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multivariate Timerseries/.ipynb_checkpoints/Template-checkpoint.ipynb

@@ -2,19 +2,144 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "7cd872e9",
+   "id": "f248adc5",
    "metadata": {},
    "source": [
     "# Template"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "67ffcf07",
+   "id": "1a956727",
    "metadata": {},
    "source": [
-    "### "
+    "### Default libraries"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "59d3e244",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
+    "\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e03b267",
+   "metadata": {},
+   "source": [
+    "### Default Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a9f3e954",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Custom prediction function\n",
+    "def custom_predict(X, model):\n",
+    "    \"\"\"\n",
+    "    Custom prediction function that overrides model predictions based on a 'flagged' column in the input data.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    - X (DataFrame): Input data with features including a 'flagged' column.\n",
+    "    - model (model object): Trained model object that has a predict method.\n",
+    "    \n",
+    "    Returns:\n",
+    "    - y_custom_pred (array): Array of predictions with overridden values based on 'flagged' column.\n",
+    "    \"\"\"\n",
+    "    y_pred = model.predict(X)\n",
+    "    \n",
+    "    # Apply the flagged logic\n",
+    "    y_custom_pred = np.where(X['flagged'] == 1, 0, y_pred)\n",
+    "    \n",
+    "    return y_custom_pred\n",
+    "\n",
+    "def metrics(y_test, y_pred):\n",
+    "    \"\"\"\n",
+    "    Computes and prints several evaluation metrics for regression models.\n",
+    "    \n",
+    "    Parameters:\n",
+    "    - y_test (array): True target values.\n",
+    "    - y_pred (array): Predicted target values from the model.\n",
+    "    \n",
+    "    Prints:\n",
+    "    - MSE, RMSE, MAE, R^2, and MAPE values.\n",
+    "    \"\"\"\n",
+    "    # Previous metrics\n",
+    "    mse  = mean_squared_error(y_test, y_pred)\n",
+    "    rmse = mean_squared_error(y_test, y_pred, squared=False)\n",
+    "    mae  = mean_absolute_error(y_test, y_pred)\n",
+    "    r2   = r2_score(y_test, y_pred)\n",
+    "    mape = 100 * (sum(abs((y_test - y_pred) / y_test)) / len(y_test))\n",
+    "    \n",
+    "    print(f\" MSE = {mse}\\nRMSE = {rmse}\\n MAE = {mae}\\n  %//R^2% = {r2}\\n MAPE = {mape}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61c478bb",
+   "metadata": {},
+   "source": [
+    "### load data temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5cc43084",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the electricity consumption dataset\n",
+    "filepath = 'C:/Users/madsh/OneDrive/Dokumenter/kandidat/Fællesmappe/Speciale/Forecasting-energy-consumption-in-Denmark/Data/Combined data/'\n",
+    "data_flagged = pd.read_csv(filepath + 'combined_daily_flagged.csv')\n",
+    "\n",
+    "# Put HourDK as DataFrame index\n",
+    "data_flagged.set_index('HourDK', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48efce61",
+   "metadata": {},
+   "source": [
+    "### Deualt test and training split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e5e87d9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Re-split the data into training and test sets based on the criteria\n",
+    "SplitDate = '2022-06-30'\n",
+    "training_set = data_flagged.loc[data_flagged.index <= SplitDate]\n",
+    "test_set = data_flagged.loc[data_flagged.index > SplitDate]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "903bbff8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -33,7 +158,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,