{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example from https://towardsdatascience.com/replicate-a-logistic-regression-model-as-an-artificial-neural-network-in-keras-cd6f49cf4b2c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import InputLayer\n",
    "from tensorflow.keras.layers import Dense\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 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",
       "    </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>25.38</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>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",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer = load_breast_cancer()\n",
    "df = pd.DataFrame(cancer.data,\n",
    "                  columns=cancer.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = (df-df.mean())/df.std()\n",
    "# X = 2*(df-df.min())/(df.max()-df.min()) - 1\n",
    "# X = df\n",
    "y = pd.Series(cancer.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, \n",
    "                                                    shuffle=True, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.97\n",
      "Precision: 0.99\n",
      "Recall: 0.97\n",
      "F-Score: 0.98\n"
     ]
    }
   ],
   "source": [
    "# Initialize a logistic regression model\n",
    "log_reg_model = LogisticRegression(max_iter=2500,\n",
    "                                   random_state=42)\n",
    "\n",
    "# Train (fit) the model\n",
    "log_reg_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "y_pred = log_reg_model.predict(X_test) # Predictions\n",
    "y_true = y_test # True values\n",
    "\n",
    "# Model evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "import numpy as np\n",
    "\n",
    "print(\"Accuracy:\", np.round(accuracy_score(y_true, y_pred), 2))\n",
    "precision, recall, fscore, _ = precision_recall_fscore_support(y_true, y_pred,\n",
    "                                                               average='binary')\n",
    "print(\"Precision:\", np.round(precision, 2))\n",
    "print(\"Recall:\", np.round(recall, 2))\n",
    "print(\"F-Score:\", np.round(fscore, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(InputLayer(input_shape=(30, )))\n",
    "# No hidden layers\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.05)\n",
    "model.compile(optimizer=optimizer,\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-10-12 14:37:31.395427: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12/12 [==============================] - 0s 15ms/step - loss: 0.3646 - accuracy: 0.8407 - val_loss: 0.1326 - val_accuracy: 0.9341\n",
      "Epoch 2/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0995 - accuracy: 0.9643 - val_loss: 0.0896 - val_accuracy: 0.9560\n",
      "Epoch 3/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0793 - accuracy: 0.9808 - val_loss: 0.0776 - val_accuracy: 0.9670\n",
      "Epoch 4/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0734 - accuracy: 0.9808 - val_loss: 0.0749 - val_accuracy: 0.9560\n",
      "Epoch 5/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0705 - accuracy: 0.9780 - val_loss: 0.0735 - val_accuracy: 0.9560\n",
      "Epoch 6/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.0684 - accuracy: 0.9808 - val_loss: 0.0713 - val_accuracy: 0.9670\n",
      "Epoch 7/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0667 - accuracy: 0.9835 - val_loss: 0.0687 - val_accuracy: 0.9780\n",
      "Epoch 8/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0653 - accuracy: 0.9835 - val_loss: 0.0665 - val_accuracy: 0.9670\n",
      "Epoch 9/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0640 - accuracy: 0.9835 - val_loss: 0.0648 - val_accuracy: 0.9670\n",
      "Epoch 10/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.0628 - accuracy: 0.9835 - val_loss: 0.0635 - val_accuracy: 0.9670\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x160461eb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train,\n",
    "          epochs=10, batch_size=32,\n",
    "          validation_split=0.2,\n",
    "          shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 0.0719 - accuracy: 0.9737\n",
      "Test loss: 0.07194967567920685\n",
      "Test accuracy: 0.9736841917037964\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
    "print(\"Test loss:\", test_loss)\n",
    "print(\"Test accuracy:\", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def zigmoid(x):\n",
    "    return 0.502073021 + 0.198695283 * x - 0.001570683 * x**2 - 0.004001354 * x**3\n",
    "zigmoid_model = Sequential()\n",
    "\n",
    "zigmoid_model.add(InputLayer(input_shape=(30, )))\n",
    "# No hidden layers\n",
    "zigmoid_model.add(Dense(1, activation=zigmoid))\n",
    "\n",
    "optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.05)\n",
    "zigmoid_model.compile(optimizer=optimizer,\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "12/12 [==============================] - 0s 11ms/step - loss: 1.1248 - accuracy: 0.7720 - val_loss: 0.5485 - val_accuracy: 0.9011\n",
      "Epoch 2/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.5224 - accuracy: 0.9121 - val_loss: 0.2269 - val_accuracy: 0.9780\n",
      "Epoch 3/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2841 - accuracy: 0.9423 - val_loss: 0.1875 - val_accuracy: 0.9560\n",
      "Epoch 4/10\n",
      "12/12 [==============================] - 0s 3ms/step - loss: 0.2715 - accuracy: 0.9588 - val_loss: 0.1772 - val_accuracy: 0.9560\n",
      "Epoch 5/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2591 - accuracy: 0.9560 - val_loss: 0.1714 - val_accuracy: 0.9670\n",
      "Epoch 6/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2549 - accuracy: 0.9560 - val_loss: 0.1657 - val_accuracy: 0.9670\n",
      "Epoch 7/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2559 - accuracy: 0.9533 - val_loss: 0.1639 - val_accuracy: 0.9670\n",
      "Epoch 8/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2508 - accuracy: 0.9533 - val_loss: 0.1605 - val_accuracy: 0.9670\n",
      "Epoch 9/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2475 - accuracy: 0.9560 - val_loss: 0.1590 - val_accuracy: 0.9670\n",
      "Epoch 10/10\n",
      "12/12 [==============================] - 0s 2ms/step - loss: 0.2490 - accuracy: 0.9588 - val_loss: 0.1591 - val_accuracy: 0.9560\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1604f03a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zigmoid_model.fit(X_train, y_train,\n",
    "          epochs=10, batch_size=32,\n",
    "          validation_split=0.2,\n",
    "          shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 1ms/step - loss: 0.1795 - accuracy: 0.9561\n",
      "Test loss: 0.1795477569103241\n",
      "Test accuracy: 0.9561403393745422\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = zigmoid_model.evaluate(X_test, y_test)\n",
    "print(\"Test loss:\", test_loss)\n",
    "print(\"Test accuracy:\", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "sklearn",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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