imf_threshold_metrics.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "\n",
    "import cufflinks as cf\n",
    "import chart_studio.plotly as plotly\n",
    "import plotly.offline\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=True, world_readable=False)\n",
    "\n",
    "path = \"imfs_exp_records/*.csv\"\n",
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    "    2: \"BOVA11\",\n",
    "    3: \"ITUB4\",\n",
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    "    7: \"ABEV3\",\n",
    "    8: \"MGLU3\",\n",
    "    9: \"VVAR3\"\n",
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    "\n",
    "xlstm_ceemdan_imfs_exp_dfs = {}\n",
    "\n",
    "for exp_name in glob.glob(path):\n",
    "    df = pd.read_csv(exp_name)\n",
    "    if 'IMF1_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF1'] = df\n",
    "    elif 'IMF2_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF2'] = df\n",
    "    elif 'IMF3_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF3'] = df\n",
    "    elif 'IMF4_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF4'] = df\n",
    "    elif 'IMF5_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF5'] = df\n",
    "    elif 'IMF6_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF6'] = df\n",
    "    elif 'IMF7_full' in exp_name:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['IMF7'] = df\n",
    "    else:\n",
    "        xlstm_ceemdan_imfs_exp_dfs['None'] = df\n",
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   "source": [
    "dfs_list = []\n",
    "for imf_model in xlstm_ceemdan_imfs_exp_dfs:\n",
    "    df = xlstm_ceemdan_imfs_exp_dfs[imf_model]['mse'].to_frame()\n",
    "    df.columns = [f'mse_{imf_model}']\n",
    "    df = df.T\n",
    "    df.columns = tickers\n",
    "    dfs_list += [df]\n",
    "\n",
    "result = pd.concat(dfs_list)\n",
    "result = result.T\n",
    "result = result[['mse_None','mse_IMF1', 'mse_IMF2', 'mse_IMF3', 'mse_IMF4', 'mse_IMF5', 'mse_IMF6']]\n",
    "result = result.T\n",
    "result.iplot(layout=space)"
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  {
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   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-29T21:02:33.489924Z",
     "start_time": "2020-07-29T21:02:33.044050Z"
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   "source": [
    "dfs_list = []\n",
    "for imf_model in xlstm_ceemdan_imfs_exp_dfs:\n",
    "    df = xlstm_ceemdan_imfs_exp_dfs[imf_model]['mape'].to_frame()\n",
    "    df.columns = [f'mape_{imf_model}']\n",
    "    df = df.T\n",
    "    df.columns = tickers\n",
    "    dfs_list += [df]\n",
    "\n",
    "result = pd.concat(dfs_list)\n",
    "result = result.T\n",
    "result = result[['mape_None','mape_IMF1', 'mape_IMF2', 'mape_IMF3', 'mape_IMF4', 'mape_IMF5', 'mape_IMF6']]\n",
    "result = result.T\n",
    "result.iplot(layout=space)"
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