breast_cancer_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breast cancer prediction\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this project I will look at a dataset of patient data relating to breast cancer, which is available on Kaggle as the [Wisconsin Breast Cancer dataset](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data). \n",
    "\n",
    "The dataset features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image in the 3-dimensional space is that described in: K. P. Bennett and O. L. Mangasarian: \"Robust Linear Programming Discrimination of Two Linearly Inseparable Sets\", Optimization Methods and Software 1, 1992, 23-34.\n",
    "\n",
    "The dataset was released in November 1995 and the original source can be found [here](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29). \n",
    "\n",
    "An example of images of cells that this data comes from of both malignant and benign tumors can be seen below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/bc_cells.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traditionally one of the main ways you could diagnose if a tumor is malignant or benign is to take a biopsy of a breast mass and then visually examine cell nuclei under a microscope. A trained specialist can then decide if there is cancer or not.\n",
    "\n",
    "We can see here in the example above that the image on the left belongs to a patient who was diagnosed with a benign tumor, and the image on the right belong to a patient who was diagnosed with a malignant tumor.\n",
    "\n",
    "The dataset we have consists of 32 features which relate to these types of images & cells and are the following:\n",
    "\n",
    "- ID number \n",
    "- Diagnosis (M = malignant, B = benign) \n",
    "\n",
    "Features 3-32 are ten real-valued features are computed for each cell nucleus: \n",
    "\n",
    "- a) radius (mean of distances from center to points on the perimeter) \n",
    "- b) texture (standard deviation of gray-scale values) \n",
    "- c) perimeter \n",
    "- d) area \n",
    "- e) smoothness (local variation in radius lengths) \n",
    "- f) compactness (perimeter^2 / area - 1.0) \n",
    "- g) concavity (severity of concave portions of the contour) \n",
    "- h) concave points (number of concave portions of the contour) \n",
    "- i) symmetry \n",
    "- j) fractal dimension (\"coastline approximation\" - 1)\n",
    "\n",
    "The mean, standard error (se) and \"worst\" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.\n",
    "\n",
    "All feature values are recoded with four significant digits.\n",
    "\n",
    "Missing attribute values: none\n",
    "\n",
    "Class distribution: 357 benign, 212 malignant\n",
    "\n",
    "We can see from the image examples that it is visually possible to distinguish between malignant and benign tumor cells. It is hoped that the data derived from these images of the cells is able to capture the differences between these types of cells.\n",
    "\n",
    "In this project, I will aim to develop a machine learning model that will aim to predict Malignant tumors with the highest accuracy.\n",
    "\n",
    "\n",
    "## Import libraries\n",
    "\n",
    "Let us first import the libraries we will need for analysis and developing our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "from mpl_toolkits.mplot3d import Axes3D  \n",
    "import sklearn\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn import ensemble\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import fbeta_score, accuracy_score\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import make_scorer, fbeta_score\n",
    "import kmapper as km\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the Census dataset\n",
    "bcancer_df = pd.read_csv(\"data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial data exploration\n",
    "\n",
    "We will now take an initial look at the dataset, its values and structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</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>...</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\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",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# For reproducability of the results\n",
    "np.random.seed(42)\n",
    "# Observe first few rows\n",
    "bcancer_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok we can see there are quite a range of continous values for each of the features. Also note something odd, the final column is called 'Unnamed: 32' and is full of nan's - it's not mentioned in the dataset description and looks like some kind of format error in the dataset csv. Let's note that for later in our cleaning phase.\n",
    "\n",
    "Lets compute some key stats for each of the features to get a feel for them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>id</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.690000e+02</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.037183e+07</td>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.250206e+08</td>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.670000e+03</td>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.692180e+05</td>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.060240e+05</td>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.813129e+06</td>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.113205e+08</td>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\n",
       "count  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \n",
       "mean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \n",
       "std    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \n",
       "min    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n",
       "25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n",
       "50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n",
       "75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \n",
       "max    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       symmetry_mean  ...  texture_worst  perimeter_worst   area_worst  \\\n",
       "count     569.000000  ...     569.000000       569.000000   569.000000   \n",
       "mean        0.181162  ...      25.677223       107.261213   880.583128   \n",
       "std         0.027414  ...       6.146258        33.602542   569.356993   \n",
       "min         0.106000  ...      12.020000        50.410000   185.200000   \n",
       "25%         0.161900  ...      21.080000        84.110000   515.300000   \n",
       "50%         0.179200  ...      25.410000        97.660000   686.500000   \n",
       "75%         0.195700  ...      29.720000       125.400000  1084.000000   \n",
       "max         0.304000  ...      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            0.222600           1.058000         1.252000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "count            569.000000      569.000000               569.000000   \n",
       "mean               0.114606        0.290076                 0.083946   \n",
       "std                0.065732        0.061867                 0.018061   \n",
       "min                0.000000        0.156500                 0.055040   \n",
       "25%                0.064930        0.250400                 0.071460   \n",
       "50%                0.099930        0.282200                 0.080040   \n",
       "75%                0.161400        0.317900                 0.092080   \n",
       "max                0.291000        0.663800                 0.207500   \n",
       "\n",
       "       Unnamed: 32  \n",
       "count          0.0  \n",
       "mean           NaN  \n",
       "std            NaN  \n",
       "min            NaN  \n",
       "25%            NaN  \n",
       "50%            NaN  \n",
       "75%            NaN  \n",
       "max            NaN  \n",
       "\n",
       "[8 rows x 32 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute key stats for each feature\n",
    "bcancer_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok quite a bit of variation in the scale and range of values (note also very variable std. dev.) we will need scaling of features later before we feed the data to our model.\n",
    "\n",
    "Lets now check for completeness of data, which according to the dataset description should not have any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      "id                         569 non-null int64\n",
      "diagnosis                  569 non-null object\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "Unnamed: 32                0 non-null float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check for nans\n",
    "bcancer_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok all looks complete (bar our 'Unnamed: 32' column which we will drop later in our cleaning phase).\n",
    "\n",
    "Let's now check the proportions of the dataset between benign & malignant target classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcancer_df['diagnosis'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's in line with our expectations from the dataset description, however it is encoded as a string/categorical value and we will need to make that numeric before feeding into our ML model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean data\n",
    "\n",
    "So we have just a couple of cleaning steps identified previously, which are to convert the diagnosis field into numeric values and to drop the id and 'Unnamed: 32' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the 'diagnosis' feature to numerical values\n",
    "bcancer_clean_df = bcancer_df.replace(['B', 'M'], [0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</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>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0          1        17.99         10.38          122.80     1001.0   \n",
       "1          1        20.57         17.77          132.90     1326.0   \n",
       "2          1        19.69         21.25          130.00     1203.0   \n",
       "3          1        11.42         20.38           77.58      386.1   \n",
       "4          1        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\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",
       "   symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\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",
       "   area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\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",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  \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": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove last column \n",
    "bcancer_clean_df = bcancer_clean_df.drop(bcancer_df.columns[len(bcancer_df.columns)-1], axis=1)\n",
    "# Remove id column\n",
    "bcancer_clean_df = bcancer_clean_df.drop(['id'], axis=1)\n",
    "# Show first few rows\n",
    "bcancer_clean_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Further data exploration\n",
    "\n",
    "Ok now we have cleaned up our dataset, we are in a better position to look more closely at it.\n",
    "\n",
    "### Compare distribution of values for each feature between benign & malignent tumor sub-groups\n",
    "\n",
    "Let's do a univariate exploration of each of the numerical features, but by dividing the dataset into sub-groups for 'diagnosis' i.e. malignant and benign sub groups, and compare the distributions for each sub groups to see what patterns there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into those with malignent and those with benign tumors\n",
    "malignent_df = bcancer_clean_df[bcancer_clean_df['diagnosis']==1]\n",
    "benign_df = bcancer_clean_df[bcancer_clean_df['diagnosis']==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function for plotting feature distributions to compare benign & malignent sub-groups\n",
    "def compare_features_benign_malignent(features, malignent_df, benign_df, rows, cols, figx, figy):\n",
    "    \n",
    "    # Define constants\n",
    "    bins = 10\n",
    "    fig, axes = plt.subplots(nrows=rows, ncols=cols, figsize=(figx, figy))\n",
    "    fig.subplots_adjust(hspace=0.5)\n",
    "    # For each axes & feature\n",
    "    for index, (ax, feature) in enumerate(zip(axes.flatten(), features)):\n",
    "        \n",
    "        ax.hist(malignent_df[feature], bins, alpha=0.5, label='Malignent')\n",
    "        ax.hist(benign_df[feature], bins, alpha=0.5, label='Benign')\n",
    "        ax.legend(loc='upper right')\n",
    "        ax.set_title(feature + \": malignent v benign tumors\")        \n",
    "\n",
    "# Get feature names (remove first element diagnosis which is target feature)\n",
    "features = bcancer_clean_df.columns\n",
    "features = features[1:]\n",
    "# Plot feature distributions to compare benign & malignent sub-groups\n",
    "compare_features_benign_malignent(features, malignent_df, benign_df, 10, 3, 20, 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see several interesting aspects to the data from these comparitive histograms.\n",
    "\n",
    "Firstly, there are some features that seem to have a significant difference between the distributions of malignant v benign, these are:\n",
    "\n",
    "- perimeter_mean\n",
    "- area_mean\n",
    "- concave points_mean\n",
    "- radius_worst\n",
    "- perimeter_worst\n",
    "- concave points_worst\n",
    "\n",
    "These might proove useful features for our model to help better predict & distinguish between and predict malignant v benign tumors. \n",
    "\n",
    "Secondly, we can see some of the distributions are particularly skewed i.e. for features:\n",
    "\n",
    "- radius_se\n",
    "- perimeter_se\n",
    "- area_se\n",
    "\n",
    "These are going to be more difficult for the ML model to work with, so we will probably want to log-transform them to spread out their values more to make them of maximum use to our model.\n",
    "\n",
    "Thirdly, we can see the units of the features and scale vary widely, with many such as 'symmetry_mean' being fractional decimals below 1, and others such as 'area_worst' has values ranging from a few hundred to over 3500. ML models are very sensitive to these differences of scale so to ensure our model treats all features equally, we will need to normalise them all so each feature is a value between 0 and 1.\n",
    "\n",
    "Finally more generally we can see comapring the malignant v benign sub-groups accross all features, we can see there are some features where there is clearly much overlap between the groups, and other features where there is a big difference between the values for each groups - so both areas of commonality and difference. \n",
    "\n",
    "Let us now move from a univariate perspective to combining multiple features to build up a bigger picture of the dataset.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### Dimensionality reduction using t-SNE and comparing cancer sub-groups\n",
    "\n",
    "[t-SNE](https://www.datacamp.com/community/tutorials/introduction-t-sne) is a recent dimensionality reduction technique that can be used to compress a multi-dimensional dataset into fewer dimensions while aiming to retain the maximum variance of the original dimensions. \n",
    "\n",
    "It is a more recent dimensionality reduction technique, say compared to [PCA](https://towardsdatascience.com/a-step-by-step-explanation-of-principal-component-analysis-b836fb9c97e2) that has various differences that I will not go into here, but are explored further in an earlier project of mine [here](https://github.com/pranath/high_dim_data_vis).\n",
    "\n",
    "In this project, I will use t-SNE on this dataset to reduce the dimensionality to 2 & 3 dimensions, and then plot these on scatterplots dividing the data into malignant & benign sub-groups, to see how the values between these sub-groups compare, and see if we can get a better overall picture of how similar or different these sub-groups are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 569 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 569 samples in 0.015s...\n",
      "[t-SNE] Computed conditional probabilities for sample 569 / 569\n",
      "[t-SNE] Mean sigma: 33.679842\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 49.567257\n",
      "[t-SNE] KL divergence after 300 iterations: 0.274702\n",
      "t-SNE done! Time elapsed: 1.104383945465088 seconds\n"
     ]
    }
   ],
   "source": [
    "# Create 2 components using t-SNE\n",
    "time_start = time.time()\n",
    "tsne = TSNE(n_components=2, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results = tsne.fit_transform(bcancer_clean_df)\n",
    "print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123f89400>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot dataset with 2 t-SNE features, using same colour for data for benign v malignent tumors\n",
    "bcancer_clean_df['tsne-2d-one'] = tsne_results[:,0]\n",
    "bcancer_clean_df['tsne-2d-two'] = tsne_results[:,1]\n",
    "plt.figure(figsize=(16,10))\n",
    "sns.scatterplot(\n",
    "    x=\"tsne-2d-one\", y=\"tsne-2d-two\",\n",
    "    hue=\"diagnosis\",\n",
    "    palette=sns.color_palette(\"hls\", 2),\n",
    "    data=bcancer_clean_df,\n",
    "    legend=\"full\",\n",
    "    alpha=0.3\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can observe various characteristics of the dataset and the subgroups.\n",
    "\n",
    "We can see that there are clear areas for the benign and malignant groups only, but also common areas of overlap between the two groups.\n",
    "\n",
    "We can also see an interesting almost symmetrical structure in this side-on 'U' shape. Even for areas of overlap between these two groups, the points follow the same 'U' pattern.\n",
    "\n",
    "These indicate the potential for a model using the dataset to be able to distinguish between the benign and malignant groups/cases.\n",
    "\n",
    "Let us now derive 3 t-SNE components from our dataset and plot these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 121 nearest neighbors...\n",
      "[t-SNE] Indexed 569 samples in 0.001s...\n",
      "[t-SNE] Computed neighbors for 569 samples in 0.014s...\n",
      "[t-SNE] Computed conditional probabilities for sample 569 / 569\n",
      "[t-SNE] Mean sigma: 23.814069\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 56.046417\n",
      "[t-SNE] KL divergence after 300 iterations: 0.856587\n",
      "t-SNE done! Time elapsed: 3.351314067840576 seconds\n"
     ]
    }
   ],
   "source": [
    "# Create 3 components using t-SNE\n",
    "time_start = time.time()\n",
    "tsne2 = TSNE(n_components=3, verbose=1, perplexity=40, n_iter=300)\n",
    "tsne_results2 = tsne2.fit_transform(bcancer_clean_df)\n",
    "print('t-SNE done! Time elapsed: {} seconds'.format(time.time()-time_start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcancer_clean_df['tsne-3d-one'] = tsne_results2[:,0]\n",
    "bcancer_clean_df['tsne-3d-two'] = tsne_results2[:,1]\n",
    "bcancer_clean_df['tsne-3d-three'] = tsne_results2[:,2]\n",
    "# Plot dataset with 3 t-SNE, using same colour for data for benign v malignent tumors\n",
    "ax = plt.figure(figsize=(16,10)).gca(projection='3d')\n",
    "ax.scatter(\n",
    "    xs=bcancer_clean_df[\"tsne-3d-one\"], \n",
    "    ys=bcancer_clean_df[\"tsne-3d-two\"], \n",
    "    zs=bcancer_clean_df[\"tsne-3d-three\"], \n",
    "    c=bcancer_clean_df[\"diagnosis\"].astype(int)\n",
    ")\n",
    "ax.set_xlabel('tsne-3d-one')\n",
    "ax.set_ylabel('tsne-3d-two')\n",
    "ax.set_zlabel('tsne-3d-three')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With 3 t-SNE components plotted, bar a few outliers both groups appear to be very close to each other in the space. It is difficult to see here visually exactly how differentiable the benign and malignant groups might be from this plot alone.\n",
    "\n",
    "Let us just have a quick look at the distributions of the values of these newly generated t-SNE components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature distributions to compare benign & malignent sub-groups for t-sne features\n",
    "tsne_features = ['tsne-2d-one', 'tsne-2d-two', 'tsne-3d-one', 'tsne-3d-two', 'tsne-3d-three']\n",
    "\n",
    "# Split dataset into those with malignent and those with benign tumors\n",
    "malignent_df = bcancer_clean_df[bcancer_clean_df['diagnosis']==1]\n",
    "benign_df = bcancer_clean_df[bcancer_clean_df['diagnosis']==0]\n",
    "\n",
    "# Plot feature distributions to compare benign & malignent sub-groups\n",
    "compare_features_benign_malignent(tsne_features, malignent_df, benign_df, 2, 3, 20, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topological data analysis for comparing cancer sub-groups\n",
    "\n",
    "We will now use a different approach to get a perspective on our data and the differences between the benign and malignant groups - using Topological Data Analysis (TDA). TDA is not a dimensionality reduction technique as such but rather, it produces a more abstract representation of the data (a 'simplicial complex' summary of the original data - an amusing mathematical oxymoron!). This complex could be said to represent the **'shape'** of the data, or a higher order representation of the data.\n",
    "\n",
    "I have gone over this technique in more detail in a [previous project](https://github.com/pranath/high_dim_data_vis/blob/master/high_dim_data_vis.ipynb) if the reader would like more familiarity with TDA I suggest you refer to that project first. \n",
    "\n",
    "In this project, I will apply TDA (using the the [kepler mapper library](https://kepler-mapper.scikit-tda.org/)) to our cancer dataset to see what this more abstract representation of the dataset might reveal about the benign and malignant groups.\n",
    "\n",
    "There are various technical and hyper-parameter settings that need to be configured to use TDA effectively, as well as a good degree of domain knowledge of the subject matter (in this case Oncology). As I have limited knowledge of both at this point, for guidance with these technical and hyper-parameter settings I have used similar settings to those used in [this](https://github.com/scikit-tda/kepler-mapper/blob/master/examples/breast-cancer/breast-cancer.ipynb) example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Pranath/.pyenv/versions/3.5.4/lib/python3.5/site-packages/sklearn/ensemble/iforest.py:237: FutureWarning: default contamination parameter 0.1 will change in version 0.22 to \"auto\". This will change the predict method behavior.\n",
      "  FutureWarning)\n",
      "/Users/Pranath/.pyenv/versions/3.5.4/lib/python3.5/site-packages/sklearn/ensemble/iforest.py:247: FutureWarning: behaviour=\"old\" is deprecated and will be removed in version 0.22. Please use behaviour=\"new\", which makes the decision_function change to match other anomaly detection algorithm API.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a custom 1-D lens with Isolation Forest\n",
    "model = ensemble.IsolationForest(random_state=1729)\n",
    "model.fit(bcancer_clean_df[features])\n",
    "lens1 = model.decision_function(bcancer_clean_df[features]).reshape((bcancer_clean_df[features].shape[0], 1))\n",
    "\n",
    "# Create another 1-D lens with L2-norm\n",
    "mapper = km.KeplerMapper(verbose=0)\n",
    "lens2 = mapper.fit_transform(bcancer_clean_df[features], projection=\"l2norm\")\n",
    "\n",
    "# Combine lenses pairwise to get a 2-D lens i.e. [Isolation Forest, L^2-Norm] lens\n",
    "lens = np.c_[lens1, lens2]\n",
    "\n",
    "# Define the simplicial complex\n",
    "graph = mapper.map(lens,\n",
    "                      bcancer_clean_df[features],\n",
    "                      nr_cubes=15,\n",
    "                      overlap_perc=0.7,\n",
    "                      clusterer=sklearn.cluster.KMeans(n_clusters=2,\n",
    "                                                       random_state=3471))\n",
    "\n",
    "# Create the visualizations (increased the graph_gravity for a tighter graph-look.)\n",
    "# Tooltips with the target diagnosis (malignent v benign) for every cluster member\n",
    "mapper.visualize(graph,\n",
    "                 title=\"Topological network of breast cancer data\",\n",
    "                 path_html=\"tda_breast_cancer.html\",\n",
    "                 color_function=bcancer_clean_df['diagnosis'].values,\n",
    "                 custom_tooltips=bcancer_clean_df['diagnosis'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/tda_breast_cancer1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows the topological network of the dataset, with the malignant group coloured in yellow, the benign group coloured in purple, and the other colours various mixtures of malignant and benign cases.\n",
    "\n",
    "The plot is actually a fully animated and interactive network that can be explored using your mouse, you can see the full interactive version of the network [here](https://pranath.github.io/breast_cancer_prediction/tda_breast_cancer.html).\n",
    "\n",
    "In this notebook let us further highlight each of the groups below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/tda_breast_cancer2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The topological network of the data reveals some interesting features. Remember that this network is a more abstract and higher level summary of all the features combined, with the malignant and bengin groups coloured.\n",
    "\n",
    "First, we can see that the benign group is more tightly packed, indicating there is less variance in the values of the features for this group.\n",
    "\n",
    "Second, and conversely, we can see that the malignant group is far more diverse, incuding many levels of disntinct sub-sub groups within it. This indicates that there is a higher variance in the values of the features for this group.\n",
    "\n",
    "We can also see there is a mixed group where the two main groups overlap, but this is relatively small.\n",
    "\n",
    "If we think back to both the example images of malignant v benign cells, as well as the univariate plots of the features this perspective seems to build a consistant picture, beautifully summerised in this topological network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/bc_cells.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The malignant cells in the images have more variance in their morphology than the bengin cells. The malignant cells are less uniform, less symmetrical, and less homogenous than the benign cells. We can see this dicotomy in many of the corresponding univariate distributions of the measurements of these cells in our features.\n",
    "\n",
    "This indicates that a ML model might have a good chance of distinguishing well between benign and malignant instances based on the features in the dataset and thus predict each case with a good degree of accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data for ML model\n",
    "\n",
    "### Log transform skewed features\n",
    "\n",
    "We identified earier some features that seem particularly skewed (which can be problematic for ML models). Lets log-transform these to try and even thier distributions out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Log-transform the skewed features\n",
    "skewed = ['radius_se', 'perimeter_se', 'area_se']\n",
    "features_log_transformed = pd.DataFrame(data = bcancer_clean_df)\n",
    "features_log_transformed[skewed] = features_log_transformed[skewed].apply(lambda x: np.log(x + 1))\n",
    "\n",
    "# Split dataset into those with malignent and those with benign tumors\n",
    "malignent_log_transformed_df = features_log_transformed[features_log_transformed['diagnosis']==1]\n",
    "benign_log_transformed_df = features_log_transformed[features_log_transformed['diagnosis']==0]\n",
    "\n",
    "# Display the distributions of previously skewed features\n",
    "compare_features_benign_malignent(skewed, malignent_log_transformed_df, benign_log_transformed_df, 1, 3, 20, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok the log transform seems to have improved these distributions.\n",
    "\n",
    "### Normalise all numerical features\n",
    "\n",
    "We also noticed earlier what differing scales of values each of the features has (which again can be problematic for ML models). Lets normalise the values for all features to be betweeen 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>tsne-2d-one</th>\n",
       "      <th>tsne-2d-two</th>\n",
       "      <th>tsne-3d-one</th>\n",
       "      <th>tsne-3d-two</th>\n",
       "      <th>tsne-3d-three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.521037</td>\n",
       "      <td>0.022658</td>\n",
       "      <td>0.545989</td>\n",
       "      <td>0.363733</td>\n",
       "      <td>0.593753</td>\n",
       "      <td>0.792037</td>\n",
       "      <td>0.703140</td>\n",
       "      <td>0.731113</td>\n",
       "      <td>0.686364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619292</td>\n",
       "      <td>0.568610</td>\n",
       "      <td>0.912027</td>\n",
       "      <td>0.598462</td>\n",
       "      <td>0.418864</td>\n",
       "      <td>0.923367</td>\n",
       "      <td>0.259824</td>\n",
       "      <td>0.289057</td>\n",
       "      <td>0.598870</td>\n",
       "      <td>0.857757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.643144</td>\n",
       "      <td>0.272574</td>\n",
       "      <td>0.615783</td>\n",
       "      <td>0.501591</td>\n",
       "      <td>0.289880</td>\n",
       "      <td>0.181768</td>\n",
       "      <td>0.203608</td>\n",
       "      <td>0.348757</td>\n",
       "      <td>0.379798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154563</td>\n",
       "      <td>0.192971</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.233590</td>\n",
       "      <td>0.222878</td>\n",
       "      <td>0.898479</td>\n",
       "      <td>0.218212</td>\n",
       "      <td>0.286267</td>\n",
       "      <td>0.604902</td>\n",
       "      <td>0.854164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.601496</td>\n",
       "      <td>0.390260</td>\n",
       "      <td>0.595743</td>\n",
       "      <td>0.449417</td>\n",
       "      <td>0.514309</td>\n",
       "      <td>0.431017</td>\n",
       "      <td>0.462512</td>\n",
       "      <td>0.635686</td>\n",
       "      <td>0.509596</td>\n",
       "      <td>...</td>\n",
       "      <td>0.385375</td>\n",
       "      <td>0.359744</td>\n",
       "      <td>0.835052</td>\n",
       "      <td>0.403706</td>\n",
       "      <td>0.213433</td>\n",
       "      <td>0.821036</td>\n",
       "      <td>0.235794</td>\n",
       "      <td>0.293010</td>\n",
       "      <td>0.608937</td>\n",
       "      <td>0.850731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.210090</td>\n",
       "      <td>0.360839</td>\n",
       "      <td>0.233501</td>\n",
       "      <td>0.102906</td>\n",
       "      <td>0.811321</td>\n",
       "      <td>0.811361</td>\n",
       "      <td>0.565604</td>\n",
       "      <td>0.522863</td>\n",
       "      <td>0.776263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.814012</td>\n",
       "      <td>0.548642</td>\n",
       "      <td>0.884880</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.773711</td>\n",
       "      <td>0.268163</td>\n",
       "      <td>0.785747</td>\n",
       "      <td>0.266365</td>\n",
       "      <td>0.650881</td>\n",
       "      <td>0.817218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629893</td>\n",
       "      <td>0.156578</td>\n",
       "      <td>0.630986</td>\n",
       "      <td>0.489290</td>\n",
       "      <td>0.430351</td>\n",
       "      <td>0.347893</td>\n",
       "      <td>0.463918</td>\n",
       "      <td>0.518390</td>\n",
       "      <td>0.378283</td>\n",
       "      <td>...</td>\n",
       "      <td>0.172415</td>\n",
       "      <td>0.319489</td>\n",
       "      <td>0.558419</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>0.142595</td>\n",
       "      <td>0.794181</td>\n",
       "      <td>0.274170</td>\n",
       "      <td>0.292770</td>\n",
       "      <td>0.616846</td>\n",
       "      <td>0.849632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0          1     0.521037      0.022658        0.545989   0.363733   \n",
       "1          1     0.643144      0.272574        0.615783   0.501591   \n",
       "2          1     0.601496      0.390260        0.595743   0.449417   \n",
       "3          1     0.210090      0.360839        0.233501   0.102906   \n",
       "4          1     0.629893      0.156578        0.630986   0.489290   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0         0.593753          0.792037        0.703140             0.731113   \n",
       "1         0.289880          0.181768        0.203608             0.348757   \n",
       "2         0.514309          0.431017        0.462512             0.635686   \n",
       "3         0.811321          0.811361        0.565604             0.522863   \n",
       "4         0.430351          0.347893        0.463918             0.518390   \n",
       "\n",
       "   symmetry_mean  ...  compactness_worst  concavity_worst  \\\n",
       "0       0.686364  ...           0.619292         0.568610   \n",
       "1       0.379798  ...           0.154563         0.192971   \n",
       "2       0.509596  ...           0.385375         0.359744   \n",
       "3       0.776263  ...           0.814012         0.548642   \n",
       "4       0.378283  ...           0.172415         0.319489   \n",
       "\n",
       "   concave points_worst  symmetry_worst  fractal_dimension_worst  tsne-2d-one  \\\n",
       "0              0.912027        0.598462                 0.418864     0.923367   \n",
       "1              0.639175        0.233590                 0.222878     0.898479   \n",
       "2              0.835052        0.403706                 0.213433     0.821036   \n",
       "3              0.884880        1.000000                 0.773711     0.268163   \n",
       "4              0.558419        0.157500                 0.142595     0.794181   \n",
       "\n",
       "   tsne-2d-two  tsne-3d-one  tsne-3d-two  tsne-3d-three  \n",
       "0     0.259824     0.289057     0.598870       0.857757  \n",
       "1     0.218212     0.286267     0.604902       0.854164  \n",
       "2     0.235794     0.293010     0.608937       0.850731  \n",
       "3     0.785747     0.266365     0.650881       0.817218  \n",
       "4     0.274170     0.292770     0.616846       0.849632  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize a scaler, then apply it to the features\n",
    "scaler = MinMaxScaler() # default=(0, 1)\n",
    "\n",
    "numerical_features = features_log_transformed.columns\n",
    "numerical_features = numerical_features[1:]\n",
    "\n",
    "features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)\n",
    "features_log_minmax_transform[numerical_features] = scaler.fit_transform(features_log_transformed[numerical_features])\n",
    "\n",
    "features_log_minmax_transform.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metrics and naive predictor\n",
    "\n",
    "In this section we will define our measure of success for our models and also define a naive predictor to act as a baseline measure.\n",
    "\n",
    "So in this project, what is most important? we are going to define the key priority as being diagnosing those cases that actually have a malignant tumor most accurately. Of course, as a secondary priority - we do also want to minimise the number of cases where the tumor is actually benign, but we predict it to be malignant.\n",
    "\n",
    "To get a measure of how well our models are doing for both of these at the same time, we can use the F1 score. We will also calculate an accuarcy score, although the F1 score will be the most important measure. The F1 score is also a good measure to use for our relatively small dataset (570 rows only).\n",
    "\n",
    "We also want to establish some kind of benchmark to judge our models against, a baseline that we hope to do much better than with our sophisticated ML models! A common baseline is to define a 'naive' predictor - in this case that would be a predictor that classifies all tumors as malignant.\n",
    "\n",
    "We will now define a naive predictor and calculate an F1 and accuracy score for it - this will be our baseline model and score that we will judge all our other models against."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive Predictor: [Accuracy score: 0.3726, F-score: 0.4260]\n"
     ]
    }
   ],
   "source": [
    "# Naive predictor\n",
    "'''\n",
    "TP = np.sum(features_log_minmax_transform['diagnosis']) # Counting the ones as this is the naive case. \n",
    "FP = features_log_minmax_transform['diagnosis'].count() - TP # Specific to the naive case\n",
    "\n",
    "TN = 0 # No predicted negatives in the naive case\n",
    "FN = 0 # No predicted negatives in the naive case\n",
    "'''\n",
    "# Calculate accuracy, precision and recall\n",
    "accuracy = np.sum(features_log_minmax_transform['diagnosis']) / features_log_minmax_transform.shape[0]\n",
    "recall = 1\n",
    "precision = accuracy\n",
    "\n",
    "# Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.\n",
    "beta_squared = 0.5 * 0.5\n",
    "fscore = (1 + beta_squared) * ((precision * recall) / ((beta_squared * precision) + recall))\n",
    "\n",
    "# Print the results \n",
    "print(\"Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]\".format(accuracy, fscore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML model selection & development\n",
    "\n",
    "For this problem - I have judged the following features are appropriate & desirable in guiding the selection of a machine learning model.\n",
    "\n",
    "**Model criteria**\n",
    "\n",
    "- **Memory & speed:** not primarily important but for what is feasible to be run within a notebook in a reasonable time, the aim is assumed to be a one off run of the model to predict rather than a model run continously where significant computational resources might be more an issue. \n",
    "- **Not overfitting:** I would regard this as desirable/important, we want a model that generalises well, its going to be run most likely periodically if used and we want to maximise the predictive power each time its run.\n",
    "- **Time for learning/fitting:** like memory & speed, not so important beyond that which is reasonable to be run in the notebook without the browser timing out!\n",
    "- **Time for predicting:** again like time for learning/fitting, not so important beyond what can be done in the notebook/browser. However, if we want to deploy and it's useful, we may want to run it on bigger datsets of patient data - so it is a factor worth taking into account.\n",
    "- **Parameterisation:** slightly important. We will be using grid search later to optimise our solution, this will multiply the computational requirements used for a single model. Given the limit of our computatioal resources available in the notebook/browser a more simple parameterisation would allow the model to scale & deliver within this limited environment for a grid search.\n",
    "- **Feature tolerance:** not so important, we have just under 40 features and there is no indication these will grow.\n",
    "\n",
    "With these in mind, these are my selected models, their features - and the rationale for choosing them.\n",
    "\n",
    "**Model 1 - LogisticRegression**\n",
    "\n",
    "- **Strengths of model:** Fast to train, no parameter tuning, features don't need scaling, more tolerant to correlated features, excellent for 2-class classification problems\n",
    "- **Weaknesses of model:** Not so good for large number of features, not good with non-linear decision boundries, not flexible enough to capture more complex relationships, adversely effected by features not relevent to output variable\n",
    "- **Why model is a good candidate for this problem:** 1. The problem is a binary classification one, logistic regression is one of the best & simplest models for this kind of problem - worth considering occams razor! 2. The computational resources required for this model are low and ensure I can be certain this model will have no problem running in the notebook/browser on this dataset! I would like at least one of my models to be likely to run well with given computational resources. \n",
    "\n",
    "**Model 2 - GaussianNB (Gaussian Naive Bayes)**\n",
    "\n",
    "- **Strengths of model:** Easy and fast to predict class of test data set. If assumption of independence holds reasonably well, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data, also good resistance to missing and noisy data\n",
    "- **Weaknesses of model:** The assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent, also its hypothesis function is linear, thus it’s very simplistic in most scenarios.\n",
    "- **Why model is a good candidate for this problem:** 1. This model could be useful if we want to predict on larger datasets for more patients, as it is likely to scale well. 2. Good to evaluate another 'fast' model in addition to logistic regression.\n",
    "\n",
    "**Model 3 - GradientBoostingClassifier**\n",
    "\n",
    "- **Strengths of model:** Can handle big datasets, very accurate, can approximate most non-linear classification boundries, one of best boosting models for many classification problems (best in class)\n",
    "- **Weaknesses of model:** Slow to train (concern over available resources in notebook/browser sufficient?), prone to overfitting, not easy to understand, sensitive to outliers & noisy data, parameterisation can make a real difference to performance - therefore could be more work/fiddly to optimise & get best results.\n",
    "- **Why model is a good candidate for this problem:** 1. This model is one of the most popular (getting some of the best results) models for classification problems - worth serious consideration! 2. Paramaterisation can make a real difference to performance with this model, good chance of improvement in optimisation/grid search phase for one of the best solutions (assuming computational resources of notebook/browser sufficient).\n",
    "\n",
    "References\n",
    "\n",
    "I found the following tables & cheatsheets particularly useful in selecting my models:\n",
    "\n",
    "https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html\n",
    "\n",
    "https://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/\n",
    "\n",
    "https://www.dataschool.io/comparing-supervised-learning-algorithms/\n",
    "\n",
    "https://recast.ai/blog/machine-learning-algorithms/2/\n",
    "\n",
    "We will now split our dataset into training and testing subsets, and develop functions that will act as a training & testing pipleline, as well as displaying the results from all our models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train & test\n",
    "X = features_log_minmax_transform[numerical_features]\n",
    "y = features_log_minmax_transform['diagnosis']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)                                                                                                                                                                                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function that will act as pipeline for training and testing our models (and returning the results)\n",
    "def train_predict(learner, sample_size, X_train, y_train, X_test, y_test): \n",
    "    '''\n",
    "    inputs:\n",
    "       - learner: the learning algorithm to be trained and predicted on\n",
    "       - sample_size: the size of samples (number) to be drawn from training set\n",
    "       - X_train: features training set\n",
    "       - y_train: income training set\n",
    "       - X_test: features testing set\n",
    "       - y_test: income testing set\n",
    "    '''\n",
    "    \n",
    "    results = {}\n",
    "    \n",
    "    # Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])\n",
    "    start = time.time() # Get start time\n",
    "    learner.fit(X_train[:sample_size], y_train[:sample_size])\n",
    "    end = time.time() # Get end time\n",
    "    \n",
    "    # Calculate the training time\n",
    "    results['train_time'] = end - start\n",
    "        \n",
    "    # Get the predictions on the test set(X_test),\n",
    "    # then get predictions on the first 300 training samples(X_train) using .predict()\n",
    "    start = time.time() # Get start time\n",
    "    predictions_test = learner.predict(X_test)\n",
    "    predictions_train = learner.predict(X_train[:300])\n",
    "    end = time.time() # Get end time\n",
    "    \n",
    "    # Calculate the total prediction time\n",
    "    results['pred_time'] = end - start\n",
    "            \n",
    "    # Compute accuracy on the first 300 training samples which is y_train[:300]\n",
    "    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)\n",
    "        \n",
    "    # Compute accuracy on test set using accuracy_score()\n",
    "    results['acc_test'] = accuracy_score(y_test, predictions_test)\n",
    "    \n",
    "    # Compute F-score on the the first 300 training samples using fbeta_score()\n",
    "    results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta=0.5)\n",
    "        \n",
    "    # Compute F-score on the test set which is y_test\n",
    "    results['f_test'] = fbeta_score(y_test, predictions_test, beta=0.5)\n",
    "       \n",
    "    # Success\n",
    "    print(\"{} trained on {} samples.\".format(learner.__class__.__name__, sample_size))\n",
    "        \n",
    "    # Return the results\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define function that will take the results of our model training & testing and plot the results\n",
    "def evaluate(results, accuracy, f1):\n",
    "    \"\"\"\n",
    "    Visualization code to display results of various learners.\n",
    "\n",
    "    inputs:\n",
    "      - learners: a list of supervised learners\n",
    "      - stats: a list of dictionaries of the statistic results from 'train_predict()'\n",
    "      - accuracy: The score for the naive predictor\n",
    "      - f1: The score for the naive predictor\n",
    "    \"\"\"\n",
    "\n",
    "    # Create figure\n",
    "    fig, ax = plt.subplots(2, 3, figsize = (13,9))\n",
    "\n",
    "    # Constants\n",
    "    bar_width = 0.3\n",
    "    colors = ['#A00000','#00A0A0','#00A000']\n",
    "\n",
    "    # Super loop to plot four panels of data\n",
    "    for k, learner in enumerate(results.keys()):\n",
    "        for j, metric in enumerate(['train_time', 'acc_train', 'f_train', 'pred_time', 'acc_test', 'f_test']):\n",
    "            for i in np.arange(3):\n",
    "                # Creative plot code\n",
    "                ax[j//3, j%3].bar(i+k*bar_width, results[learner][i][metric], width = bar_width, color = colors[k])\n",
    "                ax[j//3, j%3].set_xticks([0.45, 1.45, 2.45])\n",
    "                ax[j//3, j%3].set_xticklabels([\"10%\", \"50%\", \"100%\"])\n",
    "                ax[j//3, j%3].set_xlabel(\"Training Set Size\")\n",
    "                ax[j//3, j%3].set_xlim((-0.1, 3.0))\n",
    "\n",
    "    # Add unique y-labels\n",
    "    ax[0, 0].set_ylabel(\"Time (in seconds)\")\n",
    "    ax[0, 1].set_ylabel(\"Accuracy Score\")\n",
    "    ax[0, 2].set_ylabel(\"F-score\")\n",
    "    ax[1, 0].set_ylabel(\"Time (in seconds)\")\n",
    "    ax[1, 1].set_ylabel(\"Accuracy Score\")\n",
    "    ax[1, 2].set_ylabel(\"F-score\")\n",
    "\n",
    "    # Add titles\n",
    "    ax[0, 0].set_title(\"Model Training\")\n",
    "    ax[0, 1].set_title(\"Accuracy Score on Training Subset\")\n",
    "    ax[0, 2].set_title(\"F-score on Training Subset\")\n",
    "    ax[1, 0].set_title(\"Model Predicting\")\n",
    "    ax[1, 1].set_title(\"Accuracy Score on Testing Set\")\n",
    "    ax[1, 2].set_title(\"F-score on Testing Set\")\n",
    "\n",
    "    # Add horizontal lines for naive predictors\n",
    "    ax[0, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')\n",
    "    ax[1, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')\n",
    "    ax[0, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')\n",
    "    ax[1, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')\n",
    "\n",
    "    # Set y-limits for score panels\n",
    "    ax[0, 1].set_ylim((0, 1))\n",
    "    ax[0, 2].set_ylim((0, 1))\n",
    "    ax[1, 1].set_ylim((0, 1))\n",
    "    ax[1, 2].set_ylim((0, 1))\n",
    "\n",
    "    # Create patches for the legend\n",
    "    patches = []\n",
    "    for i, learner in enumerate(results.keys()):\n",
    "        patches.append(mpatches.Patch(color = colors[i], label = learner))\n",
    "    plt.legend(handles = patches, bbox_to_anchor = (-.80, 2.53), \\\n",
    "               loc = 'upper center', borderaxespad = 0., ncol = 3, fontsize = 'x-large')\n",
    "\n",
    "    # Aesthetics\n",
    "    plt.suptitle(\"Performance Metrics for Three Supervised Learning Models\", fontsize = 16, y = 1.10)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression trained on 45 samples.\n",
      "LogisticRegression trained on 227 samples.\n",
      "LogisticRegression trained on 455 samples.\n",
      "GaussianNB trained on 45 samples.\n",
      "GaussianNB trained on 227 samples.\n",
      "GaussianNB trained on 455 samples.\n",
      "GradientBoostingClassifier trained on 45 samples.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Pranath/.pyenv/versions/3.5.4/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/Pranath/.pyenv/versions/3.5.4/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/Pranath/.pyenv/versions/3.5.4/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoostingClassifier trained on 227 samples.\n",
      "GradientBoostingClassifier trained on 455 samples.\n"
     ]
    }
   ],
   "source": [
    "# Initialize the three models\n",
    "clf_A = linear_model.LogisticRegression()\n",
    "clf_B = GaussianNB()\n",
    "clf_C = GradientBoostingClassifier()\n",
    "\n",
    "# Calculate the number of samples for 10%, 50% and 100% of the training data\n",
    "samples_100 = len(y_train)\n",
    "samples_50 = int((samples_100 / 100) * 50)\n",
    "samples_10 = int((samples_100 / 100) * 10)\n",
    "\n",
    "# Collect results on the learners\n",
    "results = {}\n",
    "for clf in [clf_A, clf_B, clf_C]:\n",
    "    clf_name = clf.__class__.__name__\n",
    "    results[clf_name] = {}\n",
    "    for i, samples in enumerate([samples_10, samples_50, samples_100]):\n",
    "        results[clf_name][i] = \\\n",
    "        train_predict(clf, samples, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x648 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run metrics visualization for the three supervised learning models chosen\n",
    "evaluate(results, accuracy, fscore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe the results of our model training and evaluation in the above plots of the metric scores for each model type.\n",
    "\n",
    "Firstly we can see that all 3 models have done quite well, with all models getting at least 0.80 on the testing dataset, even when only 10% or 50% of that testing set was used.\n",
    "\n",
    "For the model training, we can see that the GradientBoostingClassifier took by far the most time to train, and in terms of speed of predicting unsurprisingly LogisticRegression was the fastest. However, in terms of the key metric using 100% of the test dataset, we can see that the GradientBoostingClassifier seems to perform the best.\n",
    "\n",
    "Therefore we will select the GradientBoostingClassifier as our best model and the one we want to optimise going forward.\n",
    "\n",
    "### Model tuning\n",
    "\n",
    "We will now use a GradientBoostingClassifier to optimse using grid search on various hyper-parameters, using F1 score as the scorer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Pranath/.pyenv/versions/3.5.4/lib/python3.5/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method BaseEstimator.get_params of GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
      "                           learning_rate=0.1, loss='deviance', max_depth=6,\n",
      "                           max_features=None, max_leaf_nodes=None,\n",
      "                           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                           min_samples_leaf=40, min_samples_split=100,\n",
      "                           min_weight_fraction_leaf=0.0, n_estimators=300,\n",
      "                           n_iter_no_change=None, presort='auto',\n",
      "                           random_state=0, subsample=0.8, tol=0.0001,\n",
      "                           validation_fraction=0.1, verbose=0, warm_start=True)>\n",
      "Unoptimized model\n",
      "------\n",
      "Accuracy score on testing data: 0.9649\n",
      "F-score on testing data: 0.9574\n",
      "\n",
      "Optimized Model\n",
      "------\n",
      "Final accuracy score on the testing data: 0.9912\n",
      "Final F-score on the testing data: 0.9957\n"
     ]
    }
   ],
   "source": [
    "# Initialize the classifier\n",
    "clf = GradientBoostingClassifier(random_state=0)\n",
    "\n",
    "# Create the parameters we want to tune\n",
    "parameters = {'n_estimators': [10, 50, 300], 'learning_rate': [0.1], 'max_depth': [6], 'min_samples_split': [100], 'warm_start': [True], 'subsample': [0.8], 'min_samples_leaf': [40]} \n",
    "    \n",
    "# Make an fbeta_score scoring object using make_scorer()\n",
    "scorer = make_scorer(fbeta_score, beta=0.5)\n",
    "\n",
    "# Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n",
    "grid_obj = GridSearchCV(clf, parameters, scoring=scorer)\n",
    "\n",
    "# Fit the grid search object to the training data and find the optimal parameters using fit()\n",
    "grid_fit = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Get the estimator\n",
    "best_clf = grid_fit.best_estimator_\n",
    "print(grid_fit.best_estimator_.get_params)\n",
    "\n",
    "# Make predictions using the unoptimized and model\n",
    "predictions = (clf.fit(X_train, y_train)).predict(X_test)\n",
    "best_predictions = best_clf.predict(X_test)\n",
    "\n",
    "# Report the before-and-afterscores\n",
    "print(\"Unoptimized model\\n------\")\n",
    "print(\"Accuracy score on testing data: {:.4f}\".format(accuracy_score(y_test, predictions)))\n",
    "print(\"F-score on testing data: {:.4f}\".format(fbeta_score(y_test, predictions, beta = 0.5)))\n",
    "print(\"\\nOptimized Model\\n------\")\n",
    "print(\"Final accuracy score on the testing data: {:.4f}\".format(accuracy_score(y_test, best_predictions)))\n",
    "print(\"Final F-score on the testing data: {:.4f}\".format(fbeta_score(y_test, best_predictions, beta = 0.5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML model evaluation\n",
    "\n",
    "Below is a summary table of the key results from developing our ML model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Metric | Benchmark Model | Unoptimized Model | Optimized Model |\n",
    "| --- | --- | --- | --- |\n",
    "| Accuracy Score | 0.3726 | 0.9649 | 0.9912 |\n",
    "| F-score | 0.4260 | 0.9574 | 0.9957 |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the unoptimized model performs significantly better on both accuracy and F-score than the benchmark model. We can also see that further parameter tuning with grid search made even more gains on both metrics - to produce an **exceptional F-score of 0.9957** for the optimised model.\n",
    "\n",
    "Let us now examine the model to see which features it found most useful for prediction.\n",
    "\n",
    "## Feature importance\n",
    "\n",
    "We will now define a function to extract and plot the top 5 most useful features for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_plot(importances, X_train, y_train):\n",
    "\n",
    "    # Display the five most important features\n",
    "    indices = np.argsort(importances)[::-1]\n",
    "    columns = X_train.columns.values[indices[:5]]\n",
    "    values = importances[indices][:5]\n",
    "\n",
    "    # Creat the plot\n",
    "    fig = plt.figure(figsize = (9,5))\n",
    "    plt.title(\"Normalized Weights for First Five Most Predictive Features\", fontsize = 16)\n",
    "    plt.bar(np.arange(5), values, width = 0.6, align=\"center\", color = '#00A000', \\\n",
    "          label = \"Feature Weight\")\n",
    "    plt.bar(np.arange(5) - 0.3, np.cumsum(values), width = 0.2, align = \"center\", color = '#00A0A0', \\\n",
    "          label = \"Cumulative Feature Weight\")\n",
    "    plt.xticks(np.arange(5), columns)\n",
    "    plt.xlim((-0.5, 4.5))\n",
    "    plt.ylabel(\"Weight\", fontsize = 12)\n",
    "    plt.xlabel(\"Feature\", fontsize = 12)\n",
    "\n",
    "    plt.legend(loc = 'upper center')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the feature importances \n",
    "importances = best_clf.feature_importances_ \n",
    "\n",
    "# Plot\n",
    "feature_plot(importances, X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the extracted feature importances that the top 5 features used by the model are:\n",
    "\n",
    "- concave points_worst\n",
    "- perimeter worst\n",
    "- concave points_mean\n",
    "- area_worst\n",
    "- texture_worst\n",
    "\n",
    "Interestingly, these were features we highlighted earlier in the univariate analysis as features that had a very different distribution of values for the malignant and benign tumor sub-groups.\n",
    "\n",
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we have looked at a dataset relating to breast cancer, and have developed a model able to predict malignant tumors with a very high degree of accuracy (an F-score of 0.9957).\n",
    "\n",
    "We have also gained an understanding of the reasons why our model is able to predict this well. Our earlier analysis showed the difference in morphology beween cell metrics for malignant v benign tumors, which could be seen visually in the images and were expressed in different distributions of values for particular feature measurements of the cells that we observed.\n",
    "\n",
    "Our use of higher level analytical tools such as TDA also allowed us to gain a much better understanding of the dataset, in particular a better idea of the range of feature values that were typical for malignant and benign tumors as 'groups' within the dataset."
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}