claim_model_LRC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression Classifyer\n",
    "___\n",
    "\n",
    "This model is based on:\n",
    "\n",
    "```Bibtex\n",
    "@inproceedings{levyContextDependentClaim2014a,\n",
    "  title = {Context Dependent Claim Detection},\n",
    "  author = {Levy, Ran and Bilu, Yonatan and Hershcovich, Daniel and Aharoni, Ehud and Slonim, Noam},\n",
    "  date = {2014},\n",
    "  url = {https://aclanthology.org/C14-1141/},\n",
    "}\n",
    "```\n",
    "\n",
    "Features:\n",
    "- sentence-topic similarity\n",
    "- Linguistic expansion\n",
    "- Keyword that\n",
    "- sentiment\n",
    "- subjectivity\n",
    "\n",
    "Parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import wandb\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (accuracy_score, classification_report, f1_score,\n",
    "                             precision_score, recall_score)\n",
    "from sklearn.pipeline import FeatureUnion, Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from config import DATASETS, PROJECT_NAME\n",
    "from src.dataset import load_dataset\n",
    "from src.features import (SentenceTopicSimilarity, Sentiment, Subjectivity,\n",
    "                          ThatToken)\n",
    "from src.evaluation import confusion_matrix_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_NAME = \"LogisticRegression\"\n",
    "TRACKING = True\n",
    "FALSE_CLASS_BALANCE = 1.0\n",
    "\n",
    "dataset = DATASETS[\"dataset_2014\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = load_dataset(dataset_path=os.path.join(dataset[\"base_path\"], dataset[\"data\"]), false_class_balance=FALSE_CLASS_BALANCE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Encode features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_features = FeatureUnion(transformer_list=[(\"tf-idf\", TfidfVectorizer())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "column_trans = ColumnTransformer(\n",
    "    [\n",
    "        (\"tf-idf\", text_features, \"Sentence\"),\n",
    "        (\"that\", ThatToken(), \"Sentence\"),\n",
    "        (\"sentiment\", Sentiment(), \"Sentence\"),\n",
    "        (\"subjectivity\", Subjectivity(), \"Sentence\"),\n",
    "        (\"similarity\", SentenceTopicSimilarity(), [\"Sentence\", \"Article\"]),\n",
    "    ],\n",
    "    remainder=\"drop\",\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = Pipeline(\n",
    "    [\n",
    "        (\"preprocessing\", column_trans),\n",
    "        (\"scaler\", StandardScaler(with_mean=False)),\n",
    "        (\"classify\", LogisticRegression(max_iter=200)),\n",
    "    ],\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mjueri\u001b[0m (use `wandb login --relogin` to force relogin)\n",
      "/home/jueri/.local/share/virtualenvs/claim_model_comparison-fePjuQRa/lib/python3.8/site-packages/IPython/html.py:12: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  warn(\"The `IPython.html` package has been deprecated since IPython 4.0. \"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                    Syncing run <strong><a href=\"https://wandb.ai/jueri/Claim%20detection%20models/runs/owzzlv35\" target=\"_blank\">chocolate-wave-63</a></strong> to <a href=\"https://wandb.ai/jueri/Claim%20detection%20models\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
       "\n",
       "                "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if TRACKING:\n",
    "    wandb.init(project=PROJECT_NAME,\n",
    "            config={\n",
    "                \"model\": MODEL_NAME,\n",
    "                \"setup\": str(pipe.get_feature_names_out),\n",
    "                \"dataset\": dataset[\"name\"],\n",
    "                \"train_data_size\": len(X_train),\n",
    "                \"validation_data_size\": 0,\n",
    "                \"test_data_size\": len(X_test),\n",
    "                \"batch_size\": None,\n",
    "                \"learning_rate\": None,\n",
    "                \"epochs\": None,\n",
    "                \"false_class_balance\": FALSE_CLASS_BALANCE\n",
    "            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ColumnTransformer] ........ (1 of 5) Processing tf-idf, total=   0.1s\n",
      "[ColumnTransformer] .......... (2 of 5) Processing that, total=   0.0s\n",
      "[ColumnTransformer] ..... (3 of 5) Processing sentiment, total=   0.4s\n",
      "[ColumnTransformer] .. (4 of 5) Processing subjectivity, total=   0.4s\n",
      "[ColumnTransformer] .... (5 of 5) Processing similarity, total=   2.1s\n",
      "[Pipeline] ..... (step 1 of 3) Processing preprocessing, total=   2.9s\n",
      "[Pipeline] ............ (step 2 of 3) Processing scaler, total=   0.0s\n",
      "[Pipeline] .......... (step 3 of 3) Processing classify, total=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('preprocessing',\n",
       "                 ColumnTransformer(transformers=[('tf-idf',\n",
       "                                                  FeatureUnion(transformer_list=[('tf-idf',\n",
       "                                                                                  TfidfVectorizer())]),\n",
       "                                                  'Sentence'),\n",
       "                                                 ('that', ThatToken(),\n",
       "                                                  'Sentence'),\n",
       "                                                 ('sentiment', Sentiment(),\n",
       "                                                  'Sentence'),\n",
       "                                                 ('subjectivity',\n",
       "                                                  Subjectivity(), 'Sentence'),\n",
       "                                                 ('similarity',\n",
       "                                                  SentenceTopicSimilarity(),\n",
       "                                                  ['Sentence', 'Article'])],\n",
       "                                   verbose=True)),\n",
       "                ('scaler', StandardScaler(with_mean=False)),\n",
       "                ('classify', LogisticRegression(max_iter=200))],\n",
       "         verbose=True)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Predict results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Evaluate results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.74      0.71      0.72       235\n",
      "        True       0.74      0.78      0.76       259\n",
      "\n",
      "    accuracy                           0.74       494\n",
      "   macro avg       0.74      0.74      0.74       494\n",
      "weighted avg       0.74      0.74      0.74       494\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = f1_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "accuracy = accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "Error",
     "evalue": "You must call wandb.init() before wandb.log()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mError\u001b[0m                                     Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_7820/498450173.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mTRACKING\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'test_f1'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'test_recall'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mrecall\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'test_precision'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprecision\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'test_accuracy'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/share/virtualenvs/claim_model_comparison-fePjuQRa/lib/python3.8/site-packages/wandb/sdk/lib/preinit.py\u001b[0m in \u001b[0;36mpreinit_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mPreInitCallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdestination\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# noqa: N802\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpreinit_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You must call wandb.init() before {}()\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mpreinit_wrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mError\u001b[0m: You must call wandb.init() before wandb.log()"
     ]
    }
   ],
   "source": [
    "if TRACKING:\n",
    "    wandb.log({'test_f1': f1})\n",
    "    wandb.log({'test_recall': recall})\n",
    "    wandb.log({'test_precision': precision})\n",
    "    wandb.log({'test_accuracy': accuracy})\n",
    "    wandb.finish()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "confusion_matrix_plot(y_test, y_pred, label=[False, True], title=MODEL_NAME+\" confusion matrix\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
  },
  "kernelspec": {
   "display_name": "Python 3.9.9 64-bit",
   "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": "3.8.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}