXE9bBm2k+fydaxIrni688CyEH59eSakpDk6GjEx68MV9MVKgDwmIRZL9kzMMIxs1Q53d3cmT55Mly5daNy4cbZjbg3HGIbB/PnzWbVqFW3atLFXIJKSkti0aZN9yKZDhw707NmTuLg4qlSpctc4czscc4ubmxvbt2/n1KlTvPTSS9SsWZMmTZrcdv+dO3fy/PPP4+HhAUCnTp2IjIzM9fnyJTVdHwzyx0hJ03tNCldqesG3aQPDVvDN3jdM2rc8TUz19vbm8uXLWbZduHCB0qVLZ9u3Zs2aDB06lLCwMH799dcc27NYLPTu3RtfX18GDhyIzXbzVf7444+x2WyEhYXh7+/P22+/jdVqZcmSJXkJN5stW7bYJ6r26tWL69evs2nTJvvz1atX5+mnnyYmJuaubRm/uWjb1VUXGYmIiORVnpIQPz8/li5dSmZmJgDXr19nxYoVtGjRIsf9O3fuTIMGDVi2bNkd2x0xYgRHjx4lMjISm83GihUrGD58OFu3brU/Ro0axccff8zVq1fzEnIWAQEBREVFERUVxfz583Fzc2P06NHs2LEDuDnZdd++fTz99NN3bKdFixZs2LCBlJQUMjIy8j3EIiIiBcOGBZth4geaE8I777zDhAkTCAwMxNXVlYyMDNq0acPrr79+22NGjRrFgQMH7tiuj48P/fr1Y8qUKRQvXpyUlBQ6duyYZZ+goCDmzJnDypUr6dWr1x3by2lOiL+/P6GhoVm2ubu7M3fuXMaOHcvkyZOxWCwMHz6cxx9//I7td+nShdjYWAIDAyldujS1a9e+p+RIRESkKLIYhlkXgzWH5ORkYmJiuN7/X9iOJzo6HDEzT3ceWDOQXztO1pwQKVTWmj4Un/EqderUwcvL657auvU30vOdWbicOltAEd5/MqtXIiWiX4G8ZvcTTWbIwYkTJxgwYECOz3l6erJixYo/OCIRERHzURKSgxo1amieh4iISCFTEiIiIk7PsJn83jEm7ZvuoisiIiIOoUqIiIg4PwNMfZmFSfumSoiIiIg4hCohIiLi9AzD5HNCTHoDO1VCRERExCFUCXESjwXaKHY509FhiIllumbyM9DwpUxcMvRek8Jzo7SN0wXcps2wgEmrBfDf/pmQKiEiIiLiEEpCRERExCE0HCMiIk7PMCymnbwJmpgqIiIiUqBUCREREeenxcqckiohIiIi4hCqhIiIiNOzARaTzpsA0xZCVAkRERERx1ASIiIiIg6h4RgREXF6hslXTNUluiIiIiIFSJUQERFxeoaBeWdvYt7Lj1UJEREREYcokCTE39+fffv23XGfdevW4efnR48ePRgyZAizZ8++a7uzZs1i06ZNAERGRjJz5syCCDeb1atX8+qrr+br2KioKHr27AlkjfHs2bMEBgYSFBTEzp076du3L61bt2bevHkFFreIiNxkGBZsJn6YdU7IHzYcs27dOkJCQujcuTNDhw7N1TF79uyhSpUqAHTv3r3AY0pKSmLSpEls3LiRJ5544p7b+22M0dHRlChRgsjISOLj49m6dSsHDhzA3d09X21nurqS6Zq/Y0Vyw/bf95dN7zMpZJmumgkgNxXoO8Hf35/g4GC+/fZb4uPjCQgIIDw8nDFjxnDw4EFiY2O5cOFClmMOHjzIBx98QFpaGufOnaNevXpMnjyZjz76iEOHDhEXF4fFYuH06dMkJCQQERFBYmIio0aN4vTp01gsFvz8/AgNDcXNze22MeRkw4YNVKlShcGDB/Pll1/mqo8ZGRmMHz+e7du3U6ZMGR566CH7czNmzCAhIYHnn3+eqVOncuXKFZ599llcXFwwDIPOnTszYcIEfH198/zaxvn/Lc/HiOTHiaBQR4cgkmdmvzoGw4IZe1fg6ejVq1dZvnw558+fp3Xr1nTr1o3w8HCOHj1Kp06dCA4OzlIJWbp0Kb179+aZZ54hLS2N4OBgtm3bxiuvvMKWLVvo1KkTQUFBzJgxw35MWFgYjRs3ZtasWdy4cYM+ffowf/58+vbte9sYatWqlS3WV155BYC1a9fmun8rV67k8OHDbNy4EYvFwhtvvJFtn6ZNm/Lmm2+yYcMGFi9eTFxcHG3atCEqKirX5/m9KluXUOzyuXwfL3I3Nld3TgSFUmPDNKwZaY4OR0zsRukK+mIlQCEkIf7+/gCUL1+ecuXKkZSUdMf9IyIi+Pe//828efM4efIkFy9eJDk5+bb7Jycns2/fPubMmQNAsWLF6NGjB/PmzbMnIXmNIS927tzJ888/j4eHBwCdOnUiMjKywNq/HZeMDFz0wSB/AGtGmt5rUqhcMjIcHYLcJwo8Cbn14QxgsVgw7nJdUc+ePalevTp+fn4EBAQQGxt7x2MMw8j2vM1mIzMz844xREZGsmLFCgDq1atHREREnvr1+xhucdXYpoiIw9kw+XAMlgK/nPXQoUOMHDmS69evU7x4cT744ANq1KiRbb8bN27w/vvv85///IfMzExee+01unXrBsDevXt5/fXXs0xNmDRpUo6jDzlx6Cfo1atX2b9/P9OnT6dChQocPnyYQ4cOERwcDICLi0uW5AKgePHiPPnkkyxZsoR+/fpx48YNVqxYQbNmze54ru7duxfI5NYWLVqwdu1aOnbsiJub2z0NsYiIiDhCWloaISEhjBkzhmbNmrFlyxZCQkLsUw1+a8aMGWRkZPDZZ59x+fJlunXrRu3atXniiSfYt28fXbt2ZciQIfmKw6FJSMmSJenXrx/dunWjdOnSFC9enEaNGnH69Gng5rDK5MmTSU1NzXLcxIkTGT16NIGBgaSlpdG8eXNCQkL+kJi7dOlCbGwsgYGBlC5dmtq1a3P16tU/5NwiIpIzk67lVWgOHjyIi4uL/Qt8QEAAY8aM4cCBAzRo0CDLvl999RURERFYLBa8vb15/vnnWbduHU888QTfffcdqampdOzYEYvFwj/+8Q+ee+65XMdhMe42XiIOlZycTExMDNW+XEixywmODkdMLNPVnZ87vE2ttf+nOSFSqG6UrsjpNq9Rp04dvLy87qmtW38j099aCMdN/DeyZkXcpr6Gu7s7Vmv2gZny5ctToUKFbNt37NhBnz59sm3v27cve/bsYdmyZfZt3bp1429/+xtt27bNsu/jjz/Opk2b7EMuq1atYvPmzcybN48BAwbQokULgoOD+fnnn+nZsyezZ8+mYcOGueqWJjT8zokTJxgwYECOz3l6etrnlYiIyP3DMMx7kzcAy3/LBWFhYZw6dSrb8yEhIfTv3z/bdj8/Pw4fPpxt+6effkp0dHS27TklOIZhZBuiubXflClT7Ntq1apFu3bt2Lx5s5KQ/KpRo4bmeYiIyH1p4sSJt62E5EXlypVJTEzMsu3cuXNUqlQp274PPvggiYmJ9sVDExMTqVSpEtevX2fJkiX07t3bfpGGYRh5umBDSYiIiDg9w8RLmwP2K39q1qx5z0NYAPXr1yc1NZVvvvmGpk2bsn37diwWC3Xr1s22b+vWrVm5ciUNGzbk6tWrfPrpp7z//vt4eXmxZs0aKlSoQKdOnYiNjeXzzz9n0aJFuY5DN7ATEREpYlxdXZkzZw7Tpk3j+eefZ+bMmcyYMQMXFxcAevXqxZYtWwDo168fbm5uBAYG0qVLF7p160aTJk2wWCzMmjWLdevWERgYyOuvv054eDi1a9fOfRyF0jsRERG5rz322GOsXLkyx+fmz59v/9nT0/O2a2vVrl2b5cuX5zsGJSEiIuL0bJj7Ml2zDjRpOEZEREQcQpUQERExAQuGaesFYM576CoJcRq2X1OwXbru6DDExGxuN28qZrucjCU99S57i+SfzSXF0SHIfUJJiIiIOD2bcfNhWibtm+aEiIiIiEMoCRERERGH0HCMiIg4PRsWbCadvHmTOfumSoiIiIg4hCohIiJiAua+RFeVEBEREZECpEqIiIg4Pdt/H2ZlzjqIKiEiIiLiIKqEiIiI0zPA1HNCTLpWmSohIiIi4hhKQkRERMQhNBwjIiJOz8DcE1PNWjEokH75+/uzb9++O+6zbt06/Pz86NGjB0OGDGH27Nl3bXfWrFls2rQJgMjISGbOnFkQ4dp9+eWXtG/fnuDgYNq3b8+OHTvy3Mbs2bMZOnQoANOmTWP16tUAHDlyhFatWtG+fXuOHDlCjx49aNeuHevXry/ILoiIiDitP6wSsm7dOkJCQujcubP9Q/tu9uzZQ5UqVQDo3r17gcZz5swZ3n33XVatWkW1atWIiYnhpZdeYuvWrZQuXTpfbYaGhtp/3rx5M0888QQTJ05k7969nDlzJl9Jzi02Vzcy3TzyfbzI3djc3LP8V6Sw2FzdCr5NzF0JMWvfCjQJ8ff3Jzg4mG+//Zb4+HgCAgIIDw9nzJgxHDx4kNjYWC5cuJDlmIMHD/LBBx+QlpbGuXPnqFevHpMnT+ajjz7i0KFDxMXFYbFYOH36NAkJCURERJCYmMioUaM4ffo0FosFPz8/QkNDcXNzu20Mv2e1Whk9ejTVqlUD4JFHHsFisXDx4sU7JiHXr19nxIgRHDp0iDJlylC6dGlKlSoFwNChQ6latSqVK1cmMjKSjIwM+vXrx7Fjx7h48SLBwcEsXrwYb2/vPL+2Z4Jfz/MxIvlxqucwR4cgIkVEgVdCrl69yvLlyzl//jytW7emW7duhIeHc/ToUTp16kRwcHCWSsjSpUvp3bs3zzzzDGlpaQQHB7Nt2zZeeeUVtmzZQqdOnQgKCmLGjBn2Y8LCwmjcuDGzZs3ixo0b9OnTh/nz59O3b9/bxlCrVq0scVaqVIlKlSrZf582bRoPPfQQNWrUuGP/Zs6cSWZmJp999hnJycl0797dnoTc8uKLLxIbG2tPmqKjowkPDycqKirfr+uDUf/E42JCvo8XuRubmzuneg6j+tJxWNPTHB2OmFhq2YoF/sXKMPmy7WbtW4EnIf7+/gCUL1+ecuXKkZSUdMf9IyIi+Pe//828efM4efIkFy9eJDk5+bb7Jycns2/fPubMmQNAsWLF6NGjB/PmzbMnIXmJIS0tjTFjxhAdHc2//vUvLJY7/0Pv2rWLQYMGYbVaKVGiBIGBgZw4ceKOxxQEa0Y6LumphX4eEWt6mt5rUqisGemODkHuEwWehHh4/G/egsViwTDuvMRKz549qV69On5+fgQEBBAbG3vHYwzDyPa8zWYjMzPzjjFERkayYsUKAOrVq0dERAQXLlwgJCSE4sWLs2rVqmwVjTvFcIubW8GPbYqISN4YFrCZs1gA3OyfGTn0qp+rV6+yf/9+Bg4cSNu2bUlNTeXQoUP2hMLFxSVLcgFQvHhxnnzySZYsWQLAjRs3WLFiBc2aNbvjubp3705UVBRRUVFERERw7do1Xn75ZerVq8f8+fNznYC0aNGCNWvWkJGRQUpKCp999lk+ei4iIiIOXSekZMmS9OvXj27dulG6dGmKFy9Oo0aNOH36NHBzWGXy5MmkpmYtDU+cOJHRo0cTGBhIWloazZs3JyQkJE/nXrVqFSdPnsTd3Z327dvbt48ZM4bHH3/8tsf169ePUaNG0bZtW8qUKUPVqlXzdF4RERG5yWLcbbxEHCo5OZmYmBgeWjOTYhfiHR2OmFimmwcn/v4eNRaN1JwQKVQ3ylUmtmMIderUwcvL657auvU3MvHNpaQfP1dAEd5/3GpWwGd6zwJ5ze4nWjH1d/bs2cO4ceNyfK5atWpMnz79D45IRETEnJSE/E7jxo3v6VJaERH54xmY906zYN6+mXU5ehEREbnPqRIiIiJOz+w3sFMlRERERKQAKQkRERERh9BwjIiIOD2bxYLtLrfdcGZm7ZsqISIiIuIQqoSIiIjT0yW6zklJiJNIP5eK9WyKo8MQE7N53Ly2IP1sCpmpWjFVCk+6Te8vuUlJiIiIOD1douucNCdEREREHEKVEBERcXo2y82HWZm1b6qEiIiIiEMoCRERERGH0HCMiIg4PRsWbJh0zAJM2zdVQkRERMQhVAkRERFTMOtlrGamSoiIiIg4hCohIiLi9GyY9zJWMO9CbKqEiIiIiEOoEiIiIk7PhnmrBWDevhVIJcTf3599+/bdcZ9169bh5+dHjx49GDJkCLNnz75ru7NmzWLTpk0AREZGMnPmzIII12779u28+OKLBAUF0aVLF/7zn//kuY3Zs2czdOhQAKZNm8bq1asBOHLkCK1ataJ9+/YcOXKEHj160K5dO9avX1+APRAREXFef1glZN26dYSEhNC5c2f7h/bd7NmzhypVqgDQvXv3Ao3n6tWrhIWFsWrVKmrUqMHWrVsZOHAgW7duzXeboaGh9p83b97ME088wcSJE9m7dy9nzpxhx44d+W7bcHfD5uGR7+NF7sbm7pHlvyKFxXB3c3QIcp8o0CTE39+f4OBgvv32W+Lj4wkICCA8PJwxY8Zw8OBBYmNjuXDhQpZjDh48yAcffEBaWhrnzp2jXr16TJ48mY8++ohDhw4RFxeHxWLh9OnTJCQkEBERQWJiIqNGjeL06dNYLBb8/PwIDQ3Fzc3ttjH8XsmSJdm9ezdubm4YhkFcXBylS5e+ax+vX7/OiBEjOHToEGXKlKF06dKUKlUKgKFDh1K1alUqV65MZGQkGRkZ9OvXj2PHjnHx4kWCg4NZvHgx3t7eeX5tE197M8/HiOTHmbdHOjoEkXzRJbrOp8ArIVevXmX58uWcP3+e1q1b061bN8LDwzl69CidOnUiODg4SyVk6dKl9O7dm2eeeYa0tDSCg4PZtm0br7zyClu2bKFTp04EBQUxY8YM+zFhYWE0btyYWbNmcePGDfr06cP8+fPp27fvbWOoVatWtljd3NyIi4uje/fuXLp0iSlTpty1fzNnziQzM5PPPvuM5ORkunfvbk9CbnnxxReJjY21J03R0dGEh4cTFRWV35cVn4XTcU+Iz/fxIndjc/fgzNsjefD/3sOalurocMTE0ipW1hcrAQohCfH39wegfPnylCtXjqSkpDvuHxERwb///W/mzZvHyZMnuXjxIsnJybfdPzk5mX379jFnzhwAihUrRo8ePZg3b549CclLDFWqVGHnzp388MMPvPrqq9SqVYuHH374tvvv2rWLQYMGYbVaKVGiBIGBgZw4ceKOfSwIlrR0rKn6YJDCZ01L1XtNCpUlLb3A29RddJ1TgSchHr+Zt2CxWDCMOxfIevbsSfXq1fHz8yMgIIDY2Ng7HmMYRrbnbTYbmZmZd4whMjKSFStWAFCvXj0GDhzIgQMHaNmyJQANGjSgZs2aHDt27I5JyK0YbnFz09imiIhIfjh0nZCrV6+yf/9+Bg4cSNu2bUlNTeXQoUP2hMLFxSVLcgFQvHhxnnzySZYsWQLAjRs3WLFiBc2aNbvjubp3705UVBRRUVFERESQmZnJoEGDOHLkCAA//vgjsbGxNGjQ4I7ttGjRgjVr1pCRkUFKSgqfffZZfrsvIiIFxFYEHmbk0HVCSpYsSb9+/ejWrRulS5emePHiNGrUiNOnTwM3h1UmT55M6u9KwxMnTmT06NEEBgaSlpZG8+bNCQkJydO5K1SowOTJkxk2bBg2mw13d3emTJlCxYoV73hcv379GDVqFG3btqVMmTJUrVo1b50WERERACzG3cZLxKGSk5OJiYmh4pxJeJyNc3Q4YmI2Dw9i3xnPQxFDNSdEClVqpSok9BlEnTp18PLyuqe2bv2NPDxgOcknzhVQhPcfrxoVeGzKSwXymt1PtGLq7+zZs4dx48bl+Fy1atWYPn36HxyRiIiIOSkJ+Z3GjRvf06W0IiIikjtKQkRExOkZlpsPszJr33QXXREREXEIVUJERMTpmfkyVjBv31QJEREREYdQJURERJyegXmrBWDem/OpEiIiIiIOoSREREREHELDMU6ixMj38HLXP5cUnkybAXFXKDlnDi5Wk14PKPeF5LQMSLhWoG0amHfIAszbN1VCRERExCH01VpERJyezXLzYVZm7ZsqISIiIuIQqoSIiIjT0yW6zkmVEBEREXEIVUJERMTpadl256RKiIiIiDiEkhARERFxCA3HiIiI09NiZc5JlRARERFxCFVCRETE6RkmX6zMMGnfVAkRERERh8hXEjJ37lxatGjBW2+9letjWrduTXR0NADr1q3Dz8+PHj16YBj3PtKVkJCAr69vvo+fOnUq77zzTr6OnT17NkOHDgVg2rRprF69GoAjR47QqlUr2rdvz5EjR+jRowft2rVj/fr1+Y5TRERyZisCDzPK13DM6tWrGTt2LE2bNs3XSdetW0dISAidO3fO1/EFJS4ujnHjxrFr1y5eeOGFe24vNDTU/vPmzZt54oknmDhxInv37uXMmTPs2LEj323bbP+9y6lIIbn1/tL7TAqbzayfqE7m0KFDjBw5kuvXr1O8eHE++OADatSocdv94+Pj6dq1K6tXr6ZixYoAXLp0iWHDhvHLL7+QmZnJ22+/TatWrXIdQ56TkDfffJPExETef/99XnrpJY4dO0ZMTAzp6enUr1+fYcOGUaJECfbv38+oUaOw2WzUqlWLGzduADBmzBgOHjxIbGwsFy5c4PTp01StWpW+ffsC8M4771CxYkX69++Pv78/wcHBfPvtt8THxxMQEEB4eDgAq1atYuHChRQvXpx69erZ44uLi2PYsGFcv34dwzB45plnGDBgQI59WblyJU2bNuWRRx7h/Pnzuer/9evXGTFiBIcOHaJMmTKULl2aUqVKATB06FCqVq1K5cqViYyMJCMjg379+nHs2DEuXrxIcHAwixcvxtvbO68vOyfOFextr0Vu50j8VUeHIJJnujomb9LS0ggJCWHMmDE0a9aMLVu2EBISwsaNG7FYsk5AMQyDjz/+mNmzZ3Pu3Lksz40cOZJHH32UuXPnEhcXR9euXalbty6VKlXKVRx5TkKmT5+Ov78/Y8eOZd26ddSoUYOIiAgMw2D8+PFMmjSJ4cOH079/f0aPHk3Lli3ZvXs3GzduBCA8PJyjR4/SqVMngoOD7UMZt3P16lWWL1/O+fPnad26Nd26dSMzM5NJkyYRFRVFxYoVmT59un3/ZcuWUb9+fd5++22Sk5MZPnw4ly5dyvGDf9CgQQDMmDEj1/2fOXMmmZmZfPbZZyQnJ9O9e3d7EnLLiy++SGxsLAkJCURERBAdHU14eDhRUVG5Ps/v1ahQAk93l3wfL3I3mTaDI/FXqV25JC5Wk86Ck/tCSlqmvlg52MGDB3FxcaFZs2YABAQEMGbMGA4cOECDBg2y7PvLL7/w9ddfs2DBAtq1a2ffnpGRwbZt2/jss88AqFKlCs2aNeOTTz6hd+/euYrjnq6O2bp1K/v372fDhg0ApKenU7ZsWY4dO0ZGRgYtW7YEoEmTJjz88MP5Ooe/vz8A5cuXp1y5ciQlJfHjjz/SqFEjezmoR48ezJo1C4CWLVvSv39/jh8/TuPGjRk0aFC+Kg+3s2vXLgYNGoTVaqVEiRIEBgZy4sSJAmv/dqxW9MEgfwgXq0XvNSlUVl0SkW/Hjx/HmsMLWL58eSpUqJBt+44dO+jTp0+27X379s1WrfDx8SE+Pj5bElKtWjWmTp2arY1Lly5x48YN+2cxQMWKFTl79mxuu3NvSYjNZmPixIk89thjAFy7do309HTi4+OzTTh1dc35VBaLJcu+6enpWZ738PC47b45td2oUSO2bNnC7t27iY6OpnPnzkyfPp3du3ezdetW4GZi89v5G3n12xjc3Nzy3Y6IiBQMAwObiQdkjP/2LSwsjFOnTmV7PiQkhP79+2fb7ufnx+HDh7Nt//TTT+0Xi/xWTgnObWP672fh74dv8tLGPSUhzZs3Z8mSJYwdOxa4OSeiTJkyjBgxgmLFivHll1/Spk0bDhw4wPHjx3Nsw9vbm5iYGOBmVrVv3z4eeuihu553zpw5xMbG8tBDD7F27Vr7c++99x7ly5cnJCSE1q1bc+zYMY4fP05oaOg9JR63tGjRgjVr1tC8eXPS09P57LPP7jiRR0REpKBMnDjxtpWQvKhcuTKJiYlZtp07dy7XczkAypYti4eHB+fOncPHxweAxMREatWqles27qkodmuSaFBQEM8//zyenp4MGTIEV1dXZs+ezbx58wgKCmLBggW3HY55+eWXuXDhAs8++yyDBg2iUaNGdz1vrVq1CA8Pp1evXnTo0CHLC9mrVy+io6MJDAykQ4cOVK5cmfbt299LN7Po168fJUuWpG3btrzyyitUrVq1wNoWEZH8cfTls3/UJbo1a9akbt262R45DcXcSf369UlNTeWbb74BYPv27VgsFurWrZvrNlxcXAgICOCjjz4C4MyZM+zcuTNPV8dYjIJYqEMKTXJyMjExMdSqWAIvdy1wK4Un02bwY9wV6lYppTkhUqiS0zL4OeEaderUwcvL697a+u/fyF1hy7h64tzdD3BSJWtUoNnElwvkNbvl8OHDjBw5kmvXruHp6cmoUaPs0yt69epFt27dCAgIyHKMr68vO3bssM8DSUpKYsSIEZw6dcp+RWhgYGCuY9Cn2u/s2bOHcePG5fhctWrVslyJIyIi9wddopt3jz32GCtXrszxufnz5+e4/ejRo1l+L1OmDDNnzsx3DEpCfqdx48b3dCmtiIiI5I4ulBIRERGHUCVEREScnoF5768C5h1qUiVEREREHEKVEBERcXo2wGbii7rMWuVRJUREREQcQpUQERFxejaTL9tu1r6pEiIiIiIOoUqIk8g8+QMZxg1HhyEmlokVvOqQcWwPhmlHoOV+kGkpBp41C7xdc9YKzE2VEBEREXEIJSEiIiLiEBqOERERp/fbO82akVn7pkqIiIiIOIQqISIi4vR0ia5zUiVEREREHEKVEBERMQVz1grMTZUQERERcQhVQkRExOnp6hjnpEqIiIiIOISSEBEREXEIDceIiIjT0yW6zinflZC5c+fSokUL3nrrrVwf07p1a6KjowFYt24dfn5+9OjRA8O49xc3ISEBX1/fPB8XGRlJYGAgQUFBdO3alQMHDuS5jXfeeYcZM2bYf96xYwcAX3/9NS1btqRDhw6cOXPGfp5vvvkmz+cQERExm3xXQlavXs3YsWNp2rRpvo5ft24dISEhdO7cOb8h3LPvv/+eefPmsWbNGsqUKcO2bdvo27cvO3fuxGKx5KvNiIgI+8+ffvopQUFBDBgwgPXr11OiRAkiIyPz1a4N6827nIoUklvvL73PpLDZCuk9Zs5agbnlKwl58803SUxM5P333+ell17i2LFjxMTEkJ6eTv369Rk2bBglSpRg//79jBo1CpvNRq1atbhx4+at6MeMGcPBgweJjY3lwoULnD59mqpVq9K3b1/gZjWhYsWK9O/fH39/f4KDg/n222+Jj48nICCA8PBwAFatWsXChQspXrw49erVs8cXFxfHsGHDuH79OoZh8MwzzzBgwIBs/ShVqhSjR4+mTJkyANSvX5+LFy+SkpKCl5fXbft//vx5hg0bRnx8POXLl8dms1GxYkUAevbsSadOnUhMTGTLli24u7uzefNmrl+/zpUrV+jcuTOrV6/O82t+2vPhPB8jkh8/e+W9oigikh/5SkKmT5+Ov78/Y8eOZd26ddSoUYOIiAgMw2D8+PFMmjSJ4cOH079/f0aPHk3Lli3ZvXs3GzduBCA8PJyjR4/SqVMngoODGTp06B3Pd/XqVZYvX8758+dp3bo13bp1IzMzk0mTJhEVFUXFihWZPn26ff9ly5ZRv3593n77bZKTkxk+fDiXLl3C29s7S7s1a9akZs2aANhsNsaOHUuLFi3umIAAjB49mkceeYQFCxZw7tw5OnTowJ///Ocs+/Tu3ZsTJ07Yk6u1a9eyYcMGFi9enNuXOYtqKScpZtzI17EiuZGJlZ+9fKmVfBQX014QKPeDG5ZiBf7FSpfoOqd7npi6detW9u/fz4YNGwBIT0+nbNmyHDt2jIyMDFq2bAlAkyZNePjh/L3p/P39AShfvjzlypUjKSmJH3/8kUaNGtkrED169GDWrFkAtGzZkv79+3P8+HEaN27MoEGDsiUgv3Xt2jUGDx5MUlIS8+bNu2s8u3btYuXKlQBUqFCBgICAfPUrL6zY9MEgfwgXvdekkFn1/pL/uuckxGazMXHiRB577DHg5gd6eno68fHx2SacurrmfDqLxZJl3/T09CzPe3h43HbfnNpu1KgRW7ZsYffu3URHR9O5c2emT5/O7t272bp1K3AzsQkNDeXkyZP06dOH+vXrM2XKlCznupPfxuDm5parY0REROR/7nl2UPPmzVmyZAmZmZlkZmYydOhQpkyZgq+vL8WKFePLL78E4MCBAxw/fjzHNry9vYmJiQHg0qVL7Nu3L1fn3bNnD7GxsQCsXbvW/tx7773HkiVLaNOmDeHh4TzyyCMcP36c0NBQoqKiiIqKIjQ0lPj4eF566SU6d+7MhAkTcp2AtGjRglWrVtnjvZXYiIiIYxiAYer/mdM9V0LCw8MZN24cQUFBZGZm8vjjjzNkyBBcXV2ZPXs27733HjNnzqR69eq3HY55+eWXGThwIM8++ywPPvggjRo1uut5a9WqRXh4OL169cLLy4unn37a/lyvXr0YNmwYX3zxBa6urjz66KO0b98+WxsLFy7k6tWrbNiwwT6cBDBv3jx8fHxue+53332X8PBwnnvuOcqVK5evS4NFRESKOotREIt0SKFJTk4mJiaGh1OO46mJqVKIMrFy1KsOvskxmhMihSrFUoyTnjWpU6fOXS8EuJtbfyM3hC3h4snEAorw/lP2YR+CJv6tQF6z+4lWTM3Bhg0bWLhwYY7PPf300/ZLhEVERCT/lITkICgoiKCgIEeHISIiuaRl252TlkYUERERh1AlREREnJ6BuZdtN2vfVAkRERERh1ASIiIiIg6h4RgREXF6mpjqnFQJEREREYdQJURERJyegXnvNAvmnZiqJMRZuHkAFkdHIab23/eXezHM+ydP7g/ujg5A7hNKQkRExOmZ+zZvmLZvmhMiIiIiDqFKiIiIOD0b5p4TYta+qRIiIiIiDqEkRERERBxCwzEiImIC5p6YatYr1lQJEREREYdQJURERJyeJqY6J1VCRERExCFUCREREadnA2yGOedNgCohIiIiIgVKlRARETEF89ZBzCvflZC5c+fSokUL3nrrrVwf07p1a6KjowFYt24dfn5+9OjRA6MASmgJCQn4+vrm+bg1a9YQGBhIYGAgf/3rXzl58mSe23jnnXeYMWOG/ecdO3YA8PXXX9OyZUs6dOjAmTNnCAwMJCgoiG+++SbP5xARETGbfFdCVq9ezdixY2natGm+jl+3bh0hISF07tw5vyHcsxMnTjBp0iQ++eQTypYty9KlSxkxYgRLly7Nd5sRERH2nz/99FOCgoIYMGAA69evp0SJEkRGRuarXRsWMnUXXSlEt95fep9JYbPpPSb/la8k5M033yQxMZH333+fl156iWPHjhETE0N6ejr169dn2LBhlChRgv379zNq1ChsNhu1atXixo0bAIwZM4aDBw8SGxvLhQsXOH36NFWrVqVv377AzWpCxYoV6d+/P/7+/gQHB/Ptt98SHx9PQEAA4eHhAKxatYqFCxdSvHhx6tWrZ48vLi6OYcOGcf36dQzD4JlnnmHAgAHZ+lGjRg127NiBm5sbGRkZxMfH4+3tfdf+nz9/nmHDhhEfH0/58uWx2WxUrFgRgJ49e9KpUycSExPZsmUL7u7ubN68mevXr3PlyhU6d+7M6tWr8/yan3Z9MM/HiOTHz67VHR2CSJ7ZMLCZeEDGrH3LVxIyffp0/P39GTt2LOvWraNGjRpERERgGAbjx49n0qRJDB8+nP79+zN69GhatmzJ7t272bhxIwDh4eEcPXqUTp06ERwczNChQ+94vqtXr7J8+XLOnz9P69at6datG5mZmUyaNImoqCgqVqzI9OnT7fsvW7aM+vXr8/bbb5OcnMzw4cO5dOlSjgmGm5sb+/btIzQ0lJSUFBYuXHjX/o8ePZpHHnmEBQsWcO7cOTp06MCf//znLPv07t2bEydO2JOrtWvXsmHDBhYvXpyLVzi7ahlnKEZavo4VyY1MLPzsWp1aGadwMekfPLk/3MBdX6wEKICJqVu3bmX//v1s2LABgPT0dMqWLcuxY8fIyMigZcuWADRp0oSHH344X+fw9/cHoHz58pQrV46kpCR+/PFHGjVqZK9A9OjRg1mzZgHQsmVL+vfvz/Hjx2ncuDGDBg26Y4Xjqaee4uuvv2bz5s307t2bLVu2ULJkydvuv2vXLlauXAlAhQoVCAgIyFe/8sKKoQ8G+UO46L0mhcxaCO8vw+TLtpu1b/echNhsNiZOnMhjjz0GwLVr10hPTyc+Pj7bhFNX15xPZ7FYsuybnp6e5XkPD4/b7ptT240aNWLLli3s3r2b6OhoOnfuzPTp09m9ezdbt24FbiY2HTt2JDY2liZNmgDQqlUrRowYwS+//JJleCcnv43Bzc3tjvuKiIhIdve8Tkjz5s1ZsmQJmZmZZGZmMnToUKZMmYKvry/FihXjyy+/BODAgQMcP348xza8vb2JiYkB4NKlS+zbty9X592zZw+xsbEArF271v7ce++9x5IlS2jTpg3h4eE88sgjHD9+nNDQUKKiooiKiiI0NJQrV67w1ltvkZCQAMCOHTuwWq3UrFnzjudu0aIFq1atssd7K7ERERHHMPjf0u1mfJizDlIAlZDw8HDGjRtHUFAQmZmZPP744wwZMgRXV1dmz57Ne++9x8yZM6levfpth2NefvllBg4cyLPPPsuDDz5Io0aN7nreWrVqER4eTq9evfDy8uLpp5+2P9erVy+GDRvGF198gaurK48++ijt27fP1kbdunUZMmQIvXr1wmq1UrJkSebPn4+np+cdz/3uu+8SHh7Oc889R7ly5fJ1abCIiEhRZzEKYpEOKTTJycnExMTwcEYcnpqYKoUoEwtHXR/GN+Ok5oRIoUrBnZOuVahTpw5eXl731Natv5FLwuaTeCKhgCK8//jUqMjfJvYqkNfsfqIVU3OwYcOG214l8/TTT9svERYRkfuDLtF1TkpCchAUFERQUJCjwxARETE1JSEiIuL0dImuc9JddEVERMQhVAkRERGnd+sSXbMyZx1ElRARERFxEFVCRETE6RkGOa6mbRZm7ZoqISIiIuIQSkJERETEITQc4ySMhNMYaVcdHYaYmGFxhYcfxoj9GcPIcHQ4YmKGe0moUqVA29RiZc5JlRARERFxCFVCRETE6ekSXeekSoiIiIg4hCohIiLi9LRsu3NSJUREREQcQpUQERFxero6xjmpEiIiIiIOoSREREREHEJJiIiIOD3DMEz/KGiHDh2ic+fOtGvXjs6dO3PixIk77h8fH0/z5s1JSEiwb9u7dy9/+tOfCA4Otj9+/vnnXMegOSEiIiJFTFpaGiEhIYwZM4ZmzZqxZcsWQkJC2LhxIxaLJcu+hmHw8ccfM3v2bM6dO5fluX379tG1a1eGDBmSrzhUCREREad3a7Eysz4Kug5y8OBBXFxcaNasGQABAQGkpKRw4MCBbPv+8ssvfP311yxYsCDbc9999x2HDh2iY8eOdOrUic8//zxPcagS8geYOnUq58+fJyIiwtGhiIiIEzt+/DhWa/b6Qfny5alQoUK27Tt27KBPnz7Ztvft25dKlSpl2ebj40N8fDwNGjTIsr1atWpMnTo1x3geeOABAgMD7cMwPXv2xMfHh4YNG+aqP0pCClFcXBzjxo1j165dvPDCC44OR0TEtAzMu6AX/K8SEhYWxqlTp7I9HxISQv/+/bNt9/Pz4/Dhw9m2f/rpp0RHR2fbnlOCcydTpkyx/1yrVi3atWvH5s2blYTk1pUrVxgyZAgJCQlYLBbq1q3L66+/Tvfu3fHz8+PHH38kNTWVsLAwAgICmDVrFps3b2bVqlWkpqbSvn17QkJCCA4Oztb2ypUradq0KY888gjnz5+/pzhtFhcyLUX+n0sKUabFJct/RQqLTe+xfJs4ceJtKyF5UblyZRITE7NsO3fuXLbqyJ1cv36dJUuW0Lt3b1xdb34+GYZh/zk3ivyn2oYNGyhWrBjr168nMzOTESNGkJaWxvnz52nYsCERERH88MMPvPrqq2zevJk+ffqwd+9eZs2axcmTJ2nevHmOCQjAoEGDAJgxY8Y9x/nLg43vuQ2R3Dhe3d/RIYjIbdSsWRMvL697bqd+/fqkpqbyzTff0LRpU7Zv327/Ip5bXl5erFmzhgoVKtCpUydiY2P5/PPPWbRoUa7bKPJJSJMmTZg7dy6vvPIKjRs35pVXXsHDw4PixYvTqVMnABo0aED16tXZu3cvzz77LBMnTiQoKIiKFSvyf//3f39InFXP7KFY2q9/yLmkaMq0uHC8uj81T23Fxch0dDhiYjfcHyjwL1ZaMTVvXF1dmTNnDiNHjiQiIgJPT09mzJiBi8vNKlWvXr3o1q0bAQEBt23DYrEwa9YsRo8ezZIlS8jMzCQ8PJzatWvnPo577omTq1WrFlu2bCE6Opro6GheffVVevbsmWM56dY/ztmzZ7FarZw7d46LFy9SqVIlpk2bxtatWwHw9/cnNDS0QOO0Gpm4GBkF2qZITlz0XpNCZlWSe1947LHHWLlyZY7PzZ8/P8ftR48ezfJ77dq1Wb58eb5jKPJJyLx58zh16hRjx47Fz8+PpKQkjhw5wpUrV9i8eTOtWrVi//79xMXF8fTTT3Pt2jUGDBjAiBEj+OWXXxgwYADLli0jNDS0wBMPERHJpUJa0Ou+YdK+Ffl1Qrp27cqlS5do164dHTp04OrVq/z973/HarWyefNmXnzxRUaNGsWMGTMoVaoU4eHhPP3007Rp04a///3vuLm53fbSJREREbm9Il8JKVWqFHPmzMmyLS4uDovFwvjx47Pt/9uEw2q1snTp0rueI6fLpkREpOBoTohzKvKVEBEREXGMIl8JyUmVKlVyXNxFRETuT8Z//2dWZu2bKiEiIiLiEEpCRERExCE0HCMiIk7PMMBm0stYwbRX6KoSIiIiIo6hSoiIiDg9A0w6dfMms/ZNlRARERFxCFVCRETE6WmxMuekSoiIiIg4hCohTsJSrjIWo4yjwxATs/z3O4mlYlUs2BwcjZiZxVKswNtUJcQ5qRIiIiIiDqEkRERERBxCwzEiImICBoZZV/QCzHqRriohIiIi4hCqhIiIiNPTxFTnpEqIiIiIOIQqISIi4vRuLttuzmoBmHVGiCohIiIi4iBKQkRERMQhNBwjIiJOzzDMfYmuWfumSoiIiIg4hCohhSgyMpKPPvoIi8WCp6cn77zzDvXr13d0WCIipmOY/BJds066VRJSSL7//nvmzZvHmjVrKFOmDNu2baNv377s3LkTi8Xi6PBEREQcrsgnIVeuXGHIkCEkJCRgsVioW7cur7/+Ot27d8fPz48ff/yR1NRUwsLCCAgIYNasWWzevJlVq1aRmppK+/btCQkJITg4OEu7pUqVYvTo0ZQpc/POt/Xr1+fixYukpKTg5eWV5zhtWMjU6JkUolvvL73PpLDZKPgvYpoT4pyKfBKyYcMGihUrxvr168nMzGTEiBGkpaVx/vx5GjZsSEREBD/88AOvvvoqmzdvpk+fPuzdu5dZs2Zx8uRJmjdvni0BAahZsyY1a9YEwGazMXbsWFq0aJGvBATgdLHq99JNkVz72fMRR4cgIkVEkU9CmjRpwty5c3nllVdo3Lgxr7zyCh4eHhQvXpxOnToB0KBBA6pXr87evXt59tlnmThxIkFBQVSsWJH/+7//u2P7165dY/DgwSQlJTFv3rx8x1ntximKGan5Pl7kbjKx8rPnI9RK+QkXbI4OR0zshsWjwL9Yadl251Tkk5BatWqxZcsWoqOjiY6O5tVXX6Vnz564umZ/aVxcXAA4e/YsVquVc+fOcfHiRSpVqsS0adPYunUrAP7+/oSGhnLy5En69OlD/fr1mTJlCh4eHvmO04qhDwb5Q7hg03tNCpXVpB+okndFPgmZN28ep06dYuzYsfj5+ZGUlMSRI0e4cuUKmzdvplWrVuzfv5+4uDiefvpprl27xoABAxgxYgS//PILAwYMYNmyZYSGhhIaGmpvNz4+npdeeonXXnuN1157zYE9FBERuT8V+SSka9euDB06lHbt2lGsWDEqVqxInz59+Pzzz9m8eTMzZ87EYrEwY8YMSpUqxVtvvcXTTz9NmzZtsNls7Nixg6lTpxIWFpal3YULF3L16lU2bNjAhg0b7NvnzZuHj4/PH91NERGTM0x7GetN5uxbkU9CSpUqxZw5c7Jsi4uLw2KxMH78+Gz7T5061f6z1Wpl6dKlObb77rvv8u677xZorCIiImZS5JMQERFxfjYDbCa9jBVu9s+MtCBADqpUqcLhw4cdHYaIiIipqRIiIiJOzzD5nBCz9k2VEBEREXEIVUJERMTpGYZh6jkhZl22XZUQERERcQglISIiIuIQGo4RERGnp4mpzkmVEBEREXEIVUJERMTp2TD3xFTdRVccyuJZAoulmKPDEBOzGBYwwOJVCovFnH/w5P5gMVzNeisUySMlISIi4vQMzDtvAsybs2lOiIiIiDiEkhARERFxCA3HiIiI09OKqc5JlRARERFxCFVCRETE6WmxMuekSoiIiIg4hCohIiLi9AzDhmHYHB1GoTFr31QJEREREYdQJURERJyegXmXNgctViYiIiJSoFQJKURr1qxh8eLFAHh7ezNy5EgefvhhxwYlIiJyn1ASUkhOnDjBpEmT+OSTTyhbtixLly5lxIgRLF261NGhiYiYjmEYpl3QC8y7WFmRT0KuXLnCkCFDSEhIwGKxULduXV5//XW6d++On58fP/74I6mpqYSFhREQEMCsWbPYvHkzq1atIjU1lfbt2xMSEkJwcHCWdmvUqMGOHTtwc3MjIyOD+Ph4vL298x2nzbCQieVeuytyW5mGJct/RQqLTe8x+a8in4Rs2LCBYsWKsX79ejIzMxkxYgRpaWmcP3+ehg0bEhERwQ8//MCrr77K5s2b6dOnD3v37mXWrFmcPHmS5s2bZ0tAbnFzc2Pfvn2EhoaSkpLCwoUL8x3nKcqZd2aS3Fd+wkfvNXE6NgxTT0w1a9+KfBLSpEkT5s6dyyuvvELjxo155ZVX8PDwoHjx4nTq1AmABg0aUL16dfbu3cuzzz7LxIkTCQoKomLFivzf//3fHdt/6qmn+Prrr9m8eTO9e/dmy5YtlCxZMs9xVucCxSwZ+eqjSG5kGhZ+wodHSMTFYs4/eHJ/uGG43vxiJUVekU9CatWqxZYtW4iOjiY6OppXX32Vnj174uqa/aVxcXEB4OzZs1itVs6dO8fFixepVKkS06ZNY+vWrQD4+/vTsWNHYmNjadKkCQCtWrVixIgR/PLLL9SrVy/PcVothj4YpPAZ4KL3mhQyK0aBV9s0J8Q5FfkkZN68eZw6dYqxY8fi5+dHUlISR44c4cqVK2zevJlWrVqxf/9+4uLiePrpp7l27RoDBgywJxQDBgxg2bJlhIaGEhoaam/3xx9/5K233iIqKoqKFSuyY8cOrFYrNWvWdGBvRURE7h9FPgnp2rUrQ4cOpV27dhQrVoyKFSvSp08fPv/8czZv3szMmTOxWCzMmDGDUqVK8dZbb/H000/Tpk0bbDYbO3bsYOrUqYSFhWVpt27dugwZMoRevXphtVopWbIk8+fPx9PT00E9FRExLwMDm0mrBWDeG9gV+SSkVKlSzJkzJ8u2uLg4LBYL48ePz7b/1KlT7T9brdY7XnLboUMHOnToUGCxioiImIlWTBURERGHKPKVkJxUqVKFw4cPOzoMERHJJQPzDlmAea+aVyVEREREHEKVEBERcXq6RNc5qRIiIiIiDqFKiIiIOD3D5Mu2m3W+iyohIiIi4hCqhIiIiNPTnBDnpEqIiIiIOIQqIfc5m80GQKqhfyopXDbDAty8w6nVpOPPcn+49ffs1t83Kbr0yXafS01NBeAM3uZdrUbuK6cop/ea/CFSU1MpUaJEgbRlM8x97xiz9k1JyH2uVKlSVK9eHQ8PD6xWjZ5J4Tl+/DhhYWFMnDhRd3uWQmWz2UhNTaVUqVKODkUcTEnIfc7V1ZWyZcs6OgwpAqxWK6dOncJqteLl5eXocMTkCqoCcothmHfyJtzsnxnpq7WIiIg4hCohIiLi9LRYmXNSJURERKQIOnToEJ07d6Zdu3Z07tyZEydO5Ljf2bNnef311wkKCqJdu3Z88MEHZGZmAhAbG0vPnj15/vnnCQwMZP/+/XmKQUmIiIhIEZOWlkZISAihoaFs2rSJN954g5CQkBzn1bzzzjs8+eSTbNiwgbVr1/LDDz+wcuVKAAYOHMgLL7zAxo0bGTduHP379yc5OTnXcSgJEREAypcvT0hICOXLl3d0KCJ5ZmDYV0015aOAh2MOHjyIi4sLzZo1AyAgIICUlBQOHDiQbd+goCC6dOkCQLFixXj00Uc5c+YMiYmJHDlyhA4dOgBQr149qlevzvbt23Mdh+aEiAgAFSpUoH///o4OQ0Tu4Pjx4zku11C+fHkqVKiQbfuOHTvo06dPtu19+/alUqVKWbb5+PgQHx9PgwYNsmx/8cUX7T//+OOPfPLJJ/zrX//i7NmzlCtXDjc3t2xt5JaSEBERcXpFZbGysLAwTp06le35kJCQHL9E+Pn5cfjw4WzbP/30U6Kjo7Ntv9N6VFu3bmXYsGGMGDGC+vXrs3//fiwWS57a+D0lISIiIk5i4sSJt62E5EXlypVJTEzMsu3cuXPZqiO3zJw5k48++ogZM2bw5z//2d7GhQsXyMjIwNXV1d5Gq1atch2HkhARETGBgp83cX+52beaNWsWyGKC9evXJzU1lW+++YamTZuyfft2LBYLdevWzbbvpEmT2LJlC2vWrMmSpPj4+FC7dm3WrVtH586dOXz4MD/99BNNmzbNdRxKQkRERIoYV1dX5syZw8iRI4mIiMDT05MZM2bg4uICQK9evejWrRtPPPEECxYswMfHhzfeeMN+vL+/P6GhoUyaNIl3332XDz/8EIDJkyfnaTl+i2HmdW5FRMTUkpOTiYmJ4e9/D+PYsZzXuTCDRx+twaJFE6lTp46pbqugS3RFRETEIZSEiIiIiENoToiIiDi9W4t6mZVZ+6ZKiIiIiDiEKiEiIuL0DJNfomvWvqkSIiIiIg6hSoiIiDg9zQlxTqqEiIiIiEOoEiIiIs7P5JUQTNo3VUJERETEIZSEiIiIiENoOEZERJyeASa9iPUms/ZNSYiIiDi96tUfcnQIhcqs/dNddEVExGmlpaXx448/YrPZHB1KobNardStWxd3d3dHh1JglISIiIhTS0tLIyMjw9FhFDpXV1dTJSCgJEREREQcRFfHiIiIiEMoCRERERGHUBIiIiIiDqEkRERERBxCSYiIiIg4hJIQERERcQglISIiIuIQ/w9M3e+6gS5FagAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ff_higher_25\"].values.reshape(-1, 1))), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### On ff_higher_50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x8500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Créer un DataFrame pandas à partir des données d'entrée X et de la variable à prédire y\n",
    "df_for_corr = pd.DataFrame(np.hstack((X, df[df[\"TRAIN\"] == 1 ][\"ff_higher_50\"].values.reshape(-1, 1))), \n",
    "    columns=list_col_corr)\n",
    "# Calculer la matrice de corrélation\n",
    "corr_matrix = df_for_corr.corr()\n",
    "corr_matrix_targets = corr_matrix.copy().loc[list_feat, list_col_targets]\n",
    "fig = plot_corr(corr_matrix_targets, aspect=0.1, size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test some models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(823, 17)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(823, 1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 17)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 1)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 17)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_for_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import clone\n",
    "def choose_target(df_y, target, nb_test):\n",
    "    print(\"\\nTarget : \", target)\n",
    "    arr_target = df_y[target].values\n",
    "    ytrain = arr_target[:nb_test]\n",
    "    ytest = arr_target[nb_test:]\n",
    "    Y = ytrain.reshape(-1, 1)\n",
    "    Y_test = ytest.reshape(-1, 1)\n",
    "    return Y, Y_test\n",
    "\n",
    "\n",
    "def fit_clf(clf, X, Y, X_test, Y_test, X_for_pred):\n",
    "    clf.fit(X, Y)\n",
    "    print(\"TRAIN score :\", clf.score(X, Y))\n",
    "    print(\"TEST score :\", clf.score(X_test, Y_test))\n",
    "    print(\"Next month : \", \n",
    "      clf.predict(X_for_pred),\n",
    "      np.max(clf.predict_proba(X_for_pred)[0]),\n",
    "    )\n",
    "    return clf\n",
    "    \n",
    "def multi_target_fit(clf, df_y, nb_test, list_targets):\n",
    "    list_clf = []\n",
    "    for target in list_targets:\n",
    "        Y, Y_test = choose_target(df_y, target=target, nb_test=nb_test)\n",
    "        new_clf = fit_clf(clone(clf), X, Y, X_test, Y_test, X_for_pred)\n",
    "        list_clf.append(new_clf)\n",
    "    return list_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  ff_lower_50\n",
      "TRAIN score : 0.8493317132442284\n",
      "TEST score : 1.0\n",
      "Next month :  [False] 0.9081770317324858\n",
      "\n",
      "Target :  ff_lower_25\n",
      "TRAIN score : 0.8760631834750912\n",
      "TEST score : 0.9333333333333333\n",
      "Next month :  [False] 0.5055047790068529\n",
      "\n",
      "Target :  ff_stable\n",
      "TRAIN score : 0.6549210206561361\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [False] 0.5441765966361798\n",
      "\n",
      "Target :  ff_higher_25\n",
      "TRAIN score : 0.7885783718104495\n",
      "TEST score : 0.8666666666666667\n",
      "Next month :  [False] 0.914492320046412\n",
      "\n",
      "Target :  ff_higher_50\n",
      "TRAIN score : 0.8420413122721749\n",
      "TEST score : 0.8666666666666667\n",
      "Next month :  [False] 0.9935695742437043\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "clf = BernoulliNB()\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, list_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>FEDFUNDS_diff</th>\n",
       "      <th>ff_lower_50</th>\n",
       "      <th>ff_lower_25</th>\n",
       "      <th>ff_stable</th>\n",
       "      <th>ff_higher_25</th>\n",
       "      <th>ff_higher_50</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>-0.02</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.45</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>-0.10</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.06</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>0.00</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>838 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FEDFUNDS_diff  ff_lower_50  ff_lower_25  ff_stable  ff_higher_25  \\\n",
       "81           -0.02        False        False       True         False   \n",
       "82            0.45        False        False      False         False   \n",
       "83            0.11        False        False      False          True   \n",
       "84           -0.10        False         True      False         False   \n",
       "85            0.06        False        False      False          True   \n",
       "..             ...          ...          ...        ...           ...   \n",
       "914           0.00        False        False       True         False   \n",
       "915           0.00        False        False       True         False   \n",
       "916           0.00        False        False       True         False   \n",
       "917           0.00        False        False       True         False   \n",
       "918           0.00        False        False       True         False   \n",
       "\n",
       "     ff_higher_50  class  \n",
       "81          False      2  \n",
       "82           True      4  \n",
       "83          False      3  \n",
       "84          False      1  \n",
       "85          False      3  \n",
       "..            ...    ...  \n",
       "914         False      2  \n",
       "915         False      2  \n",
       "916         False      2  \n",
       "917         False      2  \n",
       "918         False      2  \n",
       "\n",
       "[838 rows x 7 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.4848116646415553\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [1] 0.5330985242046086\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "clf = BernoulliNB()\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.4422843256379101\n",
      "TEST score : 0.06666666666666667\n",
      "Next month :  [3] 0.5762329089316995\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'clf.fit(X, Y)\\nprint(\"TRAIN score :\", clf.score(X, Y))\\nprint(\"TEST score :\", clf.score(X_test, Y_test))\\nprint(\"Next month : \", clf.predict(X_for_pred))\\nprint(\"classes : \", clf.classes_)\\nprint(\"classes prob : \", clf.predict_proba(X_for_pred))'"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "clf = GaussianNB()\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "\n",
    "\"\"\"clf.fit(X, Y)\n",
    "print(\"TRAIN score :\", clf.score(X, Y))\n",
    "print(\"TEST score :\", clf.score(X_test, Y_test))\n",
    "print(\"Next month : \", clf.predict(X_for_pred))\n",
    "print(\"classes : \", clf.classes_)\n",
    "print(\"classes prob : \", clf.predict_proba(X_for_pred))\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### DecisionTree model : fine tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.8784933171324423\n",
      "TEST score : 0.8\n",
      "Next month :  [2] 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(\n",
    "    random_state=2,\n",
    "    max_features=6,\n",
    "    min_samples_leaf=2,\n",
    ")\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "predictions = list_clf[0].predict(X)\n",
    "cm = confusion_matrix(Y, predictions, labels=list_clf[0].classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=list_clf[0].classes_)\n",
    "disp.plot()\n",
    "plt.title(\"Confusion matrix on TRAIN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "predictions = list_clf[0].predict(X_test)\n",
    "cm = confusion_matrix(Y_test, predictions, labels=list_clf[0].classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=list_clf[0].classes_)\n",
    "disp.plot()\n",
    "plt.title(\"Confusion matrix on TEST\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_clf[-1].max_features_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.08125057, 0.10392983, 0.13766819, 0.06201539, 0.00736324,\n",
       "       0.03315812, 0.03482648, 0.03015178, 0.06289023, 0.05878835,\n",
       "       0.02835062, 0.12733259, 0.08470274, 0.02417126, 0.04243297,\n",
       "       0.0388793 , 0.04208833])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_clf[-1].feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FEDFUNDS</th>\n",
       "      <td>0.137668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-1_diff</th>\n",
       "      <td>0.127333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation</th>\n",
       "      <td>0.103930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-2_diff</th>\n",
       "      <td>0.084703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE</th>\n",
       "      <td>0.081251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-1_diff</th>\n",
       "      <td>0.062890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPX_diff</th>\n",
       "      <td>0.062015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-2_diff</th>\n",
       "      <td>0.058788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-1</th>\n",
       "      <td>0.042433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-3</th>\n",
       "      <td>0.042088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spx-2</th>\n",
       "      <td>0.038879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-2_diff</th>\n",
       "      <td>0.034826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-1_diff</th>\n",
       "      <td>0.033158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UNRATE-3_diff</th>\n",
       "      <td>0.030152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation-3_diff</th>\n",
       "      <td>0.028351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fedfunds-3_diff</th>\n",
       "      <td>0.024171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_month</th>\n",
       "      <td>0.007363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Importance\n",
       "FEDFUNDS            0.137668\n",
       "fedfunds-1_diff     0.127333\n",
       "Inflation           0.103930\n",
       "fedfunds-2_diff     0.084703\n",
       "UNRATE              0.081251\n",
       "Inflation-1_diff    0.062890\n",
       "SPX_diff            0.062015\n",
       "Inflation-2_diff    0.058788\n",
       "spx-1               0.042433\n",
       "spx-3               0.042088\n",
       "spx-2               0.038879\n",
       "UNRATE-2_diff       0.034826\n",
       "UNRATE-1_diff       0.033158\n",
       "UNRATE-3_diff       0.030152\n",
       "Inflation-3_diff    0.028351\n",
       "fedfunds-3_diff     0.024171\n",
       "num_month           0.007363"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(\n",
    "    index=list_feat,\n",
    "    data=list_clf[-1].feature_importances_,\n",
    "    columns=[\"Importance\"]).sort_values(\n",
    "        by=\"Importance\", \n",
    "        ascending=False,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6114649681528662\n",
      "TEST score : 0.5761904761904761\n",
      "Next month :  [2] 0.4053825271895583\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf = SVC(gamma='auto', probability=True)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.3248407643312102\n",
      "TEST score : 0.2\n",
      "Next month :  [3] 0.5805170280191708\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/utils/validation.py:1183: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "clf = SGDClassifier(random_state=0, loss=\"log_loss\")\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  ff_lower_50\n",
      "TRAIN score : 0.9171974522292994\n",
      "TEST score : 0.9095238095238095\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Next month :  [False] 1.0\n",
      "\n",
      "Target :  ff_lower_25\n",
      "TRAIN score : 0.8773885350318471\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST score : 0.9142857142857143\n",
      "Next month :  [False] 1.0\n",
      "\n",
      "Target :  ff_stable\n",
      "TRAIN score : 0.8184713375796179\n",
      "TEST score : 0.49523809523809526\n",
      "Next month :  [ True] 0.6666666666666666\n",
      "\n",
      "Target :  ff_higher_25\n",
      "TRAIN score : 0.8280254777070064\n",
      "TEST score : 0.680952380952381\n",
      "Next month :  [False] 0.6666666666666666\n",
      "\n",
      "Target :  ff_higher_50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8789808917197452\n",
      "TEST score : 0.9476190476190476\n",
      "Next month :  [False] 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, list_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-class : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.6114649681528662\n",
      "TEST score : 0.38095238095238093\n",
      "Next month :  [2] 0.6666666666666666\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, list_targets=[\"class\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.5573248407643312\n",
      "TEST score : 0.4523809523809524\n",
      "Next month :  [2] 0.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:233: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors=5)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, list_targets=[\"class\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradienBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "one-by-one class :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  ff_lower_50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9777070063694268\n",
      "TEST score : 0.9380952380952381\n",
      "Next month :  [False] 0.9807474684045564\n",
      "\n",
      "Target :  ff_lower_25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9538216560509554\n",
      "TEST score : 0.9142857142857143\n",
      "Next month :  [False] 0.928844466375127\n",
      "\n",
      "Target :  ff_stable\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9378980891719745\n",
      "TEST score : 0.4714285714285714\n",
      "Next month :  [False] 0.8439643475610926\n",
      "\n",
      "Target :  ff_higher_25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9299363057324841\n",
      "TEST score : 0.8476190476190476\n",
      "Next month :  [False] 0.6687286546221147\n",
      "\n",
      "Target :  ff_higher_50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9697452229299363\n",
      "TEST score : 0.37142857142857144\n",
      "Next month :  [False] 0.9837382304889356\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier(random_state=2)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, list_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All classes : method argmax of one by one prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  ff_lower_50\n",
      "\n",
      "Target :  ff_lower_25\n",
      "\n",
      "Target :  ff_stable\n",
      "\n",
      "Target :  ff_higher_25\n",
      "\n",
      "Target :  ff_higher_50\n",
      "Accuracy train :  0.9585987261146497\n",
      "Accuracy test :  0.2523809523809524\n"
     ]
    }
   ],
   "source": [
    "arr_proba_train = np.zeros((nb_test, len(list_targets)))\n",
    "arr_proba_test = np.zeros((df_y.shape[0]-nb_test, len(list_targets)))\n",
    "# loop over clf\n",
    "for i_clf, target in enumerate(list_targets):\n",
    "    clf_curr =  list_clf[i_clf]\n",
    "    Y, Y_test = choose_target(df_y, target=target, nb_test=nb_test)\n",
    "    arr_proba_train[:,i_clf] = clf_curr.predict_proba(X)[:, np.argmax(clf_curr.classes_ == True)]\n",
    "    arr_proba_test[:,i_clf] = clf_curr.predict_proba(X_test)[:, np.argmax(clf_curr.classes_ == True)]\n",
    "# final accuracy\n",
    "Y_pred = np.argmax(arr_proba_train, axis=1)\n",
    "Y_test_pred = np.argmax(arr_proba_test, axis=1)\n",
    "Y_multi = df_y.iloc[:nb_test][\"class\"].values\n",
    "Y_multi_test = df_y.iloc[nb_test:][\"class\"].values\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy train : \", accuracy_score(Y_multi, Y_pred))\n",
    "print(\"Accuracy test : \", accuracy_score(Y_multi_test, Y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only first and second class : ur_lower and ur_stable :  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ff_lower_50', 'ff_lower_25']\n",
      "\n",
      "Target :  ff_lower_50\n",
      "\n",
      "Target :  ff_lower_25\n",
      "Accuracy train :  0.5270700636942676\n",
      "Accuracy test :  0.6952380952380952\n"
     ]
    }
   ],
   "source": [
    "list_targets_red = list_targets[:2]\n",
    "print(list_targets_red)\n",
    "arr_proba_train = np.zeros((nb_test, len(list_targets_red)))\n",
    "arr_proba_test = np.zeros((df_y.shape[0]-nb_test, len(list_targets_red)))\n",
    "# loop over clf\n",
    "for i_clf, target in enumerate(list_targets_red):\n",
    "    clf_curr =  list_clf[i_clf]\n",
    "    Y, Y_test = choose_target(df_y, target=target, nb_test=nb_test)\n",
    "    arr_proba_train[:,i_clf] = clf_curr.predict_proba(X)[:, np.argmax(clf_curr.classes_ == True)]\n",
    "    arr_proba_test[:,i_clf] = clf_curr.predict_proba(X_test)[:, np.argmax(clf_curr.classes_ == True)]\n",
    "# prediction , last class if other pred < 0.5\n",
    "Y_pred = np.argmax(arr_proba_train, axis=1)\n",
    "Y_pred[np.all(arr_proba_train < 0.5, axis=1)] = 2\n",
    "\n",
    "Y_test_pred = np.argmax(arr_proba_test, axis=1)\n",
    "Y_test_pred[np.all(arr_proba_test < 0.5, axis=1)] = 2\n",
    "\n",
    "Y_multi = df_y.iloc[:nb_test][\"class\"].values\n",
    "Y_multi_test = df_y.iloc[nb_test:][\"class\"].values\n",
    "# final accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy train : \", accuracy_score(Y_multi, Y_pred))\n",
    "print(\"Accuracy test : \", accuracy_score(Y_multi_test, Y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-class : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9777070063694268\n",
      "TEST score : 0.23809523809523808\n"
     ]
    }
   ],
   "source": [
    "Y, Y_test = choose_target(df_y, target=\"class\", nb_test=nb_test)\n",
    "clf = GradientBoostingClassifier(random_state=0)\n",
    "clf.fit(X, Y)\n",
    "print(\"TRAIN score :\", clf.score(X, Y))\n",
    "print(\"TEST score :\", clf.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9777070063694268\n",
      "TEST score : 0.22857142857142856\n",
      "Next month :  [3] 0.5902646112997721\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier(random_state=1)\n",
    "list_clf = multi_target_fit(\n",
    "    clf,\n",
    "    df_y,\n",
    "    nb_test,\n",
    "    list_targets=[\"class\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9808917197452229\n",
      "TEST score : 0.2\n",
      "Next month :  [3] 0.5876121000404897\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "clf = GradientBoostingClassifier(random_state=2)\n",
    "list_clf = multi_target_fit(\n",
    "    clf,\n",
    "    df_y,\n",
    "    nb_test,\n",
    "    list_targets=[\"class\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multi-class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6576433121019108\n",
      "TEST score : 0.5761904761904761\n",
      "Next month :  [2] 0.6357002246953709\n",
      "nb iteration :  200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "clf = MLPClassifier(random_state=0)\n",
    "list_clf = multi_target_fit(\n",
    "    clf,\n",
    "    df_y,\n",
    "    nb_test,\n",
    "    list_targets=[\"class\"],\n",
    ")\n",
    "print(\"nb iteration : \", list_clf[-1].n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.9585987261146497\n",
      "TEST score : 0.48095238095238096\n",
      "Next month :  [2] 0.8146730931229321\n",
      "nb iteration :  1000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(random_state=0, \n",
    "                    max_iter=1000,\n",
    "                    n_iter_no_change=1000)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "print(\"nb iteration : \", list_clf[-1].n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.9968152866242038\n",
      "TEST score : 0.44761904761904764\n",
      "Next month :  [2] 0.819490300434549\n",
      "nb iteration :  1500\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1500) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(random_state=0, \n",
    "                    max_iter=1500,\n",
    "                    n_iter_no_change=1500)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "print(\"nb iteration : \", list_clf[-1].n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.46656050955414013\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.40137865980310766\n",
      "nb iteration :  48000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (48000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes=(4),\n",
    "                    random_state=0, \n",
    "                    max_iter=48000,\n",
    "                    n_iter_no_change=48000)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "print(\"nb iteration : \", list_clf[-1].n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.5429936305732485\n",
      "TEST score : 0.44761904761904764\n",
      "Next month :  [2] 0.5482002029965664\n",
      "nb iteration :  40000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (40000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf = MLPClassifier(hidden_layer_sizes=(8),\n",
    "                    random_state=4, \n",
    "                    max_iter=40000,\n",
    "                    n_iter_no_change=40000)\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "print(\"nb iteration : \", list_clf[-1].n_iter_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MLP CV Alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.4856687898089172\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.5775603113964229\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.4856687898089172\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.5778940013098389\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.4856687898089172\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.576814028034131\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.49203821656050956\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.5734478968980077\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5238853503184714\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.5633802624694483\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5191082802547771\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.5651598483146911\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5191082802547771\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.5650174114653512\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5127388535031847\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.5739097575280582\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5079617834394905\n",
      "TEST score : 0.638095238095238\n",
      "Next month :  [2] 0.31567505030112214\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5079617834394905\n",
      "TEST score : 0.638095238095238\n",
      "Next month :  [2] 0.3163356746847794\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5079617834394905\n",
      "TEST score : 0.638095238095238\n",
      "Next month :  [2] 0.31643977948777563\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5079617834394905\n",
      "TEST score : 0.6333333333333333\n",
      "Next month :  [2] 0.31609522230382503\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5238853503184714\n",
      "TEST score : 0.6047619047619047\n",
      "Next month :  [3] 0.40941095699007246\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5238853503184714\n",
      "TEST score : 0.6047619047619047\n",
      "Next month :  [3] 0.40994511996394706\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5222929936305732\n",
      "TEST score : 0.6047619047619047\n",
      "Next month :  [3] 0.409763876142825\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5222929936305732\n",
      "TEST score : 0.6142857142857143\n",
      "Next month :  [3] 0.40999213591942124\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.4984076433121019\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.584504782837792\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5015923566878981\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.5865706407334783\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.4984076433121019\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.5847623070349096\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 1000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5\n",
      "TEST score : 0.5190476190476191\n",
      "Next month :  [2] 0.5825792378692068\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5095541401273885\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.6173233281404726\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5111464968152867\n",
      "TEST score : 0.5047619047619047\n",
      "Next month :  [2] 0.6176717688538887\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5111464968152867\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.6157157337061856\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.517515923566879\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.6162998021201166\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5270700636942676\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.6201072457070447\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5302547770700637\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.6221279372246348\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5286624203821656\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.6192832915743511\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5318471337579618\n",
      "TEST score : 0.5428571428571428\n",
      "Next month :  [2] 0.6317685987240648\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5270700636942676\n",
      "TEST score : 0.638095238095238\n",
      "Next month :  [2] 0.4107631142931485\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5270700636942676\n",
      "TEST score : 0.6428571428571429\n",
      "Next month :  [2] 0.4072999085682688\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5254777070063694\n",
      "TEST score : 0.6428571428571429\n",
      "Next month :  [2] 0.40823427768664483\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5222929936305732\n",
      "TEST score : 0.6428571428571429\n",
      "Next month :  [2] 0.4189702757134055\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5222929936305732\n",
      "TEST score : 0.5761904761904761\n",
      "Next month :  [3] 0.5138002239533758\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5222929936305732\n",
      "TEST score : 0.5761904761904761\n",
      "Next month :  [3] 0.5141502816851197\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5207006369426752\n",
      "TEST score : 0.5714285714285714\n",
      "Next month :  [3] 0.5062646375307106\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5207006369426752\n",
      "TEST score : 0.5714285714285714\n",
      "Next month :  [3] 0.5085465429908157\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5254777070063694\n",
      "TEST score : 0.44761904761904764\n",
      "Next month :  [2] 0.6685251453906783\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5302547770700637\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.6588896542412013\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5286624203821656\n",
      "TEST score : 0.44761904761904764\n",
      "Next month :  [2] 0.6687473733871956\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 2000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.517515923566879\n",
      "TEST score : 0.4523809523809524\n",
      "Next month :  [2] 0.6125053901614514\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5286624203821656\n",
      "TEST score : 0.47619047619047616\n",
      "Next month :  [2] 0.5096637093001409\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5366242038216561\n",
      "TEST score : 0.4714285714285714\n",
      "Next month :  [2] 0.5211844944602229\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5302547770700637\n",
      "TEST score : 0.47619047619047616\n",
      "Next month :  [2] 0.48279365143367325\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5461783439490446\n",
      "TEST score : 0.47619047619047616\n",
      "Next month :  [2] 0.45970992773957314\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5605095541401274\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.6765978014679982\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5573248407643312\n",
      "TEST score : 0.5428571428571428\n",
      "Next month :  [2] 0.6770983027467685\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5621019108280255\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.6757999164369217\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5652866242038217\n",
      "TEST score : 0.5476190476190477\n",
      "Next month :  [2] 0.6750498628233804\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5366242038216561\n",
      "TEST score : 0.6142857142857143\n",
      "Next month :  [1] 0.5183297847196606\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5334394904458599\n",
      "TEST score : 0.6190476190476191\n",
      "Next month :  [1] 0.5166640578646438\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5318471337579618\n",
      "TEST score : 0.6238095238095238\n",
      "Next month :  [1] 0.5169642980634054\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5270700636942676\n",
      "TEST score : 0.6476190476190476\n",
      "Next month :  [1] 0.4559032481421512\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5477707006369427\n",
      "TEST score : 0.6285714285714286\n",
      "Next month :  [3] 0.5248394083740846\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5477707006369427\n",
      "TEST score : 0.5952380952380952\n",
      "Next month :  [3] 0.5357069130194578\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.552547770700637\n",
      "TEST score : 0.5857142857142857\n",
      "Next month :  [3] 0.5236414552007508\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5493630573248408\n",
      "TEST score : 0.5857142857142857\n",
      "Next month :  [2] 0.5793670129088395\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5334394904458599\n",
      "TEST score : 0.4714285714285714\n",
      "Next month :  [2] 0.693890137356207\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5334394904458599\n",
      "TEST score : 0.4857142857142857\n",
      "Next month :  [2] 0.6790930626145372\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5270700636942676\n",
      "TEST score : 0.4857142857142857\n",
      "Next month :  [2] 0.691687689519412\n",
      "\n",
      "nb_neurons: 8 , nb_iter: 4000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5286624203821656\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.6264345053140701\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5875796178343949\n",
      "TEST score : 0.4523809523809524\n",
      "Next month :  [2] 0.5834260518312566\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.589171974522293\n",
      "TEST score : 0.4523809523809524\n",
      "Next month :  [2] 0.5826659155852268\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5843949044585988\n",
      "TEST score : 0.44285714285714284\n",
      "Next month :  [2] 0.5831334734885988\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5828025477707006\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.5809182604635761\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5589171974522293\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.6424701769161147\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5605095541401274\n",
      "TEST score : 0.5428571428571428\n",
      "Next month :  [2] 0.6409031509013284\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5621019108280255\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.6398177183102276\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5636942675159236\n",
      "TEST score : 0.5523809523809524\n",
      "Next month :  [2] 0.6315556078588392\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5875796178343949\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.577241604452646\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5875796178343949\n",
      "TEST score : 0.5285714285714286\n",
      "Next month :  [2] 0.5770146488544758\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5859872611464968\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.5757332492977123\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5875796178343949\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.5744576811316073\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6050955414012739\n",
      "TEST score : 0.5761904761904761\n",
      "Next month :  [2] 0.3765497049989284\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5971337579617835\n",
      "TEST score : 0.5857142857142857\n",
      "Next month :  [2] 0.37255882957439207\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6003184713375797\n",
      "TEST score : 0.5857142857142857\n",
      "Next month :  [2] 0.3719062233059188\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6035031847133758\n",
      "TEST score : 0.5952380952380952\n",
      "Next month :  [2] 0.3741680583673328\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5780254777070064\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.5934378611598704\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5796178343949044\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.5970920510228012\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5780254777070064\n",
      "TEST score : 0.5476190476190477\n",
      "Next month :  [2] 0.5978536845043464\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 1000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.5843949044585988\n",
      "TEST score : 0.5476190476190477\n",
      "Next month :  [2] 0.6007311811154592\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6512738853503185\n",
      "TEST score : 0.3904761904761905\n",
      "Next month :  [2] 0.6967839392977924\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6512738853503185\n",
      "TEST score : 0.3952380952380952\n",
      "Next month :  [2] 0.7090368408025373\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6464968152866242\n",
      "TEST score : 0.3952380952380952\n",
      "Next month :  [2] 0.7029931453954411\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6512738853503185\n",
      "TEST score : 0.4\n",
      "Next month :  [2] 0.7213013447143787\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6273885350318471\n",
      "TEST score : 0.5047619047619047\n",
      "Next month :  [2] 0.7176976351970566\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6273885350318471\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.7246020380571537\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6226114649681529\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.7254727141520115\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6273885350318471\n",
      "TEST score : 0.5190476190476191\n",
      "Next month :  [2] 0.7112042769448691\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6449044585987261\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.6689598325371507\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6496815286624203\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.6727236955240479\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6512738853503185\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.666584932496659\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6512738853503185\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [2] 0.6592414549609048\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6289808917197452\n",
      "TEST score : 0.6095238095238096\n",
      "Next month :  [4] 0.38572585251177766\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6337579617834395\n",
      "TEST score : 0.6095238095238096\n",
      "Next month :  [4] 0.35794624618921367\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6369426751592356\n",
      "TEST score : 0.6095238095238096\n",
      "Next month :  [4] 0.3988083021207376\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6353503184713376\n",
      "TEST score : 0.5904761904761905\n",
      "Next month :  [4] 0.3369770037719171\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6289808917197452\n",
      "TEST score : 0.49047619047619045\n",
      "Next month :  [2] 0.7072075924781396\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6305732484076433\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.7167898858310676\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6369426751592356\n",
      "TEST score : 0.5047619047619047\n",
      "Next month :  [2] 0.7007352336616294\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 2000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6321656050955414\n",
      "TEST score : 0.48095238095238096\n",
      "Next month :  [2] 0.7006685561410805\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6815286624203821\n",
      "TEST score : 0.34285714285714286\n",
      "Next month :  [2] 0.7184577748789349\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6863057324840764\n",
      "TEST score : 0.32857142857142857\n",
      "Next month :  [2] 0.7233831803315982\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6799363057324841\n",
      "TEST score : 0.3238095238095238\n",
      "Next month :  [2] 0.7232391457635999\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6878980891719745\n",
      "TEST score : 0.34285714285714286\n",
      "Next month :  [2] 0.730624693106936\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.660828025477707\n",
      "TEST score : 0.5095238095238095\n",
      "Next month :  [2] 0.7278305616287598\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6624203821656051\n",
      "TEST score : 0.49523809523809526\n",
      "Next month :  [2] 0.7082033229673853\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6576433121019108\n",
      "TEST score : 0.5095238095238095\n",
      "Next month :  [2] 0.7268031407980847\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6624203821656051\n",
      "TEST score : 0.5095238095238095\n",
      "Next month :  [2] 0.7109770076073955\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6878980891719745\n",
      "TEST score : 0.5333333333333333\n",
      "Next month :  [3] 0.3993008175341369\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6799363057324841\n",
      "TEST score : 0.5523809523809524\n",
      "Next month :  [2] 0.49577310759287607\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.678343949044586\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [2] 0.42679061893353837\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6719745222929936\n",
      "TEST score : 0.5428571428571428\n",
      "Next month :  [2] 0.43881645509293793\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6512738853503185\n",
      "TEST score : 0.5380952380952381\n",
      "Next month :  [4] 0.39166458319767794\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6385350318471338\n",
      "TEST score : 0.5666666666666667\n",
      "Next month :  [2] 0.3926720230312467\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6449044585987261\n",
      "TEST score : 0.5714285714285714\n",
      "Next month :  [2] 0.3888810873738716\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6480891719745223\n",
      "TEST score : 0.5619047619047619\n",
      "Next month :  [2] 0.3850858252149548\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6624203821656051\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [2] 0.8472519926738264\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6496815286624203\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.8564534599054838\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.64171974522293\n",
      "TEST score : 0.4714285714285714\n",
      "Next month :  [2] 0.7859708449620274\n",
      "\n",
      "nb_neurons: 16 , nb_iter: 4000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6449044585987261\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [2] 0.8139652498894089\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7308917197452229\n",
      "TEST score : 0.4142857142857143\n",
      "Next month :  [2] 0.6613651399155426\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7213375796178344\n",
      "TEST score : 0.4142857142857143\n",
      "Next month :  [2] 0.6805771310207055\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7213375796178344\n",
      "TEST score : 0.4142857142857143\n",
      "Next month :  [2] 0.6682959297528149\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7165605095541401\n",
      "TEST score : 0.4095238095238095\n",
      "Next month :  [2] 0.6733276177795893\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7372611464968153\n",
      "TEST score : 0.5476190476190477\n",
      "Next month :  [2] 0.4922409236900206\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7340764331210191\n",
      "TEST score : 0.5571428571428572\n",
      "Next month :  [2] 0.5075728486417213\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7340764331210191\n",
      "TEST score : 0.5571428571428572\n",
      "Next month :  [2] 0.49178556235500154\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7356687898089171\n",
      "TEST score : 0.5619047619047619\n",
      "Next month :  [2] 0.48921834744961956\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7388535031847133\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.45974371067205627\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7420382165605095\n",
      "TEST score : 0.5190476190476191\n",
      "Next month :  [2] 0.44781924731859624\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7388535031847133\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.4672164430838298\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7404458598726115\n",
      "TEST score : 0.49523809523809526\n",
      "Next month :  [2] 0.49599262798307125\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7213375796178344\n",
      "TEST score : 0.43333333333333335\n",
      "Next month :  [2] 0.6498576500125942\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7261146496815286\n",
      "TEST score : 0.43333333333333335\n",
      "Next month :  [2] 0.6517227764746327\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7229299363057324\n",
      "TEST score : 0.43333333333333335\n",
      "Next month :  [2] 0.6501899509402026\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7213375796178344\n",
      "TEST score : 0.43333333333333335\n",
      "Next month :  [2] 0.6458522431747675\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7133757961783439\n",
      "TEST score : 0.4\n",
      "Next month :  [2] 0.5029367090415438\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7117834394904459\n",
      "TEST score : 0.4142857142857143\n",
      "Next month :  [2] 0.5195271444122243\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7165605095541401\n",
      "TEST score : 0.3952380952380952\n",
      "Next month :  [2] 0.5148140468432751\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 1000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7181528662420382\n",
      "TEST score : 0.42857142857142855\n",
      "Next month :  [2] 0.4774599661108524\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8343949044585988\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.8241998851539496\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8264331210191083\n",
      "TEST score : 0.46190476190476193\n",
      "Next month :  [2] 0.8432279528753908\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8264331210191083\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [2] 0.8315817690367534\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8248407643312102\n",
      "TEST score : 0.47619047619047616\n",
      "Next month :  [2] 0.8361484329059456\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8184713375796179\n",
      "TEST score : 0.5\n",
      "Next month :  [2] 0.46076277898305557\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8121019108280255\n",
      "TEST score : 0.48095238095238096\n",
      "Next month :  [2] 0.5778885506659602\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.802547770700637\n",
      "TEST score : 0.4857142857142857\n",
      "Next month :  [2] 0.5535996519211182\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8168789808917197\n",
      "TEST score : 0.46190476190476193\n",
      "Next month :  [2] 0.5256512393333231\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.821656050955414\n",
      "TEST score : 0.5238095238095238\n",
      "Next month :  [3] 0.4239471782907444\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8264331210191083\n",
      "TEST score : 0.4714285714285714\n",
      "Next month :  [3] 0.5732475990775545\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8168789808917197\n",
      "TEST score : 0.5047619047619047\n",
      "Next month :  [3] 0.4427253251220666\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8168789808917197\n",
      "TEST score : 0.5095238095238095\n",
      "Next month :  [3] 0.37274004139538525\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7834394904458599\n",
      "TEST score : 0.43333333333333335\n",
      "Next month :  [2] 0.7119375585742694\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7834394904458599\n",
      "TEST score : 0.40476190476190477\n",
      "Next month :  [2] 0.696797146914101\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.785031847133758\n",
      "TEST score : 0.4095238095238095\n",
      "Next month :  [2] 0.7022216899284641\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7834394904458599\n",
      "TEST score : 0.3952380952380952\n",
      "Next month :  [2] 0.6685894842366834\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7961783439490446\n",
      "TEST score : 0.42857142857142855\n",
      "Next month :  [2] 0.4326406850333136\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7945859872611465\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.46971686985758726\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.7993630573248408\n",
      "TEST score : 0.4238095238095238\n",
      "Next month :  [2] 0.40298867166094227\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 2000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8009554140127388\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.4536156764133869\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 0, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8980891719745223\n",
      "TEST score : 0.42857142857142855\n",
      "Next month :  [2] 0.8724376260857298\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 0, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9012738853503185\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [2] 0.9184283130698242\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 0, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8789808917197452\n",
      "TEST score : 0.44761904761904764\n",
      "Next month :  [2] 0.9163665129506768\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 0, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8853503184713376\n",
      "TEST score : 0.4714285714285714\n",
      "Next month :  [2] 0.9135218892523544\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 1, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9124203821656051\n",
      "TEST score : 0.47619047619047616\n",
      "Next month :  [2] 0.7204925693359262\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 1, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.893312101910828\n",
      "TEST score : 0.4380952380952381\n",
      "Next month :  [2] 0.6037470615163842\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 1, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9060509554140127\n",
      "TEST score : 0.41904761904761906\n",
      "Next month :  [2] 0.48452269106479434\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 1, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9012738853503185\n",
      "TEST score : 0.4666666666666667\n",
      "Next month :  [3] 0.4694337884651261\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 2, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8821656050955414\n",
      "TEST score : 0.5190476190476191\n",
      "Next month :  [3] 0.6293518905289353\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 2, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8869426751592356\n",
      "TEST score : 0.49047619047619045\n",
      "Next month :  [3] 0.766182257317042\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 2, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9028662420382165\n",
      "TEST score : 0.49523809523809526\n",
      "Next month :  [3] 0.7303579379342036\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 2, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8869426751592356\n",
      "TEST score : 0.5\n",
      "Next month :  [3] 0.6867601904400394\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 3, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8630573248407644\n",
      "TEST score : 0.3761904761904762\n",
      "Next month :  [2] 0.7434631068728282\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 3, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8694267515923567\n",
      "TEST score : 0.38095238095238093\n",
      "Next month :  [2] 0.7204831785431693\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 3, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8789808917197452\n",
      "TEST score : 0.40476190476190477\n",
      "Next month :  [2] 0.7903764083448372\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 3, alpha: 0.01\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.8630573248407644\n",
      "TEST score : 0.3904761904761905\n",
      "Next month :  [2] 0.7691324193993724\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 4, alpha: 0.0001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9044585987261147\n",
      "TEST score : 0.38095238095238093\n",
      "Next month :  [0] 0.4934974909322196\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 4, alpha: 0.0005\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9012738853503185\n",
      "TEST score : 0.3904761904761905\n",
      "Next month :  [0] 0.5589970065540677\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 4, alpha: 0.001\n",
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n",
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.9044585987261147\n",
      "TEST score : 0.4\n",
      "Next month :  [1] 0.4484581705921921\n",
      "\n",
      "nb_neurons: 32 , nb_iter: 4000, random_state: 4, alpha: 0.01\n",
      "\n",
      "Target :  class\n",
      "TRAIN score : 0.893312101910828\n",
      "TEST score : 0.38095238095238093\n",
      "Next month :  [0] 0.5505128865266634\n",
      "new best param :  (8, 4000, 2, 0.01)\n",
      "TRAIN score  best : 0.893312101910828\n",
      "TEST score best : 0.6476190476190476\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (4000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n",
      "/var/folders/m4/ng3wlwj109df2b8j9f4y4m7m0000gn/T/ipykernel_18049/3307376772.py:36: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  df_res_alpha = df_res_alpha.append(\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>nb_neurons</th>\n",
       "      <th>nb_iter</th>\n",
       "      <th>random_state</th>\n",
       "      <th>alpha</th>\n",
       "      <th>accuracy</th>\n",
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       "      <th>0</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>0.0001</td>\n",
       "      <td>0.485669</td>\n",
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       "      <th>1</th>\n",
       "      <td>8.0</td>\n",
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       "      <td>0.0005</td>\n",
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       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.485669</td>\n",
       "      <td>0.533333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.492038</td>\n",
       "      <td>0.538095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.523885</td>\n",
       "      <td>0.528571</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.863057</td>\n",
       "      <td>0.390476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>0.904459</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.901274</td>\n",
       "      <td>0.390476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.904459</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.893312</td>\n",
       "      <td>0.380952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nb_neurons  nb_iter  random_state   alpha  accuracy  accuracy_test\n",
       "0           8.0   1000.0           0.0  0.0001  0.485669       0.533333\n",
       "1           8.0   1000.0           0.0  0.0005  0.485669       0.533333\n",
       "2           8.0   1000.0           0.0  0.0010  0.485669       0.533333\n",
       "3           8.0   1000.0           0.0  0.0100  0.492038       0.538095\n",
       "4           8.0   1000.0           1.0  0.0001  0.523885       0.528571\n",
       "..          ...      ...           ...     ...       ...            ...\n",
       "175        32.0   4000.0           3.0  0.0100  0.863057       0.390476\n",
       "176        32.0   4000.0           4.0  0.0001  0.904459       0.380952\n",
       "177        32.0   4000.0           4.0  0.0005  0.901274       0.390476\n",
       "178        32.0   4000.0           4.0  0.0010  0.904459       0.400000\n",
       "179        32.0   4000.0           4.0  0.0100  0.893312       0.380952\n",
       "\n",
       "[180 rows x 6 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "list_nb_iter = [1000, 2000, 4000]\n",
    "list_random_state = [0, 1, 2, 3, 4]\n",
    "list_nb_cell = [8, 16, 32]\n",
    "list_alpha = [0.0001, 0.0005, 0.001, 0.01]\n",
    "df_res_alpha = pd.DataFrame(\n",
    "    columns=[\n",
    "        \"nb_neurons\",\n",
    "        \"nb_iter\",\n",
    "        \"random_state\",\n",
    "        \"alpha\",\n",
    "        \"accuracy\",\n",
    "        \"accuracy_test\",\n",
    "    ]\n",
    ")\n",
    "\n",
    "Y, Y_test = choose_target(df_y, \"class\", nb_test)\n",
    "\n",
    "acc_best = 0\n",
    "for nb_neurons in list_nb_cell:\n",
    "    for nb_iter in list_nb_iter:\n",
    "        for random_state in list_random_state:\n",
    "            for alpha in list_alpha:\n",
    "                param = (nb_neurons, nb_iter, random_state, alpha)\n",
    "                print(f\"\\nnb_neurons: {nb_neurons} , nb_iter: {nb_iter}, random_state: {random_state}, alpha: {alpha}\")\n",
    "                clf = MLPClassifier(\n",
    "                    hidden_layer_sizes=(nb_neurons),\n",
    "                    random_state=random_state, \n",
    "                    max_iter=nb_iter,\n",
    "                    n_iter_no_change=nb_iter,\n",
    "                    alpha=alpha,\n",
    "                )\n",
    "                list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "                acc_curr = list_clf[0].score(X, Y)\n",
    "                acc_curr_test = list_clf[0].score(X_test, Y_test)\n",
    "                df_res_alpha = df_res_alpha.append(\n",
    "                    {\n",
    "                        \"nb_neurons\": nb_neurons, \n",
    "                        \"nb_iter\": nb_iter, \n",
    "                        \"random_state\": random_state, \n",
    "                        \"alpha\": alpha,\n",
    "                        \"accuracy\": acc_curr, \n",
    "                        \"accuracy_test\": acc_curr_test,\n",
    "                    }, \n",
    "                    ignore_index=True,\n",
    "                )\n",
    "\n",
    "                if acc_curr_test > acc_best:\n",
    "                    acc_best = acc_curr_test\n",
    "                    param_best = param\n",
    "                    clf_best = list_clf[0]\n",
    "\n",
    "print(\"new best param : \", param_best)\n",
    "print(\"TRAIN score  best :\", list_clf[0].score(X, Y))\n",
    "print(\"TEST score best :\", acc_best)\n",
    "df_res_alpha.to_csv(f\"data/res_FEDFUNDS_mlp_{ratio_test}_alpha.csv\", index=False)\n",
    "df_res_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for param best :  (8, 4000, 2, 0.01)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "print(\"for param best : \", param_best)\n",
    "predictions = clf_best.predict(X_test)\n",
    "cm = confusion_matrix(Y_test, predictions, labels=clf_best.classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=clf_best.classes_)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>nb_neurons</th>\n",
       "      <th>nb_iter</th>\n",
       "      <th>random_state</th>\n",
       "      <th>alpha</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>accuracy_test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.527070</td>\n",
       "      <td>0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0010</td>\n",
       "      <td>0.525478</td>\n",
       "      <td>0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>8.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.522293</td>\n",
       "      <td>0.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.527070</td>\n",
       "      <td>0.647619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    nb_neurons  nb_iter  random_state   alpha  accuracy  accuracy_test\n",
       "29         8.0   2000.0           2.0  0.0005  0.527070       0.642857\n",
       "30         8.0   2000.0           2.0  0.0010  0.525478       0.642857\n",
       "31         8.0   2000.0           2.0  0.0100  0.522293       0.642857\n",
       "51         8.0   4000.0           2.0  0.0100  0.527070       0.647619"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res_alpha[df_res_alpha[\"accuracy_test\"] >= 0.64]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x33c5cb3a0>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 581.875x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df_res_alpha,\n",
    "            kind=\"bar\",\n",
    "            x=\"nb_iter\",\n",
    "            y=\"accuracy_test\",\n",
    "            hue=\"alpha\",\n",
    "            row=\"nb_neurons\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x32ff54340>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 581.875x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df_res_alpha,\n",
    "            kind=\"bar\",\n",
    "            x=\"nb_iter\",\n",
    "            y=\"accuracy\",\n",
    "            hue=\"alpha\",\n",
    "            row=\"nb_neurons\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Target :  class\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:1102: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN score : 0.6289808917197452\n",
      "TEST score : 0.6095238095238096\n",
      "Next month :  [4] 0.38572585251177766\n",
      "nb iteration :  2000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gregory/miniforge3/envs/ocean_tf_2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (2000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "#16.0\t2000.0\t3.0\t0.0001\t0.660099\t0.846154\n",
    "clf = MLPClassifier(\n",
    "    hidden_layer_sizes=(16),\n",
    "    random_state=3, \n",
    "    max_iter=2000,\n",
    "    n_iter_no_change=2000,\n",
    "    alpha=0.0001,\n",
    ")\n",
    "list_clf = multi_target_fit(clf, df_y, nb_test, [\"class\"])\n",
    "print(\"nb iteration : \", list_clf[-1].n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "predictions = list_clf[0].predict(X_test)\n",
    "cm = confusion_matrix(Y_test, predictions, labels=list_clf[0].classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=list_clf[0].classes_)\n",
    "disp.plot()\n",
    "plt.title(\"Confusion matrix on TEST\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "predictions = list_clf[0].predict(X)\n",
    "cm = confusion_matrix(Y, predictions, labels=list_clf[0].classes_)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
    "                              display_labels=list_clf[0].classes_)\n",
    "disp.plot()\n",
    "plt.title(\"Confusion matrix on TRAIN\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best : nb_neurons 32, nb_iter=4000,\trandom_state=3,\talpha=0.0100  \n",
    "\n",
    "because False predictions are more homogenous between classes"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ocean_tf_2",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}