linear_bin_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- coding: utf-8 -*-\n",
    "#\n",
    "# Copyright (c) 2022 Milan Ondrašovič <milan.ondrasovic@gmail.com>\n",
    "#\n",
    "# MIT License\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "# of this software and associated documentation files (the \"Software\"), to deal\n",
    "# in the Software without restriction, including without limitation the rights\n",
    "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
    "# copies of the Software, and to permit persons to whom the Software is\n",
    "# furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in all\n",
    "# copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, ConfusionMatrixDisplay\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import config as cfg\n",
    "from dataset import (\n",
    "    load_dataset_and_extract_features, bin_class_labeL_getter\n",
    ")\n",
    "from visual import plot_linear_feature_importance\n",
    "\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_names = ['no', 'yes']\n",
    "label_getter = bin_class_labeL_getter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1298/1298 [01:38<00:00, 13.13it/s]\n",
      "100%|██████████| 319/319 [00:21<00:00, 14.68it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((1298, 192), (319, 192))"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_train, labels_train = load_dataset_and_extract_features(\n",
    "    cfg.TRAIN_DIR_PATH, label_getter\n",
    ")\n",
    "features_test, labels_test = load_dataset_and_extract_features(\n",
    "    cfg.TEST_DIR_PATH, label_getter\n",
    ")\n",
    "\n",
    "features_train.shape, features_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Pipeline] ............ (step 1 of 2) Processing scaler, total=   0.0s\n",
      "[Pipeline] ............. (step 2 of 2) Processing model, total=   0.1s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('model',\n",
       "                 LogisticRegression(max_iter=5000, random_state=731995))],\n",
       "         verbose=True)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(max_iter=5000, random_state=731995)\n",
    "pipeline = Pipeline(\n",
    "    [\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('model', model)\n",
    "    ],\n",
    "    verbose=True\n",
    ")\n",
    "pipeline.fit(features_train, labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "          no       1.00      0.97      0.98        29\n",
      "         yes       1.00      1.00      1.00       290\n",
      "\n",
      "    accuracy                           1.00       319\n",
      "   macro avg       1.00      0.98      0.99       319\n",
      "weighted avg       1.00      1.00      1.00       319\n",
      "\n"
     ]
    }
   ],
   "source": [
    "labels_pred = pipeline.predict(features_test)\n",
    "print(classification_report(labels_test, labels_pred, target_names=label_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay.from_predictions(\n",
    "    labels_test, labels_pred, display_labels=label_names\n",
    ")\n",
    "disp.plot()\n",
    "plt.savefig('CM_bin_linear_classifer.png', dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\venvs\\siam-mot\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_linear_feature_importance(model)\n",
    "fig.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "f91a5d6e4747d83f2ca8abaaaf7aacd52eac54e528eee770e3eb228d51a3694d"
  },
  "kernelspec": {
   "display_name": "Python 3.7.9 64-bit (system)",
   "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": "3.7.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}