catboost_model_eval.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostRegressor at 0x16329bd2790>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import catboost as cb\n",
    "\n",
    "model_cb = cb.CatBoostRegressor()\n",
    "model_cb.load_model(\"cb_no1_no_bool_lookback_24.cbm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "from msal import PublicClientApplication\n",
    "\n",
    "from cognite.client import CogniteClient\n",
    "from cognite.client.data_classes import TimeSeries, Asset\n",
    "\n",
    "# Log-in detaljer\n",
    "TENANT_ID = os.getenv(\"AZURE_TENANT_ID\")\n",
    "CLIENT_ID = os.getenv(\"AZURE_CLIENT_ID\")\n",
    "CDF_CLUSTER = \"az-power-no-northeurope\"\n",
    "COGNITE_PROJECT = \"heco-dev\"\n",
    "\n",
    "# Code to log-in WIHTOUT client_secret\n",
    "SCOPES = [f\"https://{CDF_CLUSTER}.cognitedata.com/.default\"]\n",
    "\n",
    "AUTHORITY_HOST_URI = \"https://login.microsoftonline.com\"\n",
    "AUTHORITY_URI = AUTHORITY_HOST_URI + \"/\" + TENANT_ID\n",
    "PORT = 53000\n",
    "\n",
    "\n",
    "def authenticate_azure():\n",
    "\n",
    "    app = PublicClientApplication(client_id=CLIENT_ID, authority=AUTHORITY_URI)\n",
    "\n",
    "    # interactive login - make sure you have http://localhost:port in Redirect URI in App Registration as type \"Mobile and desktop applications\"\n",
    "    creds = app.acquire_token_interactive(scopes=SCOPES, port=PORT)\n",
    "    return creds\n",
    "\n",
    "\n",
    "creds = authenticate_azure()\n",
    "\n",
    "client = CogniteClient(\n",
    "    token_url=creds[\"id_token_claims\"][\"iss\"],\n",
    "    token=creds[\"access_token\"],\n",
    "    token_client_id=creds[\"id_token_claims\"][\"aud\"],\n",
    "    project=COGNITE_PROJECT,\n",
    "    base_url=f\"https://{CDF_CLUSTER}.cognitedata.com\",\n",
    "    client_name=\"cognite-python-dev\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NO1_consumption_per_15min\n",
      "NO1_el_price_per_hour\n",
      "NO1_temperature_per_15min\n",
      "NO1_cloud_coverage_per_15min\n",
      "NO1_consumption_forecast_per_15min\n"
     ]
    }
   ],
   "source": [
    "# Defining curves\n",
    "curves = [\"NO1_consumption_per_15min\",\n",
    "\"NO1_el_price_per_hour\",\n",
    "\"NO1_temperature_per_15min\",\n",
    "\"NO1_cloud_coverage_per_15min\",\n",
    "\"NO1_consumption_forecast_per_15min\"\n",
    "]\n",
    "\n",
    "# Defining relevant dates\n",
    "start_date = datetime(2022,6,4)\n",
    "end_date = datetime(2022,6,7)\n",
    "\n",
    "df_watt = pd.DataFrame()\n",
    "for curve in curves:\n",
    "    print(curve)\n",
    "    hm = client.datapoints.retrieve_dataframe(\n",
    "        start=start_date,\n",
    "        end=end_date,\n",
    "        aggregates=[\"average\"],\n",
    "        granularity=\"1h\",\n",
    "        id=client.time_series.retrieve(external_id=curve).id,)\n",
    "    df_watt = pd.merge(df_watt, hm, left_index=True, right_index=True, how=\"outer\")\n",
    "\n",
    "df_watt.columns = [\"cons_actual\",\"price_forecast\",\"temp_forecast\",\"cc_forecast\",\"cons_forecast\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>cons_actual</th>\n",
       "      <th>price_forecast</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>cc_forecast</th>\n",
       "      <th>cons_forecast</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-06-04 00:00:00</th>\n",
       "      <td>2383.500</td>\n",
       "      <td>143.11</td>\n",
       "      <td>9.0875</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>2506.146925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 01:00:00</th>\n",
       "      <td>2367.125</td>\n",
       "      <td>142.79</td>\n",
       "      <td>8.2750</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>2444.991048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 02:00:00</th>\n",
       "      <td>2320.250</td>\n",
       "      <td>142.47</td>\n",
       "      <td>8.1875</td>\n",
       "      <td>0.31375</td>\n",
       "      <td>2394.595393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 03:00:00</th>\n",
       "      <td>2281.500</td>\n",
       "      <td>142.61</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0.35875</td>\n",
       "      <td>2327.598184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 04:00:00</th>\n",
       "      <td>2302.375</td>\n",
       "      <td>144.39</td>\n",
       "      <td>10.6375</td>\n",
       "      <td>0.48250</td>\n",
       "      <td>2314.102861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 19:00:00</th>\n",
       "      <td>2727.125</td>\n",
       "      <td>140.02</td>\n",
       "      <td>17.7000</td>\n",
       "      <td>0.89875</td>\n",
       "      <td>2576.793434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 20:00:00</th>\n",
       "      <td>2670.750</td>\n",
       "      <td>140.15</td>\n",
       "      <td>16.1375</td>\n",
       "      <td>0.96125</td>\n",
       "      <td>2556.965603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 21:00:00</th>\n",
       "      <td>2589.000</td>\n",
       "      <td>130.16</td>\n",
       "      <td>14.9125</td>\n",
       "      <td>0.97250</td>\n",
       "      <td>2534.456478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 22:00:00</th>\n",
       "      <td>2402.125</td>\n",
       "      <td>121.35</td>\n",
       "      <td>14.2000</td>\n",
       "      <td>0.99125</td>\n",
       "      <td>2532.415520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 23:00:00</th>\n",
       "      <td>2264.375</td>\n",
       "      <td>120.41</td>\n",
       "      <td>13.3875</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2424.336558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     cons_actual  price_forecast  temp_forecast  cc_forecast  \\\n",
       "2022-06-04 00:00:00     2383.500          143.11         9.0875      0.20500   \n",
       "2022-06-04 01:00:00     2367.125          142.79         8.2750      0.18750   \n",
       "2022-06-04 02:00:00     2320.250          142.47         8.1875      0.31375   \n",
       "2022-06-04 03:00:00     2281.500          142.61         9.0000      0.35875   \n",
       "2022-06-04 04:00:00     2302.375          144.39        10.6375      0.48250   \n",
       "...                          ...             ...            ...          ...   \n",
       "2022-06-06 19:00:00     2727.125          140.02        17.7000      0.89875   \n",
       "2022-06-06 20:00:00     2670.750          140.15        16.1375      0.96125   \n",
       "2022-06-06 21:00:00     2589.000          130.16        14.9125      0.97250   \n",
       "2022-06-06 22:00:00     2402.125          121.35        14.2000      0.99125   \n",
       "2022-06-06 23:00:00     2264.375          120.41        13.3875      1.00000   \n",
       "\n",
       "                     cons_forecast  \n",
       "2022-06-04 00:00:00    2506.146925  \n",
       "2022-06-04 01:00:00    2444.991048  \n",
       "2022-06-04 02:00:00    2394.595393  \n",
       "2022-06-04 03:00:00    2327.598184  \n",
       "2022-06-04 04:00:00    2314.102861  \n",
       "...                            ...  \n",
       "2022-06-06 19:00:00    2576.793434  \n",
       "2022-06-06 20:00:00    2556.965603  \n",
       "2022-06-06 21:00:00    2534.456478  \n",
       "2022-06-06 22:00:00    2532.415520  \n",
       "2022-06-06 23:00:00    2424.336558  \n",
       "\n",
       "[72 rows x 5 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "from pandasql import sqldf\n",
    "pysqldf = lambda q: sqldf(q,globals())\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>cons_actual</th>\n",
       "      <th>price_forecast</th>\n",
       "      <th>temp_forecast</th>\n",
       "      <th>cc_forecast</th>\n",
       "      <th>cons_forecast</th>\n",
       "      <th>Day sin</th>\n",
       "      <th>Day cos</th>\n",
       "      <th>Week sin</th>\n",
       "      <th>Week cos</th>\n",
       "      <th>Year sin</th>\n",
       "      <th>Year cos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-06-04 00:00:00</th>\n",
       "      <td>2383.500</td>\n",
       "      <td>143.11</td>\n",
       "      <td>9.0875</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>2506.146925</td>\n",
       "      <td>-1.513536e-12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974928</td>\n",
       "      <td>-0.222521</td>\n",
       "      <td>0.466789</td>\n",
       "      <td>-0.884369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 01:00:00</th>\n",
       "      <td>2367.125</td>\n",
       "      <td>142.79</td>\n",
       "      <td>8.2750</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>2444.991048</td>\n",
       "      <td>2.588190e-01</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.466154</td>\n",
       "      <td>-0.884703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 02:00:00</th>\n",
       "      <td>2320.250</td>\n",
       "      <td>142.47</td>\n",
       "      <td>8.1875</td>\n",
       "      <td>0.31375</td>\n",
       "      <td>2394.595393</td>\n",
       "      <td>5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>0.955573</td>\n",
       "      <td>-0.294755</td>\n",
       "      <td>0.465520</td>\n",
       "      <td>-0.885037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 03:00:00</th>\n",
       "      <td>2281.500</td>\n",
       "      <td>142.61</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>0.35875</td>\n",
       "      <td>2327.598184</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>0.943883</td>\n",
       "      <td>-0.330279</td>\n",
       "      <td>0.464886</td>\n",
       "      <td>-0.885371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-04 04:00:00</th>\n",
       "      <td>2302.375</td>\n",
       "      <td>144.39</td>\n",
       "      <td>10.6375</td>\n",
       "      <td>0.48250</td>\n",
       "      <td>2314.102861</td>\n",
       "      <td>8.660254e-01</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.930874</td>\n",
       "      <td>-0.365341</td>\n",
       "      <td>0.464251</td>\n",
       "      <td>-0.885704</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>2022-06-06 19:00:00</th>\n",
       "      <td>2727.125</td>\n",
       "      <td>140.02</td>\n",
       "      <td>17.7000</td>\n",
       "      <td>0.89875</td>\n",
       "      <td>2576.793434</td>\n",
       "      <td>-9.659258e-01</td>\n",
       "      <td>0.258819</td>\n",
       "      <td>-0.916562</td>\n",
       "      <td>-0.399892</td>\n",
       "      <td>0.423795</td>\n",
       "      <td>-0.905758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 20:00:00</th>\n",
       "      <td>2670.750</td>\n",
       "      <td>140.15</td>\n",
       "      <td>16.1375</td>\n",
       "      <td>0.96125</td>\n",
       "      <td>2556.965603</td>\n",
       "      <td>-8.660254e-01</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.930874</td>\n",
       "      <td>-0.365341</td>\n",
       "      <td>0.423146</td>\n",
       "      <td>-0.906062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 21:00:00</th>\n",
       "      <td>2589.000</td>\n",
       "      <td>130.16</td>\n",
       "      <td>14.9125</td>\n",
       "      <td>0.97250</td>\n",
       "      <td>2534.456478</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.943883</td>\n",
       "      <td>-0.330279</td>\n",
       "      <td>0.422496</td>\n",
       "      <td>-0.906365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 22:00:00</th>\n",
       "      <td>2402.125</td>\n",
       "      <td>121.35</td>\n",
       "      <td>14.2000</td>\n",
       "      <td>0.99125</td>\n",
       "      <td>2532.415520</td>\n",
       "      <td>-5.000000e-01</td>\n",
       "      <td>0.866025</td>\n",
       "      <td>-0.955573</td>\n",
       "      <td>-0.294755</td>\n",
       "      <td>0.421847</td>\n",
       "      <td>-0.906667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-06-06 23:00:00</th>\n",
       "      <td>2264.375</td>\n",
       "      <td>120.41</td>\n",
       "      <td>13.3875</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2424.336558</td>\n",
       "      <td>-2.588190e-01</td>\n",
       "      <td>0.965926</td>\n",
       "      <td>-0.965926</td>\n",
       "      <td>-0.258819</td>\n",
       "      <td>0.421197</td>\n",
       "      <td>-0.906969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     cons_actual  price_forecast  temp_forecast  cc_forecast  \\\n",
       "2022-06-04 00:00:00     2383.500          143.11         9.0875      0.20500   \n",
       "2022-06-04 01:00:00     2367.125          142.79         8.2750      0.18750   \n",
       "2022-06-04 02:00:00     2320.250          142.47         8.1875      0.31375   \n",
       "2022-06-04 03:00:00     2281.500          142.61         9.0000      0.35875   \n",
       "2022-06-04 04:00:00     2302.375          144.39        10.6375      0.48250   \n",
       "...                          ...             ...            ...          ...   \n",
       "2022-06-06 19:00:00     2727.125          140.02        17.7000      0.89875   \n",
       "2022-06-06 20:00:00     2670.750          140.15        16.1375      0.96125   \n",
       "2022-06-06 21:00:00     2589.000          130.16        14.9125      0.97250   \n",
       "2022-06-06 22:00:00     2402.125          121.35        14.2000      0.99125   \n",
       "2022-06-06 23:00:00     2264.375          120.41        13.3875      1.00000   \n",
       "\n",
       "                     cons_forecast       Day sin   Day cos  Week sin  \\\n",
       "2022-06-04 00:00:00    2506.146925 -1.513536e-12  1.000000  0.974928   \n",
       "2022-06-04 01:00:00    2444.991048  2.588190e-01  0.965926  0.965926   \n",
       "2022-06-04 02:00:00    2394.595393  5.000000e-01  0.866025  0.955573   \n",
       "2022-06-04 03:00:00    2327.598184  7.071068e-01  0.707107  0.943883   \n",
       "2022-06-04 04:00:00    2314.102861  8.660254e-01  0.500000  0.930874   \n",
       "...                            ...           ...       ...       ...   \n",
       "2022-06-06 19:00:00    2576.793434 -9.659258e-01  0.258819 -0.916562   \n",
       "2022-06-06 20:00:00    2556.965603 -8.660254e-01  0.500000 -0.930874   \n",
       "2022-06-06 21:00:00    2534.456478 -7.071068e-01  0.707107 -0.943883   \n",
       "2022-06-06 22:00:00    2532.415520 -5.000000e-01  0.866025 -0.955573   \n",
       "2022-06-06 23:00:00    2424.336558 -2.588190e-01  0.965926 -0.965926   \n",
       "\n",
       "                     Week cos  Year sin  Year cos  \n",
       "2022-06-04 00:00:00 -0.222521  0.466789 -0.884369  \n",
       "2022-06-04 01:00:00 -0.258819  0.466154 -0.884703  \n",
       "2022-06-04 02:00:00 -0.294755  0.465520 -0.885037  \n",
       "2022-06-04 03:00:00 -0.330279  0.464886 -0.885371  \n",
       "2022-06-04 04:00:00 -0.365341  0.464251 -0.885704  \n",
       "...                       ...       ...       ...  \n",
       "2022-06-06 19:00:00 -0.399892  0.423795 -0.905758  \n",
       "2022-06-06 20:00:00 -0.365341  0.423146 -0.906062  \n",
       "2022-06-06 21:00:00 -0.330279  0.422496 -0.906365  \n",
       "2022-06-06 22:00:00 -0.294755  0.421847 -0.906667  \n",
       "2022-06-06 23:00:00 -0.258819  0.421197 -0.906969  \n",
       "\n",
       "[72 rows x 11 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_watt['Seconds'] = df_watt.index.map(pd.Timestamp.timestamp)\n",
    "day = 60*60*24\n",
    "year = 365.2425*day\n",
    "week = day*7\n",
    "\n",
    "df_watt['Day sin'] = np.sin(df_watt['Seconds'] * (2* np.pi / day))\n",
    "df_watt['Day cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / day))\n",
    "df_watt['Week sin'] = np.sin(df_watt['Seconds'] * (2 * np.pi / week))\n",
    "df_watt['Week cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / week))\n",
    "df_watt['Year sin'] = np.sin(df_watt['Seconds'] * (2 * np.pi / year))\n",
    "df_watt['Year cos'] = np.cos(df_watt['Seconds'] * (2 * np.pi / year))\n",
    "\n",
    "df_watt=df_watt.drop(['Seconds'], axis=1)\n",
    "df_watt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['price_forecast', 'temp_forecast', 'cc_forecast', 'cons_forecast', 'Day sin', 'Day cos', 'Week sin', 'Week cos', 'Year sin', 'Year cos']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16329c27610>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = list(df_watt)[1:]\n",
    "print(cols)\n",
    "X_val = df_watt[cols]\n",
    "y_val = df_watt['cons_actual']\n",
    "pred_future = model_cb.predict(X_val)\n",
    "\"\"\"rmse = (np.sqrt(mean_squared_error(y_val, pred)))\n",
    "r2 = r2_score(y_val, pred)\"\"\"\n",
    "dates = df_watt.index\n",
    "plt.figure(figsize=(20,12))\n",
    "# plt.plot(y_val, label='actual')\n",
    "plt.plot(dates, pred_future, label='prediction')\n",
    "plt.plot(df_watt['cons_actual'], label='consumption')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE Volue: 198404.57576889626\n",
      "MAE Volue: 424.7627203473029\n",
      "MSLE Volue: 0.027364936583782615\n",
      "R2 Volue: -2.3635697160464093\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_squared_log_error, r2_score\n",
    "\n",
    "# df_watt = df_watt.dropna()\n",
    "\n",
    "cons_actual = df_watt[\"cons_actual\"].values\n",
    "cons_forecast = pred_future\n",
    "\n",
    "# cons_actual = cons_actual[~np.isnan(cons_actual)]\n",
    "# cons_forecast = cons_forecast[~np.isnan(cons_forecast)]\n",
    "\n",
    "print(f\"MSE Volue: {mean_squared_error(pred_future, cons_actual)}\")\n",
    "print(f\"MAE Volue: {mean_absolute_error(pred_future, cons_actual)}\")\n",
    "print(f\"MSLE Volue: {mean_squared_log_error(pred_future, cons_actual)}\")\n",
    "print(f\"R2 Volue: {r2_score(pred_future, cons_actual)}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('p39_sommer')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0a8d2357f8c9ab0bd5f00168acbe48004ca520c58a19fa70219e2bdec9ce91e8"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}