4.2.2 - K-Means.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "This notebook was created just to explore different cluster patterns and to discuss the predictive value (or accuracy) of each cluster permutation.  Not much text for this notebook since it is almost identical to the curriculum notebook.  The conclusion at the end of this notebook is the main focus of this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import normalize\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 1 1 0 0 0 1\n",
      " 1 1 0 1 0 0 0 1 1 0 1 0 0 0 0 1 0 1 1 1 1 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1\n",
      " 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 1 1 1 1 1\n",
      " 1 0 1 1 0 0 0 1 1 1 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 1 1 0\n",
      " 0 0 0 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 1 0 0 1 1 0 0 1\n",
      " 0 0 1 1 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0\n",
      " 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 0 0\n",
      " 1 0 1 0 0 1 1 1 1 1 0 1 0 1 0 1 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 1 0 1 1 1 0\n",
      " 1 1 1 1 1 1 0]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/Thinkful-Ed/data-201-resources/master/cleveland.csv\", \n",
    "                 header=None, error_bad_lines=False)\n",
    "\n",
    "# Define the features and the outcome.\n",
    "X = df.iloc[:, :13]\n",
    "y = df.iloc[:, 13]\n",
    "\n",
    "# Replace missing values (marked by ?) with a 0.\n",
    "X = X.replace(to_replace='?', value=0)\n",
    "\n",
    "# Binarize y so that 1 means heart disease diagnosis and 0 means no diagnosis.\n",
    "y = np.where(y > 0, 1, 0)\n",
    "print(y)\n",
    "\n",
    "# y1 = np.where(y > 0.5, 1, 0)\n",
    "# print(y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 14)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>13</th>\n",
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       "      <th>0</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>233.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>2.3</td>\n",
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       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>286.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>56.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>268.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>63.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
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       "      <th>9</th>\n",
       "      <td>53.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
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      "text/plain": [
       "     0    1    2      3      4    5    6      7    8    9    10   11   12  13\n",
       "0  63.0  1.0  1.0  145.0  233.0  1.0  2.0  150.0  0.0  2.3  3.0  0.0  6.0   0\n",
       "1  67.0  1.0  4.0  160.0  286.0  0.0  2.0  108.0  1.0  1.5  2.0  3.0  3.0   2\n",
       "2  67.0  1.0  4.0  120.0  229.0  0.0  2.0  129.0  1.0  2.6  2.0  2.0  7.0   1\n",
       "3  37.0  1.0  3.0  130.0  250.0  0.0  0.0  187.0  0.0  3.5  3.0  0.0  3.0   0\n",
       "4  41.0  0.0  2.0  130.0  204.0  0.0  2.0  172.0  0.0  1.4  1.0  0.0  3.0   0\n",
       "5  56.0  1.0  2.0  120.0  236.0  0.0  0.0  178.0  0.0  0.8  1.0  0.0  3.0   0\n",
       "6  62.0  0.0  4.0  140.0  268.0  0.0  2.0  160.0  0.0  3.6  3.0  2.0  3.0   3\n",
       "7  57.0  0.0  4.0  120.0  354.0  0.0  0.0  163.0  1.0  0.6  1.0  0.0  3.0   0\n",
       "8  63.0  1.0  4.0  130.0  254.0  0.0  2.0  147.0  0.0  1.4  2.0  1.0  7.0   2\n",
       "9  53.0  1.0  4.0  140.0  203.0  1.0  2.0  155.0  1.0  3.1  3.0  0.0  7.0   1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([164.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 139.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing k-means clusters against the data:\n",
      "col_0   0   1\n",
      "row_0        \n",
      "0      21  40\n",
      "1      67  37\n",
      "2      20  42\n",
      "3      56  20\n",
      "Duration: 0:00:00.309017\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "# Normalize the data.\n",
    "X_norm = normalize(X)\n",
    "\n",
    "# Reduce it to two components.\n",
    "X_pca = PCA(2).fit_transform(X_norm)\n",
    "\n",
    "# Calculate predicted values.\n",
    "y_pred = KMeans(n_clusters=4, random_state=42).fit_predict(X_pca)\n",
    "\n",
    "# Plot the solution.\n",
    "plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_pred)\n",
    "plt.show()\n",
    "\n",
    "# Check the solution against the data.\n",
    "print('Comparing k-means clusters against the data:')\n",
    "print(pd.crosstab(y_pred, y))\n",
    "\n",
    "end_time = datetime.now()\n",
    "print('Duration: {}'.format(end_time - start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparing k-means and mini batch k-means solutions:\n",
      "col_0   0   1   2   3\n",
      "row_0                \n",
      "0       0   0   1  47\n",
      "1       8   1  39   0\n",
      "2      53  45   0   0\n",
      "3       0  58  22  29\n",
      "Comparing mini batch k-means to acuual values:\n",
      "col_0   0   1\n",
      "row_0        \n",
      "0      35  13\n",
      "1      16  32\n",
      "2      43  55\n",
      "3      70  39\n",
      "Duration: 0:00:00.092006\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "start_time = datetime.now()\n",
    "\n",
    "# Each batch will be made up of 200 data points.\n",
    "minibatchkmeans = MiniBatchKMeans(\n",
    "    init='random',\n",
    "    n_clusters=4,\n",
    "    batch_size=200)\n",
    "minibatchkmeans.fit(X_pca)\n",
    "\n",
    "# Add the new predicted cluster memberships to the data frame.\n",
    "predict_mini = minibatchkmeans.predict(X_pca)\n",
    "\n",
    "# Check the MiniBatch model against our earlier one.\n",
    "print('Comparing k-means and mini batch k-means solutions:')\n",
    "print(pd.crosstab(predict_mini, y_pred))\n",
    "\n",
    "print('Comparing mini batch k-means to acuual values:')\n",
    "print(pd.crosstab(predict_mini, y))\n",
    "\n",
    "end_time = datetime.now()\n",
    "print('Duration: {}'.format(end_time - start_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "From trial and error, 4 clusters produced values that clearly fit into either of the 2 groups (have heart disease or not).  \n",
    "Here is the crosstab info:  \n",
    "\n",
    "Comparing k-means clusters against the data:  \n",
    "\n",
    "row 0:  21  40  \n",
    "row 1:  67  37  \n",
    "row 2:  20  42  \n",
    "row 3:  56  20  \n",
    "\n",
    "\n",
    "One can easily see that if a person falls in cluster 0, the prediction will be 'has heart disease' (40 1's vs. 21 0's); cluster 1 is 'no heart disease'; cluster 2 is 'has heart disease' and cluster 3 is 'no heart disease'.  Many other number of clusters were tested, but this seemed to yield the best result -- keeping in mind that the more clusters used, the higher the potential for overfitting.  \n",
    "\n",
    "Another conslusion that can be drawn from this notebook is that the mini-Batch K-means method is about 3 times faster to compute, however it yielded different results:  clusters 0 and 1 would have been classified as the opposite compared to the KMeans method."
   ]
  }
 ],
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