logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "62db0eaa",
   "metadata": {
    "id": "62db0eaa",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:09.642076900Z",
     "start_time": "2024-11-27T05:53:09.496166900Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e66a9cf95b568f9a",
   "metadata": {
    "id": "e66a9cf95b568f9a",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:09.685464300Z",
     "start_time": "2024-11-27T05:53:09.507344100Z"
    }
   },
   "outputs": [],
   "source": [
    "# load breast cancer\n",
    "data = load_breast_cancer()\n",
    "indices = np.random.permutation(len(data.target))\n",
    "data.data, data.target = data.data[indices], data.target[indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "[data sepecification](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html)"
   ],
   "metadata": {
    "collapsed": false,
    "id": "a6c2ddd45323b7e7"
   },
   "id": "a6c2ddd45323b7e7"
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "outputs": [],
   "source": [
    "# define Dataset\n",
    "class Dataset:\n",
    "    def __init__(self, dataset):\n",
    "      self.x, self.y = dataset\n",
    "    def __getitem__(self, item): return self.x[item], self.y[item]\n",
    "    def __len__(self): return len(self.y)\n",
    "# Dataset\n",
    "\n",
    "def dataset_visualize(dataset: Dataset):\n",
    "  for x, y in dataset: print(f\"{'-' * 16}\\nx: {x}\\ny: {y}\")\n",
    "# dataset_visualize\n",
    "\n",
    "# init Datasets\n",
    "trainset = Dataset((data.data[:], data.target[:]))\n",
    "testset = Dataset((data.data[0:100], data.target[0:100]))\n",
    "###dataset_visualize(trainset)"
   ],
   "metadata": {
    "id": "70149775527c0926",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:09.687441900Z",
     "start_time": "2024-11-27T05:53:09.535414300Z"
    }
   },
   "id": "70149775527c0926"
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8e431387",
   "metadata": {
    "id": "8e431387",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:09.687441900Z",
     "start_time": "2024-11-27T05:53:09.553703600Z"
    }
   },
   "outputs": [],
   "source": [
    "def sigmoid(x): return 1 / (1 + np.exp(-1 * np.clip(x, -1e2, 1e2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "outputs": [],
   "source": [
    "class LogisticRegression:\n",
    "    def __init__(self, n_inpt, act_func):\n",
    "      self.weight = np.zeros(shape=(n_inpt))\n",
    "      self.act_func = act_func\n",
    "    # __init__\n",
    "\n",
    "    def gdr(self, x, y, lr):\n",
    "      indications = self.__call__(x)\n",
    "      self.weight -= (lr / x.shape[0]) * np.dot(x.T, (indications - y))\n",
    "    # gdr\n",
    "\n",
    "    def train(self, dataset, iters: int, lr=0.01):\n",
    "        for _ in range(iters):\n",
    "            for x, y in dataset:\n",
    "                self.gdr(x, y, lr=lr)\n",
    "    # train\n",
    "\n",
    "    def __call__(self, x): return sigmoid(np.dot(x, self.weight))\n",
    "\n",
    "    def loss(self, dataset):\n",
    "        x, y = dataset.x, dataset.y\n",
    "        probability = self.__call__(x)\n",
    "        loss = np.mean((y - probability) ** 2)\n",
    "        print(f\"loss: {loss:.3f}\")\n",
    "    # loss\n",
    "# LogisticRegression"
   ],
   "metadata": {
    "id": "9ac7d18d7d81de70",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:09.688464Z",
     "start_time": "2024-11-27T05:53:09.566065600Z"
    }
   },
   "id": "9ac7d18d7d81de70"
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f2becd52",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2becd52",
    "outputId": "92b1e3ad-635d-4d1f-b3c2-e8f963ddea59",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:10.944161Z",
     "start_time": "2024-11-27T05:53:09.580883200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 0.250\n",
      "loss: 0.057\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(len(trainset.x[0]), sigmoid)\n",
    "model.loss(trainset)\n",
    "model.train(trainset, iters=100, lr=0.0001)\n",
    "model.loss(testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4f4b9b467cabc899",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 579
    },
    "id": "4f4b9b467cabc899",
    "outputId": "5432aa6b-fd41-487b-e3d3-11bfbc4ee991",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:11.368356800Z",
     "start_time": "2024-11-27T05:53:10.945901300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x11b52ac47d0>"
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# init a testset\n",
    "index = random.randrange(0, 50)\n",
    "test_x, test_y = testset[index]\n",
    "\n",
    "malignant = np.array([list(trainset.x[_]) for _ in range(trainset.__len__()) if trainset.y[_] == 0])\n",
    "benign = np.array([list(trainset.x[_]) for _ in range(trainset.__len__()) if trainset.y[_] == 1])\n",
    "pred = model(test_x)\n",
    "\n",
    "fig, ax1 = plt.subplots(1, 1, figsize=(12, 6))\n",
    "ax1.set_xlabel('radius')\n",
    "ax1.set_ylabel('texture')\n",
    "ax1.grid(True, alpha=0.25)\n",
    "\n",
    "ax1.scatter(x=benign[:, 0], y=benign[:, 1], s=5, alpha=1, c=\"yellow\", label=\"WDBC-Benign\")\n",
    "ax1.scatter(x=malignant[:, 0], y=malignant[:, 1], s=5, alpha=1, c=\"violet\", label=\"WDBC-Malignant\")\n",
    "ax1.scatter(x=test_x[0], y=test_x[1], s=100, marker=\"+\", alpha=1, c=\"r\", label=f\"prediction\")\n",
    "ax1.legend(fontsize=\"10\", loc=\"best\", title=\"Breast cancer wisconsin\")"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(f\"predicted class: {pred:.2f} actual class: {test_y}\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rfkrlg6wXsti",
    "outputId": "bba3d83d-9f28-41b3-db48-3aff2c1f137a",
    "ExecuteTime": {
     "end_time": "2024-11-27T05:53:11.381692800Z",
     "start_time": "2024-11-27T05:53:11.374442500Z"
    }
   },
   "id": "Rfkrlg6wXsti",
   "execution_count": 59,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted class: 0.89 actual class: 1\n"
     ]
    }
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "colab": {
   "provenance": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}