{
 "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../DATA/cancer_tumor_data_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA with Scikit-Learn\n",
    "\n",
    "\n",
    "### Scaling Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_X = scaler.fit_transform(df)"
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-Learn Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class PCA in module sklearn.decomposition._pca:\n",
      "\n",
      "class PCA(sklearn.decomposition._base._BasePCA)\n",
      " |  PCA(n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)\n",
      " |  \n",
      " |  Principal component analysis (PCA).\n",
      " |  \n",
      " |  Linear dimensionality reduction using Singular Value Decomposition of the\n",
      " |  data to project it to a lower dimensional space. The input data is centered\n",
      " |  but not scaled for each feature before applying the SVD.\n",
      " |  \n",
      " |  It uses the LAPACK implementation of the full SVD or a randomized truncated\n",
      " |  SVD by the method of Halko et al. 2009, depending on the shape of the input\n",
      " |  data and the number of components to extract.\n",
      " |  \n",
      " |  It can also use the scipy.sparse.linalg ARPACK implementation of the\n",
      " |  truncated SVD.\n",
      " |  \n",
      " |  Notice that this class does not support sparse input. See\n",
      " |  :class:`TruncatedSVD` for an alternative with sparse data.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <PCA>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  n_components : int, float, None or str\n",
      " |      Number of components to keep.\n",
      " |      if n_components is not set all components are kept::\n",
      " |  \n",
      " |          n_components == min(n_samples, n_features)\n",
      " |  \n",
      " |      If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's\n",
      " |      MLE is used to guess the dimension. Use of ``n_components == 'mle'``\n",
      " |      will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'``.\n",
      " |  \n",
      " |      If ``0 < n_components < 1`` and ``svd_solver == 'full'``, select the\n",
      " |      number of components such that the amount of variance that needs to be\n",
      " |      explained is greater than the percentage specified by n_components.\n",
      " |  \n",
      " |      If ``svd_solver == 'arpack'``, the number of components must be\n",
      " |      strictly less than the minimum of n_features and n_samples.\n",
      " |  \n",
      " |      Hence, the None case results in::\n",
      " |  \n",
      " |          n_components == min(n_samples, n_features) - 1\n",
      " |  \n",
      " |  copy : bool, default=True\n",
      " |      If False, data passed to fit are overwritten and running\n",
      " |      fit(X).transform(X) will not yield the expected results,\n",
      " |      use fit_transform(X) instead.\n",
      " |  \n",
      " |  whiten : bool, optional (default False)\n",
      " |      When True (False by default) the `components_` vectors are multiplied\n",
      " |      by the square root of n_samples and then divided by the singular values\n",
      " |      to ensure uncorrelated outputs with unit component-wise variances.\n",
      " |  \n",
      " |      Whitening will remove some information from the transformed signal\n",
      " |      (the relative variance scales of the components) but can sometime\n",
      " |      improve the predictive accuracy of the downstream estimators by\n",
      " |      making their data respect some hard-wired assumptions.\n",
      " |  \n",
      " |  svd_solver : str {'auto', 'full', 'arpack', 'randomized'}\n",
      " |      If auto :\n",
      " |          The solver is selected by a default policy based on `X.shape` and\n",
      " |          `n_components`: if the input data is larger than 500x500 and the\n",
      " |          number of components to extract is lower than 80% of the smallest\n",
      " |          dimension of the data, then the more efficient 'randomized'\n",
      " |          method is enabled. Otherwise the exact full SVD is computed and\n",
      " |          optionally truncated afterwards.\n",
      " |      If full :\n",
      " |          run exact full SVD calling the standard LAPACK solver via\n",
      " |          `scipy.linalg.svd` and select the components by postprocessing\n",
      " |      If arpack :\n",
      " |          run SVD truncated to n_components calling ARPACK solver via\n",
      " |          `scipy.sparse.linalg.svds`. It requires strictly\n",
      " |          0 < n_components < min(X.shape)\n",
      " |      If randomized :\n",
      " |          run randomized SVD by the method of Halko et al.\n",
      " |  \n",
      " |      .. versionadded:: 0.18.0\n",
      " |  \n",
      " |  tol : float >= 0, optional (default .0)\n",
      " |      Tolerance for singular values computed by svd_solver == 'arpack'.\n",
      " |  \n",
      " |      .. versionadded:: 0.18.0\n",
      " |  \n",
      " |  iterated_power : int >= 0, or 'auto', (default 'auto')\n",
      " |      Number of iterations for the power method computed by\n",
      " |      svd_solver == 'randomized'.\n",
      " |  \n",
      " |      .. versionadded:: 0.18.0\n",
      " |  \n",
      " |  random_state : int, RandomState instance, default=None\n",
      " |      Used when ``svd_solver`` == 'arpack' or 'randomized'. Pass an int\n",
      " |      for reproducible results across multiple function calls.\n",
      " |      See :term:`Glossary <random_state>`.\n",
      " |  \n",
      " |      .. versionadded:: 0.18.0\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  components_ : array, shape (n_components, n_features)\n",
      " |      Principal axes in feature space, representing the directions of\n",
      " |      maximum variance in the data. The components are sorted by\n",
      " |      ``explained_variance_``.\n",
      " |  \n",
      " |  explained_variance_ : array, shape (n_components,)\n",
      " |      The amount of variance explained by each of the selected components.\n",
      " |  \n",
      " |      Equal to n_components largest eigenvalues\n",
      " |      of the covariance matrix of X.\n",
      " |  \n",
      " |      .. versionadded:: 0.18\n",
      " |  \n",
      " |  explained_variance_ratio_ : array, shape (n_components,)\n",
      " |      Percentage of variance explained by each of the selected components.\n",
      " |  \n",
      " |      If ``n_components`` is not set then all components are stored and the\n",
      " |      sum of the ratios is equal to 1.0.\n",
      " |  \n",
      " |  singular_values_ : array, shape (n_components,)\n",
      " |      The singular values corresponding to each of the selected components.\n",
      " |      The singular values are equal to the 2-norms of the ``n_components``\n",
      " |      variables in the lower-dimensional space.\n",
      " |  \n",
      " |      .. versionadded:: 0.19\n",
      " |  \n",
      " |  mean_ : array, shape (n_features,)\n",
      " |      Per-feature empirical mean, estimated from the training set.\n",
      " |  \n",
      " |      Equal to `X.mean(axis=0)`.\n",
      " |  \n",
      " |  n_components_ : int\n",
      " |      The estimated number of components. When n_components is set\n",
      " |      to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this\n",
      " |      number is estimated from input data. Otherwise it equals the parameter\n",
      " |      n_components, or the lesser value of n_features and n_samples\n",
      " |      if n_components is None.\n",
      " |  \n",
      " |  n_features_ : int\n",
      " |      Number of features in the training data.\n",
      " |  \n",
      " |  n_samples_ : int\n",
      " |      Number of samples in the training data.\n",
      " |  \n",
      " |  noise_variance_ : float\n",
      " |      The estimated noise covariance following the Probabilistic PCA model\n",
      " |      from Tipping and Bishop 1999. See \"Pattern Recognition and\n",
      " |      Machine Learning\" by C. Bishop, 12.2.1 p. 574 or\n",
      " |      http://www.miketipping.com/papers/met-mppca.pdf. It is required to\n",
      " |      compute the estimated data covariance and score samples.\n",
      " |  \n",
      " |      Equal to the average of (min(n_features, n_samples) - n_components)\n",
      " |      smallest eigenvalues of the covariance matrix of X.\n",
      " |  \n",
      " |  See Also\n",
      " |  --------\n",
      " |  KernelPCA : Kernel Principal Component Analysis.\n",
      " |  SparsePCA : Sparse Principal Component Analysis.\n",
      " |  TruncatedSVD : Dimensionality reduction using truncated SVD.\n",
      " |  IncrementalPCA : Incremental Principal Component Analysis.\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  For n_components == 'mle', this class uses the method of *Minka, T. P.\n",
      " |  \"Automatic choice of dimensionality for PCA\". In NIPS, pp. 598-604*\n",
      " |  \n",
      " |  Implements the probabilistic PCA model from:\n",
      " |  Tipping, M. E., and Bishop, C. M. (1999). \"Probabilistic principal\n",
      " |  component analysis\". Journal of the Royal Statistical Society:\n",
      " |  Series B (Statistical Methodology), 61(3), 611-622.\n",
      " |  via the score and score_samples methods.\n",
      " |  See http://www.miketipping.com/papers/met-mppca.pdf\n",
      " |  \n",
      " |  For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.\n",
      " |  \n",
      " |  For svd_solver == 'randomized', see:\n",
      " |  *Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).\n",
      " |  \"Finding structure with randomness: Probabilistic algorithms for\n",
      " |  constructing approximate matrix decompositions\".\n",
      " |  SIAM review, 53(2), 217-288.* and also\n",
      " |  *Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).\n",
      " |  \"A randomized algorithm for the decomposition of matrices\".\n",
      " |  Applied and Computational Harmonic Analysis, 30(1), 47-68.*\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> import numpy as np\n",
      " |  >>> from sklearn.decomposition import PCA\n",
      " |  >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
      " |  >>> pca = PCA(n_components=2)\n",
      " |  >>> pca.fit(X)\n",
      " |  PCA(n_components=2)\n",
      " |  >>> print(pca.explained_variance_ratio_)\n",
      " |  [0.9924... 0.0075...]\n",
      " |  >>> print(pca.singular_values_)\n",
      " |  [6.30061... 0.54980...]\n",
      " |  \n",
      " |  >>> pca = PCA(n_components=2, svd_solver='full')\n",
      " |  >>> pca.fit(X)\n",
      " |  PCA(n_components=2, svd_solver='full')\n",
      " |  >>> print(pca.explained_variance_ratio_)\n",
      " |  [0.9924... 0.00755...]\n",
      " |  >>> print(pca.singular_values_)\n",
      " |  [6.30061... 0.54980...]\n",
      " |  \n",
      " |  >>> pca = PCA(n_components=1, svd_solver='arpack')\n",
      " |  >>> pca.fit(X)\n",
      " |  PCA(n_components=1, svd_solver='arpack')\n",
      " |  >>> print(pca.explained_variance_ratio_)\n",
      " |  [0.99244...]\n",
      " |  >>> print(pca.singular_values_)\n",
      " |  [6.30061...]\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      PCA\n",
      " |      sklearn.decomposition._base._BasePCA\n",
      " |      sklearn.base.TransformerMixin\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, n_components=None, *, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  fit(self, X, y=None)\n",
      " |      Fit the model with X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape (n_samples, n_features)\n",
      " |          Training data, where n_samples is the number of samples\n",
      " |          and n_features is the number of features.\n",
      " |      \n",
      " |      y : None\n",
      " |          Ignored variable.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Returns the instance itself.\n",
      " |  \n",
      " |  fit_transform(self, X, y=None)\n",
      " |      Fit the model with X and apply the dimensionality reduction on X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape (n_samples, n_features)\n",
      " |          Training data, where n_samples is the number of samples\n",
      " |          and n_features is the number of features.\n",
      " |      \n",
      " |      y : None\n",
      " |          Ignored variable.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : array-like, shape (n_samples, n_components)\n",
      " |          Transformed values.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This method returns a Fortran-ordered array. To convert it to a\n",
      " |      C-ordered array, use 'np.ascontiguousarray'.\n",
      " |  \n",
      " |  score(self, X, y=None)\n",
      " |      Return the average log-likelihood of all samples.\n",
      " |      \n",
      " |      See. \"Pattern Recognition and Machine Learning\"\n",
      " |      by C. Bishop, 12.2.1 p. 574\n",
      " |      or http://www.miketipping.com/papers/met-mppca.pdf\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array, shape(n_samples, n_features)\n",
      " |          The data.\n",
      " |      \n",
      " |      y : None\n",
      " |          Ignored variable.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      ll : float\n",
      " |          Average log-likelihood of the samples under the current model.\n",
      " |  \n",
      " |  score_samples(self, X)\n",
      " |      Return the log-likelihood of each sample.\n",
      " |      \n",
      " |      See. \"Pattern Recognition and Machine Learning\"\n",
      " |      by C. Bishop, 12.2.1 p. 574\n",
      " |      or http://www.miketipping.com/papers/met-mppca.pdf\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array, shape(n_samples, n_features)\n",
      " |          The data.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      ll : array, shape (n_samples,)\n",
      " |          Log-likelihood of each sample under the current model.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.decomposition._base._BasePCA:\n",
      " |  \n",
      " |  get_covariance(self)\n",
      " |      Compute data covariance with the generative model.\n",
      " |      \n",
      " |      ``cov = components_.T * S**2 * components_ + sigma2 * eye(n_features)``\n",
      " |      where S**2 contains the explained variances, and sigma2 contains the\n",
      " |      noise variances.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      cov : array, shape=(n_features, n_features)\n",
      " |          Estimated covariance of data.\n",
      " |  \n",
      " |  get_precision(self)\n",
      " |      Compute data precision matrix with the generative model.\n",
      " |      \n",
      " |      Equals the inverse of the covariance but computed with\n",
      " |      the matrix inversion lemma for efficiency.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      precision : array, shape=(n_features, n_features)\n",
      " |          Estimated precision of data.\n",
      " |  \n",
      " |  inverse_transform(self, X)\n",
      " |      Transform data back to its original space.\n",
      " |      \n",
      " |      In other words, return an input X_original whose transform would be X.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape (n_samples, n_components)\n",
      " |          New data, where n_samples is the number of samples\n",
      " |          and n_components is the number of components.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_original array-like, shape (n_samples, n_features)\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      If whitening is enabled, inverse_transform will compute the\n",
      " |      exact inverse operation, which includes reversing whitening.\n",
      " |  \n",
      " |  transform(self, X)\n",
      " |      Apply dimensionality reduction to X.\n",
      " |      \n",
      " |      X is projected on the first principal components previously extracted\n",
      " |      from a training set.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like, shape (n_samples, n_features)\n",
      " |          New data, where n_samples is the number of samples\n",
      " |          and n_features is the number of features.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X_new : array-like, shape (n_samples, n_components)\n",
      " |      \n",
      " |      Examples\n",
      " |      --------\n",
      " |      \n",
      " |      >>> import numpy as np\n",
      " |      >>> from sklearn.decomposition import IncrementalPCA\n",
      " |      >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])\n",
      " |      >>> ipca = IncrementalPCA(n_components=2, batch_size=3)\n",
      " |      >>> ipca.fit(X)\n",
      " |      IncrementalPCA(batch_size=3, n_components=2)\n",
      " |      >>> ipca.transform(X) # doctest: +SKIP\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.TransformerMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : mapping of string to any\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as pipelines). The latter have parameters of the form\n",
      " |      ``<component>__<parameter>`` so that it's possible to update each\n",
      " |      component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "principal_components = pca.fit_transform(scaled_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# REQUIRES INTERNET CONNECTION AND FIREWALL ACCESS\n",
    "cancer_dictionary = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dictionary.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dictionary['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second Principal Component')"
      ]
     },
     "execution_count": 22,
     "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')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitted Model Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.21890244,  0.10372458,  0.22753729,  0.22099499,  0.14258969,\n",
       "         0.23928535,  0.25840048,  0.26085376,  0.13816696,  0.06436335,\n",
       "         0.20597878,  0.01742803,  0.21132592,  0.20286964,  0.01453145,\n",
       "         0.17039345,  0.15358979,  0.1834174 ,  0.04249842,  0.10256832,\n",
       "         0.22799663,  0.10446933,  0.23663968,  0.22487053,  0.12795256,\n",
       "         0.21009588,  0.22876753,  0.25088597,  0.12290456,  0.13178394],\n",
       "       [-0.23385713, -0.05970609, -0.21518136, -0.23107671,  0.18611302,\n",
       "         0.15189161,  0.06016536, -0.0347675 ,  0.19034877,  0.36657547,\n",
       "        -0.10555215,  0.08997968, -0.08945723, -0.15229263,  0.20443045,\n",
       "         0.2327159 ,  0.19720728,  0.13032156,  0.183848  ,  0.28009203,\n",
       "        -0.21986638, -0.0454673 , -0.19987843, -0.21935186,  0.17230435,\n",
       "         0.14359317,  0.09796411, -0.00825724,  0.14188335,  0.27533947]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this numpy matrix array, each row represents a principal component, Principal axes in feature space, representing the directions of maximum variance in the data. The components are sorted by explained_variance_.\n",
    "\n",
    "We can visualize this relationship with a heatmap:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = pd.DataFrame(pca.components_,index=['PC1','PC2'],columns=df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>PC1</th>\n",
       "      <td>0.218902</td>\n",
       "      <td>0.103725</td>\n",
       "      <td>0.227537</td>\n",
       "      <td>0.220995</td>\n",
       "      <td>0.142590</td>\n",
       "      <td>0.239285</td>\n",
       "      <td>0.258400</td>\n",
       "      <td>0.260854</td>\n",
       "      <td>0.138167</td>\n",
       "      <td>0.064363</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227997</td>\n",
       "      <td>0.104469</td>\n",
       "      <td>0.236640</td>\n",
       "      <td>0.224871</td>\n",
       "      <td>0.127953</td>\n",
       "      <td>0.210096</td>\n",
       "      <td>0.228768</td>\n",
       "      <td>0.250886</td>\n",
       "      <td>0.122905</td>\n",
       "      <td>0.131784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>-0.233857</td>\n",
       "      <td>-0.059706</td>\n",
       "      <td>-0.215181</td>\n",
       "      <td>-0.231077</td>\n",
       "      <td>0.186113</td>\n",
       "      <td>0.151892</td>\n",
       "      <td>0.060165</td>\n",
       "      <td>-0.034768</td>\n",
       "      <td>0.190349</td>\n",
       "      <td>0.366575</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.219866</td>\n",
       "      <td>-0.045467</td>\n",
       "      <td>-0.199878</td>\n",
       "      <td>-0.219352</td>\n",
       "      <td>0.172304</td>\n",
       "      <td>0.143593</td>\n",
       "      <td>0.097964</td>\n",
       "      <td>-0.008257</td>\n",
       "      <td>0.141883</td>\n",
       "      <td>0.275339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "PC1     0.218902      0.103725        0.227537   0.220995         0.142590   \n",
       "PC2    -0.233857     -0.059706       -0.215181  -0.231077         0.186113   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "PC1          0.239285        0.258400             0.260854       0.138167   \n",
       "PC2          0.151892        0.060165            -0.034768       0.190349   \n",
       "\n",
       "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
       "PC1                0.064363  ...      0.227997       0.104469   \n",
       "PC2                0.366575  ...     -0.219866      -0.045467   \n",
       "\n",
       "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       "PC1         0.236640    0.224871          0.127953           0.210096   \n",
       "PC2        -0.199878   -0.219352          0.172304           0.143593   \n",
       "\n",
       "     worst concavity  worst concave points  worst symmetry  \\\n",
       "PC1         0.228768              0.250886        0.122905   \n",
       "PC2         0.097964             -0.008257        0.141883   \n",
       "\n",
       "     worst fractal dimension  \n",
       "PC1                 0.131784  \n",
       "PC2                 0.275339  \n",
       "\n",
       "[2 rows x 30 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 3000x450 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,3),dpi=150)\n",
    "sns.heatmap(df_comp,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.44272026, 0.18971182])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6324320765155944"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pca.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=30)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_30 = PCA(n_components=30)\n",
    "pca_30.fit(scaled_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.42720256e-01, 1.89711820e-01, 9.39316326e-02, 6.60213492e-02,\n",
       "       5.49576849e-02, 4.02452204e-02, 2.25073371e-02, 1.58872380e-02,\n",
       "       1.38964937e-02, 1.16897819e-02, 9.79718988e-03, 8.70537901e-03,\n",
       "       8.04524987e-03, 5.23365745e-03, 3.13783217e-03, 2.66209337e-03,\n",
       "       1.97996793e-03, 1.75395945e-03, 1.64925306e-03, 1.03864675e-03,\n",
       "       9.99096464e-04, 9.14646751e-04, 8.11361259e-04, 6.01833567e-04,\n",
       "       5.16042379e-04, 2.72587995e-04, 2.30015463e-04, 5.29779290e-05,\n",
       "       2.49601032e-05, 4.43482743e-06])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_30.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pca_30.explained_variance_ratio_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "explained_variance = []\n",
    "\n",
    "for n in range(1,30):\n",
    "    pca = PCA(n_components=n)\n",
    "    pca.fit(scaled_X)\n",
    "    \n",
    "    explained_variance.append(np.sum(pca.explained_variance_ratio_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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": [
    "plt.plot(range(1,30),explained_variance)\n",
    "plt.xlabel(\"Number of Components\")\n",
    "plt.ylabel(\"Variance Explained\");"
   ]
  }
 ],
 "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
}