{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "# Using PCA"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goals\n",
    "\n",
    "After completing this notebook, you will be able to:\n",
    "1. Standardize data with `scikit-learn`\n",
    "2. Perform Principal Component Analysis (PCA) on data\n",
    "3. Evaluate the influence of different principal components by seeing how much variance they explain\n",
    "4. Be able to transform data into lower dimensions uing PCA\n",
    "5. Be able to use KernelPCA to separate nonlinearly separable data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Our Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# PCA Imports\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.decomposition import KernelPCA\n",
    "\n",
    "# Import for 3d plotting\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "# For making nonlinear data\n",
    "from sklearn.datasets import make_circles\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Getting Example Data\n",
    "\n",
    "Today we'll be looking at sklearn's breast cancer identification dataset. You could get this directly from sklearn with `from sklearn.datasets import load_breast_cancer`, but its good to practice reading in data so we'll do it by hand. There are two files you'll need for this data:\n",
    "\n",
    "- `cancer.csv` contains the cell measurements\n",
    "- `target.csv` has if each cell is malignant (1) or benign (0).\n",
    "\n",
    "cancer.csv link here\n",
    "\n",
    "target.csv link here\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Do This - Read in these files as separate DataFrames with `pd.read_csv()`and print ther `head()`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "      <th>mean concave points</th>\n",
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       "      <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",
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       "      <td>122.80</td>\n",
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       "      <td>0.2419</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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>...</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",
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       "      <th>2</th>\n",
       "      <td>2</td>\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>...</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>3</td>\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>...</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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\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>...</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",
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       "   Unnamed: 0  mean radius  mean texture  mean perimeter  mean area  \\\n",
       "0           0        17.99         10.38          122.80     1001.0   \n",
       "1           1        20.57         17.77          132.90     1326.0   \n",
       "2           2        19.69         21.25          130.00     1203.0   \n",
       "3           3        11.42         20.38           77.58      386.1   \n",
       "4           4        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   mean symmetry  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0         0.2419  ...         25.38          17.33           184.60   \n",
       "1         0.1812  ...         24.99          23.41           158.80   \n",
       "2         0.2069  ...         23.57          25.53           152.50   \n",
       "3         0.2597  ...         14.91          26.50            98.87   \n",
       "4         0.1809  ...         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 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "target = pd.read_csv(\"target.csv\", names = ['target'])\n",
    "data = pd.read_csv('cancer.csv')\n",
    "target.head()\n",
    "data.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Getting the right columns\n",
    "\n",
    "Note that the dataframe from `cancer.csv` has a column that is just the index again. This isn't actually part of the data so you should get rid of it. \n",
    "\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Use the DataFrame `drop()` method to make a new DataFrame that doesen't have this extra column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <td>2019.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
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       "      <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",
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       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
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       "      <th>2</th>\n",
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       "      <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",
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       "    <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",
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       "      <td>0.1625</td>\n",
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       "   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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "data_dropped = data.drop('Unnamed: 0', axis = 1)\n",
    "data_dropped.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many features does this data have? Does all of the data fall on the same scale?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Scaling the Data\n",
    "\n",
    "\n",
    "Like you saw in the previous notebook, to use PCA to reduce the number of features, the data needs to be scaled. \n",
    "\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Do this - use `sklearn.preprocessing.StandardScaler` to scale the `cancer.csv` data. "
   ]
  },
  {
   "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>1.097064</td>\n",
       "      <td>-2.073335</td>\n",
       "      <td>1.269934</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>1.568466</td>\n",
       "      <td>3.283515</td>\n",
       "      <td>2.652874</td>\n",
       "      <td>2.532475</td>\n",
       "      <td>2.217515</td>\n",
       "      <td>2.255747</td>\n",
       "      <td>...</td>\n",
       "      <td>1.886690</td>\n",
       "      <td>-1.359293</td>\n",
       "      <td>2.303601</td>\n",
       "      <td>2.001237</td>\n",
       "      <td>1.307686</td>\n",
       "      <td>2.616665</td>\n",
       "      <td>2.109526</td>\n",
       "      <td>2.296076</td>\n",
       "      <td>2.750622</td>\n",
       "      <td>1.937015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.829821</td>\n",
       "      <td>-0.353632</td>\n",
       "      <td>1.685955</td>\n",
       "      <td>1.908708</td>\n",
       "      <td>-0.826962</td>\n",
       "      <td>-0.487072</td>\n",
       "      <td>-0.023846</td>\n",
       "      <td>0.548144</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>-0.868652</td>\n",
       "      <td>...</td>\n",
       "      <td>1.805927</td>\n",
       "      <td>-0.369203</td>\n",
       "      <td>1.535126</td>\n",
       "      <td>1.890489</td>\n",
       "      <td>-0.375612</td>\n",
       "      <td>-0.430444</td>\n",
       "      <td>-0.146749</td>\n",
       "      <td>1.087084</td>\n",
       "      <td>-0.243890</td>\n",
       "      <td>0.281190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.579888</td>\n",
       "      <td>0.456187</td>\n",
       "      <td>1.566503</td>\n",
       "      <td>1.558884</td>\n",
       "      <td>0.942210</td>\n",
       "      <td>1.052926</td>\n",
       "      <td>1.363478</td>\n",
       "      <td>2.037231</td>\n",
       "      <td>0.939685</td>\n",
       "      <td>-0.398008</td>\n",
       "      <td>...</td>\n",
       "      <td>1.511870</td>\n",
       "      <td>-0.023974</td>\n",
       "      <td>1.347475</td>\n",
       "      <td>1.456285</td>\n",
       "      <td>0.527407</td>\n",
       "      <td>1.082932</td>\n",
       "      <td>0.854974</td>\n",
       "      <td>1.955000</td>\n",
       "      <td>1.152255</td>\n",
       "      <td>0.201391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.768909</td>\n",
       "      <td>0.253732</td>\n",
       "      <td>-0.592687</td>\n",
       "      <td>-0.764464</td>\n",
       "      <td>3.283553</td>\n",
       "      <td>3.402909</td>\n",
       "      <td>1.915897</td>\n",
       "      <td>1.451707</td>\n",
       "      <td>2.867383</td>\n",
       "      <td>4.910919</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.281464</td>\n",
       "      <td>0.133984</td>\n",
       "      <td>-0.249939</td>\n",
       "      <td>-0.550021</td>\n",
       "      <td>3.394275</td>\n",
       "      <td>3.893397</td>\n",
       "      <td>1.989588</td>\n",
       "      <td>2.175786</td>\n",
       "      <td>6.046041</td>\n",
       "      <td>4.935010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.750297</td>\n",
       "      <td>-1.151816</td>\n",
       "      <td>1.776573</td>\n",
       "      <td>1.826229</td>\n",
       "      <td>0.280372</td>\n",
       "      <td>0.539340</td>\n",
       "      <td>1.371011</td>\n",
       "      <td>1.428493</td>\n",
       "      <td>-0.009560</td>\n",
       "      <td>-0.562450</td>\n",
       "      <td>...</td>\n",
       "      <td>1.298575</td>\n",
       "      <td>-1.466770</td>\n",
       "      <td>1.338539</td>\n",
       "      <td>1.220724</td>\n",
       "      <td>0.220556</td>\n",
       "      <td>-0.313395</td>\n",
       "      <td>0.613179</td>\n",
       "      <td>0.729259</td>\n",
       "      <td>-0.868353</td>\n",
       "      <td>-0.397100</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     1.097064     -2.073335        1.269934   0.984375         1.568466   \n",
       "1     1.829821     -0.353632        1.685955   1.908708        -0.826962   \n",
       "2     1.579888      0.456187        1.566503   1.558884         0.942210   \n",
       "3    -0.768909      0.253732       -0.592687  -0.764464         3.283553   \n",
       "4     1.750297     -1.151816        1.776573   1.826229         0.280372   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0          3.283515        2.652874             2.532475       2.217515   \n",
       "1         -0.487072       -0.023846             0.548144       0.001392   \n",
       "2          1.052926        1.363478             2.037231       0.939685   \n",
       "3          3.402909        1.915897             1.451707       2.867383   \n",
       "4          0.539340        1.371011             1.428493      -0.009560   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                2.255747  ...      1.886690      -1.359293         2.303601   \n",
       "1               -0.868652  ...      1.805927      -0.369203         1.535126   \n",
       "2               -0.398008  ...      1.511870      -0.023974         1.347475   \n",
       "3                4.910919  ...     -0.281464       0.133984        -0.249939   \n",
       "4               -0.562450  ...      1.298575      -1.466770         1.338539   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0    2.001237          1.307686           2.616665         2.109526   \n",
       "1    1.890489         -0.375612          -0.430444        -0.146749   \n",
       "2    1.456285          0.527407           1.082932         0.854974   \n",
       "3   -0.550021          3.394275           3.893397         1.989588   \n",
       "4    1.220724          0.220556          -0.313395         0.613179   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0              2.296076        2.750622                 1.937015  \n",
       "1              1.087084       -0.243890                 0.281190  \n",
       "2              1.955000        1.152255                 0.201391  \n",
       "3              2.175786        6.046041                 4.935010  \n",
       "4              0.729259       -0.868353                -0.397100  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(data_dropped)\n",
    "\n",
    "scaled = scaler.transform(data_dropped)\n",
    "\n",
    "scaled_df = pd.DataFrame(scaled, columns = list(data_dropped.columns))\n",
    "\n",
    "scaled_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Appplying PCA\n",
    "\n",
    "Now that the data is scaled appropriatley, we're ready to actually use Principal Component analysis. \n",
    "\n",
    "The syntax for doing PCA with scikit-learn is similar to other classes we've worked with from sklearn. You first initialize an instance of the PCA class (this is where you choose hyperparameters), then fit it to your data.  ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html))\n",
    "\n",
    "\n",
    "### 2.1 Task\n",
    "<font size=8 color=\"#009600\">&#9998;</font>  Create and fit a PCA object without specifying any hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "pca_full = PCA()\n",
    "pca_full.fit(scaled_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=8 color=\"#009600\">&#9998;</font> What is the total explained variance from all of the principal components? How many principal components are there? (you'll probably want to write some code to figure this out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Explained Variance: 1.0\n",
      "Number of principal components: 30\n"
     ]
    }
   ],
   "source": [
    "print('Total Explained Variance:',round(sum(pca_full.explained_variance_ratio_),4))\n",
    "print('Number of principal components:', len(pca_full.explained_variance_ratio_))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "answer here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Explained Variance Curve\n",
    "\n",
    "It can be useful to visually see how much variance each principal component explains. \n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Make a plot of the running total of the explained variance from each principal component. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(np.cumsum(pca_full.explained_variance_ratio_))\n",
    "plt.title('Explained Variance Curve')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Explained Variance')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "### ALT. ANSWER ###\n",
    "\n",
    "explained_variance = []\n",
    "variance = 0\n",
    "for i in range(len(scaled_df.columns)):\n",
    "    variance += pca_full.explained_variance_ratio_[i]\n",
    "    explained_variance.append(variance)\n",
    "\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(explained_variance)\n",
    "plt.title('Explained Variance Curve')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Explained Variance')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=8 color=\"#009600\">&#9998;</font> About how many Principal Components does it take to cover $90\\%$ of the variance in the data?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "answer here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Transforming Data with PCA\n",
    "\n",
    "To move data into a lower dimensional space with PCA, we can use the `transform(data)` method. Since we can only visualize data in $2$ and $3$ dimensions, let's try transforming data using $2$ and $3$ principal components.\n",
    "\n",
    "#### 2.3.1 2d Transform\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Do this - Create a new pca object with only 2 principal components and transform the data into this 2d space. How much of the total variance is explained by these two principal components?\n",
    "\n",
    "(note: Is there another method that lets you fit and transform with only one line of code?)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total explained variance: 0.632\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal Component 1</th>\n",
       "      <th>Principal Component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.192837</td>\n",
       "      <td>1.948583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.387802</td>\n",
       "      <td>-3.768172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.733896</td>\n",
       "      <td>-1.075174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.122953</td>\n",
       "      <td>10.275589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.935302</td>\n",
       "      <td>-1.948072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal Component 1  Principal Component 2\n",
       "0               9.192837               1.948583\n",
       "1               2.387802              -3.768172\n",
       "2               5.733896              -1.075174\n",
       "3               7.122953              10.275589\n",
       "4               3.935302              -1.948072"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "pca_2 = PCA(n_components=2) # specify 2 principal components\n",
    "\n",
    "pca_2.fit(scaled_df)\n",
    "\n",
    "trans_pca_2 = pca_2.transform(scaled_df)  # could also do trans_pca_2 = pca.fit_transform(scaled_df) OR pca_2.fit(scaled_df).transform(scaled_df)\n",
    "\n",
    "print(\"total explained variance:\",round(sum(pca_2.explained_variance_ratio_),3))\n",
    "pd.DataFrame(trans_pca_2,columns=['Principal Component 1', 'Principal Component 2']).head()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.2 2d Transformed Data Plot\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Do this - Make a scatter plot of the transformed data (now in 2d) with the two principal components as axes. Color the points in the scatter plot by if the corresponding cell is malignant or not.\n",
    "\n",
    "Hint: To get the colors working in the `c = ` argument, try casting the dataframe with the target data as a numpy array. You'll need to transpose / index the array to get the data in the form matplotlib needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### ANSWER ###\n",
    "\n",
    "plt.figure(figsize = (10,6))\n",
    "plt.scatter(trans_pca_2[:,0],trans_pca_2[:,1], c = target.to_numpy().T[0], s = 5)\n",
    "plt.title(str(round(sum(pca_2.explained_variance_ratio_),3)) + ' of variance captured')\n",
    "plt.xlabel(\"Principal Component 1\")\n",
    "plt.ylabel(\"Principal Component 2\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.3 Observations\n",
    "\n",
    " What kind of structure do you see in this transformed data? Is it seperable? If yes (or mostly yes), **how** is it separable?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "answer here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.3.3 3d Transform\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Repeat the procedure from 2.3.1 - 2.3.3 but in 3d. Here some [documentation](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) on `mplot3d`, which you can use to make a 3d scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total explained variance: 0.726\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_3 = PCA(n_components=3)\n",
    "pca_3.fit(scaled_df)\n",
    "trans_pca_3 = pca_3.transform(scaled_df)\n",
    "print(\"total explained variance:\",round(sum(pca_3.explained_variance_ratio_),3))\n",
    "\n",
    "fig = plt.figure(figsize = (10,10))\n",
    "ax = plt.axes(projection = '3d')\n",
    "ax.scatter3D(trans_pca_3[:,0],trans_pca_3[:,1],trans_pca_3[:,2], c = target.to_numpy().T[0])\n",
    "ax.set_title(str(round(sum(pca_3.explained_variance_ratio_),3)) + ' of variance captured')\n",
    "ax.set_zlabel('Principal Component 3')\n",
    "ax.set_ylabel('Principal Component 2')\n",
    "ax.set_xlabel('Principal Component 1')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. (Time Permitting) Kernel PCA\n",
    "\n",
    "Standard PCA is extremely useful for data that is linearly separable, but it falls short when data has nonlinear structure. In those cases, Kernel PCA may be of use. Kernel PCA is similar in concept to Support Vector Machines (SVMs), as it works by using a kernel function to move data into a higher dimensional space, where the data may become linearly seperable, and hence a typical PCA is effective.\n",
    "\n",
    "Here's some circles like we saw last class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_circles(n_samples=500, factor=0.3, noise=0.06, random_state=10)\n",
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Standard PCA on Nonlinear Data\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Try using a regular PCA to transform this data (but don't change the dimensionality) and plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_pca = pca.fit_transform(X)\n",
    "\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y)\n",
    "plt.axis('equal')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=8 color=\"#009600\">&#9998;</font> What does your group observe?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "answer here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Trying Kernel PCA\n",
    "\n",
    "<font size=8 color=\"#009600\">&#9998;</font> Now try using a Kernel PCA to transform the data. Try to find a kernel and set of parameters that separate the data linearly. What do you observe? (Hint: the `'rbf'` kernel is sensitive to different values of `gamma`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "kernel_pca = KernelPCA(kernel=\"rbf\", gamma=2)\n",
    "\n",
    "X_kernel_pca = kernel_pca.fit(X).transform(X)\n",
    "\n",
    "plt.scatter(X_kernel_pca[:, 0], X_kernel_pca[:, 1], c=y)\n",
    "plt.axis('equal')\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "answer here"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Few Other Dimension Reduction Techniques of note:\n",
    "\n",
    "- Linear Discriminant Analysis (LDA): Used with classifiers, finds the linear combination of features for separating the data.\n",
    "- t-distributed stochastic neighbor embedding (t-SNE): Nonlinear Method, good for visualizing datasets in 2 and 3 dimensions. \n",
    " - Incremental PCA: Like a standard PCA but can be much more memory efficient in some cases.\n",
    "\n",
    "This list is very much non-exhaustive."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}