08_logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b694dee4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:33:57.385901Z",
     "start_time": "2024-04-08T13:33:57.373760Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "542aadbd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:33:57.417217Z",
     "start_time": "2024-04-08T13:33:57.390401Z"
    }
   },
   "outputs": [],
   "source": [
    "# 0) Prepare data\n",
    "bc = datasets.load_breast_cancer()\n",
    "X, y = bc.data, bc.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "778e5fcb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:33:57.432439Z",
     "start_time": "2024-04-08T13:33:57.423298Z"
    }
   },
   "outputs": [],
   "source": [
    "n_samples, n_features = X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b168b23e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:33:57.447683Z",
     "start_time": "2024-04-08T13:33:57.435518Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "68c0d72c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:33:57.462703Z",
     "start_time": "2024-04-08T13:33:57.452705Z"
    }
   },
   "outputs": [],
   "source": [
    "# scale\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6b4a12c8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:33:57.477936Z",
     "start_time": "2024-04-08T13:33:57.465706Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train = torch.from_numpy(X_train.astype(np.float32))\n",
    "X_test = torch.from_numpy(X_test.astype(np.float32))\n",
    "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
    "y_test = torch.from_numpy(y_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e13d482b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-08T13:35:16.282470Z",
     "start_time": "2024-04-08T13:35:16.246986Z"
    },
    "lines_to_next_cell": 1
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.]])\n",
      "tensor([[1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [1.],\n",
      "        [0.],\n",
      "        [0.]])\n"
     ]
    }
   ],
   "source": [
    "y_train = y_train.view(y_train.shape[0], 1)\n",
    "print(y_train)\n",
    "y_test = y_test.view(y_test.shape[0], 1)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3b1b8b7",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "# 1) Model\n",
    "# Linear model f = wx + b , sigmoid at the end\n",
    "class Model(nn.Module):\n",
    "    def __init__(self, n_input_features):\n",
    "        super(Model, self).__init__()\n",
    "        self.linear = nn.Linear(n_input_features, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        y_pred = torch.sigmoid(self.linear(x))\n",
    "        return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cd8f9ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(n_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df93f0c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2) Loss and optimizer\n",
    "num_epochs = 100\n",
    "learning_rate = 0.01\n",
    "criterion = nn.BCELoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9198d44b",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 10, loss = 0.6068\n",
      "epoch: 20, loss = 0.4985\n",
      "epoch: 30, loss = 0.4305\n",
      "epoch: 40, loss = 0.3837\n",
      "epoch: 50, loss = 0.3494\n",
      "epoch: 60, loss = 0.3228\n",
      "epoch: 70, loss = 0.3016\n",
      "epoch: 80, loss = 0.2841\n",
      "epoch: 90, loss = 0.2694\n",
      "epoch: 100, loss = 0.2567\n"
     ]
    }
   ],
   "source": [
    "# 3) Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    # Forward pass and loss\n",
    "    y_pred = model(X_train)\n",
    "    loss = criterion(y_pred, y_train)\n",
    "\n",
    "    # Backward pass and update\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # zero grad before new step\n",
    "    optimizer.zero_grad()\n",
    "\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f'epoch: {epoch+1}, loss = {loss.item():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38830728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.3684\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    y_predicted = model(X_test)\n",
    "    y_predicted_cls = y_predicted.round()\n",
    "    acc = y_predicted_cls.eq(y_test).sum() / float(y_test.shape[0])\n",
    "    print(f'accuracy: {acc.item():.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e55d638e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ee5a23c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "main_language": "python",
   "notebook_metadata_filter": "-all"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}