{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "\n",
    "<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
    "___\n",
    "<center><em>Copyright by Pierian Data Inc.</em></center>\n",
    "<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "Breast cancer wisconsin (diagnostic) dataset\n",
    "--------------------------------------------\n",
    "\n",
    "**Data Set Characteristics:**\n",
    "\n",
    "    :Number of Instances: 569\n",
    "\n",
    "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
    "\n",
    "    :Attribute Information:\n",
    "        - radius (mean of distances from center to points on the perimeter)\n",
    "        - texture (standard deviation of gray-scale values)\n",
    "        - perimeter\n",
    "        - area\n",
    "        - smoothness (local variation in radius lengths)\n",
    "        - compactness (perimeter^2 / area - 1.0)\n",
    "        - concavity (severity of concave portions of the contour)\n",
    "        - concave points (number of concave portions of the contour)\n",
    "        - symmetry\n",
    "        - fractal dimension (\"coastline approximation\" - 1)\n",
    "\n",
    "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
    "        worst/largest values) of these features were computed for each image,\n",
    "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
    "        10 is Radius SE, field 20 is Worst Radius.\n",
    "\n",
    "        - class:\n",
    "                - WDBC-Malignant\n",
    "                - WDBC-Benign\n",
    "\n",
    "    :Summary Statistics:\n",
    "\n",
    "    ===================================== ====== ======\n",
    "                                           Min    Max\n",
    "    ===================================== ====== ======\n",
    "    radius (mean):                        6.981  28.11\n",
    "    texture (mean):                       9.71   39.28\n",
    "    perimeter (mean):                     43.79  188.5\n",
    "    area (mean):                          143.5  2501.0\n",
    "    smoothness (mean):                    0.053  0.163\n",
    "    compactness (mean):                   0.019  0.345\n",
    "    concavity (mean):                     0.0    0.427\n",
    "    concave points (mean):                0.0    0.201\n",
    "    symmetry (mean):                      0.106  0.304\n",
    "    fractal dimension (mean):             0.05   0.097\n",
    "    radius (standard error):              0.112  2.873\n",
    "    texture (standard error):             0.36   4.885\n",
    "    perimeter (standard error):           0.757  21.98\n",
    "    area (standard error):                6.802  542.2\n",
    "    smoothness (standard error):          0.002  0.031\n",
    "    compactness (standard error):         0.002  0.135\n",
    "    concavity (standard error):           0.0    0.396\n",
    "    concave points (standard error):      0.0    0.053\n",
    "    symmetry (standard error):            0.008  0.079\n",
    "    fractal dimension (standard error):   0.001  0.03\n",
    "    radius (worst):                       7.93   36.04\n",
    "    texture (worst):                      12.02  49.54\n",
    "    perimeter (worst):                    50.41  251.2\n",
    "    area (worst):                         185.2  4254.0\n",
    "    smoothness (worst):                   0.071  0.223\n",
    "    compactness (worst):                  0.027  1.058\n",
    "    concavity (worst):                    0.0    1.252\n",
    "    concave points (worst):               0.0    0.291\n",
    "    symmetry (worst):                     0.156  0.664\n",
    "    fractal dimension (worst):            0.055  0.208\n",
    "    ===================================== ====== ======\n",
    "\n",
    "    :Missing Attribute Values: None\n",
    "\n",
    "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
    "\n",
    "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
    "\n",
    "    :Donor: Nick Street\n",
    "\n",
    "    :Date: November, 1995\n",
    "\n",
    "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
    "https://goo.gl/U2Uwz2\n",
    "\n",
    "Features are computed from a digitized image of a fine needle\n",
    "aspirate (FNA) of a breast mass.  They describe\n",
    "characteristics of the cell nuclei present in the image.\n",
    "\n",
    "Separating plane described above was obtained using\n",
    "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
    "Construction Via Linear Programming.\" Proceedings of the 4th\n",
    "Midwest Artificial Intelligence and Cognitive Science Society,\n",
    "pp. 97-101, 1992], a classification method which uses linear\n",
    "programming to construct a decision tree.  Relevant features\n",
    "were selected using an exhaustive search in the space of 1-4\n",
    "features and 1-3 separating planes.\n",
    "\n",
    "The actual linear program used to obtain the separating plane\n",
    "in the 3-dimensional space is that described in:\n",
    "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
    "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
    "Optimization Methods and Software 1, 1992, 23-34].\n",
    "\n",
    "This database is also available through the UW CS ftp server:\n",
    "\n",
    "ftp ftp.cs.wisc.edu\n",
    "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
    "\n",
    ".. topic:: References\n",
    "\n",
    "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
    "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
    "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
    "     San Jose, CA, 1993.\n",
    "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
    "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
    "     July-August 1995.\n",
    "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
    "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
    "     163-171."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/cancer_tumor_data_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual Construction of PCA\n",
    "\n",
    "\n",
    "### Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X = scaler.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Calculate Covariance Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Because we scaled the data, this won't produce any change.\n",
    "# We've left if here because you would need to do this for unscaled data\n",
    "scaled_X -= scaled_X.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
       "         2.75062224,  1.93701461],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
       "        -0.24388967,  0.28118999],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
       "         1.152255  ,  0.20139121],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
       "        -1.10454895, -0.31840916],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
       "         1.91908301,  2.21963528],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
       "        -0.04813821, -0.75120669]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grab Covariance Matrix\n",
    "covariance_matrix = np.cov(scaled_X, rowvar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get Eigen Vectors and Eigen Values\n",
    "eigen_values, eigen_vectors = np.linalg.eig(covariance_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose som number of components\n",
    "num_components=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get index sorting key based on Eigen Values\n",
    "sorted_key = np.argsort(eigen_values)[::-1][:num_components]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get num_components of Eigen Values and Eigen Vectors\n",
    "eigen_values, eigen_vectors = eigen_values[sorted_key], eigen_vectors[:, sorted_key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dot product of original data and eigen_vectors are the principal component values\n",
    "# This is the \"projection\" step of the original points on to the Principal Component\n",
    "principal_components=np.dot(scaled_X,eigen_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.19283683,  1.94858307],\n",
       "       [ 2.3878018 , -3.76817174],\n",
       "       [ 5.73389628, -1.0751738 ],\n",
       "       ...,\n",
       "       [ 1.25617928, -1.90229671],\n",
       "       [10.37479406,  1.67201011],\n",
       "       [-5.4752433 , -0.67063679]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principal_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(principal_components[:,0],principal_components[:,1])\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second Principal Component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# REQUIRES INTERNET CONNECTION AND FIREWALL ACCESS\n",
    "cancer_dictionary = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dictionary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 0, 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, 0, 0, 0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dictionary['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(principal_components[:,0],principal_components[:,1],c=cancer_dictionary['target'])\n",
    "plt.xlabel('First principal component')\n",
    "plt.ylabel('Second Principal Component')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}