DS proj - 3 .ipynb

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    "### Predicting recurrance of breast cancer based on histopathological findings\n",
    "\n",
    "First we import the required datasets and do some data cleaning and then generate a logistic regression model to \n",
    
    "(A) serve as baseline to compare with an MLP model\n",
    "(B) to compute the coefficients inorder to identify patterns in nuclear morphology that are indicative of high risk of recurrance of breast cancer.\n",
    "\n",
    
    "Next I used PCA to identify the 9 principle components that use reduce the number of dimensions inputed from 24 to 9. These 9 components which are linear combinations of the 24 initial features, explain 90 percent of the variance of the original dataset.\n",
    "Using PCA's component function, i then try to identify the 3 main contributors to each of the 9 newly generated components to see if they match up with the coefficients of the logistic regressions calculated earlier. This insight will be the compared to the clinical literature to see if pathologists use the same criteria to evaluate recurrence and if not, my work could improve the criteria and predictive abilities in clinical practice.\n",
    "\n",
    "I then built a MLP model and train it on the 32 featured data set ad get an f1 score of .69. I then train the same model on 9 component PCA transformed data. The f1 score improved marginally to 0.71.\n",
    "\n",
    "\n",
    "Attribute Information:\n",
    "\n",
    "1) ID number\n",
    "\n",
    "2) Recurrance \n",
    "\n",
    "3-32)\n",
    "\n",
    "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",
    "\n",
    "b) texture (standard deviation of gray-scale values)\n",
    "\n",
    "c) perimeter\n",
    "\n",
    "d) area\n",
    "\n",
    "e) smoothness (local variation in radius lengths)\n",
    "\n",
    "f) compactness (perimeter^2 / area - 1.0)\n",
    "\n",
    "g) concavity (severity of concave portions of the contour)\n",
    "\n",
    "h) concave points (number of concave portions of the contour)\n",
    "\n",
    "i) symmetry\n",
    "\n",
    "j) fractal dimension (\"coastline approximation\" - 1)\n",
    "\n",
    "\n",
    "The mean, standard error and \"worst\" or largest (mean of the three\n",
    "largest values) of these features were computed for each image,\n",
    "resulting in 30 features.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b875e57d",
   "metadata": {},
   "source": [
    "### 1. Data description\n",
    "We first import the relevant packages and load the dataset. We do some basic data exploration during which we find some missing values (?)  in the lymph node column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f66aefc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np , seaborn as sns, matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b535b31",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath = 'D:\\DATA SCIENCE\\DS PROJECTS\\proj 3\\wpbc.csv'\n",
    "data = pd.read_csv(filepath,sep =',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3349f9bf",
   "metadata": {},
   "outputs": [
    {
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       "float64    31\n",
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       "object      2\n",
       "dtype: int64"
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     "execution_count": 3,
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     "output_type": "execute_result"
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   ],
   "source": [
    "data.dtypes\n",
    "data.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a56add93",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Time</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>Tumor Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.980000e+02</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.00000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "      <td>198.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.990469e+06</td>\n",
       "      <td>46.732323</td>\n",
       "      <td>17.412323</td>\n",
       "      <td>22.27601</td>\n",
       "      <td>114.856566</td>\n",
       "      <td>970.040909</td>\n",
       "      <td>0.102681</td>\n",
       "      <td>0.142648</td>\n",
       "      <td>0.156243</td>\n",
       "      <td>0.086776</td>\n",
       "      <td>...</td>\n",
       "      <td>30.139091</td>\n",
       "      <td>140.347778</td>\n",
       "      <td>1404.958586</td>\n",
       "      <td>0.143921</td>\n",
       "      <td>0.365102</td>\n",
       "      <td>0.436685</td>\n",
       "      <td>0.178778</td>\n",
       "      <td>0.323404</td>\n",
       "      <td>0.090828</td>\n",
       "      <td>2.847475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.889025e+06</td>\n",
       "      <td>34.462870</td>\n",
       "      <td>3.161676</td>\n",
       "      <td>4.29829</td>\n",
       "      <td>21.383402</td>\n",
       "      <td>352.149215</td>\n",
       "      <td>0.012522</td>\n",
       "      <td>0.049898</td>\n",
       "      <td>0.070572</td>\n",
       "      <td>0.033877</td>\n",
       "      <td>...</td>\n",
       "      <td>6.017777</td>\n",
       "      <td>28.892279</td>\n",
       "      <td>586.006972</td>\n",
       "      <td>0.022004</td>\n",
       "      <td>0.163965</td>\n",
       "      <td>0.173625</td>\n",
       "      <td>0.045181</td>\n",
       "      <td>0.075161</td>\n",
       "      <td>0.021172</td>\n",
       "      <td>1.937964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.423000e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.950000</td>\n",
       "      <td>10.38000</td>\n",
       "      <td>71.900000</td>\n",
       "      <td>361.600000</td>\n",
       "      <td>0.074970</td>\n",
       "      <td>0.046050</td>\n",
       "      <td>0.023980</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>...</td>\n",
       "      <td>16.670000</td>\n",
       "      <td>85.100000</td>\n",
       "      <td>508.100000</td>\n",
       "      <td>0.081910</td>\n",
       "      <td>0.051310</td>\n",
       "      <td>0.023980</td>\n",
       "      <td>0.028990</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.557452e+05</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>15.052500</td>\n",
       "      <td>19.41250</td>\n",
       "      <td>98.160000</td>\n",
       "      <td>702.525000</td>\n",
       "      <td>0.093900</td>\n",
       "      <td>0.110200</td>\n",
       "      <td>0.106850</td>\n",
       "      <td>0.063670</td>\n",
       "      <td>...</td>\n",
       "      <td>26.210000</td>\n",
       "      <td>118.075000</td>\n",
       "      <td>947.275000</td>\n",
       "      <td>0.129325</td>\n",
       "      <td>0.248700</td>\n",
       "      <td>0.322150</td>\n",
       "      <td>0.152650</td>\n",
       "      <td>0.275950</td>\n",
       "      <td>0.076577</td>\n",
       "      <td>1.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.863390e+05</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>17.290000</td>\n",
       "      <td>21.75000</td>\n",
       "      <td>113.700000</td>\n",
       "      <td>929.100000</td>\n",
       "      <td>0.101900</td>\n",
       "      <td>0.131750</td>\n",
       "      <td>0.151350</td>\n",
       "      <td>0.086075</td>\n",
       "      <td>...</td>\n",
       "      <td>30.135000</td>\n",
       "      <td>136.500000</td>\n",
       "      <td>1295.000000</td>\n",
       "      <td>0.141850</td>\n",
       "      <td>0.351300</td>\n",
       "      <td>0.402350</td>\n",
       "      <td>0.179250</td>\n",
       "      <td>0.310300</td>\n",
       "      <td>0.086890</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.279958e+05</td>\n",
       "      <td>72.750000</td>\n",
       "      <td>19.580000</td>\n",
       "      <td>24.65500</td>\n",
       "      <td>129.650000</td>\n",
       "      <td>1193.500000</td>\n",
       "      <td>0.110975</td>\n",
       "      <td>0.172200</td>\n",
       "      <td>0.200500</td>\n",
       "      <td>0.103925</td>\n",
       "      <td>...</td>\n",
       "      <td>33.555000</td>\n",
       "      <td>159.875000</td>\n",
       "      <td>1694.250000</td>\n",
       "      <td>0.154875</td>\n",
       "      <td>0.423675</td>\n",
       "      <td>0.541050</td>\n",
       "      <td>0.207125</td>\n",
       "      <td>0.358800</td>\n",
       "      <td>0.101375</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.411300e+06</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>27.220000</td>\n",
       "      <td>39.28000</td>\n",
       "      <td>182.100000</td>\n",
       "      <td>2250.000000</td>\n",
       "      <td>0.144700</td>\n",
       "      <td>0.311400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>232.200000</td>\n",
       "      <td>3903.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.170000</td>\n",
       "      <td>0.290300</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 33 columns</p>\n",
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      "text/plain": [
       "                 id        Time  radius_mean  texture_mean  perimeter_mean  \\\n",
       "count  1.980000e+02  198.000000   198.000000     198.00000      198.000000   \n",
       "mean   1.990469e+06   46.732323    17.412323      22.27601      114.856566   \n",
       "std    2.889025e+06   34.462870     3.161676       4.29829       21.383402   \n",
       "min    8.423000e+03    1.000000    10.950000      10.38000       71.900000   \n",
       "25%    8.557452e+05   14.000000    15.052500      19.41250       98.160000   \n",
       "50%    8.863390e+05   39.500000    17.290000      21.75000      113.700000   \n",
       "75%    9.279958e+05   72.750000    19.580000      24.65500      129.650000   \n",
       "max    9.411300e+06  125.000000    27.220000      39.28000      182.100000   \n",
       "\n",
       "         area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "count   198.000000       198.000000        198.000000      198.000000   \n",
       "mean    970.040909         0.102681          0.142648        0.156243   \n",
       "std     352.149215         0.012522          0.049898        0.070572   \n",
       "min     361.600000         0.074970          0.046050        0.023980   \n",
       "25%     702.525000         0.093900          0.110200        0.106850   \n",
       "50%     929.100000         0.101900          0.131750        0.151350   \n",
       "75%    1193.500000         0.110975          0.172200        0.200500   \n",
       "max    2250.000000         0.144700          0.311400        0.426800   \n",
       "\n",
       "       concave points_mean  ...  texture_worst  perimeter_worst   area_worst  \\\n",
       "count           198.000000  ...     198.000000       198.000000   198.000000   \n",
       "mean              0.086776  ...      30.139091       140.347778  1404.958586   \n",
       "std               0.033877  ...       6.017777        28.892279   586.006972   \n",
       "min               0.020310  ...      16.670000        85.100000   508.100000   \n",
       "25%               0.063670  ...      26.210000       118.075000   947.275000   \n",
       "50%               0.086075  ...      30.135000       136.500000  1295.000000   \n",
       "75%               0.103925  ...      33.555000       159.875000  1694.250000   \n",
       "max               0.201200  ...      49.540000       232.200000  3903.000000   \n",
       "\n",
       "       smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "count        198.000000         198.000000       198.000000   \n",
       "mean           0.143921           0.365102         0.436685   \n",
       "std            0.022004           0.163965         0.173625   \n",
       "min            0.081910           0.051310         0.023980   \n",
       "25%            0.129325           0.248700         0.322150   \n",
       "50%            0.141850           0.351300         0.402350   \n",
       "75%            0.154875           0.423675         0.541050   \n",
       "max            0.222600           1.058000         1.170000   \n",
       "\n",
       "       concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "count            198.000000      198.000000               198.000000   \n",
       "mean               0.178778        0.323404                 0.090828   \n",
       "std                0.045181        0.075161                 0.021172   \n",
       "min                0.028990        0.156500                 0.055040   \n",
       "25%                0.152650        0.275950                 0.076577   \n",
       "50%                0.179250        0.310300                 0.086890   \n",
       "75%                0.207125        0.358800                 0.101375   \n",
       "max                0.290300        0.663800                 0.207500   \n",
       "\n",
       "       Tumor Size  \n",
       "count  198.000000  \n",
       "mean     2.847475  \n",
       "std      1.937964  \n",
       "min      0.400000  \n",
       "25%      1.500000  \n",
       "50%      2.500000  \n",
       "75%      3.500000  \n",
       "max     10.000000  \n",
       "\n",
       "[8 rows x 33 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "074d421e",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Time</th>\n",
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       "      <th>perimeter_mean</th>\n",
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       "      <th>concavity_mean</th>\n",
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       "      <th>Tumor Size</th>\n",
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       "      <th>0</th>\n",
       "      <td>119513</td>\n",
       "      <td>31</td>\n",
       "      <td>18.02</td>\n",
       "      <td>27.60</td>\n",
       "      <td>117.50</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>0.09489</td>\n",
       "      <td>0.1036</td>\n",
       "      <td>0.1086</td>\n",
       "      <td>0.07055</td>\n",
       "      <td>...</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>0.1926</td>\n",
       "      <td>0.3140</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>0.2677</td>\n",
       "      <td>0.08113</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8423</td>\n",
       "      <td>61</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>0.2776</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</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>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>842517</td>\n",
       "      <td>116</td>\n",
       "      <td>21.37</td>\n",
       "      <td>17.44</td>\n",
       "      <td>137.50</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>0.08836</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>0.1255</td>\n",
       "      <td>0.08180</td>\n",
       "      <td>...</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>0.1188</td>\n",
       "      <td>0.3449</td>\n",
       "      <td>0.3414</td>\n",
       "      <td>0.2032</td>\n",
       "      <td>0.4334</td>\n",
       "      <td>0.09067</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>843483</td>\n",
       "      <td>123</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>0.2839</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</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>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>843584</td>\n",
       "      <td>27</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>0.1328</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</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>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Time  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0  119513    31        18.02         27.60          117.50     1013.0   \n",
       "1    8423    61        17.99         10.38          122.80     1001.0   \n",
       "2  842517   116        21.37         17.44          137.50     1373.0   \n",
       "3  843483   123        11.42         20.38           77.58      386.1   \n",
       "4  843584    27        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.09489            0.1036          0.1086              0.07055   \n",
       "1          0.11840            0.2776          0.3001              0.14710   \n",
       "2          0.08836            0.1189          0.1255              0.08180   \n",
       "3          0.14250            0.2839          0.2414              0.10520   \n",
       "4          0.10030            0.1328          0.1980              0.10430   \n",
       "\n",
       "   ...  area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0  ...      1436.0            0.1195             0.1926           0.3140   \n",
       "1  ...      2019.0            0.1622             0.6656           0.7119   \n",
       "2  ...      1949.0            0.1188             0.3449           0.3414   \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  Tumor Size  \\\n",
       "0                0.1170          0.2677                  0.08113         5.0   \n",
       "1                0.2654          0.4601                  0.11890         3.0   \n",
       "2                0.2032          0.4334                  0.09067         2.5   \n",
       "3                0.2575          0.6638                  0.17300         2.0   \n",
       "4                0.1625          0.2364                  0.07678         3.5   \n",
       "\n",
       "   Lymph node status  Recurrence  \n",
       "0                5.1           N  \n",
       "1                  2           N  \n",
       "2                  0           N  \n",
       "3                  0           N  \n",
       "4                  0           R  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "29b0a4d4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 198 entries, 0 to 197\n",
      "Data columns (total 35 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       198 non-null    int64  \n",
      " 1   Time                     198 non-null    int64  \n",
      " 2   radius_mean              198 non-null    float64\n",
      " 3   texture_mean             198 non-null    float64\n",
      " 4   perimeter_mean           198 non-null    float64\n",
      " 5   area_mean                198 non-null    float64\n",
      " 6   smoothness_mean          198 non-null    float64\n",
      " 7   compactness_mean         198 non-null    float64\n",
      " 8   concavity_mean           198 non-null    float64\n",
      " 9   concave points_mean      198 non-null    float64\n",
      " 10  symmetry_mean            198 non-null    float64\n",
      " 11  fractal_dimension_mean   198 non-null    float64\n",
      " 12  radius_se                198 non-null    float64\n",
      " 13  texture_se               198 non-null    float64\n",
      " 14  perimeter_se             198 non-null    float64\n",
      " 15  area_se                  198 non-null    float64\n",
      " 16  smoothness_se            198 non-null    float64\n",
      " 17  compactness_se           198 non-null    float64\n",
      " 18  concavity_se             198 non-null    float64\n",
      " 19  concave points_se        198 non-null    float64\n",
      " 20  symmetry_se              198 non-null    float64\n",
      " 21  fractal_dimension_se     198 non-null    float64\n",
      " 22  radius_worst             198 non-null    float64\n",
      " 23  texture_worst            198 non-null    float64\n",
      " 24  perimeter_worst          198 non-null    float64\n",
      " 25  area_worst               198 non-null    float64\n",
      " 26  smoothness_worst         198 non-null    float64\n",
      " 27  compactness_worst        198 non-null    float64\n",
      " 28  concavity_worst          198 non-null    float64\n",
      " 29  concave points_worst     198 non-null    float64\n",
      " 30  symmetry_worst           198 non-null    float64\n",
      " 31  fractal_dimension_worst  198 non-null    float64\n",
      " 32  Tumor Size               198 non-null    float64\n",
      " 33  Lymph node status        198 non-null    object \n",
      " 34  Recurrence               198 non-null    object \n",
      "dtypes: float64(31), int64(2), object(2)\n",
      "memory usage: 54.3+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45342659",
   "metadata": {},
   "source": [
    "### 2. Data cleaning\n",
    "These ?  are replaced with nan and then these rows are then dropped. The id number is also dropped as it has no significance in predicting recurrence. The recurrence values are strings R and N and we therefore use label binarizer to convert them to 0 and 1s.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62dd5b74",
   "metadata": {},
   "outputs": [],
   "source": [
    "data =data.drop(['id','Time'],axis=1)\n",
    "\n",
    "binarize =['Recurrence']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "lb = LabelBinarizer()\n",
    "for column in binarize:\n",
    "    data[column] = lb.fit_transform(data[column])\n",
    "    \n",
    "data = data.replace('?', np.nan)\n",
    "data.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f76be6",
   "metadata": {},
   "source": [
    "### 3.Data exploration\n",
    "\n",
    "We look at the correlation of all the features with the dependent variable ( recurrence ) and we can see that compactness and smoothness have a low correlation with breast ca recurrence.\n",
    "What is time.?\n",
    "The worst or highest nucleus radius, area and perimeter observed in a slide along with the mean values for the earlier mentioned parameters have a strong positive correlation with recurrence. This makes clinical sense as nuclear polymorphism is indicative of carcinoma.\n",
    "Whereas fractal dimension which indicates roughness or complexity in the border of nucleus and symmetry of the nucleus seems protective.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d5076d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fractal_dimension_mean    -0.120151\n",
       "symmetry_mean             -0.101712\n",
       "texture_se                -0.077835\n",
       "concave points_se         -0.077726\n",
       "concavity_se              -0.071108\n",
       "texture_mean              -0.069888\n",
       "symmetry_worst            -0.062130\n",
       "smoothness_se             -0.059774\n",
       "fractal_dimension_worst   -0.053887\n",
       "texture_worst             -0.051249\n",
       "fractal_dimension_se      -0.048354\n",
       "symmetry_se               -0.040898\n",
       "compactness_worst         -0.023835\n",
       "compactness_se            -0.016591\n",
       "compactness_mean          -0.011520\n",
       "concavity_worst            0.012775\n",
       "smoothness_mean            0.012789\n",
       "smoothness_worst           0.035943\n",
       "concavity_mean             0.041231\n",
       "concave points_worst       0.067958\n",
       "concave points_mean        0.103772\n",
       "radius_se                  0.115318\n",
       "perimeter_se               0.121759\n",
       "area_se                    0.135859\n",
       "radius_mean                0.163639\n",
       "perimeter_mean             0.164672\n",
       "Tumor Size                 0.173139\n",
       "area_mean                  0.178668\n",
       "perimeter_worst            0.217082\n",
       "radius_worst               0.221101\n",
       "area_worst                 0.222812\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y= data['Recurrence']\n",
    "fields = list(data.columns[:-1])\n",
    "corr = data[fields].corrwith(y)\n",
    "corr.sort_values(inplace= True)\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b21b9239",
   "metadata": {},
   "outputs": [],
   "source": [
    "data =data.drop([ 'compactness_worst', 'compactness_se' ,'compactness_mean' ,'concavity_worst' ,'smoothness_mean' ,'smoothness_worst' ],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "107458f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_col = data.columns[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f127cbb7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2571afa7640>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 250x6500 with 26 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=data, y_vars= x_col, x_vars=['Recurrence'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c1e0eb",
   "metadata": {},
   "source": [
    "### 4. Pre processing\n",
    "\n",
    "We look at the frequency of the independent variable. We see an imbalanced data set. Attempts to use class weights to fix this in mpl have been unsucessful so far.\n",
    "\n",
    "We then split the data into dependent and independent variables, we then use standard scalar to standardize the date. Next we divide the data into test and train sets. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2986b68",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "data['Recurrence'].value_counts()\n",
    "plot1 = sns.countplot(data['Recurrence'])\n",
    "plot1.set_xticklabels(['NO recurrence','Recurrence'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c82520f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['Recurrence']\n",
    "x = data[data.columns[:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7602e52c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.19525303,  1.22546081,  0.12717922,  0.12464985, -0.6742506 ,\n",
       "        -0.47993529, -0.23126324,  0.08078597,  0.06758118,  1.17095964,\n",
       "        -0.13156742,  0.02623066, -0.79262314, -0.41089092, -0.96162794,\n",
       "        -0.38184876, -0.26191805,  0.15090023,  1.1400542 , -0.01519548,\n",
       "         0.05848299, -1.35315352, -0.73839436, -0.45256316,  1.09607526,\n",
       "         0.34547806],\n",
       "       [ 0.18576982, -2.75686661,  0.37512792,  0.090583  ,  2.03212882,\n",
       "         1.77986102,  1.77545788,  2.19630931,  1.5885731 , -0.69999136,\n",
       "         1.986189  ,  1.73514643, -0.13326817,  0.61193255,  0.13043836,\n",
       "         0.98045376,  1.12520797,  1.03643635, -2.12326631,  1.54641051,\n",
       "         1.05416933,  1.91471481,  1.86592704,  1.31933223,  0.06809073,\n",
       "        -0.22173599],\n",
       "       [ 1.25421108, -1.12415862,  1.06283472,  1.1466554 , -0.43541085,\n",
       "        -0.14782936,  1.46394522, -0.36350144, -0.06021967, -1.26011765,\n",
       "        -0.15174963,  0.24754413, -0.21107608, -0.37886795,  0.52616717,\n",
       "         1.07515928,  0.53190055,  0.92308773, -1.52017163,  0.6595296 ,\n",
       "         0.93461866,  0.54502876,  1.50451654, -0.00501531, -0.18890541,\n",
       "        -0.58768053],\n",
       "       [-1.89105239, -0.44424906, -1.74038917, -1.65505926,  1.20254933,\n",
       "         0.54295096,  2.42021665,  4.77262609, -0.35076439, -0.22365602,\n",
       "        -0.3732953 , -0.89911017,  0.77594408,  0.74958356,  0.6387139 ,\n",
       "         4.06098504,  2.6753136 , -1.43598052, -0.6080942 , -1.43524832,\n",
       "        -1.42445774,  1.74075147,  4.62320496,  3.85731319, -0.44590154,\n",
       "        -0.58768053],\n",
       "       [ 0.91281565, -1.84107006,  0.95055606,  0.93089867,  0.58919754,\n",
       "         0.51638248, -0.43410869, -0.53819029,  0.49563315, -0.93559401,\n",
       "         0.54086726,  0.50414247,  1.57414592,  0.76248834,  0.67138876,\n",
       "        -0.31732412,  0.57097452,  0.36579033, -2.23231905,  0.41955006,\n",
       "         0.29587648, -0.35121276, -1.16207035, -0.65663371,  0.32508686,\n",
       "        -0.58768053]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "x = preprocessing.StandardScaler().fit(x).transform(x)\n",
    "x[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "339c5fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3, random_state =12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bce8463d",
   "metadata": {},
   "source": [
    "### 5. Model building\n",
    "\n",
    "We first build a logistic regression model to serve as a baseline. Hyperparameter tuning was performed using gridsearch cv and obtained a f1 score of .69. We then look at the coefficients to see which of the factors contribute the most towards recurrence. We can then compare it with the correlations identified earlier.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "373cb97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "class_weights = compute_class_weight(class_weight = \"balanced\", classes= np.unique(y_train), y= y_train)\n",
    "class_weights = dict(enumerate(class_weights))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f602c81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.501311249137336,\n",
       " {'C': 0.001,\n",
       "  'class_weight': {0: 0.6553398058252428, 1: 2.109375},\n",
       "  'max_iter': 100,\n",
       "  'penalty': 'l2'})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "\n",
    "lr = LogisticRegression()\n",
    "param_grid = [{'penalty': ['l1','l2','elasticnet','none'] , 'C': np.logspace(-4,4,9) , 'max_iter': [100,1000,2000,5000], 'class_weight' : [class_weights] }]\n",
    "\n",
    "lrmodels = GridSearchCV( lr ,param_grid = param_grid,scoring='f1', cv=3, n_jobs =-1)\n",
    "lrmodels.fit(x_train, y_train)\n",
    "lrmodels.best_score_,lrmodels.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "11b50b9f",
   "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>0</th>\n",
       "      <th>Coefs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>area_worst</td>\n",
       "      <td>0.017205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Lymph node status</td>\n",
       "      <td>0.016999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>radius_worst</td>\n",
       "      <td>0.016780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>perimeter_worst</td>\n",
       "      <td>0.016307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>area_mean</td>\n",
       "      <td>0.015185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Tumor Size</td>\n",
       "      <td>0.014317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>perimeter_mean</td>\n",
       "      <td>0.014316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>radius_mean</td>\n",
       "      <td>0.014164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>concave points_mean</td>\n",
       "      <td>0.008354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>area_se</td>\n",
       "      <td>0.007474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>perimeter_se</td>\n",
       "      <td>0.004858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>concave points_worst</td>\n",
       "      <td>0.004673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>radius_se</td>\n",
       "      <td>0.004359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>concavity_mean</td>\n",
       "      <td>0.003304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>symmetry_se</td>\n",
       "      <td>-0.000861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>symmetry_worst</td>\n",
       "      <td>-0.003208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>smoothness_se</td>\n",
       "      <td>-0.005312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>fractal_dimension_se</td>\n",
       "      <td>-0.005991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>concavity_se</td>\n",
       "      <td>-0.007457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fractal_dimension_worst</td>\n",
       "      <td>-0.007641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>symmetry_mean</td>\n",
       "      <td>-0.008250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>concave points_se</td>\n",
       "      <td>-0.008726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>texture_se</td>\n",
       "      <td>-0.010025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>texture_worst</td>\n",
       "      <td>-0.012212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fractal_dimension_mean</td>\n",
       "      <td>-0.012392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>texture_mean</td>\n",
       "      <td>-0.014453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0     Coefs\n",
       "20               area_worst  0.017205\n",
       "25        Lymph node status  0.016999\n",
       "17             radius_worst  0.016780\n",
       "19          perimeter_worst  0.016307\n",
       "3                 area_mean  0.015185\n",
       "24               Tumor Size  0.014317\n",
       "2            perimeter_mean  0.014316\n",
       "0               radius_mean  0.014164\n",
       "5       concave points_mean  0.008354\n",
       "11                  area_se  0.007474\n",
       "10             perimeter_se  0.004858\n",
       "21     concave points_worst  0.004673\n",
       "8                 radius_se  0.004359\n",
       "4            concavity_mean  0.003304\n",
       "15              symmetry_se -0.000861\n",
       "22           symmetry_worst -0.003208\n",
       "12            smoothness_se -0.005312\n",
       "16     fractal_dimension_se -0.005991\n",
       "13             concavity_se -0.007457\n",
       "23  fractal_dimension_worst -0.007641\n",
       "6             symmetry_mean -0.008250\n",
       "14        concave points_se -0.008726\n",
       "9                texture_se -0.010025\n",
       "18            texture_worst -0.012212\n",
       "7    fractal_dimension_mean -0.012392\n",
       "1              texture_mean -0.014453"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lrmodels.best_estimator_.coef_\n",
    "coef_table = pd.DataFrame(list(data.columns[:-1])).copy()\n",
    "coef_table.insert(len(coef_table.columns),\"Coefs\",lrmodels.best_estimator_.coef_.transpose())\n",
    "coef_table_desc = coef_table.sort_values(['Coefs'], ascending=[False])\n",
    "coef_table_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31795d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = lrmodels.predict(x_test)\n",
    "yhat_prob = lrmodels.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ab69109b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.78      0.77        45\n",
      "           1       0.23      0.21      0.22        14\n",
      "\n",
      "    accuracy                           0.64        59\n",
      "   macro avg       0.50      0.50      0.50        59\n",
      "weighted avg       0.64      0.64      0.64        59\n",
      "\n",
      "Area under the curve is\n",
      "0.5047619047619047\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix,  roc_auc_score,f1_score\n",
    "\n",
    "print (classification_report(y_test, yhat))\n",
    "auc = roc_auc_score(y_test, yhat_prob[::,1], average='weighted')\n",
    "print(\"Area under the curve is\" )\n",
    "print(auc)\n",
    "cm= confusion_matrix(y_test, yhat)\n",
    "fig, ax = plt.subplots(figsize=(6,6))  \n",
    "ax.set(title = \"Confusion Matrix\")\n",
    "sns.heatmap(cm,annot =True,ax=ax, annot_kws={\"size\":20,\"weight\":\"bold\"})\n",
    "labels=[\"NO recurrance\",\"Recurrance\"]\n",
    "ax.set_xticklabels(labels,fontsize =15);\n",
    "ax.set_yticklabels(labels,fontsize =15);\n",
    "ax.set_ylabel('Ground Truth',fontsize =22);\n",
    "ax.set_xlabel('Prediction',fontsize =22);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ebb392c",
   "metadata": {},
   "source": [
    "\n",
    "Next we import pca and train multiple models with components ranging from 1 to 15 and check the total amount of variability explained by each of the models. We find that 90 percent of the variability of the original data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d88376d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca_list = list()\n",
    "feature_weight_list = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "085ef6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in range (1,15):\n",
    "    PCAmod =PCA(n_components=n)\n",
    "    PCAmod.fit(x_train)\n",
    "    \n",
    "    pca_list.append(pd.Series({'n':n,'model':PCAmod,'var': PCAmod.explained_variance_ratio_.sum()})) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5a0f4b4c",
   "metadata": {
    "scrolled": true
   },
   "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>model</th>\n",
       "      <th>var</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PCA(n_components=1)</td>\n",
       "      <td>0.368457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PCA(n_components=2)</td>\n",
       "      <td>0.557076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PCA(n_components=3)</td>\n",
       "      <td>0.676854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PCA(n_components=4)</td>\n",
       "      <td>0.755123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PCA(n_components=5)</td>\n",
       "      <td>0.806077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PCA(n_components=6)</td>\n",
       "      <td>0.852106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PCA(n_components=7)</td>\n",
       "      <td>0.887425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PCA(n_components=8)</td>\n",
       "      <td>0.910447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PCA(n_components=9)</td>\n",
       "      <td>0.927366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PCA(n_components=10)</td>\n",
       "      <td>0.943532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PCA(n_components=11)</td>\n",
       "      <td>0.958151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PCA(n_components=12)</td>\n",
       "      <td>0.968484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PCA(n_components=13)</td>\n",
       "      <td>0.978048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PCA(n_components=14)</td>\n",
       "      <td>0.986527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   model       var\n",
       "n                                 \n",
       "1    PCA(n_components=1)  0.368457\n",
       "2    PCA(n_components=2)  0.557076\n",
       "3    PCA(n_components=3)  0.676854\n",
       "4    PCA(n_components=4)  0.755123\n",
       "5    PCA(n_components=5)  0.806077\n",
       "6    PCA(n_components=6)  0.852106\n",
       "7    PCA(n_components=7)  0.887425\n",
       "8    PCA(n_components=8)  0.910447\n",
       "9    PCA(n_components=9)  0.927366\n",
       "10  PCA(n_components=10)  0.943532\n",
       "11  PCA(n_components=11)  0.958151\n",
       "12  PCA(n_components=12)  0.968484\n",
       "13  PCA(n_components=13)  0.978048\n",
       "14  PCA(n_components=14)  0.986527"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df = pd.concat(pca_list,axis =1).T.set_index('n')\n",
    "pca_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1510da0e",
   "metadata": {},
   "source": [
    "We then use the .components attribute to look into how much each of the original features contribute to the newly generated 9 components. We then take the absolute values of these metrics and look at the 3 most significant contributors to each of the 9 newly generated components. Symmetry, tumor size, smoothness , lymph node status seems to be contributing the most to these new components.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44050d66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      radius_mean  texture_mean  perimeter_mean  area_mean  concavity_mean  \\\n",
      "PC-1     0.287943      0.047997        0.297603   0.296760        0.244352   \n",
      "PC-2    -0.129782      0.010617       -0.102013  -0.133068        0.247580   \n",
      "PC-3    -0.050150      0.126567       -0.057173  -0.053657       -0.108026   \n",
      "PC-4    -0.026018      0.619657       -0.023838  -0.020767        0.026391   \n",
      "PC-5     0.073595      0.137068        0.084656   0.059480        0.138806   \n",
      "PC-6     0.071532     -0.011487        0.077149   0.059865        0.104284   \n",
      "PC-7     0.190794      0.015196        0.165783   0.164132       -0.000864   \n",
      "PC-8     0.009258     -0.141359        0.026865  -0.015306        0.291904   \n",
      "PC-9    -0.036440     -0.259726       -0.030063   0.005820        0.071128   \n",
      "\n",
      "      concave points_mean  symmetry_mean  fractal_dimension_mean  radius_se  \\\n",
      "PC-1             0.280267       0.060193               -0.017260   0.274285   \n",
      "PC-2             0.149711       0.304834                0.355821  -0.031243   \n",
      "PC-3            -0.123852      -0.200998               -0.146370   0.165506   \n",
      "PC-4            -0.021111      -0.020491                0.023829  -0.023844   \n",
      "PC-5             0.086131      -0.127852               -0.028512  -0.256842   \n",
      "PC-6             0.025778      -0.335795               -0.046344  -0.218683   \n",
      "PC-7            -0.009481       0.161589               -0.375011  -0.247102   \n",
      "PC-8             0.392416       0.177133                0.006469  -0.071737   \n",
      "PC-9            -0.010463      -0.025867                0.152800   0.038776   \n",
      "\n",
      "      texture_se  ...  fractal_dimension_se  radius_worst  texture_worst  \\\n",
      "PC-1    0.088760  ...              0.077533      0.284778      -0.011039   \n",
      "PC-2    0.141703  ...              0.359959     -0.159242       0.025955   \n",
      "PC-3    0.387789  ...              0.115152     -0.152497       0.008813   \n",
      "PC-4    0.252470  ...             -0.049072      0.046319       0.669303   \n",
      "PC-5   -0.055218  ...              0.045389      0.000633       0.118585   \n",
      "PC-6   -0.274344  ...              0.145598      0.047393      -0.022364   \n",
      "PC-7    0.082294  ...             -0.163795      0.036734       0.022940   \n",
      "PC-8    0.367363  ...             -0.366526     -0.163603      -0.056665   \n",
      "PC-9    0.226249  ...              0.091194      0.071763       0.004908   \n",
      "\n",
      "      perimeter_worst  area_worst  concave points_worst  symmetry_worst  \\\n",
      "PC-1         0.295366    0.285161              0.181898       -0.046579   \n",
      "PC-2        -0.125702   -0.168261              0.196167        0.230168   \n",
      "PC-3        -0.152515   -0.149122             -0.338998       -0.363863   \n",
      "PC-4         0.050450    0.057982              0.025571        0.029432   \n",
      "PC-5         0.018806   -0.023532              0.114130       -0.118048   \n",
      "PC-6         0.064232    0.032819              0.138100       -0.265877   \n",
      "PC-7         0.005380   -0.000636             -0.082872        0.332175   \n",
      "PC-8        -0.121881   -0.211830              0.200293       -0.239687   \n",
      "PC-9         0.059888    0.142518             -0.230909        0.003342   \n",
      "\n",
      "      fractal_dimension_worst  Tumor Size  Lymph node status  \n",
      "PC-1                -0.054804    0.038716           0.017945  \n",
      "PC-2                 0.274712   -0.076263          -0.039690  \n",
      "PC-3                -0.278237    0.040480           0.052794  \n",
      "PC-4                 0.087108   -0.176182          -0.090918  \n",
      "PC-5                 0.048787    0.596855           0.529340  \n",
      "PC-6                 0.064996   -0.412767          -0.314768  \n",
      "PC-7                -0.321015   -0.189239           0.001369  \n",
      "PC-8                -0.217999   -0.216209           0.080843  \n",
      "PC-9                 0.108990   -0.339485           0.470735  \n",
      "\n",
      "[9 rows x 26 columns]\n"
     ]
    }
   ],
   "source": [
    "PCA9 =PCA(n_components=9)\n",
    "PCA9.fit(x_train)\n",
    "PCA9.components_\n",
    "print(pd.DataFrame(PCA9.components_,columns=data.columns[:-1],index = ['PC-1','PC-2','PC-3','PC-4','PC-5','PC-6','PC-7','PC-8','PC-9']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "02e9cac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "PCA_comp_abs= abs(PCA9.components_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "67c783e3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component 0:['perimeter_mean', 'area_mean', 'area_se']\n",
      "Component 1:['fractal_dimension_se', 'fractal_dimension_mean', 'concavity_se']\n",
      "Component 2:['texture_se', 'symmetry_worst', 'smoothness_se']\n",
      "Component 3:['texture_worst', 'texture_mean', 'texture_se']\n",
      "Component 4:['Tumor Size', 'Lymph node status', 'radius_se']\n",
      "Component 5:['Tumor Size', 'symmetry_se', 'symmetry_mean']\n",
      "Component 6:['symmetry_se', 'fractal_dimension_mean', 'symmetry_worst']\n",
      "Component 7:['concave points_mean', 'texture_se', 'fractal_dimension_se']\n",
      "Component 8:['concave points_se', 'Lymph node status', 'Tumor Size']\n"
     ]
    }
   ],
   "source": [
    "for row in range (PCA_comp_abs.shape[0]):\n",
    "\n",
    "    temp= np.argpartition(-(PCA_comp_abs[row]),3)\n",
    "    indices = temp[np.argsort((-PCA_comp_abs[row])[temp])][:3]\n",
    "    print(f'Component {row}:{data.columns[:-1][indices].to_list()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1908b98",
   "metadata": {},
   "source": [
    "Next we build a MLP deep learning model with 5 hidden layers using scikit learn and train it on the original dataset to get a f1 score of 0.69. Next we try using the 9 components generated from pca to train the same MLP model and the f1 score increased to 0.71. In cases where the number of features are high , i.e around 100 to 200, pca may improve the performance of the model significantly but when the number of features are relatively low i.e around 32 , pca seems to have a very mild effect effect. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "844e6033",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "mlp = MLPClassifier ( hidden_layer_sizes = (512,265,128,64,32,16), activation ='relu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "53e60bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp.fit(x_train,y_train)\n",
    "ypred =mlp.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c82a6576",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.84      0.81        45\n",
      "           1       0.30      0.21      0.25        14\n",
      "\n",
      "    accuracy                           0.69        59\n",
      "   macro avg       0.54      0.53      0.53        59\n",
      "weighted avg       0.66      0.69      0.68        59\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "aa411e24",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_pca = PCA9.transform(x_train)\n",
    "x_test_pca = PCA9.fit_transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b78b24de",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_pca = MLPClassifier ( hidden_layer_sizes = (512,265,128,64,32,16), activation ='relu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "dbe967be",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_pca.fit(x_train_pca ,y_train)\n",
    "ypred_pca =mlp_pca.predict(x_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e3e897fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.87      0.82        45\n",
      "           1       0.33      0.21      0.26        14\n",
      "\n",
      "    accuracy                           0.71        59\n",
      "   macro avg       0.56      0.54      0.54        59\n",
      "weighted avg       0.67      0.71      0.69        59\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (classification_report(y_test, ypred_pca))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f946fde",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm= confusion_matrix(y_test,ypred_pca)\n",
    "fig, ax = plt.subplots(figsize=(6,6))  \n",
    "ax.set(title = \"Confusion Matrix\")\n",
    "sns.heatmap(cm,annot =True,ax=ax, annot_kws={\"size\":20,\"weight\":\"bold\"})\n",
    "labels=[\"NO recurrance\",\"Recurrance\"]\n",
    "ax.set_xticklabels(labels,fontsize =15);\n",
    "ax.set_yticklabels(labels,fontsize =15);\n",
    "ax.set_ylabel('Ground Truth',fontsize =22);\n",
    "ax.set_xlabel('Prediction',fontsize =22);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9a7849",
   "metadata": {},
   "source": [
    "Changes would like to make, \n",
    "\n",
    "(1) change pairplot to boxplot ,it would improve comprehesion but I was unable to do so due to vague errors. \n",
    "\n",
    "(2) use a combination of over and undersampling to balance the data , or use class balance in MLP . Keep getting error mesages related to balanced class generator in keras, when trying to install imblearn.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ccae779",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}