Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a58cdea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import mglearn\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "692632a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "215a485a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         1.240e-01],\n",
       "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
       "         7.039e-02]]),\n",
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       "        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
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       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
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       "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),\n",
       " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
       " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry \\n        - fractal dimension (\"coastline approximation\" - 1)\\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.  For instance, field 3 is Mean Radius, field\\n        13 is Radius SE, field 23 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
       " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "        'smoothness error', 'compactness error', 'concavity error',\n",
       "        'concave points error', 'symmetry error',\n",
       "        'fractal dimension error', 'worst radius', 'worst texture',\n",
       "        'worst perimeter', 'worst area', 'worst smoothness',\n",
       "        'worst compactness', 'worst concavity', 'worst concave points',\n",
       "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
       " 'filename': '/Users/s4558220/opt/anaconda3/envs/machlearn/lib/python3.7/site-packages/sklearn/datasets/data/breast_cancer.csv'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer = load_breast_cancer()\n",
    "cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ac70519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28bad92a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "96a65f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b52cda09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry \n",
      "        - fractal dimension (\"coastline approximation\" - 1)\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.  For instance, field 3 is Mean Radius, field\n",
      "        13 is Radius SE, field 23 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(cancer.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e2805bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sklearn.utils.Bunch"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c41ee63b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
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       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
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       "      <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",
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       "      <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>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</td>\n",
       "      <td>23.41</td>\n",
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       "      <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>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>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <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 × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cancer = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
    "df_cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c483e183",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aa6554de",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/n1/xlmr0_gd1850jr26hcgcqk0w0000gp/T/ipykernel_7264/1533103328.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'target'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcancer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
     ]
    }
   ],
   "source": [
    "df['target']=cancer.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "35ccf174",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
       "3                 0.09744  ...          26.50            98.87       567.7   \n",
       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cancer['target']=cancer.target\n",
    "df_cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d73e714a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ce646b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_col = ['target'] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a5c6451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['mean radius',\n",
       " 'mean texture',\n",
       " 'mean perimeter',\n",
       " 'mean area',\n",
       " 'mean smoothness',\n",
       " 'mean compactness',\n",
       " 'mean concavity',\n",
       " 'mean concave points',\n",
       " 'mean symmetry',\n",
       " 'mean fractal dimension',\n",
       " 'radius error',\n",
       " 'texture error',\n",
       " 'perimeter error',\n",
       " 'area error',\n",
       " 'smoothness error',\n",
       " 'compactness error',\n",
       " 'concavity error',\n",
       " 'concave points error',\n",
       " 'symmetry error',\n",
       " 'fractal dimension error',\n",
       " 'worst radius',\n",
       " 'worst texture',\n",
       " 'worst perimeter',\n",
       " 'worst area',\n",
       " 'worst smoothness',\n",
       " 'worst compactness',\n",
       " 'worst concavity',\n",
       " 'worst concave points',\n",
       " 'worst symmetry',\n",
       " 'worst fractal dimension']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cont_cols = [col for col in df_cancer.columns if col not in y_col]\n",
    "cont_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d57aba61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.7990e+01, 1.0380e+01, 1.2280e+02, 1.0010e+03, 1.1840e-01, 2.7760e-01,\n",
       "         3.0010e-01, 1.4710e-01, 2.4190e-01, 7.8710e-02, 1.0950e+00, 9.0530e-01,\n",
       "         8.5890e+00, 1.5340e+02, 6.3990e-03, 4.9040e-02, 5.3730e-02, 1.5870e-02,\n",
       "         3.0030e-02, 6.1930e-03, 2.5380e+01, 1.7330e+01, 1.8460e+02, 2.0190e+03,\n",
       "         1.6220e-01, 6.6560e-01, 7.1190e-01, 2.6540e-01, 4.6010e-01, 1.1890e-01],\n",
       "        [2.0570e+01, 1.7770e+01, 1.3290e+02, 1.3260e+03, 8.4740e-02, 7.8640e-02,\n",
       "         8.6900e-02, 7.0170e-02, 1.8120e-01, 5.6670e-02, 5.4350e-01, 7.3390e-01,\n",
       "         3.3980e+00, 7.4080e+01, 5.2250e-03, 1.3080e-02, 1.8600e-02, 1.3400e-02,\n",
       "         1.3890e-02, 3.5320e-03, 2.4990e+01, 2.3410e+01, 1.5880e+02, 1.9560e+03,\n",
       "         1.2380e-01, 1.8660e-01, 2.4160e-01, 1.8600e-01, 2.7500e-01, 8.9020e-02],\n",
       "        [1.9690e+01, 2.1250e+01, 1.3000e+02, 1.2030e+03, 1.0960e-01, 1.5990e-01,\n",
       "         1.9740e-01, 1.2790e-01, 2.0690e-01, 5.9990e-02, 7.4560e-01, 7.8690e-01,\n",
       "         4.5850e+00, 9.4030e+01, 6.1500e-03, 4.0060e-02, 3.8320e-02, 2.0580e-02,\n",
       "         2.2500e-02, 4.5710e-03, 2.3570e+01, 2.5530e+01, 1.5250e+02, 1.7090e+03,\n",
       "         1.4440e-01, 4.2450e-01, 4.5040e-01, 2.4300e-01, 3.6130e-01, 8.7580e-02],\n",
       "        [1.1420e+01, 2.0380e+01, 7.7580e+01, 3.8610e+02, 1.4250e-01, 2.8390e-01,\n",
       "         2.4140e-01, 1.0520e-01, 2.5970e-01, 9.7440e-02, 4.9560e-01, 1.1560e+00,\n",
       "         3.4450e+00, 2.7230e+01, 9.1100e-03, 7.4580e-02, 5.6610e-02, 1.8670e-02,\n",
       "         5.9630e-02, 9.2080e-03, 1.4910e+01, 2.6500e+01, 9.8870e+01, 5.6770e+02,\n",
       "         2.0980e-01, 8.6630e-01, 6.8690e-01, 2.5750e-01, 6.6380e-01, 1.7300e-01],\n",
       "        [2.0290e+01, 1.4340e+01, 1.3510e+02, 1.2970e+03, 1.0030e-01, 1.3280e-01,\n",
       "         1.9800e-01, 1.0430e-01, 1.8090e-01, 5.8830e-02, 7.5720e-01, 7.8130e-01,\n",
       "         5.4380e+00, 9.4440e+01, 1.1490e-02, 2.4610e-02, 5.6880e-02, 1.8850e-02,\n",
       "         1.7560e-02, 5.1150e-03, 2.2540e+01, 1.6670e+01, 1.5220e+02, 1.5750e+03,\n",
       "         1.3740e-01, 2.0500e-01, 4.0000e-01, 1.6250e-01, 2.3640e-01, 7.6780e-02]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert continuous variables to a tensor\n",
    "conts = np.stack([df_cancer[col].values for col in cont_cols], 1)\n",
    "conts = torch.tensor(conts, dtype=torch.float)\n",
    "conts[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ae6c9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'torch.FloatTensor'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conts.type()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fa1abb74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert labels to a tensor\n",
    "y = torch.tensor(df_cancer[y_col].values).flatten()\n",
    "\n",
    "y[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fe5f9b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([569, 30])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "75e619a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([569])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9980d52d",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TabularNoEmbedModel(nn.Module):\n",
    "\n",
    "    def __init__(self,  n_cont, out_sz, layers, p=0.5):\n",
    "        super().__init__()\n",
    "        self.bn_cont = nn.BatchNorm1d(n_cont)\n",
    "        \n",
    "        layerlist = []\n",
    "        n_in =  n_cont\n",
    "        \n",
    "        for i in layers:\n",
    "            layerlist.append(nn.Linear(n_in,i)) \n",
    "            layerlist.append(nn.ReLU(inplace=True))\n",
    "            layerlist.append(nn.BatchNorm1d(i))\n",
    "            layerlist.append(nn.Dropout(p))\n",
    "            n_in = i\n",
    "        layerlist.append(nn.Linear(layers[-1],out_sz))\n",
    "            \n",
    "        self.layers = nn.Sequential(*layerlist)\n",
    "    \n",
    "    def forward(self, x_cont):\n",
    "        x_cont = self.bn_cont(x_cont)\n",
    "        x = self.layers(x_cont)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "21130097",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(33)\n",
    "model = TabularNoEmbedModel( conts.shape[1], 2, [200,100], p=0.4) # out_sz = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d1ef19a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TabularNoEmbedModel(\n",
       "  (bn_cont): BatchNorm1d(30, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (layers): Sequential(\n",
       "    (0): Linear(in_features=30, out_features=200, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Dropout(p=0.4)\n",
       "    (4): Linear(in_features=200, out_features=100, bias=True)\n",
       "    (5): ReLU(inplace)\n",
       "    (6): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (7): Dropout(p=0.4)\n",
       "    (8): Linear(in_features=100, out_features=2, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4cf4c604",
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3ee459c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 569\n",
    "test_size = batch_size//5\n",
    "\n",
    "con_train = conts[:batch_size-test_size]\n",
    "con_test = conts[batch_size-test_size:batch_size]\n",
    "y_train = y[:batch_size-test_size]\n",
    "y_test = y[batch_size-test_size:batch_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "df8a0606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([456, 30])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "abaacaa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([113, 30])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b3cc6314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([113])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b36794ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([456])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8a54e752",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   1  loss: 0.83456796\n",
      "epoch:  26  loss: 0.11763120\n",
      "epoch:  51  loss: 0.06203300\n",
      "epoch:  76  loss: 0.03756348\n",
      "epoch: 101  loss: 0.02956760\n",
      "epoch: 126  loss: 0.02237166\n",
      "epoch: 151  loss: 0.01372856\n",
      "epoch: 176  loss: 0.00909834\n",
      "epoch: 201  loss: 0.00696914\n",
      "epoch: 226  loss: 0.00792410\n",
      "epoch: 251  loss: 0.00598626\n",
      "epoch: 276  loss: 0.00469436\n",
      "epoch: 300  loss: 0.00298132\n",
      "\n",
      "Duration: 3 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "start_time = time.time()\n",
    "\n",
    "epochs = 300\n",
    "losses = []\n",
    "\n",
    "for i in range(epochs):\n",
    "    i+=1\n",
    "    y_pred = model( con_train)\n",
    "    loss = criterion(y_pred, y_train)\n",
    "    losses.append(loss)\n",
    "    \n",
    "    # a neat trick to save screen space:\n",
    "    if i%25 == 1:\n",
    "        print(f'epoch: {i:3}  loss: {loss.item():10.8f}')\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "print(f'epoch: {i:3}  loss: {loss.item():10.8f}') # print the last line\n",
    "print(f'\\nDuration: {time.time() - start_time:.0f} seconds') # print the time elapsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "36eb9b6f",
   "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"
    }
   ],
   "source": [
    "plt.plot(range(epochs), losses)\n",
    "plt.ylabel('Cross Entropy Loss')\n",
    "plt.xlabel('epoch');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e87441c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CE Loss: 0.59505314\n"
     ]
    }
   ],
   "source": [
    "# TO EVALUATE THE ENTIRE TEST SET\n",
    "with torch.no_grad():\n",
    "    y_val = model( con_test)\n",
    "    loss = criterion(y_val, y_test)\n",
    "print(f'CE Loss: {loss:.8f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "dd3dad37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL OUTPUT               ARGMAX  Y_TEST\n",
      "tensor([ 1.9655, -1.8683])    0      1   \n",
      "tensor([ 2.8957, -3.5559])    0      1   \n",
      "tensor([-0.0777,  0.2053])    1      1   \n",
      "tensor([-3.7152,  3.6695])    1      1   \n",
      "tensor([ 5.8541, -6.2573])    0      0   \n",
      "tensor([ 7.9688, -8.0006])    0      0   \n",
      "tensor([ 1.7889, -1.6350])    0      1   \n",
      "tensor([-4.1811,  4.2243])    1      1   \n",
      "tensor([-2.7909,  3.1419])    1      1   \n",
      "tensor([-2.9524,  3.2079])    1      1   \n",
      "tensor([-2.1054,  1.8442])    1      1   \n",
      "tensor([-4.7335,  4.1996])    1      1   \n",
      "tensor([ 4.6915, -4.2685])    0      0   \n",
      "tensor([ 0.0743, -0.2690])    0      1   \n",
      "tensor([-3.8841,  4.5333])    1      1   \n",
      "tensor([-0.4769,  0.0719])    1      1   \n",
      "tensor([-3.9447,  3.9175])    1      1   \n",
      "tensor([-2.4745,  2.6045])    1      1   \n",
      "tensor([-5.5853,  6.4046])    1      1   \n",
      "tensor([-3.8659,  3.8461])    1      1   \n",
      "tensor([-1.6677,  0.8142])    1      1   \n",
      "tensor([-2.8166,  3.1415])    1      1   \n",
      "tensor([-4.6708,  4.8236])    1      1   \n",
      "tensor([ 5.3874, -5.5667])    0      0   \n",
      "tensor([-4.0915,  4.1350])    1      1   \n",
      "tensor([ 1.8981, -2.5988])    0      1   \n",
      "tensor([-2.6072,  2.5675])    1      1   \n",
      "tensor([-2.3653,  2.5413])    1      1   \n",
      "tensor([0.2559, 0.1390])      0      1   \n",
      "tensor([-4.4394,  4.3499])    1      1   \n",
      "tensor([-1.4130,  0.7345])    1      1   \n",
      "tensor([ 6.2131, -7.4908])    0      0   \n",
      "tensor([-3.7850,  4.0050])    1      1   \n",
      "tensor([-1.2955,  1.6102])    1      0   \n",
      "tensor([ 0.1275, -0.5017])    0      1   \n",
      "tensor([-0.8598,  0.8513])    1      1   \n",
      "tensor([ 4.6347, -4.5328])    0      0   \n",
      "tensor([-4.7008,  4.9777])    1      1   \n",
      "tensor([-3.7561,  3.4844])    1      1   \n",
      "tensor([-1.1777,  1.0841])    1      1   \n",
      "tensor([ 1.0935, -1.1598])    0      1   \n",
      "tensor([-6.0056,  5.8904])    1      1   \n",
      "tensor([ 3.9217, -4.4537])    0      0   \n",
      "tensor([ 7.4395, -7.1189])    0      0   \n",
      "tensor([-0.5397,  0.7139])    1      1   \n",
      "tensor([ 5.7602, -7.0432])    0      0   \n",
      "tensor([-4.0222,  4.8579])    1      1   \n",
      "tensor([ 7.1058, -7.0727])    0      0   \n",
      "tensor([-3.2818,  3.4691])    1      1   \n",
      "tensor([-1.6458,  2.0218])    1      1   \n",
      "\n",
      "41 out of 50 = 82.00% correct\n"
     ]
    }
   ],
   "source": [
    "rows = 50\n",
    "correct = 0\n",
    "print(f'{\"MODEL OUTPUT\":26} ARGMAX  Y_TEST')\n",
    "for i in range(rows):\n",
    "    print(f'{str(y_val[i]):26} {y_val[i].argmax():^7}{y_test[i]:^7}')\n",
    "    if y_val[i].argmax().item() == y_test[i]:\n",
    "        correct += 1\n",
    "print(f'\\n{correct} out of {rows} = {100*correct/rows:.2f}% correct')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a10b7765",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL OUTPUT               ARGMAX  Y_TEST\n",
      "tensor([ 1.9655, -1.8683])    0      1   \n",
      "tensor([ 2.8957, -3.5559])    0      1   \n",
      "tensor([-0.0777,  0.2053])    1      1   \n",
      "tensor([-3.7152,  3.6695])    1      1   \n",
      "tensor([ 5.8541, -6.2573])    0      0   \n",
      "tensor([ 7.9688, -8.0006])    0      0   \n",
      "tensor([ 1.7889, -1.6350])    0      1   \n",
      "tensor([-4.1811,  4.2243])    1      1   \n",
      "tensor([-2.7909,  3.1419])    1      1   \n",
      "tensor([-2.9524,  3.2079])    1      1   \n",
      "tensor([-2.1054,  1.8442])    1      1   \n",
      "tensor([-4.7335,  4.1996])    1      1   \n",
      "tensor([ 4.6915, -4.2685])    0      0   \n",
      "tensor([ 0.0743, -0.2690])    0      1   \n",
      "tensor([-3.8841,  4.5333])    1      1   \n",
      "tensor([-0.4769,  0.0719])    1      1   \n",
      "tensor([-3.9447,  3.9175])    1      1   \n",
      "tensor([-2.4745,  2.6045])    1      1   \n",
      "tensor([-5.5853,  6.4046])    1      1   \n",
      "tensor([-3.8659,  3.8461])    1      1   \n",
      "tensor([-1.6677,  0.8142])    1      1   \n",
      "tensor([-2.8166,  3.1415])    1      1   \n",
      "tensor([-4.6708,  4.8236])    1      1   \n",
      "tensor([ 5.3874, -5.5667])    0      0   \n",
      "tensor([-4.0915,  4.1350])    1      1   \n",
      "tensor([ 1.8981, -2.5988])    0      1   \n",
      "tensor([-2.6072,  2.5675])    1      1   \n",
      "tensor([-2.3653,  2.5413])    1      1   \n",
      "tensor([0.2559, 0.1390])      0      1   \n",
      "tensor([-4.4394,  4.3499])    1      1   \n",
      "tensor([-1.4130,  0.7345])    1      1   \n",
      "tensor([ 6.2131, -7.4908])    0      0   \n",
      "tensor([-3.7850,  4.0050])    1      1   \n",
      "tensor([-1.2955,  1.6102])    1      0   \n",
      "tensor([ 0.1275, -0.5017])    0      1   \n",
      "tensor([-0.8598,  0.8513])    1      1   \n",
      "tensor([ 4.6347, -4.5328])    0      0   \n",
      "tensor([-4.7008,  4.9777])    1      1   \n",
      "tensor([-3.7561,  3.4844])    1      1   \n",
      "tensor([-1.1777,  1.0841])    1      1   \n",
      "tensor([ 1.0935, -1.1598])    0      1   \n",
      "tensor([-6.0056,  5.8904])    1      1   \n",
      "tensor([ 3.9217, -4.4537])    0      0   \n",
      "tensor([ 7.4395, -7.1189])    0      0   \n",
      "tensor([-0.5397,  0.7139])    1      1   \n",
      "tensor([ 5.7602, -7.0432])    0      0   \n",
      "tensor([-4.0222,  4.8579])    1      1   \n",
      "tensor([ 7.1058, -7.0727])    0      0   \n",
      "tensor([-3.2818,  3.4691])    1      1   \n",
      "tensor([-1.6458,  2.0218])    1      1   \n",
      "tensor([-3.0960,  3.0397])    1      1   \n",
      "tensor([-3.4294,  3.3194])    1      1   \n",
      "tensor([ 0.4128, -0.0029])    0      1   \n",
      "tensor([ 6.6732, -5.9565])    0      0   \n",
      "tensor([-5.4166,  4.7457])    1      1   \n",
      "tensor([-3.4861,  3.5625])    1      1   \n",
      "tensor([ 6.0863, -8.2013])    0      0   \n",
      "tensor([-2.9966,  3.0663])    1      1   \n",
      "tensor([ 4.5670, -3.7652])    0      0   \n",
      "tensor([-3.2510,  3.9450])    1      1   \n",
      "tensor([ 6.7717, -6.3072])    0      0   \n",
      "tensor([ 6.3345, -6.5603])    0      0   \n",
      "tensor([-1.6220,  1.2495])    1      1   \n",
      "tensor([-3.1971,  2.8742])    1      1   \n",
      "tensor([-3.8055,  4.8778])    1      1   \n",
      "tensor([ 6.7706, -6.8548])    0      0   \n",
      "tensor([-2.7512,  2.9010])    1      1   \n",
      "tensor([-1.1510,  0.5572])    1      1   \n",
      "tensor([-3.5988,  3.8208])    1      1   \n",
      "tensor([-3.1358,  3.9430])    1      1   \n",
      "tensor([ 5.5076, -5.9846])    0      1   \n",
      "tensor([-5.9827,  5.9020])    1      1   \n",
      "tensor([-1.2576,  1.5782])    1      1   \n",
      "tensor([-4.3219,  4.5136])    1      1   \n",
      "tensor([-1.8594,  0.7784])    1      1   \n",
      "tensor([ 1.4963, -1.0923])    0      1   \n",
      "tensor([-0.3777,  0.4822])    1      1   \n",
      "tensor([ 5.6551, -7.2746])    0      0   \n",
      "tensor([-5.1868,  5.1274])    1      1   \n",
      "tensor([ 5.9069, -5.4976])    0      0   \n",
      "tensor([ 4.6040, -4.9823])    0      0   \n",
      "tensor([-1.3165,  1.4622])    1      1   \n",
      "tensor([-2.3559,  2.6454])    1      1   \n",
      "tensor([-8.7205,  7.9862])    1      1   \n",
      "tensor([-5.9731,  6.4282])    1      1   \n",
      "tensor([ 3.2832, -3.8648])    0      1   \n",
      "tensor([ 1.0392, -1.4223])    0      1   \n",
      "tensor([ 2.0346, -2.6195])    0      1   \n",
      "tensor([-3.4129,  2.9900])    1      1   \n",
      "tensor([-1.2118,  0.7108])    1      1   \n",
      "tensor([-4.9325,  4.4907])    1      1   \n",
      "tensor([-6.8440,  6.6086])    1      1   \n",
      "tensor([-4.3508,  4.6367])    1      1   \n",
      "tensor([-3.6592,  3.7840])    1      1   \n",
      "tensor([-3.1405,  3.6882])    1      1   \n",
      "tensor([-5.5900,  5.7689])    1      1   \n",
      "tensor([ 3.9028, -4.2361])    0      1   \n",
      "tensor([-4.0632,  4.4934])    1      1   \n",
      "tensor([-0.3536, -0.0835])    1      1   \n",
      "tensor([-5.1749,  4.9402])    1      1   \n",
      "tensor([-6.0569,  5.7915])    1      1   \n",
      "tensor([-4.4072,  4.1387])    1      1   \n",
      "tensor([-3.0018,  2.2145])    1      1   \n",
      "tensor([-5.4918,  3.8969])    1      1   \n",
      "tensor([-1.1497,  1.1053])    1      1   \n",
      "tensor([-7.5142,  7.1012])    1      1   \n",
      "tensor([ 7.8140, -7.7018])    0      0   \n",
      "tensor([ 5.6968, -5.0631])    0      0   \n",
      "tensor([ 5.7393, -6.7790])    0      0   \n",
      "tensor([ 5.7347, -5.8821])    0      0   \n",
      "tensor([ 6.0899, -6.0463])    0      0   \n",
      "tensor([ 9.4554, -9.3369])    0      0   \n",
      "tensor([-5.5452,  5.2061])    1      1   \n",
      "\n",
      "97 out of 113 = 85.84% correct  TP=72; TN=25; FP=15; FN=1\n"
     ]
    }
   ],
   "source": [
    "FP = 0\n",
    "FN = 0\n",
    "TP = 0\n",
    "TN = 0\n",
    "correct = 0\n",
    "print(f'{\"MODEL OUTPUT\":26} ARGMAX  Y_TEST')\n",
    "for i in range(len(con_test)):\n",
    "    print(f'{str(y_val[i]):26} {y_val[i].argmax():^7}{y_test[i]:^7}')\n",
    "    if y_val[i].argmax().item() == y_test[i]:\n",
    "        correct += 1\n",
    "        if y_test[i]==1:\n",
    "            TP+=1\n",
    "        else:\n",
    "            TN+=1\n",
    "    else:\n",
    "        if y_test[i]==1:\n",
    "            FP+=1\n",
    "        else:\n",
    "            FN+=1\n",
    "        \n",
    "print(f'\\n{correct} out of {len(con_test)} = {100*correct/len(con_test):.2f}% correct  TP={TP}; TN={TN}; FP={FP}; FN={FN}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a903ce84",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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