class_06_add.ipynb

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    "# Class 06 Add \n",
    "\n",
    "## Review Assignment 4 \n",
    "\n",
    "### Predicting Survival Rates for Lung Cancer Surgery Patients\n",
    "> 1. Write a program to predict the life or death of a new patient using data from existing patients!  \n",
    ">> * Actual medical record data ('data/lungcancer.csv') recording preoperative diagnosis data and postoperative survival results of lung cancer surgery patients published in 2013 by Medical University in Poland.\n",
    ">> * The medical treatment file is in the table format, and the treatment information of a total of 470 patients is recorded.\n",
    ">> <img src = './images/asg4.png' width=600 height=200>\n",
    ">> * Each patient can be divided into rows, and the columns from A to Q are the degree of disease of each patient,\n",
    ">> * The last R column is the final survival status of the patient. 0 is survival, 1 is death.\n",
    ">2. Complete the following code\n",
    ">3. Add a description to each line of code\n",
    ">4. Save as a pdf file and upload on LMS and submit a hardcopy before the next class(1000am, 4/11/2023)"
   ]
  },
  {
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   "execution_count": null,
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   "source": [
    "# Predicting Survival Rates for Lung Cancer Surgery Patients\n",
    "# \n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# \n",
    "import numpy\n",
    "import tensorflow as tf\n",
    "\n",
    "# \n",
    "seed = \n",
    "numpy.random.seed(seed)\n",
    "tf.random.set_seed(seed) \n",
    "\n",
    "# \n",
    "Data_set = __________(\"_____lungcancer.csv\", delimiter=_____)\n",
    "\n",
    "# \n",
    "X = Data_set[______________]\n",
    "Y = Data_set[______________]\n",
    "\n",
    "# \n",
    "model = Sequential()\n",
    "model.add(Dense(30, input_dim=___, activation=________))\n",
    "model.add(Dense(1, activation=___________))\n",
    "\n",
    "# \n",
    "model.compile(loss=____________, optimizer=_______, metrics=['accuracy'])\n",
    "model.fit(X, Y, epochs=_____, batch_size=______)\n",
    "\n",
    "# \n",
    "print(\"\\n Accuracy: %.4f\" % (model.evaluate(X, Y)[1]))\n",
    "\n",
    "\n",
    "# -*- coding: utf-8 -*-\n",
    "# This is the part that allows you to use Hangul inside the code.\n",
    "\n",
    "# Load the keras function needed to drive deep learning.\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "# Load the required libraries.\n",
    "import numpy\n",
    "import tensorflow as tf\n",
    "\n",
    "# This is the part that you set to output the same result every time you run it.\n",
    "seed = 0\n",
    "numpy.random.seed(seed)\n",
    "tf.random.set_seed(seed) # old : tf.set_random_seed(seed)\n",
    "\n",
    "# Load Data\n",
    "Data_set = numpy.loadtxt(\"../data/lungcancer.csv\", delimiter=\",\")\n",
    "\n",
    "# Store patient records and surgical results, separated by X and Y.\n",
    "X = Data_set[:,0:17]\n",
    "Y = Data_set[:,17]\n",
    "\n",
    "# Determine the deep learning structure (this is where the model is set up and executed).\n",
    "model = Sequential()\n",
    "model.add(Dense(30, input_dim=17, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# Run deep learning.\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])\n",
    "model.fit(X, Y, epochs=30, batch_size=10)\n",
    "\n",
    "# Outputs the results (Accuracy)\n",
    "print(\"\\n Accuracy: %.4f\" % (model.evaluate(X, Y)[1]))"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Set Information:   https://archive.ics.uci.edu/ml/datasets/Thoracic+Surgery+Data\n",
    "\n",
    "The data was collected retrospectively at Wroclaw Thoracic Surgery Centre for patients who underwent major lung resections for primary lung cancer in the years 2007â€“2011. The Centre is associated with the Department of Thoracic Surgery of the Medical University of Wroclaw and Lower-Silesian Centre for Pulmonary Diseases, Poland, while the research database constitutes a part of the National Lung Cancer Registry, administered by the Institute of Tuberculosis and Pulmonary Diseases in Warsaw, Poland.\n",
    "\n",
    "### Attribute Information:\n",
    "\n",
    "1. DGN: Diagnosis - specific combination of ICD-10 codes for primary and secondary as well multiple tumours if any (DGN3,DGN2,DGN4,DGN6,DGN5,DGN8,DGN1)\n",
    "2. PRE4: Forced vital capacity - FVC (numeric)\n",
    "3. PRE5: Volume that has been exhaled at the end of the first second of forced expiration - FEV1 (numeric)\n",
    "4. PRE6: Performance status - Zubrod scale (PRZ2,PRZ1,PRZ0)\n",
    "5. PRE7: Pain before surgery (T,F)\n",
    "6. PRE8: Haemoptysis before surgery (T,F)\n",
    "7. PRE9: Dyspnoea before surgery (T,F)\n",
    "8. PRE10: Cough before surgery (T,F)\n",
    "9. PRE11: Weakness before surgery (T,F)\n",
    "10. PRE14: T in clinical TNM - size of the original tumour, from OC11 (smallest) to OC14 (largest) (OC11,OC14,OC12,OC13)\n",
    "11. PRE17: Type 2 DM - diabetes mellitus (T,F)\n",
    "12. PRE19: MI up to 6 months (T,F)\n",
    "13. PRE25: PAD - peripheral arterial diseases (T,F)\n",
    "14. PRE30: Smoking (T,F)\n",
    "15. PRE32: Asthma (T,F)\n",
    "16. AGE: Age at surgery (numeric)\n",
    "17. Risk1Y: 1 year survival period - (T)rue value if died (T,F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# This is the part that allows you to use Hangul inside the code.\n",
    "\n",
    "# Load the keras function needed to drive deep learning.\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.callbacks import EarlyStopping\n",
    "# Load the required libraries.\n",
    "import numpy\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "# This is the part that you set to output the same result every time you run it.\n",
    "seed = 0\n",
    "numpy.random.seed(seed)\n",
    "tf.random.set_seed(seed) # old : tf.set_random_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Data\n",
    "df = pd.read_csv('./data/lungcancer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
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     "execution_count": 167,
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   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store patient records and surgical results, separated by X and Y.\n",
    "X = df.iloc[:,0:17]\n",
    "Y = df.iloc[:,17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
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       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2.96</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>98</td>\n",
       "      <td>6</td>\n",
       "      <td>3.04</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>369</td>\n",
       "      <td>6</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>406</td>\n",
       "      <td>6</td>\n",
       "      <td>5.36</td>\n",
       "      <td>3.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>25</td>\n",
       "      <td>8</td>\n",
       "      <td>4.32</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>447</td>\n",
       "      <td>8</td>\n",
       "      <td>5.20</td>\n",
       "      <td>4.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>469 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     293  1   3.8   2.8  0  0.1  0.2  0.3  0.4  0.5  12  0.6  0.7  0.8  1.1  \\\n",
       "0      1  2  2.88  2.16  1    0    0    0    1    1  14    0    0    0    1   \n",
       "1      8  2  3.19  2.50  1    0    0    0    1    0  11    0    0    1    1   \n",
       "2     14  2  3.98  3.06  2    0    0    0    1    1  14    0    0    0    1   \n",
       "3     17  2  2.21  1.88  0    0    1    0    0    0  12    0    0    0    1   \n",
       "4     18  2  2.96  1.67  0    0    0    0    0    0  12    0    0    0    1   \n",
       "..   ... ..   ...   ... ..  ...  ...  ...  ...  ...  ..  ...  ...  ...  ...   \n",
       "464   98  6  3.04  2.40  2    0    0    0    1    0  11    0    0    0    1   \n",
       "465  369  6  3.88  2.72  1    0    0    0    1    0  12    0    0    0    1   \n",
       "466  406  6  5.36  3.96  1    0    0    0    1    0  12    0    0    0    0   \n",
       "467   25  8  4.32  3.20  0    0    0    0    0    0  11    0    0    0    0   \n",
       "468  447  8  5.20  4.10  0    0    0    0    0    0  12    0    0    0    0   \n",
       "\n",
       "     0.9  62  \n",
       "0      0  60  \n",
       "1      0  66  \n",
       "2      0  80  \n",
       "3      0  56  \n",
       "4      0  61  \n",
       "..   ...  ..  \n",
       "464    0  76  \n",
       "465    0  77  \n",
       "466    0  62  \n",
       "467    0  58  \n",
       "468    0  49  \n",
       "\n",
       "[469 rows x 17 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(469,)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 13.1021 - accuracy: 0.2141\n",
      "Epoch 2/50\n",
      "24/24 [==============================] - 0s 913us/step - loss: 1.6238 - accuracy: 0.8435\n",
      "Epoch 3/50\n",
      "24/24 [==============================] - 0s 912us/step - loss: 0.7174 - accuracy: 0.8293\n",
      "Epoch 4/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.5129 - accuracy: 0.8403\n",
      "Epoch 5/50\n",
      "24/24 [==============================] - 0s 861us/step - loss: 0.5695 - accuracy: 0.8089\n",
      "Epoch 6/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4641 - accuracy: 0.8631\n",
      "Epoch 7/50\n",
      "24/24 [==============================] - 0s 954us/step - loss: 0.4176 - accuracy: 0.8746\n",
      "Epoch 8/50\n",
      "24/24 [==============================] - 0s 951us/step - loss: 0.4628 - accuracy: 0.8538\n",
      "Epoch 9/50\n",
      "24/24 [==============================] - 0s 954us/step - loss: 0.4918 - accuracy: 0.8347\n",
      "Epoch 10/50\n",
      "24/24 [==============================] - 0s 984us/step - loss: 0.4568 - accuracy: 0.8345\n",
      "Epoch 11/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4621 - accuracy: 0.8444\n",
      "Epoch 12/50\n",
      "24/24 [==============================] - 0s 970us/step - loss: 0.4651 - accuracy: 0.8395\n",
      "Epoch 13/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4899 - accuracy: 0.8426\n",
      "Epoch 14/50\n",
      "24/24 [==============================] - 0s 980us/step - loss: 0.4555 - accuracy: 0.8541\n",
      "Epoch 15/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4678 - accuracy: 0.8348\n",
      "Epoch 16/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4797 - accuracy: 0.8284\n",
      "Epoch 17/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4615 - accuracy: 0.8536\n",
      "Epoch 18/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4361 - accuracy: 0.8559\n",
      "Epoch 19/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4675 - accuracy: 0.8390\n",
      "Epoch 20/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.3732 - accuracy: 0.8850\n",
      "Epoch 21/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4245 - accuracy: 0.8573\n",
      "Epoch 22/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4706 - accuracy: 0.8431\n",
      "Epoch 23/50\n",
      "24/24 [==============================] - 0s 954us/step - loss: 0.4867 - accuracy: 0.8527\n",
      "Epoch 24/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4632 - accuracy: 0.8501\n",
      "Epoch 25/50\n",
      "24/24 [==============================] - 0s 2ms/step - loss: 0.5031 - accuracy: 0.8207\n",
      "Epoch 26/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4843 - accuracy: 0.8408\n",
      "Epoch 27/50\n",
      "24/24 [==============================] - 0s 824us/step - loss: 0.4073 - accuracy: 0.8696\n",
      "Epoch 28/50\n",
      "24/24 [==============================] - 0s 911us/step - loss: 0.4175 - accuracy: 0.8590\n",
      "Epoch 29/50\n",
      "24/24 [==============================] - 0s 954us/step - loss: 0.4476 - accuracy: 0.8461\n",
      "Epoch 30/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4053 - accuracy: 0.8604\n",
      "Epoch 31/50\n",
      "24/24 [==============================] - 0s 910us/step - loss: 0.3569 - accuracy: 0.8847\n",
      "Epoch 32/50\n",
      "24/24 [==============================] - 0s 824us/step - loss: 0.4989 - accuracy: 0.8261\n",
      "Epoch 33/50\n",
      "24/24 [==============================] - 0s 824us/step - loss: 0.4536 - accuracy: 0.8372\n",
      "Epoch 34/50\n",
      "24/24 [==============================] - 0s 911us/step - loss: 0.4396 - accuracy: 0.8512\n",
      "Epoch 35/50\n",
      "24/24 [==============================] - 0s 954us/step - loss: 0.4020 - accuracy: 0.8585\n",
      "Epoch 36/50\n",
      "24/24 [==============================] - 0s 867us/step - loss: 0.4366 - accuracy: 0.8429\n",
      "Epoch 37/50\n",
      "24/24 [==============================] - 0s 781us/step - loss: 0.4143 - accuracy: 0.8571\n",
      "Epoch 38/50\n",
      "24/24 [==============================] - 0s 780us/step - loss: 0.4845 - accuracy: 0.8362\n",
      "Epoch 39/50\n",
      "24/24 [==============================] - 0s 867us/step - loss: 0.4316 - accuracy: 0.8481\n",
      "Epoch 40/50\n",
      "24/24 [==============================] - 0s 911us/step - loss: 0.4160 - accuracy: 0.8512\n",
      "Epoch 41/50\n",
      "24/24 [==============================] - 0s 824us/step - loss: 0.4153 - accuracy: 0.8633\n",
      "Epoch 42/50\n",
      "24/24 [==============================] - 0s 824us/step - loss: 0.4331 - accuracy: 0.8446\n",
      "Epoch 43/50\n",
      "24/24 [==============================] - 0s 840us/step - loss: 0.4417 - accuracy: 0.8370\n",
      "Epoch 44/50\n",
      "24/24 [==============================] - 0s 824us/step - loss: 0.4438 - accuracy: 0.8391\n",
      "Epoch 45/50\n",
      "24/24 [==============================] - 0s 910us/step - loss: 0.4987 - accuracy: 0.8134\n",
      "Epoch 46/50\n",
      "24/24 [==============================] - 0s 867us/step - loss: 0.4204 - accuracy: 0.8425\n",
      "Epoch 47/50\n",
      "24/24 [==============================] - 0s 954us/step - loss: 0.4575 - accuracy: 0.8364\n",
      "Epoch 48/50\n",
      "24/24 [==============================] - 0s 997us/step - loss: 0.4149 - accuracy: 0.8577\n",
      "Epoch 49/50\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 0.4833 - accuracy: 0.8135\n",
      "Epoch 50/50\n",
      "24/24 [==============================] - 0s 692us/step - loss: 0.4604 - accuracy: 0.8358\n",
      "15/15 [==============================] - 0s 713us/step - loss: 0.4327 - accuracy: 0.8529\n",
      "\n",
      " Accuracy: 0.8529\n"
     ]
    }
   ],
   "source": [
    "# Build the model (Determine the deep learning structure)\n",
    "model = Sequential()\n",
    "model.add(Dense(30, input_dim=X.shape[1], activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(X, Y, epochs=50, batch_size=20)\n",
    "\n",
    "# Evaluate the model\n",
    "print(\"\\n Accuracy: %.4f\" % (model.evaluate(X, Y)[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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": [
    "# Plot the training and validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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": [
    "# Plot Accuracy\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "During the training process of a deep learning model, the model is updated based on the gradients computed from the loss function with respect to the model parameters using the training data. The purpose of the training process is to make the model learn the patterns and features of the training data so that it can generalize well to new data. However, if the model only learns the training data too well, it may not be able to perform well on new, unseen data, a phenomenon called overfitting.\n",
    "\n",
    "To prevent overfitting, we use a validation set to evaluate the model's performance on data it has not seen during training. The validation set is a portion of the original dataset that is held out and not used in the training process. Instead, it is used to evaluate the model's performance after each epoch of training.\n",
    "\n",
    "In the context of the output \"15/15\", this means that the model has evaluated its performance on all 15 batches of the validation data after the training for that epoch. This allows us to monitor the model's performance on new data and detect if it is overfitting or not. If the validation accuracy is much lower than the training accuracy, it may indicate that the model is overfitting and we need to take some measures such as early stopping, regularization, or changing the model architecture to prevent overfitting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Data_set2 = numpy.loadtxt(\"./data/lungcancer.csv\", delimiter=\",\")\n",
    "X2 = Data_set2[:,0:17]\n",
    "Y2 = Data_set2[:,17]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X2, Y2, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model (Determine the deep learning structure)\n",
    "model2 = Sequential([\n",
    "    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),\n",
    "    Dropout(0.5),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dropout(0.5),\n",
    "    Dense(1, activation='sigmoid')\n",
    "])\n",
    "model2.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "15/15 [==============================] - 0s 11ms/step - loss: 11.0137 - accuracy: 0.6195 - val_loss: 2.4502 - val_accuracy: 0.8816\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 7.0263 - accuracy: 0.7283 - val_loss: 3.5683 - val_accuracy: 0.8816\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 6.6287 - accuracy: 0.7359 - val_loss: 3.6468 - val_accuracy: 0.8947\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 7.5526 - accuracy: 0.7358 - val_loss: 2.7874 - val_accuracy: 0.9079\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 6.4772 - accuracy: 0.7058 - val_loss: 2.3311 - val_accuracy: 0.9079\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 5.0211 - accuracy: 0.7244 - val_loss: 1.9656 - val_accuracy: 0.9079\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 3.8962 - accuracy: 0.7354 - val_loss: 1.6808 - val_accuracy: 0.9079\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 4.3683 - accuracy: 0.7193 - val_loss: 1.1401 - val_accuracy: 0.9079\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 21ms/step - loss: 4.1811 - accuracy: 0.7079 - val_loss: 0.8141 - val_accuracy: 0.9079\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 2.7320 - accuracy: 0.7445 - val_loss: 0.6094 - val_accuracy: 0.9079\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 2.5004 - accuracy: 0.7588 - val_loss: 0.5508 - val_accuracy: 0.9079\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.7075 - accuracy: 0.7696 - val_loss: 0.5746 - val_accuracy: 0.9079\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 2.5090 - accuracy: 0.7273 - val_loss: 0.5171 - val_accuracy: 0.9079\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.9492 - accuracy: 0.6716 - val_loss: 0.4959 - val_accuracy: 0.8816\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.6650 - accuracy: 0.7247 - val_loss: 0.4817 - val_accuracy: 0.8158\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.1360 - accuracy: 0.7574 - val_loss: 0.4792 - val_accuracy: 0.8158\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.8452 - accuracy: 0.6994 - val_loss: 0.4875 - val_accuracy: 0.8947\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 1.0540 - accuracy: 0.7802 - val_loss: 0.4901 - val_accuracy: 0.9079\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.3747 - accuracy: 0.7104 - val_loss: 0.4546 - val_accuracy: 0.9079\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.8500 - accuracy: 0.7539 - val_loss: 0.4533 - val_accuracy: 0.9079\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 1.0871 - accuracy: 0.7765 - val_loss: 0.4910 - val_accuracy: 0.9079\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.9186 - accuracy: 0.7924 - val_loss: 0.4948 - val_accuracy: 0.9079\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.7430 - accuracy: 0.8050 - val_loss: 0.4963 - val_accuracy: 0.9079\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 1.1695 - accuracy: 0.7820 - val_loss: 0.5302 - val_accuracy: 0.9079\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.9247 - accuracy: 0.8046 - val_loss: 0.5077 - val_accuracy: 0.9079\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6830 - accuracy: 0.8291 - val_loss: 0.4747 - val_accuracy: 0.9079\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.7854 - accuracy: 0.8026 - val_loss: 0.4538 - val_accuracy: 0.9079\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.8321 - accuracy: 0.8060 - val_loss: 0.4696 - val_accuracy: 0.9079\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 0.6984 - accuracy: 0.8313 - val_loss: 0.4802 - val_accuracy: 0.9079\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 4ms/step - loss: 0.9456 - accuracy: 0.8045 - val_loss: 0.4972 - val_accuracy: 0.9079\n",
      "3/3 [==============================] - 0s 1ms/step - loss: 0.5314 - accuracy: 0.8404\n",
      "Accuracy: 0.8404\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "early_stop = EarlyStopping(patience=10, restore_best_weights=True)\n",
    "# history = model2.fit(X_train, y_train, epochs=100, batch_size=10, validation_split=0.2)\n",
    "history = model2.fit(X_train, y_train, epochs=50, batch_size=20, validation_split=0.2, callbacks=[early_stop])\n",
    "\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model2.evaluate(X_test, y_test)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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": [
    "# Plot the training and validation loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Dense(64, input_dim=17, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "# Compile model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# Fit the model\n",
    "history = model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, verbose=0)\n",
    "# list all data in history\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "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"
    },
    {
     "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": [
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-fold cross validation  \n",
    "### Log Loss  \n",
    "### Batch, Iteration, Epoch  \n",
    "\n",
    "### Final Project"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3.6 (tensorflow)",
   "language": "python",
   "name": "jeff_heaton_t81_park"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}