{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97298dda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Päivitetty 2022-10-15 12:13:25.532336\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "print(f'Päivitetty {datetime.now()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e07b41",
   "metadata": {},
   "source": [
    "# Pääkomponenttianalyysi\n",
    "\n",
    "Lasketaan kurjenmiekkojen terä- ja verholehtien pituuksien ja leveyksien perusteella kaksi pääkomponenttia (yksinkertaistetaan muuttujarakennetta)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee64b0b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a48794a8",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esimerkkiaineisto löytyy seaborn-kirjastosta\n",
    "iris = sns.load_dataset('iris')\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24ee287b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X sisältää vain terä- ja verholehtien pituudet ja leveydet\n",
    "X = iris.drop('species', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec479659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Yhdistetään neljä muuttujaa kahdeksi pääkomponentiksi\n",
    "malli = PCA(n_components=2)\n",
    "malli.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cba4d91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.92461872, 0.05306648])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Katsotaan kuinka monta prosenttia arvojen vaihtelusta pääkomponentit selittävät\n",
    "malli.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac57751",
   "metadata": {},
   "source": [
    "* Ensimmäinen pääkomponentti selittää 92,5 % vaihtelusta\n",
    "* Toinen pääkomponentti selittää 5,3 % vaihtelusta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2facad60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x23471ed1370>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lasketaan pääkomponenttien arvot ja lisätään ne alkuperäiseen dataan\n",
    "\n",
    "X_2D = malli.transform(X)\n",
    "iris['pca1'] = X_2D[:, 0]\n",
    "iris['pca2'] = X_2D[:, 1]\n",
    "\n",
    "# Katsotaan miten lajikkeet jakautuvat pääkomponenttien suhteen\n",
    "sns.jointplot(x='pca1', y='pca2', hue='species', data=iris)"
   ]
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}