Compare_models.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare results of the different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tamarahuete/opt/anaconda3/lib/python3.7/site-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
      "  import pandas.util.testing as tm\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import re\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from bioinfokit.analys import stat\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Aggregate all results in one file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'data/PROCESSED/MODEL_OUTPUTS'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = re.compile(f'.*_performance_overall.csv$') # only csv files\n",
    "files = list(filter(r.match, os.listdir(path))) \n",
    "res = sorted(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = re.compile(f'.*-minmax')\n",
    "files = list(filter(r.match, res)) \n",
    "res1 = sorted(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "res2= sorted(list(set(res) - set(res1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame()\n",
    "for f in range(len(res2)):\n",
    "    df = pd.read_csv(f'{path}/{res2[f]}')\n",
    "    df['epochs'] = df['Unnamed: 0'].apply(lambda row: int(row.split('_')[-1]) if row.split('_')[-1] in ['5','20','50','100','150','250'] else 20)\n",
    "    df['window'] = df['Unnamed: 0'].apply(lambda row: row.split('_')[-3]+'_'+row.split('_')[-2] if row.split('_')[-1] in ['5','20','50','100','150','250'] else row.split('_')[-2]+'_'+row.split('_')[-1])\n",
    "    df['freq'] = df['Unnamed: 0'].apply(lambda row: row.split('_')[-4] if row.split('_')[-1] in ['5','20','50','100','150','250'] else row.split('_')[-3])\n",
    "    df['model'] = df['Unnamed: 0'].apply(lambda row: row.split('_')[0])\n",
    "    df['station'] =res2[f].split('_')[0]\n",
    "    df['file'] = f'{res2[f]}'\n",
    "    results = results.append(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results[~results['model'].isin(['BaselineLast','BaselineRepeat'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations= ['guadalupe',\n",
    "           'banos',\n",
    "           'aeropuerto',\n",
    "         'quisapincha',\n",
    "         'chiquiurco',\n",
    "         'AJSucre',\n",
    "         'JAlvarez',\n",
    "         'pfc-hgpt',\n",
    "         'calamaca',\n",
    "         'mulaCorral',\n",
    "         'pampasSalasaca',\n",
    "         'tasinteo',\n",
    "         'pisayambo',\n",
    "         'cunchibamba']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .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>Unnamed: 0</th>\n",
       "      <th>loss_test</th>\n",
       "      <th>mae_test</th>\n",
       "      <th>loss_val</th>\n",
       "      <th>mae_val</th>\n",
       "      <th>epochs</th>\n",
       "      <th>window</th>\n",
       "      <th>freq</th>\n",
       "      <th>model</th>\n",
       "      <th>station</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultiLinear_60m_w24_12</td>\n",
       "      <td>0.483552</td>\n",
       "      <td>0.423504</td>\n",
       "      <td>0.439122</td>\n",
       "      <td>0.399172</td>\n",
       "      <td>20</td>\n",
       "      <td>w24_12</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLinear</td>\n",
       "      <td>AJSucre</td>\n",
       "      <td>AJSucre_multi_60m_w24_12_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MultiDense_60m_w24_12</td>\n",
       "      <td>0.396101</td>\n",
       "      <td>0.362826</td>\n",
       "      <td>0.365817</td>\n",
       "      <td>0.342350</td>\n",
       "      <td>20</td>\n",
       "      <td>w24_12</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiDense</td>\n",
       "      <td>AJSucre</td>\n",
       "      <td>AJSucre_multi_60m_w24_12_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MultiConv_60m_w24_12</td>\n",
       "      <td>0.391945</td>\n",
       "      <td>0.360213</td>\n",
       "      <td>0.363291</td>\n",
       "      <td>0.339760</td>\n",
       "      <td>20</td>\n",
       "      <td>w24_12</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiConv</td>\n",
       "      <td>AJSucre</td>\n",
       "      <td>AJSucre_multi_60m_w24_12_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w24_12</td>\n",
       "      <td>0.386219</td>\n",
       "      <td>0.367019</td>\n",
       "      <td>0.358438</td>\n",
       "      <td>0.346414</td>\n",
       "      <td>20</td>\n",
       "      <td>w24_12</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>AJSucre</td>\n",
       "      <td>AJSucre_multi_60m_w24_12_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AR_LSTM_60m_w24_12</td>\n",
       "      <td>0.383760</td>\n",
       "      <td>0.368215</td>\n",
       "      <td>0.357675</td>\n",
       "      <td>0.349274</td>\n",
       "      <td>20</td>\n",
       "      <td>w24_12</td>\n",
       "      <td>60m</td>\n",
       "      <td>AR</td>\n",
       "      <td>AJSucre</td>\n",
       "      <td>AJSucre_multi_60m_w24_12_performance_overall.csv</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultiLinear_60m_w72_48</td>\n",
       "      <td>0.466857</td>\n",
       "      <td>0.436734</td>\n",
       "      <td>0.367463</td>\n",
       "      <td>0.378324</td>\n",
       "      <td>20</td>\n",
       "      <td>w72_48</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLinear</td>\n",
       "      <td>tasinteo</td>\n",
       "      <td>tasinteo_multi_60m_w72_48_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MultiDense_60m_w72_48</td>\n",
       "      <td>0.416033</td>\n",
       "      <td>0.403345</td>\n",
       "      <td>0.307051</td>\n",
       "      <td>0.329152</td>\n",
       "      <td>20</td>\n",
       "      <td>w72_48</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiDense</td>\n",
       "      <td>tasinteo</td>\n",
       "      <td>tasinteo_multi_60m_w72_48_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MultiConv_60m_w72_48</td>\n",
       "      <td>0.414283</td>\n",
       "      <td>0.404766</td>\n",
       "      <td>0.308108</td>\n",
       "      <td>0.334269</td>\n",
       "      <td>20</td>\n",
       "      <td>w72_48</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiConv</td>\n",
       "      <td>tasinteo</td>\n",
       "      <td>tasinteo_multi_60m_w72_48_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w72_48</td>\n",
       "      <td>0.425136</td>\n",
       "      <td>0.422176</td>\n",
       "      <td>0.311359</td>\n",
       "      <td>0.345627</td>\n",
       "      <td>20</td>\n",
       "      <td>w72_48</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>tasinteo</td>\n",
       "      <td>tasinteo_multi_60m_w72_48_performance_overall.csv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AR_LSTM_60m_w72_48</td>\n",
       "      <td>0.437465</td>\n",
       "      <td>0.428827</td>\n",
       "      <td>0.310420</td>\n",
       "      <td>0.345731</td>\n",
       "      <td>20</td>\n",
       "      <td>w72_48</td>\n",
       "      <td>60m</td>\n",
       "      <td>AR</td>\n",
       "      <td>tasinteo</td>\n",
       "      <td>tasinteo_multi_60m_w72_48_performance_overall.csv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>570 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Unnamed: 0  loss_test  mae_test  loss_val   mae_val  \\\n",
       "1       MultiLinear_60m_w24_12   0.483552  0.423504  0.439122  0.399172   \n",
       "2        MultiDense_60m_w24_12   0.396101  0.362826  0.365817  0.342350   \n",
       "3         MultiConv_60m_w24_12   0.391945  0.360213  0.363291  0.339760   \n",
       "4   MultiLSTM_model_60m_w24_12   0.386219  0.367019  0.358438  0.346414   \n",
       "5           AR_LSTM_60m_w24_12   0.383760  0.368215  0.357675  0.349274   \n",
       "..                         ...        ...       ...       ...       ...   \n",
       "1       MultiLinear_60m_w72_48   0.466857  0.436734  0.367463  0.378324   \n",
       "2        MultiDense_60m_w72_48   0.416033  0.403345  0.307051  0.329152   \n",
       "3         MultiConv_60m_w72_48   0.414283  0.404766  0.308108  0.334269   \n",
       "4   MultiLSTM_model_60m_w72_48   0.425136  0.422176  0.311359  0.345627   \n",
       "5           AR_LSTM_60m_w72_48   0.437465  0.428827  0.310420  0.345731   \n",
       "\n",
       "    epochs  window freq        model   station  \\\n",
       "1       20  w24_12  60m  MultiLinear   AJSucre   \n",
       "2       20  w24_12  60m   MultiDense   AJSucre   \n",
       "3       20  w24_12  60m    MultiConv   AJSucre   \n",
       "4       20  w24_12  60m    MultiLSTM   AJSucre   \n",
       "5       20  w24_12  60m           AR   AJSucre   \n",
       "..     ...     ...  ...          ...       ...   \n",
       "1       20  w72_48  60m  MultiLinear  tasinteo   \n",
       "2       20  w72_48  60m   MultiDense  tasinteo   \n",
       "3       20  w72_48  60m    MultiConv  tasinteo   \n",
       "4       20  w72_48  60m    MultiLSTM  tasinteo   \n",
       "5       20  w72_48  60m           AR  tasinteo   \n",
       "\n",
       "                                                 file  \n",
       "1    AJSucre_multi_60m_w24_12_performance_overall.csv  \n",
       "2    AJSucre_multi_60m_w24_12_performance_overall.csv  \n",
       "3    AJSucre_multi_60m_w24_12_performance_overall.csv  \n",
       "4    AJSucre_multi_60m_w24_12_performance_overall.csv  \n",
       "5    AJSucre_multi_60m_w24_12_performance_overall.csv  \n",
       "..                                                ...  \n",
       "1   tasinteo_multi_60m_w72_48_performance_overall.csv  \n",
       "2   tasinteo_multi_60m_w72_48_performance_overall.csv  \n",
       "3   tasinteo_multi_60m_w72_48_performance_overall.csv  \n",
       "4   tasinteo_multi_60m_w72_48_performance_overall.csv  \n",
       "5   tasinteo_multi_60m_w72_48_performance_overall.csv  \n",
       "\n",
       "[570 rows x 11 columns]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = results[(results['station'].isin(stations)) & ~(results['window'].isin(['w576_288','w192_96', 'w96_48'])) & (results['mae_test']<0.6)]\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA Window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,7))\n",
    "ax = sns.boxplot(x='window', y='mae_test', data=results, color='#DFE3EE')\n",
    "ax = sns.swarmplot(x=\"window\", y=\"mae_test\", data=results, color='#3B5998')\n",
    "ax.set_xlabel(\"window\",fontsize=15)\n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels=results.window.unique(),rotation = 45, ha=\"right\")\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_window.png',dpi = 75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(window)</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.043555</td>\n",
       "      <td>0.006222</td>\n",
       "      <td>1.625198</td>\n",
       "      <td>0.125548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>562.0</td>\n",
       "      <td>2.151666</td>\n",
       "      <td>0.003829</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              df    sum_sq   mean_sq         F    PR(>F)\n",
       "C(window)    7.0  0.043555  0.006222  1.625198  0.125548\n",
       "Residual   562.0  2.151666  0.003829       NaN       NaN"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test ~ C(window)')\n",
    "res.anova_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Lower</th>\n",
       "      <th>Upper</th>\n",
       "      <th>q-value</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [group1, group2, Diff, Lower, Upper, q-value, p-value]\n",
       "Index: []"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.tukey_hsd(df=results, res_var='mae_test', xfac_var='window', anova_model='mae_test ~ C(window)')\n",
    "test = res.tukey_summary\n",
    "test[test['p-value']<0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Differences between the shortes and longets periods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Linear', 'MLP', 'CNN', 'LSTM', 'AR-LSTM'], dtype=object)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.model.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "results['model'].replace({'MultiLinear':'Linear','MultiDense':'MLP','MultiConv':'CNN','MultiLSTM':'LSTM','AR':'AR-LSTM'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (7,7))\n",
    "ax = sns.boxplot(x='model', y='mae_test', data=results, color='#DFE3EE')\n",
    "ax = sns.swarmplot(x='model', y=\"mae_test\", data=results, color='#3B5998')\n",
    "ax.set_xlabel('model',fontsize=15)\n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels=['Linear', 'MLP', 'CNN', 'LSTM', 'AR-LSTM'],rotation = 45, ha=\"right\")\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_model.png',dpi = 75,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(model)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.121992</td>\n",
       "      <td>0.030498</td>\n",
       "      <td>8.311397</td>\n",
       "      <td>0.000002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>565.0</td>\n",
       "      <td>2.073229</td>\n",
       "      <td>0.003669</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             df    sum_sq   mean_sq         F    PR(>F)\n",
       "C(model)    4.0  0.121992  0.030498  8.311397  0.000002\n",
       "Residual  565.0  2.073229  0.003669       NaN       NaN"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANOVA table using bioinfokit v1.0.3 or later (it uses wrapper script for anova_lm)\n",
    "from bioinfokit.analys import stat\n",
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test ~ C(model)')\n",
    "res.anova_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Lower</th>\n",
       "      <th>Upper</th>\n",
       "      <th>q-value</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear</td>\n",
       "      <td>MLP</td>\n",
       "      <td>0.039576</td>\n",
       "      <td>0.017223</td>\n",
       "      <td>0.061929</td>\n",
       "      <td>6.852152</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear</td>\n",
       "      <td>CNN</td>\n",
       "      <td>0.038690</td>\n",
       "      <td>0.016337</td>\n",
       "      <td>0.061043</td>\n",
       "      <td>6.698806</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Linear</td>\n",
       "      <td>LSTM</td>\n",
       "      <td>0.036258</td>\n",
       "      <td>0.014782</td>\n",
       "      <td>0.057734</td>\n",
       "      <td>6.534049</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Linear</td>\n",
       "      <td>AR-LSTM</td>\n",
       "      <td>0.029132</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>0.051485</td>\n",
       "      <td>5.043983</td>\n",
       "      <td>0.003587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   group1   group2      Diff     Lower     Upper   q-value   p-value\n",
       "0  Linear      MLP  0.039576  0.017223  0.061929  6.852152  0.001000\n",
       "1  Linear      CNN  0.038690  0.016337  0.061043  6.698806  0.001000\n",
       "2  Linear     LSTM  0.036258  0.014782  0.057734  6.534049  0.001000\n",
       "3  Linear  AR-LSTM  0.029132  0.006780  0.051485  5.043983  0.003587"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.tukey_hsd(df=results, res_var='mae_test', xfac_var='model', anova_model='mae_test ~ C(model)')\n",
    "test = res.tukey_summary\n",
    "test[test['p-value']<0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "####  MultiLinear is worse than others. No signiff difference in the rest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA Agregagtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AJSucre', 'JAlvarez', 'aeropuerto', 'banos', 'calamaca',\n",
       "       'chiquiurco', 'cunchibamba', 'guadalupe', 'mulaCorral',\n",
       "       'pampasSalasaca', 'pfc-hgpt', 'pisayambo', 'quisapincha',\n",
       "       'tasinteo'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.station.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = results.loc[:,['station', 'mae_test']] \\\n",
    "    .groupby(['station']) \\\n",
    "    .median() \\\n",
    "    .sort_values(by='mae_test')\n",
    "\n",
    "fig, ax =plt.subplots(figsize=(10,5))\n",
    "ax = sns.boxplot(x='station', y='mae_test', data=results, color='#DFE3EE',order=grouped.index)\n",
    "ax = sns.swarmplot(x=\"station\", y=\"mae_test\", data=results, color='#3B5998',order=grouped.index)\n",
    "ax.set_xlabel('station',fontsize=15)\n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels = grouped.index,rotation = 45, ha=\"right\")\n",
    "ax.set_ylim(0.25,0.55)\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_stations.png',dpi = 75,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = results.loc[:,['station', 'mae_test']] \\\n",
    "    .groupby(['station']) \\\n",
    "    .median() \\\n",
    "    .sort_values(by='mae_test')\n",
    "\n",
    "fig, ax =plt.subplots(figsize=(15,10))\n",
    "ax = sns.boxplot(x=\"station\", y=\"mae_test\", hue=\"model\", data=results, palette=\"Set3\",order =grouped.index) \n",
    "#ax = sns.swarmplot(x=\"station\", y=\"mae_test\", data=results, color='#3B5998',order=grouped.index)\n",
    "ax.set_xlabel('station',fontsize=15)\n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels = grouped.index,rotation = 45, ha=\"right\")\n",
    "#ax.legend(labels = ['Linear','MLP','CNN','LSTM','AR-LSTM'])\n",
    "# ax.legend().get_texts()[0].set_text('Linear')\n",
    "# ax.legend().get_texts()[1].set_text('MLP')\n",
    "# ax.legend().get_texts()[2].set_text('CNN')\n",
    "# ax.legend().get_texts()[3].set_text('LSTM')\n",
    "# ax.legend().get_texts()[3].set_text('AR-LSTM')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_stations_models.png',dpi = 75,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = results.loc[:,['station', 'mae_test']] \\\n",
    "    .groupby(['station']) \\\n",
    "    .median() \\\n",
    "    .sort_values(by='mae_test')\n",
    "\n",
    "fig, ax =plt.subplots(figsize=(15,10))\n",
    "ax = sns.boxplot(x=\"station\", y=\"mae_test\", hue=\"window\", data=results, palette=\"Set3\",order =grouped.index) \n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels = grouped.index,rotation = 45, ha=\"right\")\n",
    "#ax.legend(labels = ['Linear','MLP','CNN','LSTM','AR-LSTM'])\n",
    "# ax.legend().get_texts()[0].set_text('Linear')\n",
    "# ax.legend().get_texts()[1].set_text('MLP')\n",
    "# ax.legend().get_texts()[2].set_text('CNN')\n",
    "# ax.legend().get_texts()[3].set_text('LSTM')\n",
    "# ax.legend().get_texts()[3].set_text('AR-LSTM')\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_stations_window.png',dpi = 75,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups_all =[['cutuchi','patate','pastaza','ambato'],['high','medium','low'],['all']]\n",
    "fig, axes =plt.subplots(nrows=1, ncols=3,figsize=(15,5))\n",
    "\n",
    "for a,ax in enumerate(axes.flatten()):\n",
    "    \n",
    "    groups = results[results.station.isin(groups_all[a])]\n",
    "    grouped = groups.loc[:,['station', 'mae_test']] \\\n",
    "    .groupby(['station']) \\\n",
    "    .median() \\\n",
    "    .sort_values(by='mae_test')\n",
    "    \n",
    "    sns.boxplot(x='station', y='mae_test', data=groups, color='#DFE3EE',order=grouped.index, ax = ax)\n",
    "    sns.swarmplot(x=\"station\", y=\"mae_test\", data=groups, color='#3B5998',order=grouped.index, ax = ax)\n",
    "    if a == 0:\n",
    "        ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "    else:\n",
    "        ax.set_ylabel(\"\",fontsize=15)\n",
    "    ax.set_xlabel(\"\",fontsize=15)\n",
    "    ax.tick_params(labelsize=15)\n",
    "    ax.set_xticklabels(labels = grouped.index, ha=\"center\")\n",
    "    ax.set_ylim(0.25,0.55)\n",
    "fig.savefig(f'{path}/boxplot_groups.png',dpi = 75,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(station)</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.361819</td>\n",
       "      <td>0.051688</td>\n",
       "      <td>298.379984</td>\n",
       "      <td>1.156336e-123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>272.0</td>\n",
       "      <td>0.047119</td>\n",
       "      <td>0.000173</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               df    sum_sq   mean_sq           F         PR(>F)\n",
       "C(station)    7.0  0.361819  0.051688  298.379984  1.156336e-123\n",
       "Residual    272.0  0.047119  0.000173         NaN            NaN"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANOVA table using bioinfokit v1.0.3 or later (it uses wrapper script for anova_lm)\n",
    "from bioinfokit.analys import stat\n",
    "res = stat()\n",
    "res.anova_stat(df=groups, res_var='mae_test', anova_model='mae_test ~ C(station)')\n",
    "res.anova_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Lower</th>\n",
       "      <th>Upper</th>\n",
       "      <th>q-value</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>all</td>\n",
       "      <td>ambato</td>\n",
       "      <td>0.016989</td>\n",
       "      <td>0.007683</td>\n",
       "      <td>0.026294</td>\n",
       "      <td>7.886654</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>all</td>\n",
       "      <td>cutuchi</td>\n",
       "      <td>0.017593</td>\n",
       "      <td>0.008287</td>\n",
       "      <td>0.026898</td>\n",
       "      <td>8.167232</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>all</td>\n",
       "      <td>high</td>\n",
       "      <td>0.011103</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>0.020813</td>\n",
       "      <td>4.939615</td>\n",
       "      <td>0.012841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>all</td>\n",
       "      <td>low</td>\n",
       "      <td>0.015931</td>\n",
       "      <td>0.006626</td>\n",
       "      <td>0.025237</td>\n",
       "      <td>7.395815</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>all</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.014831</td>\n",
       "      <td>0.005526</td>\n",
       "      <td>0.024137</td>\n",
       "      <td>6.885179</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>all</td>\n",
       "      <td>pastaza</td>\n",
       "      <td>0.097279</td>\n",
       "      <td>0.087973</td>\n",
       "      <td>0.106584</td>\n",
       "      <td>45.159964</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>all</td>\n",
       "      <td>patate</td>\n",
       "      <td>0.017813</td>\n",
       "      <td>0.008508</td>\n",
       "      <td>0.027119</td>\n",
       "      <td>8.269412</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ambato</td>\n",
       "      <td>cutuchi</td>\n",
       "      <td>0.034581</td>\n",
       "      <td>0.024971</td>\n",
       "      <td>0.044192</td>\n",
       "      <td>15.544109</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ambato</td>\n",
       "      <td>high</td>\n",
       "      <td>0.028092</td>\n",
       "      <td>0.018089</td>\n",
       "      <td>0.038095</td>\n",
       "      <td>12.131653</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ambato</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.031820</td>\n",
       "      <td>0.022209</td>\n",
       "      <td>0.041430</td>\n",
       "      <td>14.302766</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ambato</td>\n",
       "      <td>pastaza</td>\n",
       "      <td>0.080290</td>\n",
       "      <td>0.070679</td>\n",
       "      <td>0.089901</td>\n",
       "      <td>36.089727</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ambato</td>\n",
       "      <td>patate</td>\n",
       "      <td>0.034802</td>\n",
       "      <td>0.025191</td>\n",
       "      <td>0.044412</td>\n",
       "      <td>15.643044</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cutuchi</td>\n",
       "      <td>low</td>\n",
       "      <td>0.033524</td>\n",
       "      <td>0.023914</td>\n",
       "      <td>0.043135</td>\n",
       "      <td>15.068856</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>cutuchi</td>\n",
       "      <td>pastaza</td>\n",
       "      <td>0.114871</td>\n",
       "      <td>0.105261</td>\n",
       "      <td>0.124482</td>\n",
       "      <td>51.633836</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>high</td>\n",
       "      <td>low</td>\n",
       "      <td>0.027034</td>\n",
       "      <td>0.017031</td>\n",
       "      <td>0.037038</td>\n",
       "      <td>11.675044</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>high</td>\n",
       "      <td>pastaza</td>\n",
       "      <td>0.108382</td>\n",
       "      <td>0.098379</td>\n",
       "      <td>0.118385</td>\n",
       "      <td>46.805541</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>low</td>\n",
       "      <td>medium</td>\n",
       "      <td>0.030763</td>\n",
       "      <td>0.021152</td>\n",
       "      <td>0.040373</td>\n",
       "      <td>13.827513</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>low</td>\n",
       "      <td>pastaza</td>\n",
       "      <td>0.081347</td>\n",
       "      <td>0.071737</td>\n",
       "      <td>0.090958</td>\n",
       "      <td>36.564980</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>low</td>\n",
       "      <td>patate</td>\n",
       "      <td>0.033744</td>\n",
       "      <td>0.024134</td>\n",
       "      <td>0.043355</td>\n",
       "      <td>15.167791</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>medium</td>\n",
       "      <td>pastaza</td>\n",
       "      <td>0.112110</td>\n",
       "      <td>0.102499</td>\n",
       "      <td>0.121720</td>\n",
       "      <td>50.392493</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>pastaza</td>\n",
       "      <td>patate</td>\n",
       "      <td>0.115092</td>\n",
       "      <td>0.105481</td>\n",
       "      <td>0.124702</td>\n",
       "      <td>51.732771</td>\n",
       "      <td>0.001000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     group1   group2      Diff     Lower     Upper    q-value   p-value\n",
       "0       all   ambato  0.016989  0.007683  0.026294   7.886654  0.001000\n",
       "1       all  cutuchi  0.017593  0.008287  0.026898   8.167232  0.001000\n",
       "2       all     high  0.011103  0.001393  0.020813   4.939615  0.012841\n",
       "3       all      low  0.015931  0.006626  0.025237   7.395815  0.001000\n",
       "4       all   medium  0.014831  0.005526  0.024137   6.885179  0.001000\n",
       "5       all  pastaza  0.097279  0.087973  0.106584  45.159964  0.001000\n",
       "6       all   patate  0.017813  0.008508  0.027119   8.269412  0.001000\n",
       "7    ambato  cutuchi  0.034581  0.024971  0.044192  15.544109  0.001000\n",
       "8    ambato     high  0.028092  0.018089  0.038095  12.131653  0.001000\n",
       "10   ambato   medium  0.031820  0.022209  0.041430  14.302766  0.001000\n",
       "11   ambato  pastaza  0.080290  0.070679  0.089901  36.089727  0.001000\n",
       "12   ambato   patate  0.034802  0.025191  0.044412  15.643044  0.001000\n",
       "14  cutuchi      low  0.033524  0.023914  0.043135  15.068856  0.001000\n",
       "16  cutuchi  pastaza  0.114871  0.105261  0.124482  51.633836  0.001000\n",
       "18     high      low  0.027034  0.017031  0.037038  11.675044  0.001000\n",
       "20     high  pastaza  0.108382  0.098379  0.118385  46.805541  0.001000\n",
       "22      low   medium  0.030763  0.021152  0.040373  13.827513  0.001000\n",
       "23      low  pastaza  0.081347  0.071737  0.090958  36.564980  0.001000\n",
       "24      low   patate  0.033744  0.024134  0.043355  15.167791  0.001000\n",
       "25   medium  pastaza  0.112110  0.102499  0.121720  50.392493  0.001000\n",
       "27  pastaza   patate  0.115092  0.105481  0.124702  51.732771  0.001000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.tukey_hsd(df=groups, res_var='mae_test', xfac_var='station', anova_model='mae_test ~ C(station)')\n",
    "test = res.tukey_summary\n",
    "test[test['p-value']<0.05]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 way ANOVA model + window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(model)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.087820</td>\n",
       "      <td>0.021955</td>\n",
       "      <td>7.061015</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(window)</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.048397</td>\n",
       "      <td>0.006914</td>\n",
       "      <td>2.223567</td>\n",
       "      <td>0.030709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(model):C(window)</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.004489</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.051557</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>670.0</td>\n",
       "      <td>2.083257</td>\n",
       "      <td>0.003109</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       df    sum_sq   mean_sq         F    PR(>F)\n",
       "C(model)              4.0  0.087820  0.021955  7.061015  0.000014\n",
       "C(window)             7.0  0.048397  0.006914  2.223567  0.030709\n",
       "C(model):C(window)   28.0  0.004489  0.000160  0.051557  1.000000\n",
       "Residual            670.0  2.083257  0.003109       NaN       NaN"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bioinfokit.analys import stat\n",
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test~C(model)+C(window)+C(model):C(window)')\n",
    "res.anova_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5f48416d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"window\", y=\"mae_test\", hue=\"model\", data=results, palette=\"Set3\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 way ANOVA window + station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/tamarahuete/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:1752: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 18, but rank is 15\n",
      "  'rank is %d' % (J, J_), ValueWarning)\n",
      "/Users/tamarahuete/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:1752: ValueWarning: covariance of constraints does not have full rank. The number of constraints is 126, but rank is 118\n",
      "  'rank is %d' % (J, J_), ValueWarning)\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>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(window)</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.056934</td>\n",
       "      <td>0.008133</td>\n",
       "      <td>34.330955</td>\n",
       "      <td>6.197033e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(station)</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1.575072</td>\n",
       "      <td>0.087504</td>\n",
       "      <td>369.351136</td>\n",
       "      <td>2.958816e-281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(window):C(station)</th>\n",
       "      <td>126.0</td>\n",
       "      <td>0.756582</td>\n",
       "      <td>0.006005</td>\n",
       "      <td>25.345283</td>\n",
       "      <td>4.768277e-165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>568.0</td>\n",
       "      <td>0.134566</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         df    sum_sq   mean_sq           F         PR(>F)\n",
       "C(window)               7.0  0.056934  0.008133   34.330955   6.197033e-40\n",
       "C(station)             18.0  1.575072  0.087504  369.351136  2.958816e-281\n",
       "C(window):C(station)  126.0  0.756582  0.006005   25.345283  4.768277e-165\n",
       "Residual              568.0  0.134566  0.000237         NaN            NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bioinfokit.analys import stat\n",
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test~C(window)+C(station)+C(window):C(station)')\n",
    "res.anova_summary\n",
    "### There is no intercation model/window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5d72b1510>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x=\"station\", y=\"mae_test\", hue=\"window\", data=results, palette=\"Set3\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 way ANOVA model + station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(model)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.087820</td>\n",
       "      <td>0.021955</td>\n",
       "      <td>185.551093</td>\n",
       "      <td>3.303560e-104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(station)</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2.023458</td>\n",
       "      <td>0.112414</td>\n",
       "      <td>950.057897</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C(model):C(station)</th>\n",
       "      <td>72.0</td>\n",
       "      <td>0.039915</td>\n",
       "      <td>0.000554</td>\n",
       "      <td>4.685261</td>\n",
       "      <td>6.962882e-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>615.0</td>\n",
       "      <td>0.072769</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        df    sum_sq   mean_sq           F         PR(>F)\n",
       "C(model)               4.0  0.087820  0.021955  185.551093  3.303560e-104\n",
       "C(station)            18.0  2.023458  0.112414  950.057897   0.000000e+00\n",
       "C(model):C(station)   72.0  0.039915  0.000554    4.685261   6.962882e-27\n",
       "Residual             615.0  0.072769  0.000118         NaN            NaN"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bioinfokit.analys import stat\n",
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test~C(model)+C(station)+C(model):C(station)')\n",
    "res.anova_summary\n",
    "### There is no intercation model/window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd5f4bfbcd0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.boxplot(x=\"station\", y=\"mae_test\", hue=\"model\", data=results, palette=\"Set3\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['w24_12', 'w24_24', 'w48_12', 'w48_24', 'w48_48', 'w72_12',\n",
       "       'w72_24', 'w72_48', 'w576_288', '24_20', 'w192_96', 'w96_48'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 4 stations 60, 30, 15 and 5 minutes for 48 +24h predictions\n",
    "results.window.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.window = results.window.replace(to_replace = ['w576_288','w192_96', 'w96_48'], value = 'w48_24')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = results[(results['window'] == 'w48_24') & \n",
    "        (results['station'].isin(['guadalupe','baños','mulaCorral','quisapincha'])) & \n",
    "        (results['model']=='MultiLSTM') & (results['epochs']==20)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>loss_test</th>\n",
       "      <th>mae_test</th>\n",
       "      <th>loss_val</th>\n",
       "      <th>mae_val</th>\n",
       "      <th>epochs</th>\n",
       "      <th>window</th>\n",
       "      <th>freq</th>\n",
       "      <th>model</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_15m_w192_96</td>\n",
       "      <td>0.276341</td>\n",
       "      <td>0.271544</td>\n",
       "      <td>0.329214</td>\n",
       "      <td>0.312905</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>15m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_30m_w96_48</td>\n",
       "      <td>0.275769</td>\n",
       "      <td>0.273131</td>\n",
       "      <td>0.329437</td>\n",
       "      <td>0.313102</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>30m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_5m_w576_288</td>\n",
       "      <td>0.283038</td>\n",
       "      <td>0.273182</td>\n",
       "      <td>0.340439</td>\n",
       "      <td>0.315666</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>5m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24</td>\n",
       "      <td>0.275618</td>\n",
       "      <td>0.273560</td>\n",
       "      <td>0.328397</td>\n",
       "      <td>0.315269</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_15m_w192_96</td>\n",
       "      <td>0.601342</td>\n",
       "      <td>0.475671</td>\n",
       "      <td>0.604503</td>\n",
       "      <td>0.457843</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>15m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_30m_w96_48</td>\n",
       "      <td>0.604446</td>\n",
       "      <td>0.478184</td>\n",
       "      <td>0.595224</td>\n",
       "      <td>0.457835</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>30m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_5m_w576_288</td>\n",
       "      <td>0.597058</td>\n",
       "      <td>0.470995</td>\n",
       "      <td>0.616320</td>\n",
       "      <td>0.458839</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>5m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24</td>\n",
       "      <td>0.612981</td>\n",
       "      <td>0.487472</td>\n",
       "      <td>0.602100</td>\n",
       "      <td>0.468679</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_15m_w192_96</td>\n",
       "      <td>0.466806</td>\n",
       "      <td>0.402464</td>\n",
       "      <td>0.429982</td>\n",
       "      <td>0.382199</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>15m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_30m_w96_48</td>\n",
       "      <td>0.474784</td>\n",
       "      <td>0.404648</td>\n",
       "      <td>0.432624</td>\n",
       "      <td>0.385006</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>30m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_5m_w576_288</td>\n",
       "      <td>0.474234</td>\n",
       "      <td>0.402034</td>\n",
       "      <td>0.438457</td>\n",
       "      <td>0.380192</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>5m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24</td>\n",
       "      <td>0.471118</td>\n",
       "      <td>0.405574</td>\n",
       "      <td>0.432854</td>\n",
       "      <td>0.389157</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Unnamed: 0  loss_test  mae_test  loss_val   mae_val  \\\n",
       "0  MultiLSTM_model_15m_w192_96   0.276341  0.271544  0.329214  0.312905   \n",
       "0   MultiLSTM_model_30m_w96_48   0.275769  0.273131  0.329437  0.313102   \n",
       "0  MultiLSTM_model_5m_w576_288   0.283038  0.273182  0.340439  0.315666   \n",
       "4   MultiLSTM_model_60m_w48_24   0.275618  0.273560  0.328397  0.315269   \n",
       "0  MultiLSTM_model_15m_w192_96   0.601342  0.475671  0.604503  0.457843   \n",
       "0   MultiLSTM_model_30m_w96_48   0.604446  0.478184  0.595224  0.457835   \n",
       "0  MultiLSTM_model_5m_w576_288   0.597058  0.470995  0.616320  0.458839   \n",
       "4   MultiLSTM_model_60m_w48_24   0.612981  0.487472  0.602100  0.468679   \n",
       "0  MultiLSTM_model_15m_w192_96   0.466806  0.402464  0.429982  0.382199   \n",
       "0   MultiLSTM_model_30m_w96_48   0.474784  0.404648  0.432624  0.385006   \n",
       "0  MultiLSTM_model_5m_w576_288   0.474234  0.402034  0.438457  0.380192   \n",
       "4   MultiLSTM_model_60m_w48_24   0.471118  0.405574  0.432854  0.389157   \n",
       "\n",
       "   epochs  window freq      model      station  \n",
       "0      20  w48_24  15m  MultiLSTM    guadalupe  \n",
       "0      20  w48_24  30m  MultiLSTM    guadalupe  \n",
       "0      20  w48_24   5m  MultiLSTM    guadalupe  \n",
       "4      20  w48_24  60m  MultiLSTM    guadalupe  \n",
       "0      20  w48_24  15m  MultiLSTM   mulaCorral  \n",
       "0      20  w48_24  30m  MultiLSTM   mulaCorral  \n",
       "0      20  w48_24   5m  MultiLSTM   mulaCorral  \n",
       "4      20  w48_24  60m  MultiLSTM   mulaCorral  \n",
       "0      20  w48_24  15m  MultiLSTM  quisapincha  \n",
       "0      20  w48_24  30m  MultiLSTM  quisapincha  \n",
       "0      20  w48_24   5m  MultiLSTM  quisapincha  \n",
       "4      20  w48_24  60m  MultiLSTM  quisapincha  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,5))\n",
    "ax = sns.boxplot(x='freq', y='mae_test', data=df, color='#DFE3EE')\n",
    "ax = sns.swarmplot(x=\"freq\", y=\"mae_test\", data=df, color='#3B5998')\n",
    "ax.set_xlabel('station',fontsize=15)\n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels = df.freq.unique(),rotation = 45, ha=\"right\")\n",
    "plt.show()\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_freqs.png',dpi = 75,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(freq)</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.033858</td>\n",
       "      <td>0.99164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>716.0</td>\n",
       "      <td>2.289261</td>\n",
       "      <td>0.003197</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             df    sum_sq   mean_sq         F   PR(>F)\n",
       "C(freq)     3.0  0.000325  0.000108  0.033858  0.99164\n",
       "Residual  716.0  2.289261  0.003197       NaN      NaN"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test ~ C(freq)')\n",
    "res.anova_summary\n",
    "### No significant difference. We keep hourly predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ANOVA epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 20, 100, 250,  50, 150])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 4 stations 60, 30, 15 and 5 minutes for 48 +24h predictions\n",
    "results.epochs.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = results[(results['window'] == 'w48_24') & \n",
    "        (results['station'].isin(['guadalupe','baños','mulaCorral','quisapincha'])) & \n",
    "        (results['model']=='MultiLSTM')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= df[(df['epochs'] != 150)  & (df['freq'] == '60m')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>loss_test</th>\n",
       "      <th>mae_test</th>\n",
       "      <th>loss_val</th>\n",
       "      <th>mae_val</th>\n",
       "      <th>epochs</th>\n",
       "      <th>window</th>\n",
       "      <th>freq</th>\n",
       "      <th>model</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_100</td>\n",
       "      <td>0.276065</td>\n",
       "      <td>0.274050</td>\n",
       "      <td>0.329681</td>\n",
       "      <td>0.318016</td>\n",
       "      <td>100</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_250</td>\n",
       "      <td>0.277113</td>\n",
       "      <td>0.273634</td>\n",
       "      <td>0.334135</td>\n",
       "      <td>0.317318</td>\n",
       "      <td>250</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_50</td>\n",
       "      <td>0.274463</td>\n",
       "      <td>0.273793</td>\n",
       "      <td>0.331377</td>\n",
       "      <td>0.318425</td>\n",
       "      <td>50</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24</td>\n",
       "      <td>0.275618</td>\n",
       "      <td>0.273560</td>\n",
       "      <td>0.328397</td>\n",
       "      <td>0.315269</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>guadalupe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_100</td>\n",
       "      <td>0.608956</td>\n",
       "      <td>0.484971</td>\n",
       "      <td>0.600186</td>\n",
       "      <td>0.467660</td>\n",
       "      <td>100</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_250</td>\n",
       "      <td>0.608108</td>\n",
       "      <td>0.483323</td>\n",
       "      <td>0.599340</td>\n",
       "      <td>0.463206</td>\n",
       "      <td>250</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_50</td>\n",
       "      <td>0.611054</td>\n",
       "      <td>0.485654</td>\n",
       "      <td>0.597371</td>\n",
       "      <td>0.465570</td>\n",
       "      <td>50</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24</td>\n",
       "      <td>0.612981</td>\n",
       "      <td>0.487472</td>\n",
       "      <td>0.602100</td>\n",
       "      <td>0.468679</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>mulaCorral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_100</td>\n",
       "      <td>0.476138</td>\n",
       "      <td>0.404731</td>\n",
       "      <td>0.431022</td>\n",
       "      <td>0.385071</td>\n",
       "      <td>100</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_250</td>\n",
       "      <td>0.478607</td>\n",
       "      <td>0.407073</td>\n",
       "      <td>0.428681</td>\n",
       "      <td>0.387155</td>\n",
       "      <td>250</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24_50</td>\n",
       "      <td>0.474572</td>\n",
       "      <td>0.406328</td>\n",
       "      <td>0.430936</td>\n",
       "      <td>0.385115</td>\n",
       "      <td>50</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiLSTM_model_60m_w48_24</td>\n",
       "      <td>0.471118</td>\n",
       "      <td>0.405574</td>\n",
       "      <td>0.432854</td>\n",
       "      <td>0.389157</td>\n",
       "      <td>20</td>\n",
       "      <td>w48_24</td>\n",
       "      <td>60m</td>\n",
       "      <td>MultiLSTM</td>\n",
       "      <td>quisapincha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Unnamed: 0  loss_test  mae_test  loss_val   mae_val  \\\n",
       "0  MultiLSTM_model_60m_w48_24_100   0.276065  0.274050  0.329681  0.318016   \n",
       "0  MultiLSTM_model_60m_w48_24_250   0.277113  0.273634  0.334135  0.317318   \n",
       "0   MultiLSTM_model_60m_w48_24_50   0.274463  0.273793  0.331377  0.318425   \n",
       "4      MultiLSTM_model_60m_w48_24   0.275618  0.273560  0.328397  0.315269   \n",
       "0  MultiLSTM_model_60m_w48_24_100   0.608956  0.484971  0.600186  0.467660   \n",
       "0  MultiLSTM_model_60m_w48_24_250   0.608108  0.483323  0.599340  0.463206   \n",
       "0   MultiLSTM_model_60m_w48_24_50   0.611054  0.485654  0.597371  0.465570   \n",
       "4      MultiLSTM_model_60m_w48_24   0.612981  0.487472  0.602100  0.468679   \n",
       "0  MultiLSTM_model_60m_w48_24_100   0.476138  0.404731  0.431022  0.385071   \n",
       "0  MultiLSTM_model_60m_w48_24_250   0.478607  0.407073  0.428681  0.387155   \n",
       "0   MultiLSTM_model_60m_w48_24_50   0.474572  0.406328  0.430936  0.385115   \n",
       "4      MultiLSTM_model_60m_w48_24   0.471118  0.405574  0.432854  0.389157   \n",
       "\n",
       "   epochs  window freq      model      station  \n",
       "0     100  w48_24  60m  MultiLSTM    guadalupe  \n",
       "0     250  w48_24  60m  MultiLSTM    guadalupe  \n",
       "0      50  w48_24  60m  MultiLSTM    guadalupe  \n",
       "4      20  w48_24  60m  MultiLSTM    guadalupe  \n",
       "0     100  w48_24  60m  MultiLSTM   mulaCorral  \n",
       "0     250  w48_24  60m  MultiLSTM   mulaCorral  \n",
       "0      50  w48_24  60m  MultiLSTM   mulaCorral  \n",
       "4      20  w48_24  60m  MultiLSTM   mulaCorral  \n",
       "0     100  w48_24  60m  MultiLSTM  quisapincha  \n",
       "0     250  w48_24  60m  MultiLSTM  quisapincha  \n",
       "0      50  w48_24  60m  MultiLSTM  quisapincha  \n",
       "4      20  w48_24  60m  MultiLSTM  quisapincha  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Gh7txFIMo6nWUy9bm+8APM3P2WvZNF/vvqFsuSZJUWc2YCiW7KKvvY7v6wohjgF2AI9Zj/9FNORFxPHA8wMSJE3uwa0mSpNbW6Ja7DmCTLsrH0nWLHhExFPgmRb+8QRGxCdA5dGZkRIyu2Tdd7L/z9Rr7z8xzM3NyZk4eP358z9+FJElSi2p0uJtFXd+6iNgGGMmafeU6jQS2Bs6iCHAdwH3lsqnAH8q/HwGW1++/fL0K+PMG1l2SJKnlNfq27DXApyNidGa+WJYdDSwBbu1mm5coRsHW2hK4CPgcMB0gM5dFxM3Ae4Af1ax7NPA7R8pKkqSBoNHh7hyKqUoujYgzgO2BU4GzaqdHiYjZwK2Z+aHMXAHcUruTiJhU/vlAZt5Vs+h04JaI+C5wGcUkxocDh/bHm5EkSWo1Db0tm5kdwEHAYIo57b4MfAc4pW7VIeU6vd3/7cC7gbcB1wHvAI7tj6dTSJIktaKGj5bNzJnAlHWsM2kdyx/nf0fB1i+7jKLVTpIkacBp9IAKSZIk9SPDnSRJUoUY7iRJkipkneEuIs6tGZ3aWXZcRIyrK9s1Ihy4IEmS1EQ9abn7MDCh80VEDAYuALarW28sxUhYSZIkNcn63pbtcqSqJEmSmss+d5IkSRViuJMkSaqQnk5ivF1EvFT+3fnkiO0jYmnNOtv3XbUkSZK0Pnoa7v67i7JfAlnzOupeS5IkqcF6Eu4O7PdaSJIkqU+sM9xl5q093VlEDN2w6kiSJGlDbPCAiihMiYgfA8/0QZ0kSZK0nnra524NEfFG4B+A9wJbAPOBqX1UL0mSJK2HXoW7iHgtRaA7BpgEvAwMAz4F/DAzV/R1BSVJktRzPXm27PYR8bmIeAC4DzgJeAg4DtiJYpTsHwx2kiRJzdeTlrvZFFOc3AX8X+CSzOwAiIix/Vg3SZIk9VJPBlQ8QdE691rgAODNEbHeffUkSZLUf9YZ7jJzO+AtwE+Bg4BpwNxydOxBOHGxJElSy+jRVCiZ+bvM/DiwFXAIcDnwLuDicpV/iYjJ/VNFSZIk9VSv5rnLzFWZeUNmfhDYEjiKIuD9PXBXRDzUD3WUJElSD633JMaZ+XJmXpaZR1PMc3ccxeALSZIkNck6B0ZExJd6sb97NqAukiRJ2kA9GfV6KrAEWEQxanZtEjhtA+skSZKk9dSTcPcoMBG4l+LxYr/OzIX9WitJkiStl55MhbIj8GbgT8DpwDMRcWlEvCcihvd3BSVJktRzPZ0KZUZmnpSZE4FDgWeAHwDzIuLCiHhrf1ZSkiRJPdPr0bKZeVtmfgTYBjgHOBr4RF9XTJIkSb3X68eIRcRbgGOAdwOjKea5+48+rpckSZLWQ4/CXUTsSRHoOue0uxb4JHBFZi7uv+pJkiSpN3oyz93/ANsB04FTgEsdLStJktSaetJytxOwFNgL2BM4M6L76e4yc0LfVE2SJEm91ZNw9+V+r4UkSZL6xDrDXWYa7iRJktpEr6dCkSRJUusy3EmSJFWI4U6SJKlCDHeSJEkVYriTJEmqEMOdJElShRjuJEmSKsRwJ0mSVCGGO0mSpAox3EmSJFVIw8NdROwWETdFxOKIeCoiTouIwevYZveIuLZcf1lEzImI8yLiVXXr/SQisoufXfv3XUmSJLWGdT5bti9FxDjgRmAmcCSwA/BtipD5hbVsOhZ4DPgZ8BSwHXAKsFdEvCEzV9SsOwv4QN32j/dF/SVJklpdQ8MdcAIwHDgqMxcCN0TEGODUiDizLFtDZv4W+G1N0S0R8SRwPbAH8PuaZYsy887+qb4kSVJra/Rt2cOA6+pC3FSKwLd/L/f1fPl7WF9UTJIkqQoaHe52pbht+orMnAMsLpetVUQMiohhEbEL8A3gHuDuutV2i4iFZd+82yOit6FRkiSpbTU63I0DFnRR3lEuW5ergWUUAXFT4O2Zuapm+R+AfwOOAP4RGExx63fvDam0JElSu2h0nzuA7KIsuimv93GKULcTxQCMayLiLZm5FCAzv7faTiOuohi88TngnWscNOJ44HiAiRMn9uItSJIktaZGt9x1AJt0UT6Wrlv0VpOZD2fmXZn5c+AQ4PXAsWtZfwlFa9+e3Sw/NzMnZ+bk8ePH96T+kiRJLa3R4W4WdX3rImIbYCR1ffHWJTOfAOYD2/dk9d7sW5IkqV01OtxdAxwSEaNryo4GlgC39mZH5aCKzSjmv+tuneEUI3Tv7X1VJUmS2k+j+9ydA5wIXBoRZ1C0up0KnFU7PUpEzAZuzcwPla+/BawA7qK4ffsa4GTgEYqpVIiIscCVwM+B2cDmwCeBrYD3NuC9SZIkNV1Dw11mdkTEQcAPgGkUQe07FAGvvl61jySbQTGY4nhgY2AOcAnw9cxcVK6zDHiWYqDFBGAp8Dtg/8yc0R/vR5IkqdU0fLRsZs4EpqxjnUl1r6dSttCtZZulwFEbWj9JkqR21ug+d5IkSepHhjtJkqQKMdxJkiRVSDOeUFEpCzrmM/3Gq5tdjX7x0ovFAOZRo8c0uSb9Y0HHfLbccouGH9PzpT0143yResrPlvbVH58thrsNMGHChGZXoV8tfGElABtvNLTJNekfW265RUP/DT1f2lujzxe/rNtXoy8E/Gxpb/3x2WK42wBTpqx10G/bmzq1GKB8zDHHNLkm1eD5op7yy7q9NfpCwM8W1TPcSVKL8cta0oZwQIUkSVKFGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVYjhTpIkqUIMd5IkSRViuJMkSaoQw50kSVKFGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVYjhTpIkqUIMd5IkSRViuJMkSaoQw50kSVKFGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVYjhTpIkqUIMd5IkSRViuJMkSaoQw50kSVKFGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVYjhTpIkqUIMd5IkSRViuJMkSaoQw50kSVKFGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVUjDw11E7BYRN0XE4oh4KiJOi4jB69hm94i4tlx/WUTMiYjzIuJVXax7ZEQ8EBFLI2JmRBzdf+9GkiSptQxp5MEiYhxwIzATOBLYAfg2Rcj8wlo2HQs8BvwMeArYDjgF2Csi3pCZK8r97wtcApwNnAgcDlwUER2ZeX2/vClJkqQW0tBwB5wADAeOysyFwA0RMQY4NSLOLMvWkJm/BX5bU3RLRDwJXA/sAfy+LP8icFtmnli+vjkidge+VK4rSZJUaY2+LXsYcF1diJtKEfj27+W+ni9/DwOIiI2AA4Ff1q03FdgnIsb2vrqSJEntpdHhbldgVm1BZs4BFpfL1ioiBkXEsIjYBfgGcA9wd7l4B2Bo/f6Bhyje584bVnVJkqTW1+hwNw5Y0EV5R7lsXa4GllEEuE2Bt2fmqpp908X+O+qWvyIijo+IGREx49lnn+3B4SVJklpbM6ZCyS7Kopvyeh8H3gT8EzAKuCYiNl7H/qO742bmuZk5OTMnjx8/vgeHlyRJam2NHlDRAWzSRflYum7RW01mPlz+eVdE/IZiBO2xwPn8bwtd/f47X69z/5IkSe2u0S13s6jrWxcR2wAjWbOv3Fpl5hPAfGD7sugRYHn9/svXq4A/r0d9JUmS2kqjw901wCERMbqm7GhgCXBrb3ZUDqrYjKL1jsxcBtwMvKdu1aOB32XmC+tbaUmSpHbR6Nuy51BMLnxpRJxB0ep2KnBW7fQoETEbuDUzP1S+/hawAriL4vbqa4CTKVrrptbs/3SKOfC+C1xGMYnx4cCh/fu2JEmSWkNDW+4yswM4CBgMTAO+DHyH4mkTtYaU63SaAewH/CdwFUVAvAR4U2Yuqtn/7cC7gbcB1wHvAI716RSSJGmgaHTLHZk5E5iyjnUm1b2eyuotdGvb9jKKVjtJkqQBpxlToUiSJKmfGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVYjhTpIkqUIMd5IkSRViuJMkSaoQw50kSVKFGO4kSZIqxHAnSZJUIYY7SZKkCjHcSZIkVYjhTpIkqUIMd5IkSRViuJMkSaoQw50kSVKFGO4kSZIqxHAnSZJUIUOaXQG1rmfmr2DxsmTJ0uUM33hos6ujFnbPA09yy/1LGDtiEEd6vmgt/vjQU1z8m5dYvGwVi4bM4ANH7cWgQdHsaqlFPfPsizz4+DLGjBhEZhLhudITttypS6efPZ2Lb1/E1fcs5phPTeWvcxc2u0pqUTf9bjaf/PpVPPj4y9wxcymf/fa1za6SWtRLi5dx8reu5ZmOlSxcnFxw6b1cMf2hZldLLeq+WU9z7Em/4Jb7l3LFnYs587zbml2lthGZ2ew6tITJkyfnjBkzml2NtZo+fTrz5s3r9+M898JKpt760mplfzNpGPvvMbzfjz1hwgSmTJnS78cZCBp1vvz6jpf46/MrVyt735RRbDJqcL8f2/OlbzTqXHli3nKm3bl4tbLttxzC4XuP7Pdje670nUadL9PuWsQTc1esVvb+g0czanj/t0u1w/kSEfdm5uSultlypzUsXb5m4O+qTAIYNnT12yQBDB3irROtabPRg6m/q7b52P6/CFB7Wrlyze+dlauaUJE2ZMtdqR1a7hplxcpVHHfyr5jz9AIAIuCsz/4db/ibrZtcM7WimbPn8a9fu5IlS5cD8J5DX8u/HveWJtdKrWrazQ/xwwvvZNGSl9l3r0l86SNT7KOpLv1mxuN87jvX0RlT3vz6iZz56cOaW6kWsraWO8NdyXC3uo4XlnDxdQ8y/4XF/O2+O/H617y62VVSC+tYuIR77n+SrbYcw+47btHs6qjFLV+xkmUvr2DUiI2aXRW1uAcfnsttMx5j6y3Gcuh+OzNsqC29nQx3PWC4kyRJ7cI+d5IkSQOE4U6SJKlCDHeSJEkVYriTJEmqEMOdJElShRjuJEmSKsRwJ0mSVCGGO0mSpAox3EmSJFWI4U6SJKlCDHeSJEkV4rNlSxHxLPBEs+vRgjYHnmt2JdQ2PF/UU54r6g3PlzVtm5nju1pguNNaRcSM7h5MLNXzfFFPea6oNzxfesfbspIkSRViuJMkSaoQw53W5dxmV0BtxfNFPeW5ot7wfOkF+9xJkiRViC13kiRJFWK4kyRJqhDDnSRJUoUY7gagiIhm10Gtz/NEveU5I7UGw90AlOUoGj+ItTbpaCv1QO3niJ8tWpuIGBQRIyJiSOc5EhHmkH7gaNkBIiJGAh8GxgEvAOdn5gvNrZVaTXmefBrYDFgBnA3MycxlTa2YWlJEjAJ+CGwJDAd+BVyZmY9FxKDMXNXUCqplRMRo4CfAq4CRwI3Af2bmzIgILyb7luFuACg/gO8FFgGjgY3Kn88DV2TmvCZWTy2iPE9+TxH+X6a4EJgIfBf4aWY+3MTqqcVExAiKz5UFwB3ArsAuwDLgA5l5jwFPABExHLgH6ACmATsBewPbAu/LzCubWL1KGtLsCqghzqT4AP574EWKgPd14HvAThHxw8yc08T6qTV8geL8eDuwIDOXRcSZwD8BO0bEVzPzgabWUK3kGIqLgPdm5l8AIuJI4OPAbRFxSGbeZsATcHD5+4OdF4kRMRn4JPDriPhQZv6sabWrIMPdwLAlcH9mPlW+fhH454j4K/AhgIg4KzPnNquCagnjgSczc25nf5jMPDki/gJ8BPh8RHwpM//c1FqqVYwGNgcWdxZk5uUR8RhwOnBdROyfmXcb8Aa8scD2wJLOgsycERGfpGh4+M+IeKE8fzxX+oAdGQeGBHbvfBERQwEy83PAeRRf3O8ql9kReuBaTnFrjczMiBhW/v19in5VbwQ+XN6+lTqAwRQXj7WfK/cDXwR+A/w8Inb0y3pgqhks8SQwH3hdWd558TgPOBW4iOJcea3nSt8w3FVYRHS2zF4CbBERHwTIzOU1H8SfB6YCp0fEBDu1Djw158l/ARtHxJcBMvPlmvPkB8CFwEcprsC9EBhgImJ0RHw8IsaXRf8FLAS+CWt8rtxP0VdzOcXtW8+XgWkQQGbeDDwFnBIRo8uLx8HlsmeBbwEPAZ+o+TzSBjDcVUzdtAQryj+nU1w5/d+IOKBc9soHMcXoyPkUfWU0AETE2Ig4B1Y7Tx6gOFfeGRIf+/kAAApuSURBVBHHlctqv7C/QPEB/KnytRcCA0Q50nE28DZgcUQMLv/9TwL2i4jzYY3z5WrgD8BR5WvPlwEgIkZGxGci4kLg3Ih4X7nogxQDKC4CyMyVnS175cXAzcD+wNAudqteMtxVSHm77CcRcV1E3FZeZe+Ymc8AxwOTgK9GxH5QfBCXm74EPE/R50oVFxFjKEbFvikixpVlkZkLKW6nLQb+tfNDufzCHlxu/heKaQw0QJTny33A/cBHM3NRZq4sF98KnAYcFRHnwWqfK1CMph1UjpZUxZXfQXdSdPPZkqI70E8i4oTMfJCi5X/fiLgiIobX3YJ9AFgF2O2jDxjuKqKcluAeYGeK/0kWAicCl0fEPpn5P8CUcvk3a66moJgW5UVgQZQaW3s1StkCcx/wGPB3mdkBr/Sxi8x8EvhHijnu/i0iPl8uXxkRG1MMwuqIiMGeJ9VXznv4APAn4AMUt9aIiFFlYFsMfBv4GnBMREyLiO0jYnh5rr0JeJai368qLCI2ouji8xRwbGYeBLwHOJ/iduskimlQjqeYBmV6REwp7yJsDhxK0ciwpIvdq5e8t10da5uWYHpEvD0zbypb7S4AvhgR/wjcDexB8T/bx7x1Ul3lBcBdwNMU0+IsLstHU9wKWVQGvEcj4miKL+wPRcRRFBcO2wD7AvvUtNyo2o6j+Hf/cRn8iYhDKb6gd6UYVHE+xYCbRyn63/2GYgTkcxSfLW/NzKWNr7oa7ECKCYq/QnEukJlzIuISiumUds7MxyPiMopb/OcCPwOGUVxs7gAclJkvNaPyVWO4q451TUtwVURMyczfRsS7gHdQNJ0fDMwD3lK27qm63k8xyeyFwMjMfDEijqDoc7kLRevtVRFxdmb+T0R8HHhDud22FOfJPpk5sxmVV1NMBbYDTouIRyn67l4J3EAR+CcAPwZek5knRcTlwMcouni8CHzYya8HjMcoJkC/ITNX1UxpciNFd449geuB5Zn5e2ByRLyTItQtAG7OzEebVPfK8QkVFVF2gD+T4srnTxExtLPvS0TsQTEaaTvg7bUhruxLNaiun4wqKiK+TtHBfRrFrbb/AK6g6E+1E0XL3BPA8Zk5u25b558agMqW3S8Dn6C4O3Aq8KPM7IiITSnuGvyAouX/7KZVVE0XESMyc3H9Z0VE3A9cm5knl68H2/rfv+xz1+ZqOrr3ZFqClyn6QLwy/UVmrjTYVV/ZX47M/HeKMPdeinPiq8D7M/MrmfnPFBcBOwAfKLtf1rbueyU4AGXmixSB7mvAxcAvO/tqUtyWnQrcBhwaxUPhB4FTnwxEmbm4/L0KVvt+WgiMqFl1REQc4TnSf2y5a0Nl36mDM/Py8vWwck6yd1DccvtVZn6wXFbbgvdzYLfM3LNZdVfjlJ3hP0bR72kZcFNmXlgu+zpFiPtMFg95f+XB3RFxMUUr3uttqVOniNgE2KbzEXS1rTMRcTUwPDMPbGYd1ZoiYhqwODOPjoixwFkUA3ReXc7moD5my12bKYPdHcCFEfEvUEw2Wy6+mWJagiOdlmBg62ZKgp+W/eg6W/DOyMzHuth8Xvl7cBfLNEBl5oKaYDe4JthNBDYB/ljTUiPVWgqMKe8gfJPiDtIbDHb9xwEVbaS8RfYtYGtgJsXw8sGZ2TkZ7YsRcQEQFKNhJ1D0k3ma4t/aaQkGgLopCT6ambPLL+AvAB+JiBsz86HMvLdcf0jnRMYRsQXF4Ir78TxRF8pW3pXl3zsC/04xxdIH7EelWjWtu4soLgDOAt5HMYDvD02tXMUZ7trL9hRz1U0DzgE+C5wYEdQEvOci4kcUQ9HPxGkJBqJ1TUmwNcWTJiiXdQa7XSlGzr6eonP8CqQ6Nbfvvwi8FdgNeJuj7VWvplvHUxTT6rwB2Ndg1/+8Ldte/kLRcndSZt5NMcXJnykC3gmdK2XmC5l5McU8VGdSdKC/Fti787aKKm2NKQnK8s4pCfaC1To7ExEfBS6nuHg4MDMfQlq7KyhGXB+QmX9sdmXU0n7F/06l9PtmV2YgcEBFm+ns+N45UCIi/g/FNAU7A/+vswWvdiCFBp6eTklQlgVF68tRwH9n5iONr7HakVNaqKeieNyYT59oEFvu2kznLZHO4JaZ9wGn8L8teMeXq24VEf/gtAQDU0+nJCjnMDssM/8EfNVgp94w2KmnDHaNZctdRUTE6yhC3i7Azyluvf098KrMnNvMuql1dDElwXconkDhlASSVBG23FVAeevtjxTh7gmKjvQHAJMNdqpTPyXBu3FKAkmqFMNdBdT0qXoG2IiiM/1+dlxVp5pBFYsoRsl3Tkmwf+eUKJKkanAqlIooJzf+LkWL3et8uLtqOSWBJA0c9rmrkIj4W+BppztRdyLi9cA1ON2JJFWW4U4aYJySQJKqzXAnSZJUIQ6okCRJqhDDnSRJUoUY7iRJkirEcCdJklQhhjtJ6kZEHB8R71zPbd8bEe/vovyWiLh4gysnSd1wtKwkdSMiZgAPZub712Pbi4HNM/OAuvLdgOWZ+XCfVFKS6viECklqIJ8eI6m/eVtW0oAWEbtHxLURMT8iFkXEQxHx0Yi4BdgL+OeIyPLn/eU2x0XE7eU2HRFxc0RMrtnnT4B3AfvXbHtquWyN27IRMSUi7oqIpRExNyLOjohRNcsPKPdxQET8KiJeiohHI+Ij/f3fR1L7seVO0kB3BTALeB+wDNgFGAN8BLgEeBQ4vVz3kfL3JOBn5ethwLHAbRHx2szsXH8isEm5H4Anuzp4eZv2WuAGikC4DfANYHvg0LrVfwz8FDgX+AfghxExIzPvXr+3LqmKDHeSBqyI2JwiRL2z5pnMN9UsXwQ8m5l31m6XmafVrDOIIpi9gSIgnpaZj0TEfGBQ/bZd+BLwBPCOzFxZ7nM+8IuI2Cczf1ez7kWZ+ZVynVuAI4CjAMOdpFd4W1bSQDYf+AtwTkQcHRETerJRRLwmIn4dEXOBlcByiha/ndejDnsDv+4MdqVLgBXAvnXrXt/5R2YuBx4Gtl6PY0qqMMOdpAErM1cBfws8A5wPPBMRv4mI13e3TUSMpghZ2wCfAvajaLW7D9h4ParxKmBuXb1WAs8Dm9atu6Du9cvreUxJFeZtWUkDWmbOAt4VEUMpgtoZwFUR0V2L2D4UrWUHl9sCEBFj17MKTwOrtRhGxGBgM4qWRUnqFVvuJIniNmdmTgfOomhN24SuW8aGl7+XdRZExJspBlnU6mmr2l3A35eBrtNRFBfft/e0/pLUyZY7SQNWROwBfAv4BcWo2HHAZ4D7MnN+RMwCDomIQyhukz4G3Am8BPw4Is6kaMU7Ffhr3e5nAUeWT7h4EngqM5/qohpfAf4AXBYR/1Hu7wzgurrBFJLUI7bcSRrInqHo7/Z54BrgbOAh4B3l8q+Ur38J3AMckZlzgfcAWwKXA58ATgBm1+37bIq+eeeX2x7fVQUy80/AYRS3Zi8tj3kR8O6+eIOSBh4fPyZJklQhttxJkiRViOFOkiSpQgx3kiRJFWK4kyRJqhDDnSRJUoUY7iRJkirEcCdJklQhhjtJkqQKMdxJkiRVyP8HhIE+YxH4XhEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(figsize=(10,5))\n",
    "ax = sns.boxplot(x='epochs', y='mae_test', data=df, color='#DFE3EE')\n",
    "ax = sns.swarmplot(x=\"epochs\", y=\"mae_test\", data=df, color='#3B5998')\n",
    "ax.set_xlabel('station',fontsize=15)\n",
    "ax.set_ylabel(\"MAE\",fontsize=15)\n",
    "ax.tick_params(labelsize=15)\n",
    "ax.set_xticklabels(labels = df.epochs.unique(),rotation = 45, ha=\"right\")\n",
    "plt.show()\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(f'{path}/boxplot_epochs.png',dpi = 75,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(epochs)</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.003074</td>\n",
       "      <td>0.000768</td>\n",
       "      <td>0.236545</td>\n",
       "      <td>0.917744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>728.0</td>\n",
       "      <td>2.365048</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              df    sum_sq   mean_sq         F    PR(>F)\n",
       "C(epochs)    4.0  0.003074  0.000768  0.236545  0.917744\n",
       "Residual   728.0  2.365048  0.003249       NaN       NaN"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.anova_stat(df=results, res_var='mae_test', anova_model='mae_test ~ C(epochs)')\n",
    "res.anova_summary\n",
    "### No significant difference. We keep hourly predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### No significative difference rainsing the epochs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare more o less variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "d= {'data':['all_variables','all_variables','all_variables','reduced_variables','reduced_variables','reduced_variables'],\n",
    "   'MAE':[0.362,0.362,0.356,0.339,0.335,0.335]}\n",
    "data = pd.DataFrame(data = d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df</th>\n",
       "      <th>sum_sq</th>\n",
       "      <th>mean_sq</th>\n",
       "      <th>F</th>\n",
       "      <th>PR(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(data)</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>0.000840</td>\n",
       "      <td>96.942308</td>\n",
       "      <td>0.000597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Residual</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           df    sum_sq   mean_sq          F    PR(>F)\n",
       "C(data)   1.0  0.000840  0.000840  96.942308  0.000597\n",
       "Residual  4.0  0.000035  0.000009        NaN       NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ANOVA table using bioinfokit v1.0.3 or later (it uses wrapper script for anova_lm)\n",
    "from bioinfokit.analys import stat\n",
    "res = stat()\n",
    "res.anova_stat(df=data, res_var='MAE', anova_model='MAE ~ C(data)')\n",
    "res.anova_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>group1</th>\n",
       "      <th>group2</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Lower</th>\n",
       "      <th>Upper</th>\n",
       "      <th>q-value</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>all_variables</td>\n",
       "      <td>reduced_variables</td>\n",
       "      <td>0.023667</td>\n",
       "      <td>0.016993</td>\n",
       "      <td>0.03034</td>\n",
       "      <td>13.924246</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          group1             group2      Diff     Lower    Upper    q-value  \\\n",
       "0  all_variables  reduced_variables  0.023667  0.016993  0.03034  13.924246   \n",
       "\n",
       "   p-value  \n",
       "0    0.001  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = stat()\n",
    "res.tukey_hsd(df=data, res_var='MAE', xfac_var='data', anova_model='MAE ~ C(data)')\n",
    "test = res.tukey_summary\n",
    "test[test['p-value']<0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.36033333333333334\n",
      "0.0030912061651652374\n"
     ]
    }
   ],
   "source": [
    "print(np.mean([0.363,0.362,0.356]))\n",
    "print(np.std([0.363,0.362,0.356]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3363333333333334\n",
      "0.0018856180831641283\n"
     ]
    }
   ],
   "source": [
    "print(np.mean([0.339,0.335,0.335]))\n",
    "print(np.std([0.339,0.335,0.335]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "he =[0.343,\n",
    "0.346,\n",
    "0.337,\n",
    "0.341,\n",
    "0.334,\n",
    "0.346]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "zero=[0.339,\n",
    "0.335,\n",
    "0.334,\n",
    "0.336,\n",
    "0.333,\n",
    "0.335]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3411666666666667 0.004450343307606213\n",
      "0.3353333333333333 0.0018856180831641283\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(he), np.std(he))\n",
    "print(np.mean(zero), np.std(zero))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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