W02_linear_polynomial_regression_solution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we load all packages that are needed in this notebook.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn import linear_model\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#------------------------------------------------------------------------------\n",
    "#                   Lineaire Regressie  Functies\n",
    "#------------------------------------------------------------------------------\n",
    "def model(X, y, degree):\n",
    "    \"\"\" function that models a polynomial\"\"\"\n",
    "    poly_features = PolynomialFeatures(degree=degree, include_bias=False)\n",
    "    X_poly = poly_features.fit_transform(X)\n",
    "    lin_reg = LinearRegression()\n",
    "    lin_reg.fit(X_poly, y)\n",
    "    print('\\nmodel outcome:')\n",
    "    print(f'intercept: {lin_reg.intercept_} coefficient {lin_reg.coef_}')\n",
    "    return [lin_reg, poly_features]\n",
    "\n",
    "\n",
    "def predict(X, model, poly_features, n=1000):\n",
    "    \"\"\" function that predicts y based on a model \"\"\"\n",
    "    X_pred=np.linspace(X.min(), X.max(), n).reshape(n, 1)\n",
    "    X_pred_poly = poly_features.transform(X_pred)\n",
    "    y_pred = model.predict(X_pred_poly)\n",
    "    return [X_pred, y_pred]\n",
    "\n",
    "\n",
    "#4. Plot the points and the regression curve\n",
    "def plot_polynomial(X,y,X_pred,y_pred):\n",
    "    \"\"\"function that plots data and prediction\"\"\"\n",
    "    plt.plot(X, y, \"b.\", label = 'Data')\n",
    "    plt.plot(X_pred, y_pred, \"r-\", linewidth=2, label=\"model trainset\")\n",
    "    plt.ylabel('Average Temperature')\n",
    "    plt.xlabel('Year')\n",
    "    plt.title('Average temperature in the Netherlands -  TRAIN')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>AverageTemperature</th>\n",
       "      <th>AverageTemperatureUncertainty</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1743-11-01</th>\n",
       "      <td>8.009</td>\n",
       "      <td>1.779</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743-12-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-02-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-03-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature  AverageTemperatureUncertainty      Country\n",
       "dt                                                                        \n",
       "1743-11-01               8.009                          1.779  Netherlands\n",
       "1743-12-01                 NaN                            NaN  Netherlands\n",
       "1744-01-01                 NaN                            NaN  Netherlands\n",
       "1744-02-01                 NaN                            NaN  Netherlands\n",
       "1744-03-01                 NaN                            NaN  Netherlands"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#------------------------------------------------------------------------------\n",
    "#                          Clean\n",
    "#------------------------------------------------------------------------------\n",
    "# Read the csv from its location\n",
    "df = pd.read_csv(\"data/GlobalLandTemperaturesByCountry.csv\", index_col=0, parse_dates=True)\n",
    "\n",
    "# Select the country (Netherlands), and drop unknown values\n",
    "df = df.loc[df[\"Country\"]==\"Netherlands\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>AverageTemperature</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1743-11-01</th>\n",
       "      <td>8.009</td>\n",
       "      <td>1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-04-01</th>\n",
       "      <td>8.443</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-05-01</th>\n",
       "      <td>11.643</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-06-01</th>\n",
       "      <td>14.773</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1744-07-01</th>\n",
       "      <td>16.277</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            AverageTemperature  Year\n",
       "dt                                  \n",
       "1743-11-01               8.009  1743\n",
       "1744-04-01               8.443  1744\n",
       "1744-05-01              11.643  1744\n",
       "1744-06-01              14.773  1744\n",
       "1744-07-01              16.277  1744"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "\n",
    "# Drop columns we won't use\n",
    "df = df.drop([\"AverageTemperatureUncertainty\",\"Country\"], axis=1)\n",
    "\n",
    "df[\"Year\"] = df.index.year\n",
    "\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/sh/djdw6j_j1zbfpbcf7k2z77rc0000gp/T/ipykernel_1692/434381446.py:6: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  data = data[df[\"Year\"].value_counts()==12]\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>AverageTemperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1753</th>\n",
       "      <td>9.440083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1754</th>\n",
       "      <td>9.111833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1755</th>\n",
       "      <td>8.924750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1756</th>\n",
       "      <td>9.568667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1757</th>\n",
       "      <td>9.436250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      AverageTemperature\n",
       "Year                    \n",
       "1753            9.440083\n",
       "1754            9.111833\n",
       "1755            8.924750\n",
       "1756            9.568667\n",
       "1757            9.436250"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take average temperature per year\n",
    "data = df.groupby([\"Year\"]).mean()\n",
    "\n",
    "# And drop years where less than 12 values are known (why do we do this?)\n",
    "# This selection can be done in a neater way, this generates a warning (but the warning can be ignored in this case)\n",
    "data = data[df[\"Year\"].value_counts()==12]\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/sh/djdw6j_j1zbfpbcf7k2z77rc0000gp/T/ipykernel_1692/3608678702.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data[\"Year\"] = data.index\n"
     ]
    },
    {
     "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": [
    "# Plot the data\n",
    "data[\"Year\"] = data.index\n",
    "plt.scatter(data[\"Year\"], data[\"AverageTemperature\"])\n",
    "plt.ylabel('Average Temperature')\n",
    "plt.xlabel('Year')\n",
    "plt.title('Average temperature in the Netherlands')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#------------------------------------------------------------------------------\n",
    "#                         Rolling mean\n",
    "#------------------------------------------------------------------------------\n",
    "\n",
    "df = data[\"AverageTemperature\"].rolling(window=10).mean()\n",
    "df = df.dropna()\n",
    "\n",
    "#df.to_csv('temperatuur.csv')\n",
    "\n",
    "y = df.values.reshape(-1,1)\n",
    "X = df.index.values\n",
    "X = X.reshape(-1,1)\n",
    "\n",
    "plt.plot(X,y)\n",
    "plt.ylabel('Average Temperature')\n",
    "plt.xlabel('Year')\n",
    "plt.title('Average temperature in the Netherlands')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#------------------------------------------------------------------------------\n",
    "#                         Regression\n",
    "#------------------------------------------------------------------------------\n",
    "#1. Normalise\n",
    "scaler = StandardScaler()\n",
    "scaler = scaler.fit(X)\n",
    "X = scaler.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train and test data are split:\n",
      "(200, 1) (200, 1)\n",
      "(51, 1) (51, 1)\n",
      "\n",
      "model outcome:\n",
      "intercept: [9.19761073] coefficient [[ 0.16002696  1.17795799  0.30556493 -1.65840727 -0.3236207   0.87284898\n",
      "   0.0905357  -0.14024564]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#2. Split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)\n",
    "print(\"train and test data are split:\")\n",
    "print(X_train.shape, y_train.shape)\n",
    "print(X_test.shape, y_test.shape)\n",
    "\n",
    "#3. Model\n",
    "degree = 8\n",
    "lin_reg, poly_features = model(X_train, y_train, degree)\n",
    "X_pred, y_pred = predict(X_train, lin_reg, poly_features)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "model outcome:\n",
      "intercept: [9.26708662] coefficient [[0.12447822 0.23135511 0.08295869]]\n"
     ]
    }
   ],
   "source": [
    "#3. Model\n",
    "degree = 3\n",
    "lin_reg, poly_features = model(X_train, y_train, degree)\n",
    "X_pred, y_pred = predict(X_train, lin_reg, poly_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.35\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#------------------------------------------------------------------------------\n",
    "#                         Evaluation\n",
    "#------------------------------------------------------------------------------\n",
    "\n",
    "#4. Plot the points and the regression curve\n",
    "plot_polynomial(X,y,X_pred,y_pred)\n",
    "\n",
    "#5 calculate mean squared error from test set\n",
    "X_pred, y_pred = predict(X_test, lin_reg, poly_features, len(y_test))\n",
    "print('Mean squared error: {:.2f}'.format(mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}