New try LSTM copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,  Dense, Dropout\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import yfinance as yf\n",
    "from pandas_datareader.data import DataReader\n",
    "import seaborn as sns\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import quandl\n",
    "from fredapi import Fred\n",
    "\n",
    "pd.set_option(\"display.max_rows\",200)\n",
    "sns.set(rc={'figure.figsize':(16,10)})\n",
    "fred_key = \"df4910b2cad947d95cf6ab16ba11d74d\"\n",
    "fred = Fred(api_key = fred_key)\n",
    "quandl.ApiConfig.api_key = 'Qq5R29Xiqp2yUbb9dzNq'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(prediction,target,grey=None,start=None,end=None):\n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(10, 3), dpi=300)\n",
    "    \n",
    "    # else:\n",
    "    #     ylim = (df.min(),df.max())\n",
    "    \n",
    "\n",
    "    if not isinstance(prediction,pd.DataFrame):\n",
    "        Results=pd.DataFrame(prediction,index=target.index)\n",
    "        Results.plot(ax=ax,legend=True)\n",
    "        ylim = (Results.min().min(),Results.max().max())\n",
    "    else:\n",
    "        prediction.plot(ax=ax,legend=True)\n",
    "        ylim = (0,1)\n",
    "    ax.fill_between(target.index, 0, ylim[1]+1e-2, target,facecolor='k', alpha=0.1)\n",
    "    if not isinstance(prediction,pd.DataFrame):\n",
    "        legend_list=[\"Prediction\", \"NBER recession indicator\"]\n",
    "    else:\n",
    "        legend_list=[\"Prediction\",'lower','upper', \"NBER recession indicator\"]\n",
    "    # if '2022-02-24' in target.index:\n",
    "    #     legend_list+=['Russian invasion of Ukraine']\n",
    "    #     ax.axvline(x='2022-02-24', color='r')\n",
    "    ax.legend(legend_list)\n",
    "        \n",
    "    return fig,ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_data=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "MacroCode=pd.read_csv(r'Data\\Macro Variables.csv')\n",
    "MacroCode.replace({'Average HOUST':'HOUST','S&P 500':'SP500'},inplace=True)\n",
    "\n",
    "errors=[]\n",
    "indicators={}\n",
    "freq={}\n",
    "for code in MacroCode['Variable']:\n",
    "    if not ('S&P' in code):\n",
    "        try:\n",
    "            col=fred.get_series(code).to_frame(code).squeeze()\n",
    "            if code == 'CPFF':\n",
    "                indicators[code]=(col) #.resample('MS').last()\n",
    "            elif code == \"ICSA\":\n",
    "                indicators[code]=(col) #.resample('MS').sum()\n",
    "            elif code == \"SP500\":\n",
    "                indicators[code]=(col) #.resample('MS').first()\n",
    "            else:\n",
    "                indicators[code]=(col) # .resample('M').interpolate()\n",
    "        except ValueError:\n",
    "            errors.append(code)\n",
    "indicators['S&P: indust']=(yf.download('^SP500-20')['Close'].to_frame('S&P: indust').squeeze()) # .resample(\"MS\").last()\n",
    "indicators['S&P div yield']=(quandl.get(\"MULTPL/SP500_DIV_YIELD_MONTH\").squeeze().to_frame('S&P div yield').squeeze()) # .resample(\"MS\").last()\n",
    "indicators['S&P PE ratio']=(quandl.get(\"MULTPL/SP500_PE_RATIO_MONTH\").squeeze().to_frame('S&P PE ratio').squeeze()) # .resample(\"MS\").first()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RPI MS\n",
      "W875RX1 MS\n",
      "DPCERA3M086SBEA MS\n",
      "CMRMTSPL MS\n",
      "RSXFS MS\n",
      "INDPRO MS\n",
      "IPFPNSS MS\n",
      "IPFINAL MS\n",
      "IPCONGD MS\n",
      "IPDCONGD MS\n",
      "IPNCONGD MS\n",
      "IPBUSEQ MS\n",
      "IPMAT MS\n",
      "IPDMAT MS\n",
      "IPNMAT MS\n",
      "IPMANSICS MS\n",
      "IPB51222S MS\n",
      "IPFUELS MS\n",
      "CUMFNS MS\n",
      "CLF16OV MS\n",
      "CE16OV MS\n",
      "UNRATE MS\n",
      "UEMPMEAN MS\n",
      "UEMPLT5 MS\n",
      "UEMP5TO14 MS\n",
      "UEMP15OV MS\n",
      "UEMP15T26 MS\n",
      "UEMP27OV MS\n",
      "ICSA W-SAT\n",
      "PAYEMS MS\n",
      "USGOOD MS\n",
      "CES1021000001 MS\n",
      "USCONS MS\n",
      "MANEMP MS\n",
      "DMANEMP MS\n",
      "NDMANEMP MS\n",
      "SRVPRD MS\n",
      "USTPU MS\n",
      "USWTRADE MS\n",
      "USTRADE MS\n",
      "USFIRE MS\n",
      "USGOVT MS\n",
      "CES0600000007 MS\n",
      "AWOTMAN MS\n",
      "AWHMAN MS\n",
      "HOUST MS\n",
      "HOUSTNE MS\n",
      "HOUSTMW MS\n",
      "HOUSTS MS\n",
      "HOUSTW MS\n",
      "PERMIT MS\n",
      "PERMITNE MS\n",
      "PERMITMW MS\n",
      "PERMITS MS\n",
      "PERMITW MS\n",
      "ACOGNO MS\n",
      "DGORDER MS\n",
      "NEWORDER MS\n",
      "AMDMUO MS\n",
      "BUSINV MS\n",
      "ISRATIO MS\n",
      "M1SL MS\n",
      "M2SL MS\n",
      "M2REAL MS\n",
      "AMBSL MS\n",
      "TOTRESNS MS\n",
      "NONBORRES MS\n",
      "BUSLOANS MS\n",
      "REALLN MS\n",
      "NONREVSL MS\n",
      "TOTALSL MS\n",
      "SP500 B\n",
      "FEDFUNDS MS\n",
      "TB3MS MS\n",
      "TB6MS MS\n",
      "GS1 MS\n",
      "GS5 MS\n",
      "GS10 MS\n",
      "AAA MS\n",
      "BAA MS\n",
      "CPFF B\n",
      "TB3SMFFM MS\n",
      "TB6SMFFM MS\n",
      "T1YFFM MS\n",
      "T5YFFM MS\n",
      "T10YFFM MS\n",
      "AAAFFM MS\n",
      "BAAFFM MS\n",
      "TWEXMMTH MS\n",
      "EXSZUS MS\n",
      "EXJPUS MS\n",
      "EXUSUK MS\n",
      "EXCAUS MS\n",
      "WPSFD49207 MS\n",
      "WPSFD49502 MS\n",
      "WPSID61 MS\n",
      "WPSID62 MS\n",
      "WTISPLC MS\n",
      "PPICMM MS\n",
      "CPIAUCSL MS\n",
      "CPIAPPSL MS\n",
      "CPITRNSL MS\n",
      "CPIMEDSL MS\n",
      "CUSR0000SAC MS\n",
      "CUSR0000SAD MS\n",
      "CUSR0000SAS MS\n",
      "CPIULFSL MS\n",
      "CUSR0000SA0L2 MS\n",
      "CUSR0000SA0L5 MS\n",
      "PCEPI MS\n",
      "DDURRG3M086SBEA MS\n",
      "DNDGRG3M086SBEA MS\n",
      "DSERRG3M086SBEA MS\n",
      "CES0600000008 MS\n",
      "CES2000000008 MS\n",
      "CES3000000008 MS\n",
      "UMCSENT MS\n",
      "MZMSL MS\n",
      "DTCOLNVHFNM MS\n",
      "DTCTHFNM MS\n",
      "INVEST MS\n",
      "S&P: indust None\n",
      "S&P div yield None\n",
      "S&P PE ratio None\n"
     ]
    }
   ],
   "source": [
    "for key in indicators:\n",
    "    print(key,pd.infer_freq(indicators[key].index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2023-05-08    838.820007\n",
       "2023-05-09    840.280029\n",
       "2023-05-10    837.559998\n",
       "2023-05-11    832.150024\n",
       "2023-05-12    832.239990\n",
       "2023-05-15    836.460022\n",
       "2023-05-16    825.059998\n",
       "2023-05-17    839.090027\n",
       "2023-05-18    844.690002\n",
       "2023-05-19    842.419983\n",
       "Name: S&P: indust, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "indicators['S&P: indust'].tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13703, 124)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "S&P PE ratio       0.000000\n",
       "S&P div yield      0.000073\n",
       "AMBSL              0.082318\n",
       "BAA                0.084069\n",
       "AAA                0.084069\n",
       "INDPRO             0.084069\n",
       "IPMANSICS          0.084069\n",
       "TB3MS              0.110341\n",
       "USCONS             0.119098\n",
       "MANEMP             0.119098\n",
       "DMANEMP            0.119098\n",
       "NDMANEMP           0.119098\n",
       "SRVPRD             0.119098\n",
       "USTRADE            0.119098\n",
       "USWTRADE           0.119098\n",
       "USGOOD             0.119098\n",
       "USFIRE             0.119098\n",
       "USGOVT             0.119098\n",
       "CES3000000008      0.119098\n",
       "USTPU              0.119098\n",
       "PAYEMS             0.119098\n",
       "AWHMAN             0.119098\n",
       "IPMAT              0.119098\n",
       "IPCONGD            0.119098\n",
       "IPFPNSS            0.119098\n",
       "IPFINAL            0.119098\n",
       "DTCTHFNM           0.126104\n",
       "DTCOLNVHFNM        0.126104\n",
       "TOTALSL            0.126104\n",
       "NONREVSL           0.126104\n",
       "WTISPLC            0.131358\n",
       "CES0600000007      0.133110\n",
       "CES2000000008      0.133110\n",
       "IPDCONGD           0.133110\n",
       "CES0600000008      0.133110\n",
       "CPITRNSL           0.133110\n",
       "CPIMEDSL           0.133110\n",
       "CPIAPPSL           0.133110\n",
       "IPNCONGD           0.133110\n",
       "CUSR0000SA0L2      0.133110\n",
       "CPIULFSL           0.133110\n",
       "IPDMAT             0.133110\n",
       "CPIAUCSL           0.133110\n",
       "PPICMM             0.133110\n",
       "BUSLOANS           0.133110\n",
       "REALLN             0.133110\n",
       "INVEST             0.133110\n",
       "IPBUSEQ            0.133110\n",
       "WPSID62            0.133547\n",
       "WPSID61            0.133547\n",
       "WPSFD49502         0.133547\n",
       "WPSFD49207         0.133547\n",
       "UNRATE             0.134861\n",
       "UEMPMEAN           0.134861\n",
       "UEMP27OV           0.134861\n",
       "UEMP15T26          0.134861\n",
       "CE16OV             0.134861\n",
       "CLF16OV            0.134861\n",
       "UEMP15OV           0.134861\n",
       "CUMFNS             0.134861\n",
       "UEMPLT5            0.134861\n",
       "UEMP5TO14          0.134861\n",
       "UMCSENT            0.143326\n",
       "GS5                0.144056\n",
       "GS10               0.144056\n",
       "GS1                0.144056\n",
       "IPNMAT             0.145370\n",
       "IPB51222S          0.145370\n",
       "IPFUELS            0.145370\n",
       "TB3SMFFM           0.146245\n",
       "T1YFFM             0.146245\n",
       "T5YFFM             0.146245\n",
       "T10YFFM            0.146245\n",
       "FEDFUNDS           0.146245\n",
       "AAAFFM             0.146245\n",
       "BAAFFM             0.146245\n",
       "CUSR0000SAS        0.148873\n",
       "AWOTMAN            0.148873\n",
       "CUSR0000SAC        0.148873\n",
       "CUSR0000SAD        0.148873\n",
       "CUSR0000SA0L5      0.150624\n",
       "CES1021000001      0.152375\n",
       "TB6SMFFM           0.153981\n",
       "TB6MS              0.153981\n",
       "MZMSL              0.154127\n",
       "DSERRG3M086SBEA    0.154127\n",
       "DNDGRG3M086SBEA    0.154127\n",
       "DDURRG3M086SBEA    0.154127\n",
       "PCEPI              0.154127\n",
       "RPI                0.154127\n",
       "M1SL               0.154127\n",
       "W875RX1            0.154127\n",
       "DPCERA3M086SBEA    0.154127\n",
       "HOUST              0.154127\n",
       "HOUSTNE            0.154127\n",
       "HOUSTMW            0.154127\n",
       "NONBORRES          0.154127\n",
       "TOTRESNS           0.154127\n",
       "M2REAL             0.154127\n",
       "M2SL               0.154127\n",
       "HOUSTS             0.154127\n",
       "HOUSTW             0.154127\n",
       "PERMIT             0.155878\n",
       "PERMITNE           0.155878\n",
       "PERMITMW           0.155878\n",
       "PERMITS            0.155878\n",
       "PERMITW            0.155878\n",
       "CMRMTSPL           0.168138\n",
       "ICSA               0.168211\n",
       "EXJPUS             0.189302\n",
       "EXUSUK             0.189302\n",
       "EXCAUS             0.189302\n",
       "EXSZUS             0.189302\n",
       "TWEXMMTH           0.200029\n",
       "ISRATIO            0.300810\n",
       "BUSINV             0.300810\n",
       "RSXFS              0.300810\n",
       "AMDMUO             0.300810\n",
       "NEWORDER           0.301248\n",
       "DGORDER            0.301248\n",
       "ACOGNO             0.301248\n",
       "S&P: indust        0.307962\n",
       "CPFF               0.390717\n",
       "SP500              0.768883\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if len(errors)>0:\n",
    "    raise Exception(\"Erorrs found\")\n",
    "data=pd.concat(indicators,axis=1).copy().fillna(method='ffill') #.loc[\"1971\":].dropna(how='all').fillna(method='ffill')\n",
    "# data.to_csv('Data/HistoricalVariables.csv')\n",
    "print(data.shape)\n",
    "data.isna().sum().sort_values()/data.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deltaX(data):\n",
    "    return data.diff()\n",
    "\n",
    "def delta2X(data):\n",
    "    return data-2*data.shift(1)+data.shift(2)\n",
    "\n",
    "def logdata(data):\n",
    "    return np.log(data)\n",
    "\n",
    "def difflog(data):\n",
    "    return np.log(data).diff()\n",
    "\n",
    "def difflog2(data):\n",
    "    return np.log(data)-2*np.log(data.shift(1))+np.log(data.shift(2))\n",
    "\n",
    "def diffpercent(data):\n",
    "    return (data/data.shift() - 1) - (data.shift()/data.shift(2) - 1)\n",
    "\n",
    "\n",
    "\n",
    "transformation={1:(lambda x: x),\n",
    "                2:deltaX,\n",
    "                3:delta2X,\n",
    "                4:logdata,\n",
    "                5:difflog,\n",
    "                6:difflog2,\n",
    "                7:diffpercent,\n",
    "                }\n",
    "\n",
    "df_transformed=pd.DataFrame()\n",
    "\n",
    "for column in data.columns:\n",
    "    type=MacroCode[MacroCode['Variable'] == column]['Transformation'].iloc[0]\n",
    "    df_transformed[column]=transformation[type](data[column])\n",
    "    df_transformed=df_transformed.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPL</th>\n",
       "      <th>RSXFS</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>CES2000000008</th>\n",
       "      <th>CES3000000008</th>\n",
       "      <th>UMCSENT</th>\n",
       "      <th>MZMSL</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>S&amp;P: indust</th>\n",
       "      <th>S&amp;P div yield</th>\n",
       "      <th>S&amp;P PE ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.013628</td>\n",
       "      <td>0.00975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.003694</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.014828</td>\n",
       "      <td>0.039006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014213</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>-0.003878</td>\n",
       "      <td>-0.00768</td>\n",
       "      <td>0.023135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.031962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014213</td>\n",
       "      <td>-0.011976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007963</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.00768</td>\n",
       "      <td>-0.023135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</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>2023-05-16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.013723</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-17</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.016862</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006652</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.002691</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-31</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11109 rows × 124 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI  W875RX1  DPCERA3M086SBEA  CMRMTSPL  RSXFS    INDPRO  \\\n",
       "1971-01-01  0.008594   0.0096         0.013628   0.00975    NaN  0.007663   \n",
       "1971-01-02  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-09  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-16  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-23  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "...              ...      ...              ...       ...    ...       ...   \n",
       "2023-05-16  0.000000   0.0000         0.000000   0.00000    0.0  0.000000   \n",
       "2023-05-17  0.000000   0.0000         0.000000   0.00000    0.0  0.000000   \n",
       "2023-05-18  0.000000   0.0000         0.000000   0.00000    0.0  0.000000   \n",
       "2023-05-19  0.000000   0.0000         0.000000   0.00000    0.0  0.000000   \n",
       "2023-05-31  0.000000   0.0000         0.000000   0.00000    0.0  0.000000   \n",
       "\n",
       "             IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  CES2000000008  \\\n",
       "1971-01-01  0.003694  0.003908  0.014828  0.039006  ...       0.014213   \n",
       "1971-01-02  0.000000  0.000000  0.000000  0.000000  ...      -0.014213   \n",
       "1971-01-09  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "1971-01-16  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "1971-01-23  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "...              ...       ...       ...       ...  ...            ...   \n",
       "2023-05-16  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "2023-05-17  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "2023-05-18  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "2023-05-19  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "2023-05-31  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "\n",
       "            CES3000000008  UMCSENT     MZMSL  DTCOLNVHFNM  DTCTHFNM    INVEST  \\\n",
       "1971-01-01       0.011976      0.0  0.007963    -0.003878  -0.00768  0.023135   \n",
       "1971-01-02      -0.011976      0.0 -0.007963     0.003878   0.00768 -0.023135   \n",
       "1971-01-09       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "1971-01-16       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "1971-01-23       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "...                   ...      ...       ...          ...       ...       ...   \n",
       "2023-05-16       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "2023-05-17       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "2023-05-18       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "2023-05-19       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "2023-05-31       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "\n",
       "            S&P: indust  S&P div yield  S&P PE ratio  \n",
       "1971-01-01          NaN           0.00      0.031962  \n",
       "1971-01-02          NaN           0.00      0.000000  \n",
       "1971-01-09          NaN           0.00      0.000000  \n",
       "1971-01-16          NaN           0.00      0.000000  \n",
       "1971-01-23          NaN           0.00      0.000000  \n",
       "...                 ...            ...           ...  \n",
       "2023-05-16    -0.013723           0.00      0.000000  \n",
       "2023-05-17     0.016862           0.00      0.000000  \n",
       "2023-05-18     0.006652           0.00      0.000000  \n",
       "2023-05-19    -0.002691           0.00      0.000000  \n",
       "2023-05-31     0.000000          -0.01      0.000000  \n",
       "\n",
       "[11109 rows x 124 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_transformed.loc['1971':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPL</th>\n",
       "      <th>RSXFS</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>CES2000000008</th>\n",
       "      <th>CES3000000008</th>\n",
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       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>S&amp;P: indust</th>\n",
       "      <th>S&amp;P div yield</th>\n",
       "      <th>S&amp;P PE ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.013628</td>\n",
       "      <td>0.00975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.003694</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.014828</td>\n",
       "      <td>0.039006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014213</td>\n",
       "      <td>0.011976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>-0.003878</td>\n",
       "      <td>-0.00768</td>\n",
       "      <td>0.023135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014213</td>\n",
       "      <td>-0.011976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007963</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.00768</td>\n",
       "      <td>-0.023135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
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       "<p>5 rows × 124 columns</p>\n",
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      "text/plain": [
       "                 RPI  W875RX1  DPCERA3M086SBEA  CMRMTSPL  RSXFS    INDPRO  \\\n",
       "1971-01-01  0.008594   0.0096         0.013628   0.00975    NaN  0.007663   \n",
       "1971-01-02  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-09  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-16  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-23  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "\n",
       "             IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  CES2000000008  \\\n",
       "1971-01-01  0.003694  0.003908  0.014828  0.039006  ...       0.014213   \n",
       "1971-01-02  0.000000  0.000000  0.000000  0.000000  ...      -0.014213   \n",
       "1971-01-09  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "1971-01-16  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "1971-01-23  0.000000  0.000000  0.000000  0.000000  ...       0.000000   \n",
       "\n",
       "            CES3000000008  UMCSENT     MZMSL  DTCOLNVHFNM  DTCTHFNM    INVEST  \\\n",
       "1971-01-01       0.011976      0.0  0.007963    -0.003878  -0.00768  0.023135   \n",
       "1971-01-02      -0.011976      0.0 -0.007963     0.003878   0.00768 -0.023135   \n",
       "1971-01-09       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "1971-01-16       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "1971-01-23       0.000000      0.0  0.000000     0.000000   0.00000  0.000000   \n",
       "\n",
       "            S&P: indust  S&P div yield  S&P PE ratio  \n",
       "1971-01-01          NaN            0.0      0.031962  \n",
       "1971-01-02          NaN            0.0      0.000000  \n",
       "1971-01-09          NaN            0.0      0.000000  \n",
       "1971-01-16          NaN            0.0      0.000000  \n",
       "1971-01-23          NaN            0.0      0.000000  \n",
       "\n",
       "[5 rows x 124 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df=yf.download(\"GE\")\n",
    "# df.head()\n",
    "n_lookback=30\n",
    "n_forecast=1\n",
    "\n",
    "# df=pd.read_csv('Data/Transformed HistoricalVariables.csv',index_col=0,parse_dates=True)\n",
    "df=df_transformed.loc['1971':].copy()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11109, 126)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RPI</th>\n",
       "      <th>W875RX1</th>\n",
       "      <th>DPCERA3M086SBEA</th>\n",
       "      <th>CMRMTSPL</th>\n",
       "      <th>RSXFS</th>\n",
       "      <th>INDPRO</th>\n",
       "      <th>IPFPNSS</th>\n",
       "      <th>IPFINAL</th>\n",
       "      <th>IPCONGD</th>\n",
       "      <th>IPDCONGD</th>\n",
       "      <th>...</th>\n",
       "      <th>UMCSENT</th>\n",
       "      <th>MZMSL</th>\n",
       "      <th>DTCOLNVHFNM</th>\n",
       "      <th>DTCTHFNM</th>\n",
       "      <th>INVEST</th>\n",
       "      <th>S&amp;P: indust</th>\n",
       "      <th>S&amp;P div yield</th>\n",
       "      <th>S&amp;P PE ratio</th>\n",
       "      <th>in a recession</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>0.008594</td>\n",
       "      <td>0.0096</td>\n",
       "      <td>0.013628</td>\n",
       "      <td>0.00975</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.003694</td>\n",
       "      <td>0.003908</td>\n",
       "      <td>0.014828</td>\n",
       "      <td>0.039006</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.007963</td>\n",
       "      <td>-0.003878</td>\n",
       "      <td>-0.00768</td>\n",
       "      <td>0.023135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.031962</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-02</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.007963</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.00768</td>\n",
       "      <td>-0.023135</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-23</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 126 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 RPI  W875RX1  DPCERA3M086SBEA  CMRMTSPL  RSXFS    INDPRO  \\\n",
       "1971-01-01  0.008594   0.0096         0.013628   0.00975    NaN  0.007663   \n",
       "1971-01-02  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-09  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-16  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "1971-01-23  0.000000   0.0000         0.000000   0.00000    NaN  0.000000   \n",
       "\n",
       "             IPFPNSS   IPFINAL   IPCONGD  IPDCONGD  ...  UMCSENT     MZMSL  \\\n",
       "1971-01-01  0.003694  0.003908  0.014828  0.039006  ...      0.0  0.007963   \n",
       "1971-01-02  0.000000  0.000000  0.000000  0.000000  ...      0.0 -0.007963   \n",
       "1971-01-09  0.000000  0.000000  0.000000  0.000000  ...      0.0  0.000000   \n",
       "1971-01-16  0.000000  0.000000  0.000000  0.000000  ...      0.0  0.000000   \n",
       "1971-01-23  0.000000  0.000000  0.000000  0.000000  ...      0.0  0.000000   \n",
       "\n",
       "            DTCOLNVHFNM  DTCTHFNM    INVEST  S&P: indust  S&P div yield  \\\n",
       "1971-01-01    -0.003878  -0.00768  0.023135          NaN            0.0   \n",
       "1971-01-02     0.003878   0.00768 -0.023135          NaN            0.0   \n",
       "1971-01-09     0.000000   0.00000  0.000000          NaN            0.0   \n",
       "1971-01-16     0.000000   0.00000  0.000000          NaN            0.0   \n",
       "1971-01-23     0.000000   0.00000  0.000000          NaN            0.0   \n",
       "\n",
       "            S&P PE ratio  in a recession  target  \n",
       "1971-01-01      0.031962             0.0     0.0  \n",
       "1971-01-02      0.000000             0.0     0.0  \n",
       "1971-01-09      0.000000             0.0     0.0  \n",
       "1971-01-16      0.000000             0.0     0.0  \n",
       "1971-01-23      0.000000             0.0     0.0  \n",
       "\n",
       "[5 rows x 126 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "recessions =  DataReader('USRECDM', 'fred', start='1800')\n",
    "df[f'in a recession']=recessions\n",
    "df['target'] = recessions.shift(-250)\n",
    "\n",
    "df.fillna(method='ffill',inplace=True)\n",
    "# df=df.fillna(-1)\n",
    "df=df['1971':] #.resample('MS').last()\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.target.value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createXY(dataset,n_lookback = 60,n_forecast = 3,target_col=-1):\n",
    "    X = []\n",
    "    Y = []\n",
    "\n",
    "    for i in range(n_lookback, len(dataset) - n_forecast + 1):\n",
    "        X.append(dataset[i - n_lookback: i, 0:dataset.shape[1]-1])\n",
    "        Y.append(dataset[i: i + n_forecast,target_col])\n",
    "\n",
    "    return np.array(X),np.array(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_for_training shape:  (5292, 126)\n",
      "df_for_testing shape:  (6135, 126)\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((5262, 30, 125), (5262, 1))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_for_training=df[:\"2004\"]\n",
    "df_for_testing=df[\"2004\":]\n",
    "print('df_for_training shape: ',df_for_training.shape)\n",
    "print('df_for_testing shape: ',df_for_testing.shape)\n",
    "\n",
    "# Scalling\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "df_for_training_scaled = scaler.fit_transform(df_for_training.fillna(0))\n",
    "df_for_testing_scaled=scaler.transform(df_for_testing.fillna(0))\n",
    "\n",
    "print()\n",
    "trainX,trainY=createXY(df_for_training_scaled,n_lookback=n_lookback,n_forecast=n_forecast)\n",
    "testX,testY=createXY(df_for_testing_scaled,n_lookback=n_lookback,n_forecast=n_forecast)\n",
    "trainX.shape,trainY.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# with recessions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CustomLoss(labels, pred,w=0.2): \n",
    "    logits=tf.math.log(pred/(1-pred))\n",
    "    return tf.nn.weighted_cross_entropy_with_logits(labels, logits, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import LSTM,  Dense, Dropout, Conv1D, MaxPool1D,Flatten,RepeatVector\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "\n",
    "PT_path='Model/CNN2-LSTM-test'\n",
    "f1 = EarlyStopping(monitor='recall', mode='max', verbose=1, patience=30)\n",
    "f2 = ModelCheckpoint(filepath=PT_path, monitor='recall', mode='max', verbose=0, save_best_only=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_16 (Conv1D)          (None, 26, 32)            20032     \n",
      "                                                                 \n",
      " conv1d_17 (Conv1D)          (None, 22, 32)            5152      \n",
      "                                                                 \n",
      " max_pooling1d_8 (MaxPooling  (None, 22, 32)           0         \n",
      " 1D)                                                             \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 22, 32)            0         \n",
      "                                                                 \n",
      " flatten_8 (Flatten)         (None, 704)               0         \n",
      "                                                                 \n",
      " repeat_vector_8 (RepeatVect  (None, 1, 704)           0         \n",
      " or)                                                             \n",
      "                                                                 \n",
      " lstm_8 (LSTM)               (None, 100)               322000    \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 347,285\n",
      "Trainable params: 347,285\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "15/15 [==============================] - 6s 148ms/step - loss: 0.1698 - recall: 0.0142 - precision: 0.0875 - auc: 0.4866 - val_loss: 0.0133 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 2/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0995 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.5202 - val_loss: 0.0242 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 3/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0992 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.5271 - val_loss: 0.0219 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 4/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0991 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.5222 - val_loss: 0.0229 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 5/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0989 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.5506 - val_loss: 0.0221 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 6/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0986 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.5906 - val_loss: 0.0192 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 7/100\n",
      "15/15 [==============================] - 1s 70ms/step - loss: 0.0974 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.6722 - val_loss: 0.0253 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 8/100\n",
      "15/15 [==============================] - 1s 67ms/step - loss: 0.0936 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.7992 - val_loss: 0.0196 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 9/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0857 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.8547 - val_loss: 0.0216 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 10/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0857 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.7876 - val_loss: 0.0088 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 11/100\n",
      "15/15 [==============================] - 1s 67ms/step - loss: 0.0804 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.8620 - val_loss: 0.0127 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 12/100\n",
      "15/15 [==============================] - 1s 63ms/step - loss: 0.0708 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9195 - val_loss: 0.0040 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 13/100\n",
      "15/15 [==============================] - 1s 64ms/step - loss: 0.0687 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9227 - val_loss: 0.0034 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 14/100\n",
      "15/15 [==============================] - 1s 64ms/step - loss: 0.0630 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9439 - val_loss: 8.3039e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 15/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0615 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9493 - val_loss: 0.0010 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 16/100\n",
      "15/15 [==============================] - 1s 66ms/step - loss: 0.0604 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9500 - val_loss: 5.8333e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 17/100\n",
      "15/15 [==============================] - 1s 64ms/step - loss: 0.0568 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9600 - val_loss: 3.2399e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 18/100\n",
      "15/15 [==============================] - 1s 69ms/step - loss: 0.0547 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9643 - val_loss: 2.0569e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 19/100\n",
      "15/15 [==============================] - 1s 74ms/step - loss: 0.0538 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9660 - val_loss: 1.4169e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 20/100\n",
      "15/15 [==============================] - 1s 74ms/step - loss: 0.0533 - recall: 0.0000e+00 - precision: 0.0000e+00 - auc: 0.9660 - val_loss: 1.1252e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 21/100\n",
      "15/15 [==============================] - 1s 85ms/step - loss: 0.0532 - recall: 0.0020 - precision: 1.0000 - auc: 0.9656 - val_loss: 1.0077e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 22/100\n",
      "15/15 [==============================] - 1s 81ms/step - loss: 0.0514 - recall: 0.0203 - precision: 1.0000 - auc: 0.9698 - val_loss: 9.5226e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 23/100\n",
      "15/15 [==============================] - 1s 78ms/step - loss: 0.0527 - recall: 0.0243 - precision: 1.0000 - auc: 0.9664 - val_loss: 8.2061e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 24/100\n",
      "15/15 [==============================] - 1s 73ms/step - loss: 0.0492 - recall: 0.0203 - precision: 1.0000 - auc: 0.9732 - val_loss: 6.5751e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 25/100\n",
      "15/15 [==============================] - 1s 79ms/step - loss: 0.0481 - recall: 0.0467 - precision: 1.0000 - auc: 0.9744 - val_loss: 5.0751e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 26/100\n",
      "15/15 [==============================] - 1s 73ms/step - loss: 0.0487 - recall: 0.0588 - precision: 1.0000 - auc: 0.9732 - val_loss: 5.1726e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 27/100\n",
      "15/15 [==============================] - 1s 64ms/step - loss: 0.0512 - recall: 0.0832 - precision: 1.0000 - auc: 0.9606 - val_loss: 5.7068e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 28/100\n",
      "15/15 [==============================] - 1s 61ms/step - loss: 0.0474 - recall: 0.0953 - precision: 1.0000 - auc: 0.9748 - val_loss: 4.9425e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 29/100\n",
      "15/15 [==============================] - 1s 64ms/step - loss: 0.0470 - recall: 0.1217 - precision: 0.9836 - auc: 0.9742 - val_loss: 4.6759e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 30/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0474 - recall: 0.1542 - precision: 0.9744 - auc: 0.9724 - val_loss: 5.9526e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 31/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0478 - recall: 0.1501 - precision: 0.9610 - auc: 0.9721 - val_loss: 4.3453e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 32/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0455 - recall: 0.1907 - precision: 0.9792 - auc: 0.9723 - val_loss: 4.0516e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 33/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0470 - recall: 0.2089 - precision: 0.9537 - auc: 0.9685 - val_loss: 3.5516e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 34/100\n",
      "15/15 [==============================] - 1s 67ms/step - loss: 0.0416 - recall: 0.2576 - precision: 0.9621 - auc: 0.9818 - val_loss: 3.6243e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 35/100\n",
      "15/15 [==============================] - 1s 75ms/step - loss: 0.0378 - recall: 0.2617 - precision: 0.9699 - auc: 0.9861 - val_loss: 2.7184e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 36/100\n",
      "15/15 [==============================] - 1s 68ms/step - loss: 0.0386 - recall: 0.3489 - precision: 0.9609 - auc: 0.9837 - val_loss: 2.3818e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 37/100\n",
      "15/15 [==============================] - 1s 83ms/step - loss: 0.0422 - recall: 0.3469 - precision: 0.9144 - auc: 0.9766 - val_loss: 2.0604e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 38/100\n",
      "15/15 [==============================] - 1s 75ms/step - loss: 0.0462 - recall: 0.3529 - precision: 0.8923 - auc: 0.9707 - val_loss: 2.3442e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 39/100\n",
      "15/15 [==============================] - 1s 63ms/step - loss: 0.0414 - recall: 0.2414 - precision: 0.9520 - auc: 0.9822 - val_loss: 2.6267e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 40/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0413 - recall: 0.3773 - precision: 0.9300 - auc: 0.9799 - val_loss: 2.4834e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 41/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0374 - recall: 0.3367 - precision: 0.9379 - auc: 0.9864 - val_loss: 2.1247e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 42/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0388 - recall: 0.4341 - precision: 0.9554 - auc: 0.9824 - val_loss: 2.0492e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 43/100\n",
      "15/15 [==============================] - 1s 68ms/step - loss: 0.0351 - recall: 0.4077 - precision: 0.9805 - auc: 0.9865 - val_loss: 1.6712e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 44/100\n",
      "15/15 [==============================] - 1s 63ms/step - loss: 0.0365 - recall: 0.4564 - precision: 0.9414 - auc: 0.9847 - val_loss: 1.4014e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 45/100\n",
      "15/15 [==============================] - 1s 68ms/step - loss: 0.0342 - recall: 0.4625 - precision: 0.9580 - auc: 0.9881 - val_loss: 1.2649e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 46/100\n",
      "15/15 [==============================] - 1s 83ms/step - loss: 0.0305 - recall: 0.5274 - precision: 0.9665 - auc: 0.9910 - val_loss: 1.1746e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 47/100\n",
      "15/15 [==============================] - 1s 80ms/step - loss: 0.0313 - recall: 0.5233 - precision: 0.9627 - auc: 0.9886 - val_loss: 1.0223e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 48/100\n",
      "15/15 [==============================] - 1s 72ms/step - loss: 0.0308 - recall: 0.5740 - precision: 0.9465 - auc: 0.9903 - val_loss: 9.3829e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 49/100\n",
      "15/15 [==============================] - 1s 73ms/step - loss: 0.0299 - recall: 0.5558 - precision: 0.9786 - auc: 0.9909 - val_loss: 9.3033e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 50/100\n",
      "15/15 [==============================] - 1s 84ms/step - loss: 0.0341 - recall: 0.4503 - precision: 0.9407 - auc: 0.9861 - val_loss: 7.2982e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 51/100\n",
      "15/15 [==============================] - 1s 76ms/step - loss: 0.0431 - recall: 0.4949 - precision: 0.9421 - auc: 0.9592 - val_loss: 1.4121e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 52/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0416 - recall: 0.4442 - precision: 0.9440 - auc: 0.9681 - val_loss: 1.3000e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 53/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0346 - recall: 0.4057 - precision: 0.9434 - auc: 0.9865 - val_loss: 1.3040e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 54/100\n",
      "15/15 [==============================] - 1s 69ms/step - loss: 0.0291 - recall: 0.5294 - precision: 0.9849 - auc: 0.9916 - val_loss: 1.3940e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 55/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0277 - recall: 0.6065 - precision: 0.9583 - auc: 0.9926 - val_loss: 1.3234e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 56/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0275 - recall: 0.5842 - precision: 0.9632 - auc: 0.9912 - val_loss: 6.1292e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 57/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0373 - recall: 0.5517 - precision: 0.9037 - auc: 0.9788 - val_loss: 4.1784e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 58/100\n",
      "15/15 [==============================] - 1s 61ms/step - loss: 0.0360 - recall: 0.5314 - precision: 0.9357 - auc: 0.9800 - val_loss: 5.2226e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 59/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0272 - recall: 0.5497 - precision: 0.9783 - auc: 0.9928 - val_loss: 5.2914e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 60/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0246 - recall: 0.6308 - precision: 0.9719 - auc: 0.9946 - val_loss: 3.7315e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 61/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0282 - recall: 0.6207 - precision: 0.9503 - auc: 0.9893 - val_loss: 3.5161e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 62/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0228 - recall: 0.6613 - precision: 0.9731 - auc: 0.9954 - val_loss: 3.4492e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 63/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0227 - recall: 0.6552 - precision: 0.9758 - auc: 0.9954 - val_loss: 3.5360e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 64/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0485 - recall: 0.5294 - precision: 0.9031 - auc: 0.9413 - val_loss: 5.0778e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 65/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0467 - recall: 0.2840 - precision: 0.9459 - auc: 0.9567 - val_loss: 2.1730e-04 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 66/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0274 - recall: 0.4787 - precision: 0.9958 - auc: 0.9947 - val_loss: 2.8239e-05 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 67/100\n",
      "15/15 [==============================] - 1s 73ms/step - loss: 0.0244 - recall: 0.6247 - precision: 0.9840 - auc: 0.9947 - val_loss: 6.1238e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 68/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0206 - recall: 0.6815 - precision: 0.9882 - auc: 0.9965 - val_loss: 4.4300e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 69/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0200 - recall: 0.7566 - precision: 0.9739 - auc: 0.9962 - val_loss: 2.5878e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 70/100\n",
      "15/15 [==============================] - 1s 56ms/step - loss: 0.0195 - recall: 0.7627 - precision: 0.9817 - auc: 0.9965 - val_loss: 2.0955e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 71/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0195 - recall: 0.7546 - precision: 0.9764 - auc: 0.9946 - val_loss: 1.3898e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 72/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0182 - recall: 0.7546 - precision: 0.9713 - auc: 0.9969 - val_loss: 1.2378e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 73/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0202 - recall: 0.7485 - precision: 0.9634 - auc: 0.9963 - val_loss: 1.4224e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 74/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0214 - recall: 0.7525 - precision: 0.9587 - auc: 0.9957 - val_loss: 1.3826e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 75/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0178 - recall: 0.7424 - precision: 0.9812 - auc: 0.9973 - val_loss: 1.2125e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 76/100\n",
      "15/15 [==============================] - 1s 69ms/step - loss: 0.0173 - recall: 0.7850 - precision: 0.9724 - auc: 0.9961 - val_loss: 1.3192e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 77/100\n",
      "15/15 [==============================] - 1s 67ms/step - loss: 0.0183 - recall: 0.7789 - precision: 0.9796 - auc: 0.9954 - val_loss: 1.1980e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 78/100\n",
      "15/15 [==============================] - 1s 69ms/step - loss: 0.0246 - recall: 0.7099 - precision: 0.9749 - auc: 0.9821 - val_loss: 1.2776e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 79/100\n",
      "15/15 [==============================] - 1s 68ms/step - loss: 0.0577 - recall: 0.2982 - precision: 0.8596 - auc: 0.9329 - val_loss: 2.0847e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 80/100\n",
      "15/15 [==============================] - 1s 70ms/step - loss: 0.0403 - recall: 0.4138 - precision: 0.9444 - auc: 0.9733 - val_loss: 2.1064e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 81/100\n",
      "15/15 [==============================] - 1s 84ms/step - loss: 0.0345 - recall: 0.5091 - precision: 0.9228 - auc: 0.9855 - val_loss: 2.1589e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 82/100\n",
      "15/15 [==============================] - 1s 66ms/step - loss: 0.0263 - recall: 0.6105 - precision: 0.9710 - auc: 0.9929 - val_loss: 2.4140e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 83/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0276 - recall: 0.6207 - precision: 0.9503 - auc: 0.9919 - val_loss: 2.3543e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 84/100\n",
      "15/15 [==============================] - 1s 62ms/step - loss: 0.0281 - recall: 0.6673 - precision: 0.9427 - auc: 0.9916 - val_loss: 2.0666e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 85/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0235 - recall: 0.7018 - precision: 0.9692 - auc: 0.9950 - val_loss: 2.1897e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 86/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0202 - recall: 0.7485 - precision: 0.9736 - auc: 0.9935 - val_loss: 2.1173e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 87/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0198 - recall: 0.7140 - precision: 0.9778 - auc: 0.9963 - val_loss: 2.9298e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 88/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0266 - recall: 0.7221 - precision: 0.9622 - auc: 0.9865 - val_loss: 2.8013e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 89/100\n",
      "15/15 [==============================] - 1s 66ms/step - loss: 0.0237 - recall: 0.7383 - precision: 0.9681 - auc: 0.9932 - val_loss: 1.2468e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 90/100\n",
      "15/15 [==============================] - 1s 57ms/step - loss: 0.0315 - recall: 0.6288 - precision: 0.9422 - auc: 0.9737 - val_loss: 5.7202e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 91/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0293 - recall: 0.6247 - precision: 0.9565 - auc: 0.9831 - val_loss: 7.1571e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 92/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0209 - recall: 0.7546 - precision: 0.9815 - auc: 0.9956 - val_loss: 4.2870e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 93/100\n",
      "15/15 [==============================] - 1s 67ms/step - loss: 0.0191 - recall: 0.7465 - precision: 0.9787 - auc: 0.9968 - val_loss: 2.4683e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 94/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0166 - recall: 0.7870 - precision: 0.9898 - auc: 0.9975 - val_loss: 1.5237e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 95/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0168 - recall: 0.7911 - precision: 0.9824 - auc: 0.9959 - val_loss: 1.3355e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 96/100\n",
      "15/15 [==============================] - 1s 59ms/step - loss: 0.0172 - recall: 0.8032 - precision: 0.9778 - auc: 0.9973 - val_loss: 1.2704e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 97/100\n",
      "15/15 [==============================] - 1s 65ms/step - loss: 0.0205 - recall: 0.7566 - precision: 0.9764 - auc: 0.9927 - val_loss: 1.1509e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 98/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0184 - recall: 0.8215 - precision: 0.9759 - auc: 0.9934 - val_loss: 1.0858e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 99/100\n",
      "15/15 [==============================] - 1s 60ms/step - loss: 0.0182 - recall: 0.7546 - precision: 0.9688 - auc: 0.9950 - val_loss: 1.0116e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n",
      "Epoch 100/100\n",
      "15/15 [==============================] - 1s 58ms/step - loss: 0.0168 - recall: 0.8174 - precision: 0.9711 - auc: 0.9974 - val_loss: 1.1021e-06 - val_recall: 0.0000e+00 - val_precision: 0.0000e+00 - val_auc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=32,kernel_size=(5,),activation='relu', input_shape=(trainX.shape[1], trainX.shape[2]),))\n",
    "model.add(Conv1D(filters=32,kernel_size=(5,),activation='relu'))\n",
    "# model.add(Conv1D(filters=32,kernel_size=(3,),activation='relu'))\n",
    "model.add(MaxPool1D(1))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(Flatten())\n",
    "model.add(RepeatVector(1))\n",
    "model.add(LSTM(100, return_sequences=False))\n",
    "model.add(Dense((n_forecast)\n",
    "            ,activation='sigmoid'\n",
    "            ))\n",
    "model.summary()\n",
    "# fit the model\n",
    "model.compile(optimizer='adam',\n",
    "              metrics=['Recall','Precision',\"AUC\"],\n",
    "              loss=CustomLoss\n",
    "            # loss='BinaryCrossentropy'\n",
    "              )\n",
    "# fit the model\n",
    "history = model.fit(trainX, trainY, epochs=100, batch_size=320, \n",
    "                    validation_split=0.1, \n",
    "                    verbose=1,  \n",
    "                    callbacks=[\n",
    "                                #  f1,\n",
    "                                f2\n",
    "                               ]\n",
    "                  )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b766683700>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(history.history['loss'], label='Training loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation loss')\n",
    "plt.legend()\n",
    "plt.show();\n",
    "plt.plot(history.history['auc'], label='AUC')\n",
    "plt.plot(history.history['recall'], label='recall')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Model/CNN2-LSTM-test'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PT_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Received incompatible tensor with shape (128,) when attempting to restore variable with shape (32,) and name layer_with_weights-0/bias/.ATTRIBUTES/VARIABLE_VALUE.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)\n",
      "Cell \u001b[1;32mIn[196], line 1\u001b[0m\n",
      "\u001b[1;32m----> 1\u001b[0m model\u001b[39m.\u001b[39;49mload_weights(PT_path)\n",
      "\n",
      "File \u001b[1;32mc:\\Users\\amine\\OneDrive\\Documents\\PFA\\.venv\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "\u001b[0;32m     68\u001b[0m     \u001b[39m# To get the full stack trace, call:\u001b[39;00m\n",
      "\u001b[0;32m     69\u001b[0m     \u001b[39m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n",
      "\u001b[1;32m---> 70\u001b[0m     \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39m\n",
      "\u001b[0;32m     71\u001b[0m \u001b[39mfinally\u001b[39;00m:\n",
      "\u001b[0;32m     72\u001b[0m     \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "\n",
      "File \u001b[1;32mc:\\Users\\amine\\OneDrive\\Documents\\PFA\\.venv\\lib\\site-packages\\tensorflow\\python\\training\\saving\\saveable_object_util.py:135\u001b[0m, in \u001b[0;36mResourceVariableSaveable.restore\u001b[1;34m(self, restored_tensors, restored_shapes)\u001b[0m\n",
      "\u001b[0;32m    132\u001b[0m   assigned_variable \u001b[39m=\u001b[39m resource_variable_ops\u001b[39m.\u001b[39mshape_safe_assign_variable_handle(\n",
      "\u001b[0;32m    133\u001b[0m       \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mhandle_op, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_shape, restored_tensor)\n",
      "\u001b[0;32m    134\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n",
      "\u001b[1;32m--> 135\u001b[0m   \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n",
      "\u001b[0;32m    136\u001b[0m       \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mReceived incompatible tensor with shape \u001b[39m\u001b[39m{\u001b[39;00mrestored_tensor\u001b[39m.\u001b[39mshape\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n",
      "\u001b[0;32m    137\u001b[0m       \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mwhen attempting to restore variable with shape \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_shape\u001b[39m}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n",
      "\u001b[0;32m    138\u001b[0m       \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mand name \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39me\u001b[39;00m\n",
      "\u001b[0;32m    139\u001b[0m \u001b[39mreturn\u001b[39;00m assigned_variable\n",
      "\n",
      "\u001b[1;31mValueError\u001b[0m: Received incompatible tensor with shape (128,) when attempting to restore variable with shape (32,) and name layer_with_weights-0/bias/.ATTRIBUTES/VARIABLE_VALUE."
     ]
    }
   ],
   "source": [
    "# model.load_weights(PT_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "165/165 [==============================] - 2s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction_train=model.predict(trainX).mean(axis=1)\n",
    "trainResults= pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 3000x900 with 1 Axes>, <Axes: >)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WLQEUzfRiNpuh0WgAsFJNdCuVF+sAcPr0aRgMBuUhWKtWrQDA4ZTHVqvVbhSZ3KnGjjOiukFOarHZbFCr1WjTpg0Ax/FenJzkMnPmTNx2220AiuI+JSUFa9euxaFDh2qw1ERUXeR6vTyzEwBkZGRgxIgR6NOnD9RqtTKb0+LFi3H16tXaKioRlUNuV3t4eCivVSoVGjdubPd+SXK93Wg0IiQkRGkDyCPHOYMbkXtiPx1Rw8K+OqK6ydUlBAVBQE5ODgoLCwEUDU7Jy8tTEtPKI8d5u3btcPvtt6Np06bK70BiYiJ+/vlnu/2IyH2ZzWYEBAQoM7gJgoA+ffqgT58+Sn3faDRizZo1OHfuXC2XlqhhKH86CCK6ZY4cOYI5c+bg2LFjAIDevXtj8ODBpRq8np6eMJvNysOvsLAwCIIAq9WqPPQqS0BAAHJycgAUTY1uMplw8eJFAKxUE90KrsS63Oj18PCAVqtVEliMRmOp80mSpDw0A4Bz587hr7/+Qm5uLmJjY9G4cWOEh4ejc+fOiI6OdqmjnYhuPYvFAlEUoVarYTKZcPbsWbRv377cjm859jt27IiJEyciNTUV165dA1DU8f7nn3+iY8eOykN2InJPoijCYDAgOTlZ2ebn5wcAuO+++3Dy5ElcvXoVgiDg6NGj2LBhA6ZPn87YJnJDoijCYrGgoKAAAJR7+7FjxzBw4ECnx8ltgIKCAiQkJCj3+DvvvNPufSJyL+ynI2oY2FdHVLe5Wo+WJAm+vr5o2rQpTp48qczWKA9MdfUcciLMuHHj8NVXXymzwe3btw/r1q3D2LFjYbPZeK8nqgHV1XbOzc1FcnIyVCoVsrOzIQgCvLy8MH78eJw6dQpnzpwBABw8eBDbt29HVFQUfH192XYnqkFMcCNyA6mpqZg7dy5Wr14NoGiUt9FoxMqVKzFkyJBSN8Lc3Fw0atQIV69ehc1mw6FDh2A2m5Upk8u6aVosFmWKZXkZJACIiooCAFaoiWpQRWJd/m9+fj5MJpMya4skSXajzeRjVCoV0tLS8Mknn2DdunWQJAkGg6FUGTp06IBp06Zh0KBBCAoKYswTuQE5jn18fGCz2WC1WqFWq5GZmelyjMr7jRkzBufOncPSpUthsVhgMBiwd+9ebNmyBePGjbsFn4aIKkuSJIiiCL1eD6CoLqDT6QAAffr0wfDhw7FkyRKYzWZIkoRffvkFffv2RceOHWuz2ETkQPHZWDdu3KjU3zdu3IjJkycjLCzMYeKLIAiwWCxYtmwZgKJZX6KiojBs2DDlfYCJbkQ1Sa5XV6StzH46orqpovHOvjqiuiU7OxsxMTGwWq24dOkSWrduDX9/f7Rv3x6enp5OjxMEAZIkoU2bNli/fj0kSUJeXh6OHTuG0aNHu5TALv9e+Pv7Y9iwYTh48CBiYmIgiiKysrLw+++/Y+DAgfD29q7Wz0xERaqjvSxJEoxGI3Q6HYxGI5o0aaL8dvTt2xeDBg3ClStXkJubCwBYs2YNunXrhn79+rG9TlSDWEsmqmVffvklBg8erCS8qNVqpTPL2TIEzZo1U0aN6PV6ZGVlYenSpQDKv2nLDe+UlBRIkqQseSBPqc7GM1HNqGisyx1jHTt2hJ+fHywWC6xWqzJzi/y+HPMrV67EnXfeiRUrVqCwsBAmkwmCIECtVkMQBOj1eoiiiNOnT+Pdd9/Fc889h+vXrzPmiWpARkYGCgsLkZiYCIPBoMS4s6UQ5Dhu27Yt9Ho9bDYbLBYLTp06pXS2l0cURUiShICAAIwZM8Yu4SUxMRGbN29GWlpaNXw6IiquovFeFkEQYDAYcOnSJSWmfX19lffvv/9+NG/eXEmEu3btGn7//XelI42Iak5l7+0jRoyAv78/zGYzBEFAfHw8vvvuOwBQHogD9u2Bo0ePYvv27cp57rjjDhw7dgwbN27E9u3bkZGRwc5yohokt5FLtpXLurezn46obqpovLOvjqju+PbbbzFlyhS8/fbbePbZZ/HZZ5/hySefxAMPPIA77rgDCxcuRFxcnNPjBUFAeHg4RFFUktmWLl2KgoICu3q8K9q2bYtx48ZBp9MpA1tPnz6N9evXV/lzElGR7OxsbN68GRs2bMD8+fOxZcsWHDp0SJlVvTIEQUBhYaGSnF5YWGhXt7/99tvRoUMHZd9Lly5h/fr1SElJqdqHIaIycQY3olqyfv16zJkzx24JInkZE7VaDZvNhubNmwOw7wyz2Wzw8fHBqFGjsHDhQhQWFgIALl26BIPBoMzyUJbz588jLy8PoigqCTZt27atzo9HRDdUNtbl/200GtG4cWPk5ORAkiTs27cPGRkZCAwMVEaLbd68GfPnz0dGRoZyvFarhcFggJ+fH9LT05XfClEUYTKZcODAAbz66qt46qmn0KVLl1vwTRDVf3/99RfWrl2LjIwMnDp1CpIkQa1Wo3Pnzhg2bBh69eqFRo0aOT1eq9VCr9cryxutX78ezz//PBo3buzSCG75d6N79+4YNmwY4uLikJubC5vNhiNHjmDHjh2YNGlStX5mooaqqvHuiM1mg9lshp+fH3JycuDl5aXM5maz2dCsWTNMnDgR8+bNQ35+PgBgxYoVGDx4MAYOHMiEF6IaUNlYl+MxODgYI0eOxNKlS5WZmX766Sc0btwYI0aMQJMmTQDcfLC+evVqzJkzB2lpaUqS+/z582G1WqHRaGA2m+Ht7Y0RI0Zg1KhRysxuRFR18swqOTk5OHLkCCIiImA0GtGnTx90794drVq1glarLTUjG/vpiOqeysY7++qI3N/OnTvx/vvvIz4+Xtkmz6go1+WvXr2KOXPmoGnTpnjzzTfRvXt3eHh4KH1vcuyPHDkSs2fPRk5ODrRaLXJzc/Hrr7/i4YcfrlCZtFotevXqhQEDBmDz5s0QBAHp6enYs2cPhgwZguDgYM7QTFQF3377LZYtW4b8/PxSA7wbN26M6dOnY8CAAWjZsmWFz52SkgKVSgWr1YqgoCCEhoYq9/rWrVtj5MiRuHjxIlJTUwEAmzZtQu/evTF27Filnc/4JqpeTHAjusXOnj2L2bNnIyYmxm57o0aNEBYWhqNHjyqjP9q1awfA/uYn3xD79u2LhQsXonnz5vjb3/6Gvn37KqM8nZHPc/nyZaUBDgAREREIDg6u1s9J1NBVNdZlJTvVzGYzjhw5guHDh0OlUiExMRFffPGF0mgPDg7GwIEDcdttt6F79+7QarW4du0atmzZgt27dyMxMVH5Hfnrr7/g4eGBZ599Fm3atKnhb4So/jp37hzmzJmDPXv22I3alh9EJycnY8OGDWjUqBHefPNN9OrVC15eXqWS1vr27Qs/Pz9kZGRAq9XCZDJh+fLleOaZZ1wewS131g0ZMgQHDx7Ezp07AQBJSUnYs2cP+vXrV+GkGyK6qbri3RFRFFFQUKCM9MzPz1cS3GT33HMPduzYgf3790MURRQWFuKXX35Bu3btEBoaCoAdZ0TVobpiPTg4GGPGjMHBgwdx6dIl6PV6FBYW4rPPPsPPP/+M22+/XZmxaefOnTh+/DiAoodwchxbrVYAUPbLy8vDqlWrsGrVKrz11lsYMWIE2/NEVRAbG4t///vfOHjwoDKbKlA0myIAbNmyBb6+vmjbti2ee+45dOjQQZmBRRRF9tMR1SFVjXcZ++qI3IskSbBarVi0aBG+/vpru8RSuf6u0WhQWFio1K3VajXi4+Pxwgsv4O6778YLL7ygxKEgCLBarfD09MSUKVPw3XffwWq1QhAEbNu2DePHj0dYWFiF2t4tWrTA4MGDcejQIWRmZgIATp48iRMnTmDo0KFswxNVQnUktDojx7dGo1Hi32q1IicnB76+vsrxY8eOxYEDB7BlyxbYbDZkZmZizZo1aNeuHVq2bGn3OyEnxrHfjqhqONcx0S2Sm5uLN998ExMnTkRMTIxy89LpdBgxYgRefPFFdOnSRbn5AjeXI3B0o2vXrh1++uknrFu3Dvfeey+aNm2qHOeMvOzJ8ePH7ZY9aN68Ofz9/avroxI1aNUZ63KD++6771a2Xbt2DWfOnFFGeS5evBixsbEAgMjISDzyyCN4+umn8eCDD6J9+/Zo2bIlBg4ciDfffBPz589Hx44dYbFYlPPt27cPixcvrqFvg6h+MxqNmD9/PqZOnYo9e/YAsF/KxGw2QxRFCIIADw8PJCUlYfbs2ViwYAEA+2VQ5HifMmUKAChxevDgQVy+fLnUuZ2Rf0fatGmDwYMH2z0YO3z4ME6fPl3Zj0vUoFVnvJclPz8fHh4eEEURvr6+CAwMVI632Wzw9vbG1KlTldkhAGDbtm3YuXMnTCYTgJud8UlJSdX18YkajJqI9Z49e+Lpp58GAKUOb7VaceXKFcyfPx9ff/015s2bh+PHjyv3cbVajYCAAERERKBXr14ICAiAxWJRZnaSH6r/5z//wddff11j3wdRfWYwGDBv3jxMmjQJu3fvhtFoVJJdZCqVSklAj4mJwTPPPIPZs2fDarXazcgAsJ+OyJ1VV7wD7KsjckeCIODChQtYtmyZkjym1WoxZMgQPPbYY/jyyy/x7bff4tVXX8XQoUPh7e0Ns9kMtVqNjIwMfPfdd5g/f77ShrbZbMo9fNCgQQgKCoLVaoUkSbh06RK2bt2qXNcVcjJL9+7d0blzZ2V7QkICTp06ZRf/RFQ2SZJgsViwcOFCvPLKK3bJbRqNRqlTW61WGI1GAPYJrV988QWA8vvp5Pi+dOmSct3Q0FD4+vpCkiRltsegoCCMHz8ekZGRSrtg586d+Ouvv5SlyfPy8rB//36sWrXK7txEVDmcwY3oFliwYAE+/fRT5WYKFN0Mu3XrhrFjx6J3795o06YN4uLiIEkSTCYT1Go1IiMjlX1L3vDCw8MRHh4OAC7NCAEUNdRtNpsyKlw+pkuXLtBoNC6fh4gcq+5YlxvS0dHRiIqKQkJCAoCiEaUTJkyAIAhYsWKFsv+0adPwwAMPKMfJ57PZbBAEAS1btsTs2bPx6aefYvv27QCKHqRv27YNffv2xejRo2v0+yGqTywWC3766Se7ZYiAoqWEfH19ERUVhaSkJCQkJODKlSswm80QBAFXr17FV199hY4dO2Lw4MHKcsVy3Hbp0gWRkZFITEwEAFy8eBH79u1D06ZNK9xxNmjQIGzduhVpaWkQBAFJSUk4ePAgBg0aVO5sEkR0U3XHe1lUKhVycnKUmSAKCgqU9+TfgDFjxmDr1q1Yu3YtbDYbbDYbFi9ejN69eyMyMhJXr17Fvn37sGfPHgwfPhzjxo2rmS+GqJ6pqVjXarUYN24cCgsLsXr1auzdu1dJVANuttOBont4dHQ0hg4diujoaPTt2xdmsxmSJOHQoUPYtWsXFixYoCS0ZmRk4Pfff0ebNm0wefLkW/RNEdV9eXl5+PLLL/HLL7/YPVRu06YN/Pz80Lx5c6SnpytJK3J/WVZWFpYuXQqVSoWpU6eiffv2ykNw9tMRuaeaiHeAfXVE7iQzMxNvvvkmzp8/r8yEfPfdd+Pee+9F69atlfjr0aMH7rvvPuzbtw/vv/8+Ll26pCw9+MMPPyAlJQVvvfUWRFFUZlpq2rQp+vfvjz/++AOCICAtLQ27du1C//79ERUV5dJMTPL7LVq0QLdu3XDw4EEUFBTAZrNh3bp1eOyxx2r8OyKqL5wltPbr1w8dO3ZEhw4d4Ovri9OnT2Pv3r2IiYlBXl6eXUKrt7c3JkyYgEaNGjmtb8u/AR4eHgBuDjx11Lc3dOhQ7Nu3D8nJyTAYDLBYLFi3bh06duwIPz8/bN++HevXr8fJkyfh7e2NUaNGKecnoopjghtRDdqxYwfef/99ZeYVWbNmzTB69GgMHToU7du3V26Q8sgurVYLtVqtdKqXV0GuSGfXuXPnlOvISTjdu3d36TpE5FhNx3pwcDC6deuGhIQECIKAM2fOYM+ePQgODkZ2djZUKhUGDBiA6dOnA7jZWVZyaWOg6AHdtGnTkJGRgePHj0MURaSnp2P9+vUYOHAgPD09q/fLIapn5Pjavn07vv/+e6UhHRQUhDvvvBNDhgzBbbfdBm9vb5hMJuTn5+OLL77Ali1bkJKSoiw9+v7772Pw4MFQq9V2nWGNGzdG9+7dkZiYqHSc7dmzB3369EHTpk0r1HEWGRmJAQMG4OjRoygsLIQkSVi1ahWefPJJJrgRuaCm492R+Ph4ZQmVgIAANGnSRHlPnplNpVLhgQcewNGjR5W6wenTp7FkyRJ07NgRu3btwubNm5GdnY3U1FT0798ffn5+Nf59EdVVNR3rcof5pEmTcOedd2LTpk1ITk5GVlYWJEnCDz/8oDxEGzx4MKZPn47u3btDp9MBKBqFLooi+vfvj/79+yM8PBxLly5FXFwc1Go18vLy8M0332DEiBEICAiote+RqC7ZvHkzli9fjsLCQgiCgICAANxxxx0YOnQoOnbsCG9vb+Weu3nzZixcuBAxMTFKMurKlSuRnJyMjz76CN7e3qUejLGfjsh9VHe8y/uyr47IfZw4cQLnz59X2tKDBw/Gv/71L/j4+AC4GX9WqxUajQYDBw7ERx99hNmzZ+Pw4cMAgOzsbCxbtgxRUVF46KGHlHOHhYWhV69e2Lt3L1JTUwEAx44dw/bt2zF9+nSX79VyXWH48OH45ZdfkJ+fD7Vajbi4OOzcuRPDhw/nsoVELqiJhFZHsSefR37mZ7PZ4OfnZ5fcJiesa7VajB8/HidOnMDJkycBFC1//s0338BkMiEmJkZJsv/www+VJc2JqHI4BIyoBly6dAmPPPIIHnvsMbuEl4CAANxzzz2YNWsWHnroIXTp0gUeHh7KjU2+MZpMJphMJrsHWlUlT5++b98+ZGdn240aadeuHQB2nBFV1K2K9eDgYHTq1AkBAQHKNMdff/01Dhw4AKCokT5q1CgAUEaAOiIf2717d4wbNw4eHh7KqJMLFy5w6UIiFwiCAJPJhK+++kp5AA4Ao0ePxowZM9C7d2+l01uj0SAgIADPPfecsjSZyWSCRqPB5cuXsWTJEgD2S5+FhoaiR48eCA0NVbbv27cPhw4dKjO+S5I748ePH4/AwEClsZ2RkaFMhy7vQ0SO1XS8Fydv1+v1MJvNUKlUMJlMyMvLs9tP7gDr3Lkzxo4dC71er7y3ePFizJkzB8uXL0d2djYA4MCBA9i0aVM1fSNE9VNNx7r8ANtms0Gj0WDcuHF4+OGHMXPmTFy+fBkWiwVWqxVt2rTBCy+8gP79+yvJbcWPl9v0U6dOxd///nfo9Xpl9HhSUhKWLVtWs18UUT0RFxeHjz76SLlXSpKEESNG4OGHH0afPn1KJayNGDECn376Ke6++25otVoARXG/fft2zJkzB0DFEtpk7Kcjqnk1Ee9yfZx9dUTuY8eOHSgsLFT63h988EH4+Pgo/V5y/BVPKLntttvw9NNPo2/fvgCK+uvNZjM+/PBDXLhwwW6W5T59+qBLly7Ksenp6dixYwdOnToFwHk7vzg5iaZly5bo06ePssShSqVSkux43ycqX/GEVkmSMGjQIPzrX/9Cu3btoFKplHgsmdDarVs3pf4tJ7T+8MMPABzHsLyvPGkFAPj7+8Nms9ntL8dtjx49MHz4cPj6+irv/fXXX9i7d6/dDLLNmzdHQUGBS78bROQYE9yIqtncuXMxduxY7Nq1S7mxqdVqDBkyBC+//DJmzpyJwYMHw9/fXzlGTnaJi4tTXvv5+SEtLa3ay7dv3z4AUGZu6datGwIDA6v9OkT13a2KdbmiO3z4cGWJQlEUce3aNfz4448AAB8fH0RHRwMou2NdLqdOp0OfPn3QtWtX5b0LFy4gJSXF7ppE5Nhff/2FU6dOKTE9aNAgvP766wgLC1P2UalUSsz5+PhgwoQJGD9+vN159uzZA5PJpMStHHuDBg1C69atIYoiBEFAdnY2Nm7ciIsXL7pcRnna9NDQUGV5Qnk02oEDB2AwGLjcEZELaireS5KPlxPmrVYrAgMDERwcbLefJElKJ/vkyZOVJHlBEFBYWIjk5GRlX71ej1dffRX33HNPpT8/UUNxK2K95LbNmzdjy5YtyvannnoKrVu3BuC4Pi4/kNPr9Rg6dCjuuusuAEUPza1WKw4dOoSMjIxKfX6ihkB+SLVhwwakpaUp8T5kyBC8/fbbdvEu18OBohgLDg7GzJkzcffddyvvA8CyZcuwbt26KpWL/XRE1e9WxTv76ohqn81mQ0xMDICiWGvRogXat2+vvHZEjqdevXrh8ccfh5+fHywWC3Q6HWw2G+bOnYu8vDzl+CZNmmDo0KGIiIhQznH48GFs3LgRRqMRgiC4FKPyPnI93mQywWq1Ijc3F8DN3y4icq4mElod/VbIx1+6dEnZJt/v5WuUnPmtadOmysyR8uxuslatWuHbb7/FN998Ax8fHya0ElUBn2oRVZPisy4U39apUyc8//zzeP7553HnnXcqleDiFV6r1QqbzaY0ri0WCwoKChAaGlpt5VOpVLh69SqOHDkC4OasLb179y5VHiJy7lbHutxADg8Px8iRI5VRKHKlW61Ww2QyVXhK4zZt2qBVq1ZQqVTKMkq7du1SrklEzskdZ3InedeuXSGKot1orJI0Gg0mTJgADw8PmM1mAEUdWVqtVvmdkOM9NDQUY8eOtRsJ/tdff2HXrl0oKCgAULH79l133QVPT0+YzWZIkoScnBylDERUtpqK95Lkurk8U4QoilCr1UrMy4o/PDt79qzTDvupU6di9+7dypJInLGRqGy3KtZlBQUF+PLLL2G1WiGKItq0aYP+/fsr75dXHw8ICMCQIUMQHBysjCBPSUnhQzGiMsht5r/++gvAzYfeAwcOLDPeiz/cfvjhh9G0aVPlITgAzJs3T1mKqKLlYT8dUc24VfHOvjqi2mWz2WAymZR+eqvVCkEQyk0UL54A07t3bzz11FMAivrqBUHArl27sHLlSrvfimHDhqFbt25Ke6GwsBDbtm2rUIzKvxHe3t5o2rSp8nr//v1KeYjIuZpMaHV0LZPJZDeDW/HnesDNvvwLFy5gwYIF+Pnnn3H16lW76wqCgDvuuAOrV6/GwIEDlQFqRFR5THAjqmbPPPMMQkJC4O/vj3/84x94+eWXcd9996FNmzbKNMSAfYVXpVLBbDbjypUrAIo6yrVard0MDNVhzZo1yrIHFosFfn5+6NevX6nyEFH5bmWsy+e677770LJlS0iSpGyzWCzw9fWtUOe3vPxCnz59lMq0IAhISkpSRowRUWlynCUmJgIADAYDACijv8qbEc3T0xNarVaZnSEjIwNms9nhPfj2229Hp06doFKplA74NWvW4Pjx4wAq1nHm4eFhNwr80KFDpZJmiMjerYz34ueTR4bKywr7+PjY3eNzcnKwa9cuzJ8/H2+//TZiY2OVeoG8X5cuXfDaa6/B09MTFovF7mEbEdm71bEuS0hIQFJSErRaLSwWC0JCQuDl5VWhsjdu3BgBAQHKCPIzZ87g+vXrFToHUUNz+vRpnD9/HsDN5YWHDx8O4GaCa1mioqLwyCOPAACMRiNUKhXi4uKwfPlypKamVrg87Kcjqjm3Kt7ZV0dUe0RRRGFhoZJQIggCPD09K3xPnjZtGrp27QqLxaIMOlu4cKHyGwIULU14xx13oGXLlsq28+fP4/fff1eu7+rAsqZNm6KgoEDZPygoCNnZ2UxuJyrDrUxoBYp+X7RarTKTqlwG4GY9Ijk5GatXr8ann36KTz75BIcPH7arw8t1g4KCAmVVJ3lpYiKqPPZyE1UTuTMKAL7++mu8+eabePjhh9GjRw+7mZ4cdVBZrVZ4eHigcePGAACz2QyTyYSgoCAAVR+1KY/o3r17NwRBUMowaNAg5RqOFL+uyWRCenp6lcpBVB/URqyLogir1QpPT0889NBDducWRREpKSk4d+6cco3yyA/qOnbsiODgYOXht8ViUaZQJqLS5NhLSEgAUJSkqlarlaTV8kaKRUREwGq1KrO8dOrUSXkgXvwaNpsNOp0OU6ZMQWhoqNJ4PnnyJNatW4ekpCS785anUaNGyr5arRZarVb5DETk2K2I9+LkOPf09FS2BQQEKIkyJpMJx48fx8KFCzF37lz89ttvdkucF39Id/ToUSxduhQGgwFqtZoPyInKcKtjXZaeno7CwkLlOHlp0rJmjCupZcuW0Ov1ygNxHx8fJrATlcNsNtslirRs2RKenp4V6nfr378/OnbsaJdA/ueff2Lfvn0uxzD76Yhq3q2Kd/bVEVUfOT5djVNJkhAQEIA2bdoor69cuaK0q105jxzLr776KtRqNYxGI9RqNRITE7F8+XK735EBAwZg+PDh8PX1Vc4fExODxYsXAyh/cIx8jNVqtbvfp6enw8/Pj213ojLcyoRWAMoqKPJvAgA0a9YMQNHAtl27duHLL7/EnDlzsGXLFphMJuW4tm3bIjIyUjnX1q1bsW3bNuTn51fqsxORPSa4EVUj+SbXoUMHjBs3rtzMcZlKpYLRaFRGW8sd4vHx8QCqPmpTFEVcv34dp0+fVpY1AqCMCi2p5MxT8fHx+PXXX/HNN9/g2LFjVSoLUX1QG7Eux+3EiRPRv39/ZfSHvH3ZsmXKNVztBMjOzkZWVpbyeUwmk8PpmImoiJyActtttwGA0uGckZGhNGIdkWP7yJEjMJvNyiitkSNHKvsUj1s5rocNG4ahQ4faJc9u2bIF69evtztvWeQH5y1atABQFOcFBQXw9/cv91iihuxWxbus5AxuQNHDMo1Gg0uXLmHp0qX48MMP8d///hcXLlxQ9lGr1XjhhRdw1113QaPRKOf59ddflRmpiMi5Wx3rMrn+L3eonzhxAoBrM8rI5bZYLHaj1wsKChAQEODS8UQNlTx7urzUUHZ2doWTwX19fdGjRw8ARXVttVqNvLw8rFy5Uont8rCfjqjm3cp4Z18dUfWQ47PkDEhl7Z+fn68kkWo0GmRmZmLTpk0uX1NeDrhTp0544IEH7N5bvnw5Tp06pZRDpVJh/PjxGDRokHL9nJwc/Pbbb9i8eTOAsmdxs9lsEAQBqampdsk0zZs3B+BaMixRfeHuCa1yAp3ZbFaOS0tLQ1xcHBYtWqQMPi0+4CQiIgJffvklVq1ahTFjxijtdUmSsHbtWpw5c8alz0pEZWOCG5GLSt5sXblZunpjttls8PDwULK/zWYzzGaz8vC5OqYm3rFjB3Jzc5WRX1qtFr1793a4r9yASE1Nxbp16/D555/jww8/xMKFC5llTvWeu8a6IAhKI3fmzJkIDg4GcLNSfvr0aaxZs0a5TnnlAIC8vDy76ZCbNGkCb29vTodODUZF410URdhsNoSGhkKr1SpxuWXLFrvpyouTZ2fIyMjA0qVLYTabYbVaMXbsWLv7cMlOdjlO77vvPkRHRyvb09LS8Mcff2DHjh0Ayu/8khNp5Zlp1Go1vL29YTQayzyOqL5x53gvfqwkScr7NpsNa9euxaeffoqPPvoIMTExdsdOnDgR27Ztwz/+8Q9Mnz4dTZo0UWZyOn/+PFavXs3ZnKjBcfdYl8vTsWNHAFDux4WFhXYPz8r7jKIoIjMzE5cvX1au1aRJE0RGRrq8NBJRXVeReJffa9q0KbRaLYxGIwRBgE6nq3BCuJeXl5JMqlarlZjbvXs39u3b53I9m/10RK6rC/HOvjqiytuzZw+++OILvP/++3jkkUfwySef4H//+x8uXrxY5qATm80GLy8vpd9MTkI7evQoDAaDywmtcnw9/vjjaNKkCSwWCzQaDQoLC/Hrr7+ioKBAOVfLli0xadIkdOjQAZIkQaPRIDc3F2+//TauXLmiJLiWNbDtwIEDsFqtSvKLnODGZQupIXHnhFZZQkICCgoKoFKpIAgCYmNj8fbbbzscfPr8889j69atGDZsGICigett27ZV9jl9+jS2bNnCGZiJqgET3IhckJeXB6vViuTkZOTn5yMnJ8fupuusgepqBVoURZjNZmRlZQEoGsVtsVhw9uzZCp3HGUmSlBEkciW5d+/eiIiIcLh/Xl4e9u7di6+//hpz587FunXrlIb5qlWrEBsbW6XyELkrd491OX67deuGKVOmKNOhC4KArKwsfPvtt8jOzi53ZKjcmD548CCMRqOyr9yYZqcZNQSViXf5gXK3bt2UGVcAYP/+/Vi2bJmynFnxKclFUYQgCNi4cSNOnjypdKyLoog33ngDH374IT7//HPs3r3b7kG6HKetW7fGXXfdhZYtWyrvnTt3DvPmzVPivTwFBQVK41m+n8uJtkQNgbvHOwDl2OzsbOU+fOjQIXz00UfYuHEjCgsLlet06dIFv/32G+bMmYOQkBCYzWa0bt0a48ePh16vV45fsmRJqc45ovqsLsS6XB5PT0+0b99e2X758mUcO3ZMWZa4LPL7hw4dQlZWFjQaDSRJQrNmzZRZaojqu4rGu/yewWBAo0aNABTFc2JiIgwGg8NjHJEfqHXq1AlAUd3aZrMpdfJVq1a5lEDDfjoi19WVeGdfHVHFnThxAtOnT8ejjz6Kb7/9Fj/++CN27dqFb775Bv/+978xffp0PPfcc8pyvyXJ8T5w4EDo9XpYLBZYrVZcuHBBGQjiClEUYbVaERAQgIceegjAzZhbv369MluqfO/t3r07Hn/8cQBFA9i1Wi3S0tLw3nvvYe/evXbHywPZ5GNTUlKwatUqAFDa+fKMcEQNQV1IaJX5+/srvyuSJOHPP/90OPh0586dePTRRwHcXE2la9euGDZsmN0s6+vWrePsy0TVwLW1D4gaqMzMTPz666+IjY1FSkoKLly4gICAAGi1WrRv3x79+/fHiBEj4O3tDQDKjAkVZbPZoNFo0KZNG8TExChLnHh5eVXpvLKsrCxcuHBBGWkmCAKGDBkCoKhSLk95brFYcO7cOezcuRPr169Xkm6Aopt+YGAg/vnPf6Jbt26VLguRO6pLsS7v8+CDD+Ly5cvYsGGDEsdnz57F7Nmz8e677zp9uCXve+HCBSxatAgAlBFj8u9CVX5viNxdVeJdbrwOHjwY3bp1w86dO6FWq2GxWLB48WKcOXMGb775Jpo0aQJJkmAymXD9+nV8/vnnWL16NYCiB1gmk0kZxS0TRRFRUVF47rnn0LNnTwQGBsJkMkGr1WLkyJFIT0/HF198oTTYT5w4gXfeeQfvvvuuMhV7SXK8r127FqdOnVJmqenXrx+8vb2rXL8gcnd1Kd4tFgsMBgPy8/OVh19paWl2D9+Cg4Px0ksv4c4771TKK3foAcC9996Lv/76C8eOHYNKpUJWVhZWrFiBli1burycOlFdVNdiXa1WIywsDM2aNcO5c+cgSRLy8/Oxfv16tGrVCr169bKbzbE4+fiEhAR88803yoN6AJgwYYJyXaL6qqpt9w4dOijJrB4eHjAajViyZAk6d+7s0vXle+65c+eUpUWDgoJw/fp1pY6+d+9etGrVqszzsJ+OqHx1Kd6tVitUKhX76ohclJeXh//+97/44YcflG1y3Ve+N4qiiKysLGzduhWxsbG4++678dhjjyn3yOLH+Pr6ol27djhy5AgA4MiRIzh79qzd7EnlkWPsgQcewNKlS3H27FnodDoYDAZ899136Nu3r3JtrVaLUaNG4cknn8R///tfJaFl+/btOH/+PN544w307t0ber3e7nNlZmbigw8+wNGjR5U2x5133om+fftW9qskqjNOnDiBDz/8EIcPH4Yoikoy265duwAA//vf/xAdHY1//vOfyjKkxRVPaP3555+VBFE5odXVeC+Z0Pruu+/aJbROnTpVicnExESYTCalTiCXQR58+uqrryoJd1arFaIoQqPRKL9h48ePx6FDh7Bjxw6o1WokJydjxYoVaNGiBQefE1UBE9yInPj222/x9ddfIy8vT3kgLAgCcnNzIYoiLly4gD///BPt2rXDqFGj8Nhjj1V6CmFRFJUObEEQoFarYTabcerUKYwcObLKDdiUlBRcu3ZNOb9Wq1WWRJEr5YmJidi3bx/WrVuHffv2KZ+3eDb7s88+W+XZ5IjcTV2LdfkcgYGBePDBB1FQUIBt27Yp7//xxx/Q6XS499570aFDBwCwe0Amx/zixYuRmpoKrVYLk8mEsWPHKr8LRPVVdcS73HH91FNPIS4uDlevXgVQNOpy586duPvuu9GiRQtoNBr4+Phgx44ddsuIygkpxe+xarUagiAgPj4eb7zxBnr16oV58+YpnfFeXl644447cP36dSxcuFA515o1a+Dr64v77rtPafiXjPfMzEysXr3a7v49cOBAAFWfIZbIndW1eJeXD/b19VUeZhVPXClZFy/+8Fu+VnBwMCZPnoz4+Hjk5eUBAH7//XcMHDgQY8eOZcxTvVQXYx0oegg3ePBgHDhwQJllNSYmBvPnz0dQUJAyc6uc7C6Tl1RZuHAhzp49qzwYGz9+PEaNGlVzXzSRG6hqvFutVmi1Wtx1113497//rcTxH3/8gRkzZqBNmzbK74Ez8sOqixcvQpIk6HQ69OjRA3/99Rdyc3MBFLW177nnHmXZMUfYT0dUtroY71arlX11RC7IzMzEF198gWXLltltDwkJQW5uLvz9/ZGRkaEsL6xWq3H16lV88cUXyM3NxQMPPIDIyEi7GG7Tpg2aN2+O48ePQxAEWCwWrFq1CmPGjHF5hmNBEOzaBU899RQsFgtEUcTu3btx4MAB9OrVS0liEQQBTz/9NNLT07FhwwZkZmZCq9Xi6tWrePHFF9GqVSuMHj0aQUFB8PT0xOnTp7FgwQJlWXGLxQIvLy+MHz8eAJwOcCGq6+pqQisAdO7cGb1798b27dsBFNUvyht8Wvz8jRs3xtixYxEfH6/MKnn27FkmrRNVkSBxbmMiOydPnsRHH32Effv2lbmfnGQiZ3vffffduPfeexEdHV1uA7kkufL6v//9D//+97+Vm/q9996LV155xa5DuzJ+/vlnvPfee0rnd2BgIHbv3g1BEJCRkYHDhw9jw4YN2L59u9JAl40ePRqzZs1CeHh4lcpA5G7qS6yfPn0azz//POLj45UYB4CIiAjMmDEDvXr1QqtWrZCfnw+9Xo+//voLn332md3U7kFBQfjvf/+LLl26sDFN9VJNxfuKFSvw66+/4vjx48roLEmSnC530rhxY4SEhCAyMhI+Pj5ITEzE5cuXkZCQoDyskuP473//O/7+97/bzbpkNpsxY8YMHDp0SNmmUqnQrFkzPPnkk+jatSsaNWqkPAzftm0bPvjgA7vzDxkyBJ999hl0Ol0VvlEi91VX433GjBkICQlBfHw8xowZo5ynZF28ZGKbTL5/WywWPP/889i4cSOAoqVO5syZwxncqN6pq7Fe/N5usVgwa9YsbNq0SZmhVZIkdOjQATNnzsSIESOU65jNZmg0GmzduhWffvopzp8/r7zn7++PTz/9FH379mVdnuql6op3+WH09evXcf/99+PKlStKEsnAgQPx7bffulSec+fO4dFHH0VycjJ69eqFt956C8899xzOnz8PlUoFi8WCd999F5MnT3baZ8B+OiLH6nK8y/dqgH11RGX55Zdf8PHHHyuDsiIjIzFq1Ch07doVPXv2hM1mQ1JSEv7880/8+eefSEtLU4718PBAz5498fXXXyv3V/leu2PHDsycORPAzYSTefPmYcSIEZVaweD+++/H4cOHlRkgJ0yYgLlz5yrvy+dMTU3Fhg0b8N5775Wq/8vkxJniOnbsiFmzZqF79+4V+wKJ6pDiCa3Flx8NDQ11mNAq31sBYMaMGQ4TWm02G15//XWsWrVK6Qfr168fvvrqK5cTWoGbvx2bN2/GU089pTyrs9ls+PHHH9GrVy8ARTPPTZ48GUD5g0+Lk+/deXl5ePnll7F9+3Y88sgjHKBCVA2Y4EaEmzea5ORkvP/++9iyZYsysiswMBA9e/ZEREQEOnXqhMuXLyM2NhZHjhxBSkqKcg6VSoWoqCgsXLgQoaGhlSrH5s2b8dJLL6GwsBCSJGH06NH4/PPPK92IlY+bO3cuFixYAEEQYLPZMGzYMHz55ZfYt28fduzYgY0bNyqj1GVt27bFa6+9ptzEieqD+hrrhw8fxquvvorLly9DkiRoNBplanQ/Pz+EhYVBr9ejoKDA7mEYUNS59vbbb2PQoEGV+ixE7qom410+t9VqxbVr1/DBBx/g4MGDyMnJAQB4enqioKBA6dDy8/PD8OHDMXToUHTs2BGNGjVSzpWRkYEff/wRmzdvxoULF5SOM71ejzfeeAO33347tFqt0mCOi4vDf/7zH2zYsAEA7OK9cePGiIiIQFBQEK5cuYKTJ0/afSctW7bEhx9+qIwYJ6ov6ku8jx07Fnq9Hq+++ip27tyJTz/9FD179gRQ1PEmL43kjNzBvn37dvz000947LHHWJeneqW+xLp8bweA48ePY968edi5c2epDv0OHTrgtttug1qthlqtxrFjx3D8+HG77yQyMhLvvvsu+vTpUzNfOlEtqcl4t9ls+OGHH/Dhhx/axd2sWbMwadIkeHt7w2KxQKVSKe1z+QFYQUEB5syZg+XLl8NqteKZZ57BzJkzMW/ePGUGZpPJhM6dO2PRokWlHnixn46otPoU78WTaNhXR1TatWvXcPfddyMrKwsAoNfr8a9//Qvjxo1TBoEUTxY9duwYFi5ciLVr1wKAUhefPn06/v73vyMsLMwu7qZNm4aYmBjo9XoUFhaiXbt2WLlyZYXKKJ9v06ZNePrpp5XtnTp1wuzZsx0unQgAixYtwp9//omjR48qvycqlcpuMBoABAQEoH///njwwQfRuXNnJrpQvVZfElqXLVuG/v37K+3+shLbipPLcvbsWQQHByM4OLhC5SIix5jgRnSDxWLBW2+9heXLlyvb2rZti6lTp2LgwIGIjIy02z8uLg7/+9//lP3lRu3IkSPxxBNPVOrh8Y4dO5RpVy0WCyIiIrBkyRKEhIRU6jPJN88333wTv/32m3LeDh06YOTIkdi6dStOnDhhd4yvry9eeOEFTJkypVLXJHJ39THWgaIljRYtWoT169cDgFLplxvQxZcykT9Hnz59MGPGDPTr16/S1yVyZzUd7/J9VpIkXLt2Denp6SgsLERMTAy+++47ZXTmtGnTMG3aNERFRSnHWq1Wu1GdcXFxmDZtGnJycqBSqWC1WtGrVy+8/fbbaNGihd11r127hu+//x6LFi0C4Fq8DxkyBM888wxatWpV1a+VyC3Vh3h/44030Lp1a7vOOHnmqMouj05U39SHWC95bz9+/Dg+/vhj7N+/H4B98npJ8j1evrdPnz4dPXr0qMI3SuS+ajLeU1NT8fzzzyMmJkaZVcXb2xvjxo3DM888Y/fwSX4wnZmZiQ8++AB//PEHAMDHxwfff/89oqOjkZGRgcGDB8NsNkMQBDRq1AjvvPMOBgwYYFdG9tMROVYf4x1gXx2RTI6tn376CbNnz4ZGo1ESRx9//PFS+xWXkpKC2bNnY+PGjUo92cPDA08++STuv/9+eHt7KysZHDhwANOnT7dLaP2///s/TJ06tcIrsVy5cgUvvfQSDh8+DKBo1uR58+aVqnsXbz9kZWXhm2++wd69e3Hp0iUYjUZlmWVPT0+MHDkSAwcOxJAhQ+Dt7V2Fb5TI/dXHhFZXBp8SUc0rP72UqIGQp/8vbvr06Zg4cWKp7HCLxYKWLVvivffeQ3h4OP773/8qHdCbNm2Ct7c3nnzySTRp0qRC2eLdunVDaGgorl+/DqCoUZuenl7ppBdRFGEymZCenm63/erVq/jvf/9bqiE9Y8YMPP/881VeEpXIndW3WJcb/j179kTnzp3RoUMH/Pnnn6VGfxbXs2dPDBgwAHfccQciIiIqfE2iuqKm412OeUEQ0LhxYzRu3BiJiYlYsmSJ8gB80qRJePnll5VRXXLMFu9UEwQBbdu2xcsvv4y5c+cqs8UcOHAAp0+fRosWLew6+SIiIvDGG2+gXbt2+PnnnxEbGwvAvrNcvrdHR0dj8ODBuOeeexAWFlb9XzKRm6gP8R4bG2uX4CaPCGVyG9FN9SHW5Xu7XM7o6Gh8+OGH+Pjjj7Fp0yYUFBQo51GpVLDZbMosT5IkoVOnThg8eDAmT57MezvVazUZ7yEhIXjxxRdx//33w2AwQBRF5OXl4bfffsOxY8fQo0cPJbmksLAQ586dw48//mi3vNiDDz6I6Oho2Gw2BAYGYtKkSfj1118hSRLS09ORmppa6jOxn47IsfoW7+yrI7In12Xl5YclSYKXlxeGDx8O4GaSiaPZzMLCwvDuu+8iMTERsbGxEAQBRqMRK1asQFBQECZNmgStVgtJktCrVy+MGDECmzdvVpJjPvnkE9x5553Q6/UVWj0lMDAQ0dHRyoxsWVlZOHr0KHr06GH3HKD44LSAgAC8/PLLKCwsREpKCg4dOgS9Xg+TyYS+ffvC39+/QksoEtVFcpxt2bIFWVlZSkLrY489hgcffNBuPzm5DQA6d+6Ml19+GRaLBRs3blRidcmSJQgODi6V0Pr0009j+vTpMBgMUKvVOHv2LJYsWVKhhFY5ftu3b49u3bopCa2JiYlKG7449s8RuQcmuBHd8L///Q95eXnKyOr77rsPkyZNAlC6w1qtVivbnn76aSQkJGDjxo0wGo0AgG3btiEkJATPP/98hTK5DQYD2rZtqyS9xMfHK43pykyrKo/slkeiyR1k+fn5sNlsyrZBgwbh1VdfRbNmzSp0fqK6qL7FulzRt9ls0Gq1ePTRR3HPPfcgLi4OGzduRFJSEgoKCuDn5wedTodx48ahVatWCA8P5xToVO/VRrwvW7YMqampEAQBXl5emDp1qt1SJY7iTj7fpEmTsH37dmzatEkZgb5y5UrcfvvtdsfJ55o8eTKGDRuGo0ePYvXq1Thz5gw0Gg0sFgsiIyMxcuRI9OjRA02bNuXIMqr36lO8y0kzrix3QNTQ1KdYl8tps9kQGhqK2bNnY8KECdi8eTO2bdsGs9mMtLQ0BAcHIz8/H506dcLo0aPRp08fNG/enPd2qvdqMt6tViuio6Mxa9YsfP/997h27RrUajWsVitiY2MRGxuLJUuWwGKxKLNCFDdkyBBMmzYNwM1479GjB1atWoXCwkIYjUYcOXIEd911V6mZWdlPR1RafYt39tUR2ZMkCQUFBbhy5QqAolkbg4OD0bp1awAos15rtVrh5+eHZ555Bp988omSKBofH4/ff/8dt912G9q1aweLxQKNRoOZM2diy5YtMBgM8PDwQHZ2Nj777DPMmjWrQmX29PREs2bNlDi2Wq04cuSI08SZ4p9Bq9WiWbNmvI9Tg1SfE1qJyD2wx5wIQHp6Ok6ePKk8FNbpdBg9ejQA58km8k1aFEVMmzYNiYmJdje/lStXonPnzhg+fLjL2eIhISHKgyx5yvSNGzeiS5culbqBymX09vZWrl98uZNmzZph1qxZGDx4cIXPTVQX1ddYB+wb0X5+fujZsyd69uypVOQNBgN0Ol2lzk1UF9VGvMfHxytLpEiShPbt2yM6OhpA2Z11giAo55syZQo2bdoEq9UKURRx9uxZXLp0Cc2bN1f2L36ugIAADB8+HMOHD0dBQQH0ej1SUlIQHh5e+S+PqI6pz/FORDfV11iXz6NWq9GvXz/069cP//znP5GVlYXMzEx4e3vDarWWWh6FqD67VfF+3333ISIiwu6BuU6ng8ViAVAUl8WXDA4ICMCkSZPw+OOPw9vb2+7BWXBwMDw8PGA2m2GxWHDy5Enk5ubCx8enVBnZT0d0U32Nd4B9dUQyOWYvX76sbJPrwvLM5c7I98uhQ4fi4sWLWLBggTJr4qlTp7B48WK888470Gg0sNls6NChAx588EH89NNPSiL5woULMX78eKUeXx45RkeMGIE5c+YoA9M1Go2SiFvWMwDO8kQNWUNIaCWi2sWUUyIAGRkZSEtLg8VigSRJCA8PR/v27ZUOaGfk96KjozFkyBB4eXkplebr16/ju+++g8lkUpYVKYvVagUAjBgxAgBgMpkAFDXyc3NzK/W5JEmCKIpo1KiRcn6z2QydTodXXnkF69evZ6cZNSj1NdZLKl7plsvJDjNqaG5lvMvvX7lyBWlpaUoMtm/fHsDNOC+LfEzfvn3RsmVLWK1WpVFd1pJExT+LXq+HIAhMbqMGp6HEO1FD1xBiXb6ur68voqKi0LlzZ7Rs2ZLJbdTg1HS8a7VaWCwWiKKIoUOH4j//+Q+eeuopBAQEwGAwwGKxwGKxwGazKQ/Qxo8fj1dffRXTp08vlewCAL1797ZLltFqtcpMUzL20xGVVl/jvST21VF9I/8dl/yvs30LCgoQGRmpxMKlS5dgNptdmrlcrqOPHz8ew4YNU7YXFhZix44d2Lx5s10ZHnvsMYSEhMBkMkGv1wMAPvroI2RlZbn02eTlwgsLC+0GqsfFxcFmszHhhagM5SW0lqV4QuvEiRMREhKivCcntAIoldAK3Iz/hQsX4vjx4y7P3iYfN2LECOh0OqWtXzyhlYjcCxPciACcOHECwM2bpyAIdqMpyyJXrseNGwdvb2+78xw5cgQ//PCDS2WQj4mKikJ4eLhy3rNnzyqV8MoaO3YsgKLEmqlTp2LXrl2YMWNGlc5JVBfV91h3hNMnU0N1K+NdbjBfuHABQFEDGICytElFElaysrLg4eEBURShVqtx7do1ZaRoebiUCTVUDTHeiRqihhDrvJcTFbkV8V68rdy8eXM89dRTWLlyJb7++mvMmjULU6ZMwd/+9jc8/fTTWLp0KV577TWMHz8eoaGhSpmKX9NoNKJVq1bK9sTERBQUFDiMa/bTEd1U3+PdEfbVUX0g/70XFBTYvXa2r7e3N9LT02G1WpV9Y2NjXbqWHDPh4eEYN26c3YzKqampWLx4MQoKCqBSqZTZop577jkARYnkoijiwIEDWLJkCXJycgCUnZBXvMyZmZlKUk6zZs0gimK5xxLVN0xoJSJ3wpo01WvlZVYXHx0tT4MOFK3zLS8pUh65QhsVFYV+/foBKLohypX0RYsW4cqVKxBFsdyZnYCipQu9vb0hCAJUKhViY2Nx4MABAHDp+OLkMvj5+eHdd9/FihUr8H//93+lpksnqusaeqwTNSTuGO/yNYODgwEARqMRwM2H3xWJ6eDgYBgMBmX0eHh4OAIDA10+nqg+YbwTNQyMdaKGwx3jvbiwsDAMHjwYf/vb3/DOO+/gpZdewpNPPol27dohMDDQaVKKKIrQaDRITk5WPkOjRo3g4+Njdw3201FD0tDjnai+io2NxTfffIM5c+Zg6tSpmDlzJh566CF8+eWX2LdvnxK7xRNg5OW5e/XqpbyXlJTkcrJZ8X06d+6M22+/XZnByWaz4dSpU1i5ciWAm8lwd999NwYMGKDM4AgAP/30EzZu3AiLxaIktZTl+PHjMBgMSsKLnEjLQSnU0DChlYjcCRPcqN5JSkrCm2++CaBoxFZZNx/5xmowGGA2m5XO6tOnTyMrK6vc42XyPt26dbObslQQBFy/ft3haDFnmjVrhubNm0OSJKhUKgiCgK1bt7p8vCNqtRqTJ09Ghw4dKnU8kTtirBM1HO4e7/I1CwsLIQiCMsvL6tWrYTQaXUp8la939uxZxMfHK6PDIiMjuWwJNSiMd8Y7NQyMdcY6NRzuHu+ucCVpJT09HYWFhXYzU4ii6PAa7Kej+orxzv48qr9SUlLw6quvYtq0afjqq6+wYMECHDt2DPv378fevXvxn//8B48++igeffRRHDt2TIlxm80GURRhNpsRFhYGtVoNtVoNg8GAjRs3AnAtwU0+n06nw4ABAzBkyBAARbGXk5ODdevWISkpCaIoKksMzpo1C82aNVOSbNLS0vDTTz9hzZo1ZV5XTpzbsmWLXYJc8QQ9ooaACa1MaCVyR6xxU72ycuVKDBs2DL/99hu++OILAGU3SuUbWu/eveHr6wuTyaQ0nnft2gXAtZuXfMPMycmB1WotdaPctGkTjh07Vm555MrApEmTAAAmkwmSJCExMREJCQnlloOooWCsEzUcdSHezWYzAGDYsGFQqVQwmUzQarXIzs7G999/79LnlMu0ceNG2Gw25UH6wIEDa2T5YiJ3xHhnvFPDwFhnrFPDURfivazyyNdyJWklISEBycnJyvl69Ojh0jJMRPUF453xTvXXggULMHLkSKxYsQI5OTkoLCy0e18QBKjValgsFuzevRsvvvgiFi1aBKAopqxWKzQaDdq3b68kmwBFg0euXbvm8ooosubNm2PkyJEICAhQZl++cOGCsnShVquFzWZDy5Yt8cgjjyiDzAVBwLlz5/D+++/j8OHDyvnkur/cZy+KIk6ePIkdO3ZAEASYTCb4+fmhY8eOyuclqs+Y0MqEViJ3xgQ3qhfkym96erpSUf3yyy+RkpJiN3KrJPkmqVKp0Lt3bwBFldjc3FycOHFCWaO7vBuYfH556lJvb2/4+voqMzNlZmbijz/+AFB2I1ke0R0eHo727dsr248ePYpr166VWQaihoCxTtRw1KV412g0sFqtCAoKUhJX5fN/9dVXiIuLU34TSnbYyY1oADh48CBWrVoFoGgUOwAMGjSo3O+KqK5jvDPeqWFgrDPWqeGoS/HuqO0ul99qtSI7O1t5cFbyuvJD9fT0dHz55ZewWq0QRRF6vR7jxo0r+0siqicY74x3qn/kv/8rV65g1qxZ+Pe//63EBgAEBwfDz88PnTp1QkREBHQ6nRKDoigiISEB8+bNw9KlSwHcjLM777wT4eHhMJvNStLL8uXLleNcLZsgCOjRowcGDx4MoOj3JDMzEzExMbh69ardZ7jjjjswffp0BAUFQZIkeHh4IDs7G6+88go++eQT5OfnKwNR5D77DRs24IknnkBKSgpEUYRKpcKjjz6K1q1bV/5LJaojmNDKhFYid8cEN6oX5MpvWloaAMDDwwOSJGHOnDl27zsTGBiINm3awNPTU9m2d+9enDhxAkD5NzC54rt//34AQEREBF566SUARTdJs9mMY8eO4fjx4wDKb5hHREQgKipKqTxnZ2djy5YtAFybJp2ovmKsEzUcdS3ei0+JHhAQALPZDJ1OB7PZjNdeew27d+8uVW5JkpRlTI4fP453330XV69ehVqthk6nw4svvoh27dqV91UR1XmMd8Y7NQyMdcY6NRx1Ld6Lt93lOAaKZqR6+eWXsX79euW68oM1OblFEARs2bIFJ0+ehEqlgsViQceOHdGqVSuniT1E9QnjnfFO9Y8cd8uWLcOWLVuUfurg4GBMmTIFL774IpYtW4alS5di6dKlmD9/PsaMGQO9Xg+bzQa1Wo3MzEx89NFHyMjIgEajgclkgiiKuOeeewAU9X1bLBbs3bsXFy9eVLa5WraIiAj069cPjRo1UuL62LFjiIuLA3BzmWStVou7774bL7zwAgDAaDQCKEre++6773DHHXfg6aefxltvvYV58+Zh8uTJePbZZ5Geng6g6Heka9euTGSleosJrUxoJaprmOBG9YJ888rPzwdQtNyfIAhYt24dDh48CEEQlBtuSfLNdvDgwWjcuDGAohtkXFwc/vjjD5w/fx4AnDZSJUmCJElYtWoV9uzZAwAICQnBgAED0L9/f2W/ixcv4vjx47DZbGU2zCVJgq+vL3r27AmbzabcZBcvXqzcYJn4Qg0VY52o4air8d6+fXs89NBDAKBMa3706FG88cYb+OabbxAXF6d0smdlZSExMRH/93//hylTpiA2NlY5rm3bthg+fHjlv0CiOoTxzninhoGxzlinhqOuxrt8rby8PPzrX//Ca6+9hu3bt+PTTz/F0qVLlYfz8sxQWVlZeOONN/Dmm28qyyOGhITgySefhKenp9LOJ6rPGO+Md6pf5MTOBQsWYP78+cjJyQEAREZG4vHHH8eTTz6JCRMmIDIyEhaLBUFBQejduzfmzJmDl156CXq9HhaLRUks+eyzzwBASSrp0aMHmjRposT/xYsXlQHfFUl6AYCePXuiQ4cOAIriOSUlRZmpqXi8a7Va3HXXXZg1axbatm2rlEcURSQlJWHz5s1YsmQJ5s2bpyTXAoBer8c999yDr776Co0aNar0d0rkzpjQyoRWorqGCW5UL8gNZXmkltzwBID3338fAKBWqx0eK1eao6OjMWDAAHh5eSk3yK1bt2L+/PkwmUzK+eTpS4tf+/Lly1i1ahVUKhVEUcSkSZMQFhaG22+/Xam4FxQU4OjRo04b9CVNmjQJvr6+MJlM0Ol0sFqt+Oqrr5RrEjVEjHWihqOuxbt8fq1Wi0ceeQR9+vSBxWJRGvrXrl3DJ598gkmTJmHGjBm4//778eyzz2LMmDH45Zdf7K4/fvx4fPfdd2jWrFlVv0aiOoHxzninhoGxzlinhqOuxXtJO3bsUJJl9Ho9kpKS8NZbb2HKlCn47LPP8M0332Du3LkYOXKkMluFbPr06ejTp0/FvzSiOorxznin+kUUReTm5mLt2rXKbEYAMGPGDDz44IMICwsDUBTrxWNbp9PhrrvuwowZMwDcTExdu3Yt4uLilH7uVq1aoW/fvspxWVlZ2Lt3L06fPq2ctzzyuRo1aoSePXtCr9cr5Vy5cqXd7IzAzUSa+++/Hx9++CG6dOkCq9WqJPPJvx/F++Kjo6PxxBNP4Pnnn4ePj08FvkGiuoMJrUxoJaqLmOBG9calS5eQnJwMjUajTB2uVqtx+vRppfFZ3mixBx54AD169FBuhIWFhVizZg1efvll/PXXXwBu3pjlCvr69evx0EMPYc+ePbBarYiOjlYats2bN7ebxnT9+vVKJriz7HRBEGC1WqHX6zF9+nSl3CqVCitXrsTp06eVKdKJGiLGOlHDUZfiXY5XuUH97rvvYvz48RBFUYltnU4Hg8GAmJgYnDhxAgcOHLAbiR4VFYUnnngCr7zyCjvPqMFhvBM1DIx1ooajLsW7fE35AdqQIUPwf//3f9BoNCgsLIQoilCr1Th79izmz5+Pzz//HD/88ANyc3OVc7Vq1Qpz5szBI488Uj1fIFEdwngnql/Wr1+vzHpotVoxZswYPPDAAwBuJps4Gpit1WoxadIkNGnSRKkvW61WJYkUKJoVqnfv3mjSpImy7dixY9i2bRvMZjMEQXApyU3+7bj99tvh6ekJi8UCjUaD5ORkbNy40W5fOZFGpVKhdevW+O677/DVV19h8uTJaNasGaxWKwIDA6HVatG7d2+88sorSowHBgZW8NsjqjuY0FqECa1EdYvjoTNEddCpU6dgNBodTgf+0UcfYcKECdBqtbDZbKUyw+XXTZo0wdSpU1FQUICYmBioVCrYbDasW7cO69atQ/fu3dGlSxfYbDZotVrs27cPx44dU86j1Wrx4IMPKpXeDh06oFGjRjh9+jS0Wi1MJhPWrVuHhx9+uMzsdPkzjB49Gr///ruyjrjVasWqVavQoUMHl7PbieobxjpRw1FX412SJERGRuL1119Hu3bt8Ouvv+Lq1atOl1UJCQnBoEGDMGrUKPTr10/ptCdqSBjvRA0DY52o4air8Q4AXl5eGDVqFN577z388ssvOHr0qLLUkjyLozyTVEBAAPr06YMpU6agV69e1f49EtUFjHei+kGO0bNnzwIomn1RkiRMnDjR7v2y+Pr6omfPnrhy5QqsVisKCwuRmZkJoCjRVa1Wo1+/fti1axeSkpJgtVqRn5+PHTt2oFu3bujbt69Lq5rIMRocHIyxY8fi559/Vn4frl+/7vQYAPD29sbAgQMxcOBAZGZmIisrC2q1GkajEa1atYIkSVxZhRoMOaFVVjKhVRCEMhNa//zzT1y58v/t3WtwVHcdxvFn92yyJCSkCY2BjrGVIAmSgiIFbYVQWiiNNL0ZpkbbFzglzFCFknaw8EIdx3IpBrQjIzKjqdSRcnPaqtBwqUJnQBKqphVaBFJuBSqXIYFssrvnrC8y55DYkCy9kM3+v5932T1nczYzz55z4Nnf/0SnQmtBQYGkK4XW3bt368SJE5KuFFq/8IUveMX4nvLmfvZMmzZNq1ev1rlz5zoVWu+55x5v264Krf/4xz9UW1ururo6HT16VAMHDlRzc7O+9KUv6c4779SECRP0+c9/ntwDfQQFNySFlpYWrV27VlJ7McTv9ysWi3Uajbp8+XLNnz//qo1w9yRaUlKigQMHas6cOTp9+rQkKRgMqq2tTfv27dO+ffs67ed+oyQYDGrevHmaNm2aJHkX0xMnTtT27dtl27Ysy9KpU6cUiUTi+gfuwYMHq7S0VKtXr1ZGRoaqqqr0rW996+P8qYA+jawD5ujLeXdvhrOzs/X4449r+vTpqq2t1Z49e3Ty5Ek1NjZq2LBhamlp0ZQpUzRu3DgNGzZM6enpn8rfEkh05B0wA1kHzNGX897R/fffr7vuuku///3v9cYbb+jQoUO6cOGC0tPTZdu2SktLdccdd+j2229n2gOMRd6B5OFOKv73v//t/WxZljfFKZ4vYg8YMKDTUoHhcFh1dXWSrhTmcnJydPfdd6uhoUGNjY2S2ouymzdvVmFhoXJycuIq07m/5+tf/7o2bNig1tZW2batY8eOSYqvkJedna3s7OwuXxdIZhRaKbQCfRUFN/Rp7s1pXV2d3n77bQUCAUWjUW8M8vLly70b59/+9rd65JFHdPPNN3v7deSewCzL0qhRo1RdXa2amhrV1taqra3Ne05qPzF2HGk6evRoVVRUdLqJdl9vzJgxuvHGG3X27FlJ0smTJ+NupWdkZKikpESZmZl69NFHlZaW9gn95YC+hawD5ki2vDuOo6ysLJWXl+ub3/ymt1xLKBRS//79u/yGO2AK8g6YgawD5ki2vMdiMWVkZKiyslIzZszQ8ePH5ff7df78eRUVFVFihdHIO5B83KX9+vfvL0kKh8OyLMubjNjTv3O7hZYRI0Zo06ZN3pKjKSkpamlp6fRv3iUlJfr73/+u06dPKxQKybZtvfHGGyoqKlJFRUVcZbqORTqfz+d9Dh05ckThcFipqakf+W8BJDsKrZ1fF0Dfwbpn6NPcG9utW7cqGo16N81FRUWqrKzUoEGDFA6HvQvnRYsWddqvO6NHj9ayZcv0k5/8RJMnT/bW9XZvwh3HUXFxsZ5++mk988wzmjJlirdvx/W7HcdRenq6AoGAfD6fjh07posXL/Z40nTfy1e+8hXNnDmTwguMRtYBcyRb3jveWLvPp6SkKDMzk/8Ah/HIO2AGsg6YI9ny3nGflJQUDRkyRLfccotGjx5N2QXGI+9A8nHzc/78eUnthRXLsrR7925JPRdBAoH2mSqnTp2SJK9QGolElJ6e7i116E5euu+++1RcXOy99vvvv69XXnlFb731lqT2Im133M8dd6nDaDQqqb3E4i6LDKBrXRVaw+Fwp0Jrd9y8jRgxQpI+VGjtuH9JSYnuuOMO75rALbRu2bJFUnxluq4KrZK8Qms8rwEgOTDBDX2W28bevHmzNmzYIKn9pFhcXKzS0lJJ0g9+8APNnTtXkUhEfr9ff/3rX7Vr1y6NHz/e+zZJd1JTU1VeXq7y8nIdP35chw8fViQSkeM4ysnJ0ZgxYxSJRLr9JkhBQYF8Pp93ss/IyFBWVlaP33Zxn6M9DtORdcAcyZ536coNO5mH6cg7YAayDpgjmfPOf5gBnZF3IDk5jiPLsjR+/Hi98847ikajsixLjY2Nunz5sleE6W5/dwqU1F54keRNhHJLqm7ORo0apdLSUp04ccIrxe3fv1+rV6/WL37xC1mW1W1e3ccbGxsVCoW8CVLu5EfyDFydu6R4x0KrJO3evVsPPPDARyq0hsNhr9Dq6lhofffdd1VXV9ep0Hrrrbfq1ltv7XLCa0fuZ4FbaA2FQpI6F1rJPGAGCm7os9w1t//0pz9JkoLBoNra2nTfffcpOztbjuNo6tSpuu2221RXV6e0tDSFQiE9++yz2rx5szcetaeTtDvWPD8/X/n5+R96vqcxx+fPn1c4HPbGI/t8PrW0tPDNLyBOZB0wB3kHzEHeATOQdcAc5B0wB3kHkpNbDvnc5z6n7OxsXbhwQY7jaO/evSovL++x4Obu/+abb3o/27bt7dexvOJ+BkyePFnHjx/Xb37zG0ntU6Fqa2v161//WjNnzozrSyRnz55VJBLxXn/48OHX+M4B81BoBdBXkXb0aa+//rq2b98uy7LU1tamgoICTZs2TdKV8cULFy6UJIVCIaWkpKixsVG/+93vOm3TnY5jzaUrY1t7Gs/qbvfBBx+oubnZ+7mgoEDp6ek97g/gCrIOmIO8A+Yg74AZyDpgDvIOmIO8A8mrsLDQK61MmjRJjz76qG644YYe93McRxcvXtSxY8e85YQl6fbbb//Qtm62b7zxRj388MOaNGmSpCtToaqrq7V161ZdvnxZ0pXlEF22bSsWi+ncuXNas2aN95qBQEDjx4//CO8aMMv/F1odx1EkEtHevXt14cKFuPfvWGiVdNVCqyRNnjxZ9957r/d4x0KrFN9UdLfQ6l5HUGgFzEPBDX3axIkTNXToUNm2rezsbFVVVWngwIGS2sehOo6joqIiTZ8+XdKVE+ry5ct16dIlBQIB7yI7Xv+//MjVbojd7fbs2aOWlhZve062wLUj64A5yDtgDvIOmIGsA+Yg74A5yDuQvEaOHKkZM2Zo3bp1WrlypUpLS5WRkdHjfn6/X6dOnVJTU5NisZgsy1JaWpqGDh3abbG0oKBAVVVVysrK8qYuSu0lN7e81nFZY3cylM/n07Zt2/Sf//zHm9Q4YsQIDRky5Jo/XwBTUWgF0NdQcEOfZlmW5s6dK6l9vOltt93W5YXy008/rX79+qm1tVX9+vVTKBTSc889d82/z22EX758WQ0NDZK6bpS72+3fv19r1qyR4ziybVt5eXmaMGHCVfcD0DWyDpiDvAPmIO+AGcg6YA7yDpiDvAPJbcaMGRo5cuQ1Tzysr69XKBSSJK8Am5eX123uYrGYCgoK9OMf/1h5eXneksLvvfeeVqxYoYULF2r79u26fPmyV3Q5c+aMFixYoB/+8Ie6dOmSotGoAoGAHnvsMfXv35/lCoE4UWgF0Ndwhkefd/fdd+v73/++FixYoMzMzE4Xyn6/X9FoVJmZmd4Nt3uie+mll3Tw4EH5/f64RqJL7Tfu58+f19KlSzV9+nRt3LjRa5I7juOdtC3LUjgc1po1a/T+++97J+OysjLl5+d/Um8dMApZB8xB3gFzkHfADGQdMAd5B8xB3oHkFovFrrkQumXLlk77jxs3rseyjPs7pk6dqjlz5qiwsFCxWEypqamSpI0bN2r27Nn69re/rYqKClVWVmrSpEnatGlTp+ObNWuWvvGNb1zT8QKg0Aqgb/HFrvXTCkhA4XBYqampchznQyeyjhfhU6dO1Xvvvae0tDSFQiGNGzdOL7zwQty/p76+Xt/97nfV1tYmScrKylJZWZkee+wx5efnKxKJKCUlRdu2bdNzzz2no0ePevsWFRXp+eef50Ya+BjIOmAO8g6Yg7wDZiDrgDnIO2AO8g5Aai+anjp1Sg8//LCampq8x1atWqWSkpIe93c/LxzH0YEDB/Tkk0/qxIkTchxHKSkpsm37qlOaCgoKVFFRoYqKCiY0Ah/RRym0fuc731F9fb38fr8cx9GDDz6oRYsWxb3/pk2b9MILL+jdd99VMBj0zvFS+7k7GAwqIyNDe/bskW3b8vl8XgnviSee0BNPPHFNxwsgOVBwgxHcNvff/vY3VVZWeuuJ27at559/XpMnT/a26c6BAwf00EMPybIsxWIx7xtm+fn5ysvLU15eno4cOaIDBw502m/IkCFavHixRo4c+em8QQCSyDpgEvIOmIO8A2Yg64A5yDtgDvIOmKO2tlZz5syR3++Xz+fTgAED9Mc//lG5ublxT1hyy7KHDh3Sli1bVFNTo0uXLnW57aBBg/S1r31NZWVlGjt2rPf5AuDTRaEVQG+i4AbjzJw5Uzt37vS+LXbTTTdpx44dkrpvqLsX1q+++qpefPFF/etf/1IgEPAed0eidxQMBlVSUqJZs2bpi1/84qf6vgB0RtYBc5B3wBzkHTADWQfMQd4Bc5B3ILnNmTNHr732mjfNacyYMXrxxRc/1msePnxYO3bs0IEDB7R//37l5uYqFArpzjvv1Fe/+lUNHz5c6enpn9A7ABAvCq0AegsFNxjDtm1ZlqXGxkbde++9kqTU1FSFw2HNmzdPM2fO9LbpinuTHYvF1NjYqJ/+9KdqaGhQc3Oz97h74S5Jo0aN0sSJE/XQQw8pLy/vur1PwHRkHTAHeQfMQd4BM5B1wBzkHTAHeQeS3/Hjx1VWVqZQKOTl8Uc/+pEeeeSRbve7Wrn1ao83NTVpwIABn9hxA/hoKLQC6C0U3GAU90Z58eLFqqmpUb9+/dTa2irLsrRr1y7l5OR0ezPdUXNzsw4ePKiNGzfqnXfekc/nU1tbm/Lz83XPPfdo1KhRuuWWWxiRCvQCsg6Yg7wD5iDvgBnIOmAO8g6Yg7wDycktor388suaP3++gsGgwuGwcnJytH79et10001xvU4oFFJaWtpVn3c/H7qb9gjg+qDQCqA3UXCDUdxxp21tbRo/fryampq8kej333+/lixZ4m0T72tJUmtrq4LBoM6ePavc3NxP+20A6AFZB8xB3gFzkHfADGQdMAd5B8xB3oHkVlVVpT//+c+yLEu2bWvcuHGqqamRbdsKBAKdtu1YZmltbdXbb7+tXbt2qaysTAUFBb1x+ADiQKEVQCKIbxFkIEn4/X5Fo1EFg0E99dRTkqRoNCpJevnll9XQ0OBtE89ruYLBoHw+HzfRQIIg64A5yDtgDvIOmIGsA+Yg74A5yDuQvI4ePao9e/ZIkrdU8IQJE+Tz+TqV29x5K+7SwocOHdLatWtVXV2tVatWacWKFWImC5C43LLZzp07JbWfx2OxmIYOHarBgwd3eQ7vmOnW1lbV19frV7/6lQ4fPnzV3+NOc6XcBqArFNxgHPeCevr06SosLFQkEvGa4s8++2ynbeLFSRZIPGQdMAd5B8xB3gEzkHXAHOQdMAd5B5KLW2Z76623dO7cOQUCAfn9fmVnZ6ukpORD27t5PX36tF599VVVV1erurpab775piRp69at2rFjx/V7AwCuGYVWAL2NghuM5LbIFy5cKKm9NR4IBPTPf/5Tr7zySqdtAPRdZB0wB3kHzEHeATOQdcAc5B0wB3kHkoc7TXHv3r3ez7Ztq6CgQDfffLNs2+5UYGlqatLOnTu1cuVKLVmyRDt27FA4HPaeHzlypIYNG3Z93wSAuFBoBZAoKLjBSIFAQLFYTGPHjtWUKVMUi8WUkpIiSVq6dKkcx1EgEPBO2AD6JrIOmIO8A+Yg74AZyDpgDvIOmIO8A8nl0qVLOnjwoCQpEolIksaOHauUlBRZliWfz6dwOKyGhgbV1NRo8eLFWrdunc6dO+e9xmc/+1n98pe/1Lp165Sfn98r7wNA9yi0AkgUFNxgLPcmecGCBZKkUCikYDCos2fP6uc//7kkMR4VSAJkHTAHeQfMQd4BM5B1wBzkHTAHeQeSR3NzsxobGyW1F14sy1JRUZH3fGNjo9avX6+lS5dq5cqVOnLkiPdcIBBQVVWVtm3bprvuuuu6HzuAa0OhFUAioOAGY1mWJdu2NWjQIFVWVnZ6btWqVTpx4oQsy+JmGujjyDpgDvIOmIO8A2Yg64A5yDtgDvIOJI+TJ0/q4sWLSk1NlW3b6tevnwoLC9XU1KS//OUvWrFihZYtW6b6+npvyUJJevDBB7Vr1y49/vjjvXj0AK4FhVYAiYCCG4zmXlDPnTtXn/nMZ9TW1uY9t379+k7bAOi7yDpgDvIOmIO8A2Yg64A5yDtgDvIOJIdIJCLLshSNRiVJ/fv31759+1RdXa0lS5botddeUygUktQ+mfHLX/6yNmzYoEWLFik7O7s3Dx3ANaLQCiARUHCD0fx+v6LRqHw+n+bNmydJGjx4sH72s5/pySef7OWjA/BJIeuAOcg7YA7yDpiBrAPmIO+AOcg7kBxOnjwp27bl97f/d/MHH3yg1atXa+3atTpz5oy3XW5urpYtW6Y//OEPKi4u7q3DBfAxUGgFkAgCvX0AQG8LBNpj8MADDygSiai8vLyXjwjAp4GsA+Yg74A5yDtgBrIOmIO8A+Yg70Dfd8MNNyg1NVXhcFh+v1+O4+jo0aPy+XzeMsOzZ8/W9773vV4+UgAfl1toDQQCchzHK7S6y5a6cnNzNX/+fE2bNq2XjhRAMvPF3CsMwGCO43jfMAGQvMg6YA7yDpiDvANmIOuAOcg7YA7yDvRt27dv1+zZs73CqiRvutPUqVP1zDPPKC8vr7cOD8AnaNu2bZo3b16nQqvf71csFqPQCuC6YYIbIHETDRiCrAPmIO+AOcg7YAayDpiDvAPmIO9A31ZcXKzc3Fz997//9R4bPny4Fi5cqDFjxvTikQH4pPl8PoXDYa/Q6i43LlFoBXD9UHADAAAAAAAAAAAAAMQtMzNTxcXFev3115WVlaWnnnqK5YaBJEWhFUAioOAGAAAAAAAAAAAAAIhbenq6Jk6cqOHDh2vWrFlKTU3t7UMC8Cmh0AogEfhi7qLIAAAAAAAAAAAAAADEIRaLyefz9fZhALgOXnrpJZ05c4ZCK4BeQ8ENAAAAAAAAAAAAAAAAXaLQCqC3+Xv7AAAAAAAAAAAAAAAAAJCYKLcB6G0U3AAAAAAAAAAAAAAAAAAACYmCGwAAAAAAAAAAAAAAAAAgIVFwAwAAAAAAAAAAAAAAAAAkJApuAAAAAAAAAAAAAAAAAICERMENAAAAAAAAAAAAAAAAAJCQKLgBAAAAAAAAAAAAAAAAABISBTcAAAAAAAAAAAAAAAAAQEKi4AYAAAAAAAAAAAAAAAAASEgU3AAAAAAAAAAAAAAAAAAACYmCGwAAAAAAAAAAAAAAAAAgIVFwAwAAAAAAAAAAAAAAAAAkJApuAAAAAAAAAAAAAAAAAICERMENAAAAAAAAAAAAAAAAAJCQKLgBAAAAAAAAAAAAAAAAABISBTcAAAAAAAAAAAAAAAAAQEKi4AYAAAAAAAAAAAAAAAAASEgU3AAAAAAAAAAAAAAAAAAACYmCGwAAAAAAAAAAAAAAAAAgIVFwAwAAAAAAAAAAAAAAAAAkJApuAAAAAAAAAAAAAAAAAICERMENAAAAAAAAAAAAAAAAAJCQKLgBAAAAAAAAAAAAAAAAABISBTcAAAAAAAAAAAAAAAAAQEKi4AYAAAAAAAAAAAAAAAAASEgU3AAAAAAAAAAAAAAAAAAACYmCGwAAAAAAAAAAAAAAAAAgIVFwAwAAAAAAAAAAAAAAAAAkJApuAAAAAAAAAAAAAAAAAICERMENAAAAAAAAAAAAAAAAAJCQKLgBAAAAAAAAAAAAAAAAABISBTcAAAAAAAAAAAAAAAAAQEKi4AYAAAAAAAAAAAAAAAAASEgU3AAAAAAAAAAAAAAAAAAACYmCGwAAAAAAAAAAAAAAAAAgIf0P8Li5Q1xX6/cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 3000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_length = min(len(df_for_training), len(prediction_train))\n",
    "trainResults['Actual']=df_for_training.iloc[-min_length:,-2]\n",
    "trainResults['Pred']=prediction_train[-min_length:]\n",
    "plot(trainResults['Pred'],trainResults['Actual'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191/191 [==============================] - 1s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "prediction=model.predict(testX)\n",
    "min_length = min(len(df_for_testing), len(prediction))\n",
    "testResults = pd.DataFrame()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 3000x900 with 1 Axes>, <Axes: >)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3000x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "testResults['Actual']=df_for_testing.iloc[-min_length:, -2]\n",
    "testResults[\"Pred\"]=prediction[-min_length:]\n",
    "plot((testResults['Pred']).rolling(1).mean(),testResults[\"Actual\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "pred=prediction\n",
    "origin=df_for_testing\n",
    "\n",
    "\n",
    "min_length = min(len(origin), len(prediction))\n",
    "pred=pred[-min_length:]\n",
    "origin=origin.iloc[-min_length:, -2]\n",
    "\n",
    "aligned = pd.DataFrame(columns=[f'pred{i}' for i in range(n_forecast)], index=pd.date_range(\n",
    "    start=origin.index[0], freq='D', periods=pred.shape[0]+n_forecast-1))\n",
    "for i in range(len(prediction)):\n",
    "    col=i % n_forecast\n",
    "    try:\n",
    "        aligned.iloc[i:i+n_forecast,col]=prediction[i]\n",
    "    except IndexError:\n",
    "        break\n",
    "CI=aligned.std(axis=1)* 1.96 / np.sqrt(aligned.shape[1])\n",
    "mean=aligned.mean(axis=1)\n",
    "aligned=pd.concat([mean,mean-CI,mean+CI],keys=['m','lc',\"uc\"],axis=1)\n",
    "plot(aligned,testResults[\"Actual\"])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}