{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA analysis using the Iris dataset.\n",
    "(See sklearn documaentation for more information.)\n",
    "The following code loads the datset and outputs a description of it.\n",
    "\n",
    "displays 2D plots of the iris dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
       "        [4.9, 3. , 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.3, 0.2],\n",
       "        [4.6, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.6, 1.4, 0.2],\n",
       "        [5.4, 3.9, 1.7, 0.4],\n",
       "        [4.6, 3.4, 1.4, 0.3],\n",
       "        [5. , 3.4, 1.5, 0.2],\n",
       "        [4.4, 2.9, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.1],\n",
       "        [5.4, 3.7, 1.5, 0.2],\n",
       "        [4.8, 3.4, 1.6, 0.2],\n",
       "        [4.8, 3. , 1.4, 0.1],\n",
       "        [4.3, 3. , 1.1, 0.1],\n",
       "        [5.8, 4. , 1.2, 0.2],\n",
       "        [5.7, 4.4, 1.5, 0.4],\n",
       "        [5.4, 3.9, 1.3, 0.4],\n",
       "        [5.1, 3.5, 1.4, 0.3],\n",
       "        [5.7, 3.8, 1.7, 0.3],\n",
       "        [5.1, 3.8, 1.5, 0.3],\n",
       "        [5.4, 3.4, 1.7, 0.2],\n",
       "        [5.1, 3.7, 1.5, 0.4],\n",
       "        [4.6, 3.6, 1. , 0.2],\n",
       "        [5.1, 3.3, 1.7, 0.5],\n",
       "        [4.8, 3.4, 1.9, 0.2],\n",
       "        [5. , 3. , 1.6, 0.2],\n",
       "        [5. , 3.4, 1.6, 0.4],\n",
       "        [5.2, 3.5, 1.5, 0.2],\n",
       "        [5.2, 3.4, 1.4, 0.2],\n",
       "        [4.7, 3.2, 1.6, 0.2],\n",
       "        [4.8, 3.1, 1.6, 0.2],\n",
       "        [5.4, 3.4, 1.5, 0.4],\n",
       "        [5.2, 4.1, 1.5, 0.1],\n",
       "        [5.5, 4.2, 1.4, 0.2],\n",
       "        [4.9, 3.1, 1.5, 0.2],\n",
       "        [5. , 3.2, 1.2, 0.2],\n",
       "        [5.5, 3.5, 1.3, 0.2],\n",
       "        [4.9, 3.6, 1.4, 0.1],\n",
       "        [4.4, 3. , 1.3, 0.2],\n",
       "        [5.1, 3.4, 1.5, 0.2],\n",
       "        [5. , 3.5, 1.3, 0.3],\n",
       "        [4.5, 2.3, 1.3, 0.3],\n",
       "        [4.4, 3.2, 1.3, 0.2],\n",
       "        [5. , 3.5, 1.6, 0.6],\n",
       "        [5.1, 3.8, 1.9, 0.4],\n",
       "        [4.8, 3. , 1.4, 0.3],\n",
       "        [5.1, 3.8, 1.6, 0.2],\n",
       "        [4.6, 3.2, 1.4, 0.2],\n",
       "        [5.3, 3.7, 1.5, 0.2],\n",
       "        [5. , 3.3, 1.4, 0.2],\n",
       "        [7. , 3.2, 4.7, 1.4],\n",
       "        [6.4, 3.2, 4.5, 1.5],\n",
       "        [6.9, 3.1, 4.9, 1.5],\n",
       "        [5.5, 2.3, 4. , 1.3],\n",
       "        [6.5, 2.8, 4.6, 1.5],\n",
       "        [5.7, 2.8, 4.5, 1.3],\n",
       "        [6.3, 3.3, 4.7, 1.6],\n",
       "        [4.9, 2.4, 3.3, 1. ],\n",
       "        [6.6, 2.9, 4.6, 1.3],\n",
       "        [5.2, 2.7, 3.9, 1.4],\n",
       "        [5. , 2. , 3.5, 1. ],\n",
       "        [5.9, 3. , 4.2, 1.5],\n",
       "        [6. , 2.2, 4. , 1. ],\n",
       "        [6.1, 2.9, 4.7, 1.4],\n",
       "        [5.6, 2.9, 3.6, 1.3],\n",
       "        [6.7, 3.1, 4.4, 1.4],\n",
       "        [5.6, 3. , 4.5, 1.5],\n",
       "        [5.8, 2.7, 4.1, 1. ],\n",
       "        [6.2, 2.2, 4.5, 1.5],\n",
       "        [5.6, 2.5, 3.9, 1.1],\n",
       "        [5.9, 3.2, 4.8, 1.8],\n",
       "        [6.1, 2.8, 4. , 1.3],\n",
       "        [6.3, 2.5, 4.9, 1.5],\n",
       "        [6.1, 2.8, 4.7, 1.2],\n",
       "        [6.4, 2.9, 4.3, 1.3],\n",
       "        [6.6, 3. , 4.4, 1.4],\n",
       "        [6.8, 2.8, 4.8, 1.4],\n",
       "        [6.7, 3. , 5. , 1.7],\n",
       "        [6. , 2.9, 4.5, 1.5],\n",
       "        [5.7, 2.6, 3.5, 1. ],\n",
       "        [5.5, 2.4, 3.8, 1.1],\n",
       "        [5.5, 2.4, 3.7, 1. ],\n",
       "        [5.8, 2.7, 3.9, 1.2],\n",
       "        [6. , 2.7, 5.1, 1.6],\n",
       "        [5.4, 3. , 4.5, 1.5],\n",
       "        [6. , 3.4, 4.5, 1.6],\n",
       "        [6.7, 3.1, 4.7, 1.5],\n",
       "        [6.3, 2.3, 4.4, 1.3],\n",
       "        [5.6, 3. , 4.1, 1.3],\n",
       "        [5.5, 2.5, 4. , 1.3],\n",
       "        [5.5, 2.6, 4.4, 1.2],\n",
       "        [6.1, 3. , 4.6, 1.4],\n",
       "        [5.8, 2.6, 4. , 1.2],\n",
       "        [5. , 2.3, 3.3, 1. ],\n",
       "        [5.6, 2.7, 4.2, 1.3],\n",
       "        [5.7, 3. , 4.2, 1.2],\n",
       "        [5.7, 2.9, 4.2, 1.3],\n",
       "        [6.2, 2.9, 4.3, 1.3],\n",
       "        [5.1, 2.5, 3. , 1.1],\n",
       "        [5.7, 2.8, 4.1, 1.3],\n",
       "        [6.3, 3.3, 6. , 2.5],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [7.1, 3. , 5.9, 2.1],\n",
       "        [6.3, 2.9, 5.6, 1.8],\n",
       "        [6.5, 3. , 5.8, 2.2],\n",
       "        [7.6, 3. , 6.6, 2.1],\n",
       "        [4.9, 2.5, 4.5, 1.7],\n",
       "        [7.3, 2.9, 6.3, 1.8],\n",
       "        [6.7, 2.5, 5.8, 1.8],\n",
       "        [7.2, 3.6, 6.1, 2.5],\n",
       "        [6.5, 3.2, 5.1, 2. ],\n",
       "        [6.4, 2.7, 5.3, 1.9],\n",
       "        [6.8, 3. , 5.5, 2.1],\n",
       "        [5.7, 2.5, 5. , 2. ],\n",
       "        [5.8, 2.8, 5.1, 2.4],\n",
       "        [6.4, 3.2, 5.3, 2.3],\n",
       "        [6.5, 3. , 5.5, 1.8],\n",
       "        [7.7, 3.8, 6.7, 2.2],\n",
       "        [7.7, 2.6, 6.9, 2.3],\n",
       "        [6. , 2.2, 5. , 1.5],\n",
       "        [6.9, 3.2, 5.7, 2.3],\n",
       "        [5.6, 2.8, 4.9, 2. ],\n",
       "        [7.7, 2.8, 6.7, 2. ],\n",
       "        [6.3, 2.7, 4.9, 1.8],\n",
       "        [6.7, 3.3, 5.7, 2.1],\n",
       "        [7.2, 3.2, 6. , 1.8],\n",
       "        [6.2, 2.8, 4.8, 1.8],\n",
       "        [6.1, 3. , 4.9, 1.8],\n",
       "        [6.4, 2.8, 5.6, 2.1],\n",
       "        [7.2, 3. , 5.8, 1.6],\n",
       "        [7.4, 2.8, 6.1, 1.9],\n",
       "        [7.9, 3.8, 6.4, 2. ],\n",
       "        [6.4, 2.8, 5.6, 2.2],\n",
       "        [6.3, 2.8, 5.1, 1.5],\n",
       "        [6.1, 2.6, 5.6, 1.4],\n",
       "        [7.7, 3. , 6.1, 2.3],\n",
       "        [6.3, 3.4, 5.6, 2.4],\n",
       "        [6.4, 3.1, 5.5, 1.8],\n",
       "        [6. , 3. , 4.8, 1.8],\n",
       "        [6.9, 3.1, 5.4, 2.1],\n",
       "        [6.7, 3.1, 5.6, 2.4],\n",
       "        [6.9, 3.1, 5.1, 2.3],\n",
       "        [5.8, 2.7, 5.1, 1.9],\n",
       "        [6.8, 3.2, 5.9, 2.3],\n",
       "        [6.7, 3.3, 5.7, 2.5],\n",
       "        [6.7, 3. , 5.2, 2.3],\n",
       "        [6.3, 2.5, 5. , 1.9],\n",
       "        [6.5, 3. , 5.2, 2. ],\n",
       "        [6.2, 3.4, 5.4, 2.3],\n",
       "        [5.9, 3. , 5.1, 1.8]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),\n",
       " 'DESCR': '.. _iris_dataset:\\n\\nIris plants dataset\\n--------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n                \\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThe famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\\nfrom Fisher\\'s paper. Note that it\\'s the same as in R, but not as in the UCI\\nMachine Learning Repository, which has two wrong data points.\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\n.. topic:: References\\n\\n   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...',\n",
       " 'feature_names': ['sepal length (cm)',\n",
       "  'sepal width (cm)',\n",
       "  'petal length (cm)',\n",
       "  'petal width (cm)'],\n",
       " 'filename': 'C:\\\\Users\\\\sc-gd\\\\anaconda3\\\\envs\\\\butterfly\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\iris.csv'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'IRIS dataset petal length vs sepal length')"
      ]
     },
     "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"
    },
    {
     "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": [
    "X = iris.data\n",
    "y = iris.target\n",
    "target_names = iris.target_names\n",
    "#first plot some relationships between the first iris attributes (try others too)\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "lw = 2\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X[y == i, 0], X[y == i, 1], color=color, alpha=.8, lw=lw,\n",
    "                label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('IRIS dataset sepal length vs sepal width')\n",
    "\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "lw = 2\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X[y == i, 0], X[y == i, 3], color=color, alpha=.8, lw=lw,\n",
    "                label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('IRIS dataset petal width vs sepal width')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code does a PCA analysis and plot of the iris dataset against the first two principle components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio (first four components): [0.92461872 0.05306648 0.01710261 0.00521218]\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": [
    "\n",
    "pca = PCA(n_components=4)\n",
    "X_r = pca.fit(X).transform(X)\n",
    "# Percentage of variance explained for each components\n",
    "print('explained variance ratio (first four components): %s'\n",
    "      % str(pca.explained_variance_ratio_))\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "lw = 2\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw,\n",
    "                label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA of IRIS dataset')\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "This codee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio (first two components): [0.92461872 0.05306648]\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"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "target_names = iris.target_names\n",
    "pca = PCA(n_components=2)\n",
    "X_r = pca.fit(X).transform(X)\n",
    "lda = LinearDiscriminantAnalysis(n_components=2)\n",
    "X_r2 = lda.fit(X, y).transform(X)\n",
    "# Percentage of variance explained for each components\n",
    "print('explained variance ratio (first two components): %s'\n",
    "      % str(pca.explained_variance_ratio_))\n",
    "plt.figure()\n",
    "colors = ['navy', 'turquoise', 'darkorange']\n",
    "lw = 2\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X_r[y == i, 0], X_r[y == i, 1], color=color, alpha=.8, lw=lw,\n",
    "                label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('PCA of IRIS dataset')\n",
    "\n",
    "plt.figure()\n",
    "for color, i, target_name in zip(colors, [0, 1, 2], target_names):\n",
    "    plt.scatter(X_r2[y == i, 0], X_r2[y == i, 1], alpha=.8, color=color,\n",
    "                label=target_name)\n",
    "plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
    "plt.title('LDA of IRIS dataset')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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