fb_2022/02_fb2022_inf_binary.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4f5e2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this model has a problem, it classifies most ads as one category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "686fdddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.model_selection as ms\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from joblib import dump, load\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "23aec1f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_models = '../models/binary_rf_v1'\n",
    "path_inference_set = 'data/fb_22_for_inf.csv'\n",
    "path_inference_set_output = 'data/fb_22_output_binary.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01c048d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#----\n",
    "# Load the inference dataset\n",
    "text_field = 'text'\n",
    "df = pd.read_csv(path_inference_set)\n",
    "df = df.dropna(subset = [text_field]) # remove NAs\n",
    "df = df[df[text_field] != '_error'] # remove errors\n",
    "df = df.reset_index(drop = True)\n",
    "\n",
    "# Load the variable labels\n",
    "with open('../data/issue_labels_65.txt', 'r') as reader:\n",
    "  labels = reader.read().split('\\n')\n",
    "labels = labels[:-1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f1f8327a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Making Predictions: 100%|███████████████████████████████████████████████| 11526320/11526320 [5:20:04<00:00, 600.18it/s]\n"
     ]
    }
   ],
   "source": [
    "preds_l = []\n",
    "\n",
    "# Batch size for predictions\n",
    "batch_size = 512\n",
    "\n",
    "# Create a single progress bar for both loops\n",
    "total_labels = len(labels)\n",
    "total_rows = len(df[text_field])\n",
    "\n",
    "with tqdm(total=total_labels * total_rows, desc='Making Predictions') as pbar:\n",
    "    for l in labels:\n",
    "        clf_rf = load(dir_models + '/issues_rf_' + l + '.joblib')\n",
    "\n",
    "        for i in range(0, total_rows, batch_size):\n",
    "            # Batch predict using NumPy for efficiency\n",
    "            batch_transcripts = df[text_field][i:i + batch_size].values\n",
    "            batch_predictions = clf_rf.predict(batch_transcripts)\n",
    "            \n",
    "            preds_l.extend(batch_predictions)\n",
    "            pbar.update(len(batch_transcripts))  # Update the single progress bar for each batch\n",
    "\n",
    "# Ensure the progress bar reaches 100% at the end\n",
    "pbar.n = total_labels * total_rows\n",
    "pbar.last_print_n = total_labels * total_rows\n",
    "pbar.refresh()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7ca61359",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_preds = pd.DataFrame({y: x for x, y in zip(preds_l, labels)}, index=df.index)\n",
    "df_results = pd.concat([df, df_preds], axis=1)\n",
    "\n",
    "df_results.to_csv(path_inference_set_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "261857b0",
   "metadata": {},
   "outputs": [
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       "                                                     text  \\\n",
       "0               Demand, I will say for computer coding...   \n",
       "1              Good afternoon.  Good afternoon. We're ...   \n",
       "2            FOR PUBLIC EDUCATION PAID FOR BY JIM PORT...   \n",
       "3            Welcome back, Madam Secretary mr. Deputy ...   \n",
       "4           Clark Tina Peters did her job and that is ...   \n",
       "...                                                   ...   \n",
       "177323  对 2022 年环境债券法进行投票\\n债券法案将于11月8日进行投票。\\n您可以选择缺席投票...   \n",
       "177324                                             選票提案 1   \n",
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       "177326                                           제안 1에 투표   \n",
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       "\n",
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