Week 1 Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "#print(cancer.DESCR) # Print the data set description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['feature_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 0(Example)\n",
    "### How many feature does the data set have?\n",
    "#### It should return an integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You should write your whole answer within the function provided. The autograder will call\n",
    "# this function and compare the return value against the correct solution value\n",
    "def answer_zero():\n",
    "    # This function returns the number of features of the breast cancer dataset, which is an integer. \n",
    "    # The assignment question description will tell you the general format the autograder is expecting\n",
    "    return len(cancer['feature_names'])\n",
    "\n",
    "# You can examine what your function returns by calling it in the cell. If you have questions\n",
    "# about the assignment formats, check out the discussion forums for any FAQs\n",
    "answer_zero() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training this model. Using a DataFrame does however help make many things easier such as munging data, so let's practice creating a classifier with a pandas DataFrame.\n",
    "\n",
    "Convert the sklearn.dataset cancer to a DataFrame.\n",
    "\n",
    "This function should return a (569, 31) DataFrame with\n",
    "\n",
    "columns =\n",
    "\n",
    "['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
    "'mean smoothness', 'mean compactness', 'mean concavity',\n",
    "'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
    "'radius error', 'texture error', 'perimeter error', 'area error',\n",
    "'smoothness error', 'compactness error', 'concavity error',\n",
    "'concave points error', 'symmetry error', 'fractal dimension error',\n",
    "'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
    "'worst smoothness', 'worst compactness', 'worst concavity',\n",
    "'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
    "'target']\n",
    "and index =\n",
    "\n",
    "RangeIndex(start=0, stop=569, step=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</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 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",
       "      <th>target</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>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</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>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</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>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</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>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</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>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 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",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                   0.07871  ...          17.33           184.60      2019.0   \n",
       "1                   0.05667  ...          23.41           158.80      1956.0   \n",
       "2                   0.05999  ...          25.53           152.50      1709.0   \n",
       "3                   0.09744  ...          26.50            98.87       567.7   \n",
       "4                   0.05883  ...          16.67           152.20      1575.0   \n",
       "..                      ...  ...            ...              ...         ...   \n",
       "564                 0.05623  ...          26.40           166.10      2027.0   \n",
       "565                 0.05533  ...          38.25           155.00      1731.0   \n",
       "566                 0.05648  ...          34.12           126.70      1124.0   \n",
       "567                 0.07016  ...          39.42           184.60      1821.0   \n",
       "568                 0.05884  ...          30.37            59.16       268.6   \n",
       "\n",
       "     worst smoothness  worst compactness  worst concavity  \\\n",
       "0             0.16220            0.66560           0.7119   \n",
       "1             0.12380            0.18660           0.2416   \n",
       "2             0.14440            0.42450           0.4504   \n",
       "3             0.20980            0.86630           0.6869   \n",
       "4             0.13740            0.20500           0.4000   \n",
       "..                ...                ...              ...   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "0                  0.2654          0.4601                  0.11890       0  \n",
       "1                  0.1860          0.2750                  0.08902       0  \n",
       "2                  0.2430          0.3613                  0.08758       0  \n",
       "3                  0.2575          0.6638                  0.17300       0  \n",
       "4                  0.1625          0.2364                  0.07678       0  \n",
       "..                    ...             ...                      ...     ...  \n",
       "564                0.2216          0.2060                  0.07115       0  \n",
       "565                0.1628          0.2572                  0.06637       0  \n",
       "566                0.1418          0.2218                  0.07820       0  \n",
       "567                0.2650          0.4087                  0.12400       0  \n",
       "568                0.0000          0.2871                  0.07039       1  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here we will get a little bit confusion because we dont understand the data properly\n",
    "#If we examine it carefully, we will understand that, cancer['Data'] is a list of lists containing all the values of all the required columns except 'target'\n",
    "#and cancer['feature_name'] contains all the column names\n",
    "#and cancer['target'] contains the values for target column\n",
    "#let 'cancer_df' be the name of the required data frame\n",
    "\n",
    "def answer_one():\n",
    "    # Your code here \n",
    "    \n",
    "    #this is to load the data into the dataframe\n",
    "    cancer_df = pd.DataFrame(cancer['data'])\n",
    "    \n",
    "    #this is to set the column names\n",
    "    cancer_df.columns = cancer['feature_names']\n",
    "    \n",
    "    #this is to set the last column 'target'\n",
    "    cancer_df['target'] = cancer['target']\n",
    "    \n",
    "    return cancer_df\n",
    "\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "What is the class distribution? (i.e. how many instances of malignant (encoded 0) and how many benign (encoded 1)?)\n",
    "\n",
    "This function should return a Series named target of length 2 with integer values and index = ['malignant', 'benign']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "malignant    212\n",
       "benign       357\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "    #first we will convert the target column into a list to directly count the nubmer of ones and zeros in that column\n",
    "    \n",
    "    malignant_count = list(cancerdf['target']).count(0)\n",
    "    benign_count = list(cancerdf['target']).count(1)\n",
    "    \n",
    "    #then, creating a Series using those values and given indexe\n",
    "    \n",
    "    target = pd.Series([malignant_count,benign_count],index=['malignant','benign'])\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return target\n",
    "\n",
    "\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quesiton 3\n",
    "Split the DataFrame into X (the data) and y (the labels).\n",
    "\n",
    "This function should return a tuple of length 2: (X, y), where\n",
    "\n",
    "    X, a pandas DataFrame, has shape (569, 30)\n",
    "    y, a pandas Series, has shape (569,)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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",
       " 564        21.56         22.39          142.00     1479.0          0.11100   \n",
       " 565        20.13         28.25          131.20     1261.0          0.09780   \n",
       " 566        16.60         28.08          108.30      858.1          0.08455   \n",
       " 567        20.60         29.33          140.10     1265.0          0.11780   \n",
       " 568         7.76         24.54           47.92      181.0          0.05263   \n",
       " \n",
       "      mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       " 0             0.27760         0.30010              0.14710         0.2419   \n",
       " 1             0.07864         0.08690              0.07017         0.1812   \n",
       " 2             0.15990         0.19740              0.12790         0.2069   \n",
       " 3             0.28390         0.24140              0.10520         0.2597   \n",
       " 4             0.13280         0.19800              0.10430         0.1809   \n",
       " ..                ...             ...                  ...            ...   \n",
       " 564           0.11590         0.24390              0.13890         0.1726   \n",
       " 565           0.10340         0.14400              0.09791         0.1752   \n",
       " 566           0.10230         0.09251              0.05302         0.1590   \n",
       " 567           0.27700         0.35140              0.15200         0.2397   \n",
       " 568           0.04362         0.00000              0.00000         0.1587   \n",
       " \n",
       "      mean fractal dimension  ...  worst radius  worst texture  \\\n",
       " 0                   0.07871  ...        25.380          17.33   \n",
       " 1                   0.05667  ...        24.990          23.41   \n",
       " 2                   0.05999  ...        23.570          25.53   \n",
       " 3                   0.09744  ...        14.910          26.50   \n",
       " 4                   0.05883  ...        22.540          16.67   \n",
       " ..                      ...  ...           ...            ...   \n",
       " 564                 0.05623  ...        25.450          26.40   \n",
       " 565                 0.05533  ...        23.690          38.25   \n",
       " 566                 0.05648  ...        18.980          34.12   \n",
       " 567                 0.07016  ...        25.740          39.42   \n",
       " 568                 0.05884  ...         9.456          30.37   \n",
       " \n",
       "      worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       " 0             184.60      2019.0           0.16220            0.66560   \n",
       " 1             158.80      1956.0           0.12380            0.18660   \n",
       " 2             152.50      1709.0           0.14440            0.42450   \n",
       " 3              98.87       567.7           0.20980            0.86630   \n",
       " 4             152.20      1575.0           0.13740            0.20500   \n",
       " ..               ...         ...               ...                ...   \n",
       " 564           166.10      2027.0           0.14100            0.21130   \n",
       " 565           155.00      1731.0           0.11660            0.19220   \n",
       " 566           126.70      1124.0           0.11390            0.30940   \n",
       " 567           184.60      1821.0           0.16500            0.86810   \n",
       " 568            59.16       268.6           0.08996            0.06444   \n",
       " \n",
       "      worst concavity  worst concave points  worst symmetry  \\\n",
       " 0             0.7119                0.2654          0.4601   \n",
       " 1             0.2416                0.1860          0.2750   \n",
       " 2             0.4504                0.2430          0.3613   \n",
       " 3             0.6869                0.2575          0.6638   \n",
       " 4             0.4000                0.1625          0.2364   \n",
       " ..               ...                   ...             ...   \n",
       " 564           0.4107                0.2216          0.2060   \n",
       " 565           0.3215                0.1628          0.2572   \n",
       " 566           0.3403                0.1418          0.2218   \n",
       " 567           0.9387                0.2650          0.4087   \n",
       " 568           0.0000                0.0000          0.2871   \n",
       " \n",
       "      worst fractal dimension  \n",
       " 0                    0.11890  \n",
       " 1                    0.08902  \n",
       " 2                    0.08758  \n",
       " 3                    0.17300  \n",
       " 4                    0.07678  \n",
       " ..                       ...  \n",
       " 564                  0.07115  \n",
       " 565                  0.06637  \n",
       " 566                  0.07820  \n",
       " 567                  0.12400  \n",
       " 568                  0.07039  \n",
       " \n",
       " [569 rows x 30 columns],\n",
       " 0      0\n",
       " 1      0\n",
       " 2      0\n",
       " 3      0\n",
       " 4      0\n",
       "       ..\n",
       " 564    0\n",
       " 565    0\n",
       " 566    0\n",
       " 567    0\n",
       " 568    1\n",
       " Length: 569, dtype: int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    \n",
    "    cancerdf = answer_one()\n",
    "    \n",
    "    #for X, we must get all the columns except the last one,so instead of giving the column names explicitly, just use the\n",
    "    #'.columns' and slice the list obtained\n",
    "    \n",
    "    X = cancerdf[list(cancerdf.columns)[:30]]\n",
    "    \n",
    "    # for creating 'y' it is self-explanatory\n",
    "    \n",
    "    y = pd.Series(list(cancerdf['target']))\n",
    "    \n",
    "    return X, y\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "Using train_test_split, split X and y into training and test sets (X_train, X_test, y_train, and y_test).\n",
    "\n",
    "Set the random number generator state to 0 using random_state=0 to make sure your results match the autograder!\n",
    "\n",
    "This function should return a tuple of length 4: (X_train, X_test, y_train, y_test), where\n",
    "\n",
    "X_train has shape (426, 30)\n",
    "X_test has shape (143, 30)\n",
    "y_train has shape (426,)\n",
    "y_test has shape (143,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       " 293       11.850         17.46           75.54      432.7          0.08372   \n",
       " 332       11.220         19.86           71.94      387.3          0.10540   \n",
       " 565       20.130         28.25          131.20     1261.0          0.09780   \n",
       " 278       13.590         17.84           86.24      572.3          0.07948   \n",
       " 489       16.690         20.20          107.10      857.6          0.07497   \n",
       " ..           ...           ...             ...        ...              ...   \n",
       " 277       18.810         19.98          120.90     1102.0          0.08923   \n",
       " 9         12.460         24.04           83.97      475.9          0.11860   \n",
       " 359        9.436         18.32           59.82      278.6          0.10090   \n",
       " 192        9.720         18.22           60.73      288.1          0.06950   \n",
       " 559       11.510         23.93           74.52      403.5          0.09261   \n",
       " \n",
       "      mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       " 293           0.05642        0.026880             0.022800         0.1875   \n",
       " 332           0.06779        0.005006             0.007583         0.1940   \n",
       " 565           0.10340        0.144000             0.097910         0.1752   \n",
       " 278           0.04052        0.019970             0.012380         0.1573   \n",
       " 489           0.07112        0.036490             0.023070         0.1846   \n",
       " ..                ...             ...                  ...            ...   \n",
       " 277           0.05884        0.080200             0.058430         0.1550   \n",
       " 9             0.23960        0.227300             0.085430         0.2030   \n",
       " 359           0.05956        0.027100             0.014060         0.1506   \n",
       " 192           0.02344        0.000000             0.000000         0.1653   \n",
       " 559           0.10210        0.111200             0.041050         0.1388   \n",
       " \n",
       "      mean fractal dimension  ...  worst radius  worst texture  \\\n",
       " 293                 0.05715  ...        13.060          25.75   \n",
       " 332                 0.06028  ...        11.980          25.78   \n",
       " 565                 0.05533  ...        23.690          38.25   \n",
       " 278                 0.05520  ...        15.500          26.10   \n",
       " 489                 0.05325  ...        19.180          26.56   \n",
       " ..                      ...  ...           ...            ...   \n",
       " 277                 0.04996  ...        19.960          24.30   \n",
       " 9                   0.08243  ...        15.090          40.68   \n",
       " 359                 0.06959  ...        12.020          25.02   \n",
       " 192                 0.06447  ...         9.968          20.83   \n",
       " 559                 0.06570  ...        12.480          37.16   \n",
       " \n",
       "      worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       " 293            84.35       517.8           0.13690            0.17580   \n",
       " 332            76.91       436.1           0.14240            0.09669   \n",
       " 565           155.00      1731.0           0.11660            0.19220   \n",
       " 278            98.91       739.1           0.10500            0.07622   \n",
       " 489           127.30      1084.0           0.10090            0.29200   \n",
       " ..               ...         ...               ...                ...   \n",
       " 277           129.00      1236.0           0.12430            0.11600   \n",
       " 9              97.65       711.4           0.18530            1.05800   \n",
       " 359            75.79       439.6           0.13330            0.10490   \n",
       " 192            62.25       303.8           0.07117            0.02729   \n",
       " 559            82.28       474.2           0.12980            0.25170   \n",
       " \n",
       "      worst concavity  worst concave points  worst symmetry  \\\n",
       " 293          0.13160               0.09140          0.3101   \n",
       " 332          0.01335               0.02022          0.3292   \n",
       " 565          0.32150               0.16280          0.2572   \n",
       " 278          0.10600               0.05185          0.2335   \n",
       " 489          0.24770               0.08737          0.4677   \n",
       " ..               ...                   ...             ...   \n",
       " 277          0.22100               0.12940          0.2567   \n",
       " 9            1.10500               0.22100          0.4366   \n",
       " 359          0.11440               0.05052          0.2454   \n",
       " 192          0.00000               0.00000          0.1909   \n",
       " 559          0.36300               0.09653          0.2112   \n",
       " \n",
       "      worst fractal dimension  \n",
       " 293                  0.07007  \n",
       " 332                  0.06522  \n",
       " 565                  0.06637  \n",
       " 278                  0.06263  \n",
       " 489                  0.07623  \n",
       " ..                       ...  \n",
       " 277                  0.05737  \n",
       " 9                    0.20750  \n",
       " 359                  0.08136  \n",
       " 192                  0.06559  \n",
       " 559                  0.08732  \n",
       " \n",
       " [426 rows x 30 columns],\n",
       "      mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       " 512        13.40         20.52           88.64      556.7          0.11060   \n",
       " 457        13.21         25.25           84.10      537.9          0.08791   \n",
       " 439        14.02         15.66           89.59      606.5          0.07966   \n",
       " 298        14.26         18.17           91.22      633.1          0.06576   \n",
       " 37         13.03         18.42           82.61      523.8          0.08983   \n",
       " ..           ...           ...             ...        ...              ...   \n",
       " 236        23.21         26.97          153.50     1670.0          0.09509   \n",
       " 113        10.51         20.19           68.64      334.2          0.11220   \n",
       " 527        12.34         12.27           78.94      468.5          0.09003   \n",
       " 76         13.53         10.94           87.91      559.2          0.12910   \n",
       " 162        19.59         18.15          130.70     1214.0          0.11200   \n",
       " \n",
       "      mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       " 512           0.14690         0.14450              0.08172         0.2116   \n",
       " 457           0.05205         0.02772              0.02068         0.1619   \n",
       " 439           0.05581         0.02087              0.02652         0.1589   \n",
       " 298           0.05220         0.02475              0.01374         0.1635   \n",
       " 37            0.03766         0.02562              0.02923         0.1467   \n",
       " ..                ...             ...                  ...            ...   \n",
       " 236           0.16820         0.19500              0.12370         0.1909   \n",
       " 113           0.13030         0.06476              0.03068         0.1922   \n",
       " 527           0.06307         0.02958              0.02647         0.1689   \n",
       " 76            0.10470         0.06877              0.06556         0.2403   \n",
       " 162           0.16660         0.25080              0.12860         0.2027   \n",
       " \n",
       "      mean fractal dimension  ...  worst radius  worst texture  \\\n",
       " 512                 0.07325  ...         16.41          29.66   \n",
       " 457                 0.05584  ...         14.35          34.23   \n",
       " 439                 0.05586  ...         14.91          19.31   \n",
       " 298                 0.05586  ...         16.22          25.26   \n",
       " 37                  0.05863  ...         13.30          22.81   \n",
       " ..                      ...  ...           ...            ...   \n",
       " 236                 0.06309  ...         31.01          34.51   \n",
       " 113                 0.07782  ...         11.16          22.75   \n",
       " 527                 0.05808  ...         13.61          19.27   \n",
       " 76                  0.06641  ...         14.08          12.49   \n",
       " 162                 0.06082  ...         26.73          26.39   \n",
       " \n",
       "      worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
       " 512           113.30       844.4           0.15740            0.38560   \n",
       " 457            91.29       632.9           0.12890            0.10630   \n",
       " 439            96.53       688.9           0.10340            0.10170   \n",
       " 298           105.80       819.7           0.09445            0.21670   \n",
       " 37             84.46       545.9           0.09701            0.04619   \n",
       " ..               ...         ...               ...                ...   \n",
       " 236           206.00      2944.0           0.14810            0.41260   \n",
       " 113            72.62       374.4           0.13000            0.20490   \n",
       " 527            87.22       564.9           0.12920            0.20740   \n",
       " 76             91.36       605.5           0.14510            0.13790   \n",
       " 162           174.90      2232.0           0.14380            0.38460   \n",
       " \n",
       "      worst concavity  worst concave points  worst symmetry  \\\n",
       " 512          0.51060               0.20510          0.3585   \n",
       " 457          0.13900               0.06005          0.2444   \n",
       " 439          0.06260               0.08216          0.2136   \n",
       " 298          0.15650               0.07530          0.2636   \n",
       " 37           0.04833               0.05013          0.1987   \n",
       " ..               ...                   ...             ...   \n",
       " 236          0.58200               0.25930          0.3103   \n",
       " 113          0.12950               0.06136          0.2383   \n",
       " 527          0.17910               0.10700          0.3110   \n",
       " 76           0.08539               0.07407          0.2710   \n",
       " 162          0.68100               0.22470          0.3643   \n",
       " \n",
       "      worst fractal dimension  \n",
       " 512                  0.11090  \n",
       " 457                  0.06788  \n",
       " 439                  0.06710  \n",
       " 298                  0.07676  \n",
       " 37                   0.06169  \n",
       " ..                       ...  \n",
       " 236                  0.08677  \n",
       " 113                  0.09026  \n",
       " 527                  0.07592  \n",
       " 76                   0.07191  \n",
       " 162                  0.09223  \n",
       " \n",
       " [143 rows x 30 columns],\n",
       " 293    1\n",
       " 332    1\n",
       " 565    0\n",
       " 278    1\n",
       " 489    0\n",
       "       ..\n",
       " 277    0\n",
       " 9      0\n",
       " 359    1\n",
       " 192    1\n",
       " 559    1\n",
       " Length: 426, dtype: int64,\n",
       " 512    0\n",
       " 457    1\n",
       " 439    1\n",
       " 298    1\n",
       " 37     1\n",
       "       ..\n",
       " 236    0\n",
       " 113    1\n",
       " 527    1\n",
       " 76     1\n",
       " 162    0\n",
       " Length: 143, dtype: int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def answer_four():\n",
    "    X, y = answer_three()\n",
    "    \n",
    "    #It is a simple syntax and it was shown in the lesson to us.\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0)\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return X_train, X_test, y_train, y_test\n",
    "answer_four()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with X_train, y_train and using one nearest neighbor (n_neighbors = 1).\n",
    "\n",
    "This function should return a sklearn.neighbors.classification.KNeighborsClassifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "def answer_five():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    \n",
    "    #these are just simple implementation of syntaxes discussed in class and given in Module 1 notebook\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors = 1)\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return knn\n",
    "answer_five()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6\n",
    "Using your knn classifier, predict the class label using the mean value for each feature.\n",
    "\n",
    "Hint: You can use cancerdf.mean()[:-1].values.reshape(1, -1) which gets the mean value for each feature, ignores the target column, and reshapes the data from 1 dimension to 2 (necessary for the precict method of KNeighborsClassifier).\n",
    "\n",
    "This function should return a numpy array either array([ 0.]) or array([ 1.])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():\n",
    "# here, dont get confused by why did he use means --- it is just used to create a row of those values just for understanding\n",
    "# the classifier (instead of using random values for the trial, they just took mean of all the values in the respective columns)\n",
    "\n",
    "#after calculating the means, to get only th values in the column, '.values' is used.\n",
    "#then, we need to reshape the array into 2-d which is the syntax for passing the data into the classifier\n",
    "#.reshpe(a,b) converts an array into the shape of a rows and b columns \n",
    "# here b is kept as '-1' so, that it automatically adjust the array into 2-d one based on the value of 'a'\n",
    "    \n",
    "    cancerdf = answer_one()\n",
    "    means = cancerdf.mean()[:-1].values.reshape(1, -1)\n",
    "    \n",
    "    knn=answer_five()\n",
    "    prediction = knn.predict(means)\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return prediction\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 7\n",
    "Using your knn classifier, predict the class labels for the test set X_test.\n",
    "\n",
    "This function should return a numpy array with shape (143,) and values either 0.0 or 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1,\n",
       "       0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_seven():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    knn = answer_five()\n",
    "    \n",
    "    #first, create a empty list to append and store all the values of predictions by the knn classifier\n",
    "    prediction_list = []\n",
    "    \n",
    "    for i in range(len(X_test)):\n",
    "        # iterate over the X_test data and append the predicton values to the list\n",
    "        prediction_list.append(knn.predict(X_test.iloc[i].values.reshape(1,-1)))\n",
    "    \n",
    "    # It is asked to return an arrys of size(143,), so convert it into an array and reshape it\n",
    "    return np.array(prediction_list).reshape(143,)\n",
    "answer_seven()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 8\n",
    "Find the score (mean accuracy) of your knn classifier using X_test and y_test.\n",
    "\n",
    "This function should return a float between 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.916083916083916"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_eight():\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "    knn = answer_five()\n",
    "    \n",
    "    #this score function is a simple fucntion to calcualte the accuracy that was discussed in the lecture and mentioned in\n",
    "    #the notebook\n",
    "    \n",
    "    score = knn.score(X_test, y_test)\n",
    "    \n",
    "    # Your code here\n",
    "    \n",
    "    return score\n",
    "answer_eight()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional Plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def accuracy_plot():\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    %matplotlib notebook\n",
    "\n",
    "    X_train, X_test, y_train, y_test = answer_four()\n",
    "\n",
    "    # Find the training and testing accuracies by target value (i.e. malignant, benign)\n",
    "    mal_train_X = X_train[y_train==0]\n",
    "    mal_train_y = y_train[y_train==0]\n",
    "    ben_train_X = X_train[y_train==1]\n",
    "    ben_train_y = y_train[y_train==1]\n",
    "\n",
    "    mal_test_X = X_test[y_test==0]\n",
    "    mal_test_y = y_test[y_test==0]\n",
    "    ben_test_X = X_test[y_test==1]\n",
    "    ben_test_y = y_test[y_test==1]\n",
    "\n",
    "    knn = answer_five()\n",
    "\n",
    "    scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), \n",
    "              knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]\n",
    "\n",
    "\n",
    "    plt.figure()\n",
    "\n",
    "    # Plot the scores as a bar chart\n",
    "    bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
    "\n",
    "    # directly label the score onto the bars\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
    "                     ha='center', color='w', fontsize=11)\n",
    "\n",
    "    # remove all the ticks (both axes), and tick labels on the Y axis\n",
    "    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
    "\n",
    "    # remove the frame of the chart\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_visible(False)\n",
    "\n",
    "    plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
    "    plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}