01_text_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#|hide\n",
    "%pip install -Uqq pandas seaborn ds_tut scikit-learn\n",
    "\n",
    "import ds_tut\n",
    "ds_tut.setup()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import pickle\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification (Reuters-21578)\n",
    "\n",
    "## Additional Information About the Dataset\n",
    "\n",
    "- [Paper comparing different text categorization methods](https://www.cs.cornell.edu/people/tj/publications/joachims_98a.pdf) (Thorsten Joachims 1998)\n",
    "- [Dataset Readme](http://www.daviddlewis.com/resources/testcollections/reuters21578/readme.txt)\n",
    "- [Link to Dataset Card on Hugging Face](https://huggingface.co/datasets/reuters21578)\n",
    "\n",
    "Maybe just use the dataset from Hugging Face instead of downloading and parsing manually? TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from ds_tut import download_from_url\n",
    "\n",
    "archive_name = \"reuters21578.tar.gz\"\n",
    "training_data_url = \"http://www.daviddlewis.com/resources/testcollections/reuters21578/{}\".format(archive_name)\n",
    "data_root = Path.cwd() / \"data\" / \"tmp\"\n",
    "data_root.mkdir(parents=True, exist_ok=True)\n",
    "training_data_path = data_root / archive_name\n",
    "reuters_dir = data_root / archive_name.split(\".\")[0]\n",
    "data_size = download_from_url(training_data_url, training_data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unpack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "import tarfile\n",
    "\n",
    "tar = tarfile.open(str(training_data_path))\n",
    "tar.extractall(path=str(reuters_dir))\n",
    "tar.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "import pickle\n",
    "\n",
    "from ds_tut.datasets import ReutersParser, ReutersCorpus\n",
    "\n",
    "documents = []\n",
    "rp = ReutersParser()\n",
    "for sgml_path in reuters_dir.glob(\"*.sgm\"):\n",
    "    for doc in rp.parse_sgml(str(sgml_path)):\n",
    "        doc[\"filename\"] = sgml_path\n",
    "        documents.append(doc)\n",
    "\n",
    "pickle_path = reuters_dir / \"documents.pkl\"\n",
    "with open(str(pickle_path), \"wb\") as f:\n",
    "    pickle.dump(documents, f)\n",
    "\n",
    "reuters = ReutersCorpus(documents)\n",
    "\n",
    "pickle_path = reuters_dir / \"corpus.pkl\"\n",
    "with open(str(pickle_path), \"wb\") as f:\n",
    "    pickle.dump(reuters, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Reuters-21578"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from ds_tut.datasets import ReutersCorpus\n",
    "\n",
    "data_root = Path.cwd() / \"data\" / \"tmp\"\n",
    "reuters_documents_path = data_root / \"reuters21578\" / \"documents.pkl\"\n",
    "reuters_corpus_path = data_root / \"reuters21578\" / \"corpus.pkl\"\n",
    "\n",
    "documents = pickle.load(open(reuters_documents_path, \"rb\"))\n",
    "reuters = pickle.load(open(reuters_corpus_path, \"rb\"))\n",
    "df, top_ten_ids, train_labels, test_labels = reuters.build_dataframe(pd=pd)\n",
    "train, test = reuters.split_modapte()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get some simple stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "number_of_samples = reuters.number_of_samples\n",
    "number_of_classes = reuters.number_of_classes\n",
    "number_of_samples_per_class = int(np.average([tc for tc in reuters.topic_counts.values() if tc > 1]))\n",
    "number_of_words_per_sample = int(np.median([len(d[\"text\"].split()) for d in reuters.docs]))\n",
    "samples_to_words_per_sample_ratio = int(number_of_samples / number_of_words_per_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples:                                   10789\n",
      "Number of classes:                                   119\n",
      "Number of samples per class:                         148\n",
      "Number of words per sample:                          89\n",
      "Number of samples/number of words per sample ratio:  121\n"
     ]
    }
   ],
   "source": [
    "#|notest\n",
    "nchars = 52\n",
    "print(\"Number of samples:\".ljust(nchars), reuters.number_of_samples)\n",
    "print(\"Number of classes:\".ljust(nchars), reuters.number_of_classes)\n",
    "print(\"Number of samples per class:\".ljust(nchars), number_of_samples_per_class)\n",
    "print(\"Number of words per sample:\".ljust(nchars), number_of_words_per_sample)\n",
    "print(\"Number of samples/number of words per sample ratio:\".ljust(nchars), samples_to_words_per_sample_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of sample length for reuters21578"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 851,
       "width": 1249
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|notest\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "sns.histplot([len(d[\"text\"]) for d in reuters.docs], kde=True, ax=ax)\n",
    "ax.set_title('Sample length distribution')\n",
    "ax.set_xlabel('Length of a sample')\n",
    "ax.set_xlim(0, 8000)\n",
    "_ = ax.set_ylabel('Number of samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word frequency distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "kwargs = {\n",
    "        'ngram_range': (1, 1),\n",
    "        'dtype': 'int32',\n",
    "        'strip_accents': 'unicode',\n",
    "        'decode_error': 'replace',\n",
    "        'analyzer': 'word',  # Split text into word tokens.\n",
    "}\n",
    "vectorizer = CountVectorizer(**kwargs)\n",
    "vectorized_texts = vectorizer.fit_transform(reuters.texts)\n",
    "all_ngrams = list(vectorizer.get_feature_names_out())\n",
    "all_counts = vectorized_texts.sum(axis=0).tolist()[0]\n",
    "all_counts, all_ngrams = zip(*[(c, n) for c, n in sorted(\n",
    "    zip(all_counts, all_ngrams), reverse=True)])\n",
    "\n",
    "num_ngrams=50\n",
    "ngrams = list(all_ngrams)[:num_ngrams]\n",
    "counts = list(all_counts)[:num_ngrams]\n",
    "idx = np.arange(num_ngrams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/vQw/BQAAAFj9xHAAAADAGumZZ57Juuuum4suuijf+MY3MmXKlAwYMCBf//rXkyze/vLMM88shXB9+/bNmWeeucQKTUOGDMnZZ5+dmTNn5k9/+lO23377JYK2a6+9thTCdezYMddff3023XTT0vmzzz47v/71r0tblX4RBg0aVArh2rVrlyuuuGKJ1drmz5+fyy67LH/9619LEdsjjzxS5ypVL730Uho3bpxf/epXS4QR/fr1y7HHHptx48Zl1qxZufvuu3PyySfXuKZ///6lEK5Jkya54IILcvjhh9e45vTTT8+xxx6bCRMm5IMPPsgNN9yQ0047Lcni1eGGDx+eJHnkkUfqjOFmzpxZikY22mijJVYDXBYXXnhhKYTbddddc8UVV6RNmzal8+ecc07OOeecDBkyZLmfXeWmm24qhXAnnnhizjnnnCWuueOOO0orCt566605+eST06xZs7Rq1Sp9+/ZN8n8xXPXX6vLMM8+kRYsWueCCC7LXXntl9uzZeeyxx7LHHnss19yr5v3Nb34zv/71r9OiRYvSubFjx+YHP/hB3n///Xz22We58MIL641/VtQ222yTbbbZJvfdd18phuvZs+dS34N/N3369Jx22mmlEK5Pnz757W9/m3bt2tW47rHHHsu5556befPmZciQIfnTn/6UU089tdZnVlRUZNiwYenZs2euvvrqGitznX322bnqqqvypz/9KUly++23p1+/fiu0Otwvf/nLUsC2zjrr5Pe//3223XbbGteMHTs2p59+et56663S9/rggw+madOm6dixY+n9ev/990vP+uY3v1nnVrLL6tNPP02S7Lnnnrn44ouX+P35+c9/Xtq69uabb16pGG7u3Lk5//zzS+HaEUcckfPOO6/GezpmzJiccsopS6xSV5fRo0cnSbbeeuucd955+epXv5qRI0fm/fffLz33hhtuKIVwPXr0yI033ljrtqQ33HBDLrvssiTJU089lbFjx6Zz5861jlv1Mzj99NNzyimn1Aj3/vrXv+ZXv/pVksWfm2RxyHrJJZeUtk1Nkm9/+9v53ve+l0WLFmXQoEGZOnVqjXn96U9/KoVwF154YY2V7pLFMecVV1yR66+/Pkly3XXXieEAAAD40lj6f+IGAAAA8CUwa9as3Hjjjcv8v3/fPq4211xzTfbcc8+0bNkyG264YY1I5s477yytHHfooYfmJz/5Sa0xyu67716KDyorK5fYYm/KlCm57bbbkiwOvP74xz/WCOGSpGnTprnooovSp0+f5XtTVlBlZWWuvPLK0te///3va922dK211sp5551XCqE+/vjjPPjgg/U++wc/+EGtUcSGG25YY9W8559/folr/vKXv5SOzz777CVCuCTp2rVrLr/88tLXf//730vH3/rWt1JeXp5kcVBS12egf//+pbDpwAMPrPf7qc1TTz1V2nawS5cuueaaa2qEPMniFaCuvfbaldoC9LXXXisd17XC3FFHHZXtt98+yeKVqqpWFFsZv/zlL3PwwQenVatWWX/99XP88cfXiNmWVc+ePfO73/1uiXs7d+6c66+/Pi1btkyy+P189913V3req8uNN95Y2lJ3q622yjXXXLNECJcs3m63KjxMFgdOVffVpk2bNrnuuutq3aLy9NNPL628NmfOnLzxxhvLPe+RI0eWfl/Ly8tz/fXXLxHCJYt/HjfffHNpdb4PP/wwDzzwwHKPtyK6deuWq666aonfn8aNG+d///d/S5+Rjz76KOPHj1/hcf7617+WtjPeZZdd8otf/GKJv+ddunTJTTfdVOvPti4dOnTIjTfemC233DLNmzfPFltskUMOOaR0vvr7+Jvf/KbWEC5Jvv/97+erX/1q6esRI0bUO+4+++yTU089dYkV7I488sjS1tHJ4p/tpZdeWiOES5Idd9wxO+20U5LFW1P/+9+Nqr89bdq0qTVCLCsry1lnnVUK9iZPnrzEdtEAAADQUKwMBwAAABTC9OnTa4QmS7PvvvumVatWdZ7v3r17vdsk3nPPPaXjqq0l67Lffvtl4403zqhRo/LKK6/UWNXnySefzMKFC5MkvXv3Tvfu3et8zrnnnpvBgwfXO9aq8Oqrr+b9999PsjhYWtoKT6ecckppJbWHHnqo3hWAjjrqqDrPbbfddqXjzz77rMa5Tz/9tLTiUfv27fPd7363zuf06tUrO+20UyorK9OtW7fMmjUrrVq1SocOHbLrrrtm8ODBmT9/fp544okcfPDBS9xffYvUFYnhBg0aVDo+4YQT0rx581qva9q0aU455ZRaV3RbFk2a/N+/unv11Vez++6713rdJZdckrKysmywwQYpKytbobGqtGvXLt/85jdX6hlVzjnnnDRu3LjWc506dcq3v/3t3HrrrUkW/0x+/OMfr5JxV7VHHnmkdHzOOefU+Ln8u3333TfbbrttXnnllcyZMyePPfZYnSua7b///nWGV40aNUqvXr1Kq4pNmjRpuef96KOPllb3+ta3vpUePXrUeW2bNm1y6qmn5rzzzkuS3HvvvbXGqKvaYYcdVueKdy1btkyPHj3yr3/9K8ni92BFt9Pt379/6bhqK+HarLvuujnuuONy9dVXL9NzDzzwwDr/ObNgwYKceuqpGTNmTObPn1/vFtNJ8rWvfS0ffPBBkpRWJK3LcccdV+vrTZo0ySabbJKJEycmSf7f//t/WWuttWq99qtf/WppJdIpU6Ys8Zyqebz//vtLBNzJ4iDu5ptvTvPmzWsEeAAAANDQrAwHAAAArJG23nrrOs9NnDixtK1pixYt0q1bt6U+r3pY98orr5SOn3vuudJxXTFTla5du9YbrKwqL730Uul4Wcbr3r17KVh54403SnHfv+vUqVPWXXfdOp/ToUOH0vH8+fNrnHv22WdLxzvvvPNSt4S8+eabc8stt+TCCy+sEaNUj9+qR0xVJkyYkJdffjnJ4p9Z165d6x2nNtXnuttuu9V7be/evVc4UKseD55++um55JJLMmzYsHz++ec1ruvUqVM6duy40iFcknz9619fYrWpFbH22munV69e9V7Tu3fv0nHVz+TLZvTo0aUVItu1a7dMW4Puv//+pePqv2v/rmpL5rpUX0Xs339flsULL7xQOq5ry+Dq9ttvv9Jn6K233srcuXOXe8zltdVWW9V7vvrfjKrVHJfXZ599Vto+eb311suWW25Z7/X77rvvMj+7vn+ONG3aNIcddljOOOOM/OQnP1nqHKdOnVr6umo719qUlZXV+z2ss846pePNN9+8zuuqr9j47+9t1d+ezz//PN/97ndz7bXXZsSIEamsrKxxXdeuXYVwAAAAfOlYGQ4AAAAohM6dO6/SVdO6dOlS57mRI0eWjufMmZOvfe1ry/Xs6tv5ffrpp6XjZdkyc4sttiiFG6tL1epDSXL77bfn9ttvX+Z7582blylTptQaQKy99tr13lt9BbWqFauqVH/Pqm8XuLx69+6dtm3bZvr06Xn++eczZcqUGkHNI488Uhr7oIMOWu7nL1q0qLQdYHl5eTbccMN6r2/VqlU6d+6cMWPGLPdYffv2zaOPPppJkyZl3rx5uemmm3LTTTelTZs22XHHHbPrrrtmt912q3WbzRVV3+/F8liW35nqP+eq+PTLpvrPbWkre1WpHiDV93OvHi3Vpvrvy79HSMui+tj1RVFVWrdunS5dumT06NFZuHBhxo8fn6985SvLPe7yWJ734N//Ziyrjz/+uHRc2wpn/27jjTdO06ZNlym+W97fl8mTJ2fUqFEZPXp0Pvnkk4wcOTJvv/12aQXAZdGuXbs6V3tLUiNmbdu27TJd9+/69euX5557LnPmzMn06dNzzTXX5Jprrsnaa6+dnXfeObvuumt23XXXOrd9BQAAgIYkhgMAAADWSG3atKnz3LRp01bq2dXvnzx58jKNWaV6uLW6TJ8+faXvry2Gq2u70GVRfZu+ZXmf6tK0adN885vfzJ133pmFCxemf//+NbZurdoitby8vMYKXstq2rRppZXZWrduvUyrsbVv336FYrj1118/t956a37+85/n1VdfLb0+Y8aMPP7443n88cdTVlaWnj175sgjj8z/+3//b6VXh2vduvVK3V+lvgintmtmzpy5SsZd1aqv1rUs31OSGluf1ve3pFmzZss8jxWJ4aqPvTxzHz16dJKV/zuxLFb0PRg2bFiN34na9OzZM9tss02Nv8HL8vlu1KhR2rZtu0xb0y7L36pZs2bl1ltvzf333196b2vTuHHjJVZ9rM3y/J1d0VUeN9tss9x888352c9+ViMOnzx5ch566KE89NBDady4cXbcccccc8wx2WOPPVZoHAAAAFgdxHAAAADAGqlx48Z1nqseJHTu3DlHH330cj27+taj1WOEZQlalrY96LKqb6zq399+++231K0K/93SVoBbEfVtC7i8Dj744Nx5551JFq8EVxXDvffee3n33XeTJHvssUeNaGl1Ki8vX+F7/+u//it33XVXhg0blv79+2fIkCE1VlGrrKzMsGHDMmzYsDz44IP505/+tFKfoSZNvrh/XVj9M7oy79GKhGKrU/Xfr1Wxde2KWpH3pfrqaw0596V59tlnc+2119Z7zWmnnZZtttmmxgpvy7q63LK+d/X9cyRZvMroiSeeWNpqt0qTJk2y4YYbZtNNN81WW22VnXbaKbfddlvuv//+pY75Rf1ctt566zzyyCN57rnnMmDAgAwZMqS0Kmay+HM+dOjQDB06NIceemh+85vffKk/MwAAAKw5xHAAAAAA/6b6aj+tWrVK3759V/hZ1YOrZVlpaVlXyFparDF//vw6z1X//nr27Jnjjz9+mcZcnarPaWVXCdt6663TrVu3fPTRRxk2bFg+/fTTrL/++vnnP/9ZumZFtkhNFq/yVl5enoqKisycOTOLFi1a6upLs2bNWqGxqttmm22yzTbb5Gc/+1nGjRuXF154Ic8++2yeeuqp0vOHDh2a6667LqeffvpKj7e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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 887,
       "width": 1249
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|notest\n",
    "fig, ax = plt.subplots(figsize=(15, 10))\n",
    "sns.barplot(x=ngrams, y=counts, ax=ax, color=\"#95BCD9\")\n",
    "ax.set_title('Sample length distribution')\n",
    "plt.xlabel('N-grams')\n",
    "_ = plt.ylabel('Frequencies')\n",
    "_ = plt.title('Frequency distribution of n-grams')\n",
    "_ = plt.xticks(idx, ngrams, rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Choose a model flowchart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div>\n",
       "    <p>Number of samples/number of words per sample ratio: 121</p>\n",
       "    <img src=\"https://developers.google.com/machine-learning/guides/text-classification/images/TextClassificationFlowchart.png\" style=\"width: 100%\">\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#|notest\n",
    "from IPython.display import display, HTML\n",
    "url = \"https://developers.google.com/machine-learning/guides/text-classification/images/TextClassificationFlowchart.png\"\n",
    "html_code = f\"\"\"\n",
    "<div>\n",
    "    <p>Number of samples/number of words per sample ratio: {samples_to_words_per_sample_ratio}</p>\n",
    "    <img src=\"{url}\" style=\"width: 100%\">\n",
    "</div>\n",
    "\"\"\"\n",
    "display(HTML(html_code))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Linear Model\n",
    "\n",
    "Ok, pretty impressive image. How about to try just a very simple linear model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get text and labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7770 3019\n"
     ]
    }
   ],
   "source": [
    "#|notest\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "train_docs, test_docs = reuters.split_modapte()\n",
    "print(len(train_docs), len(test_docs))\n",
    "\n",
    "train = [d[\"text\"] for d in train_docs]\n",
    "train_labels = reuters.get_labels(train_docs)\n",
    "y_train = MultiLabelBinarizer().fit_transform(train_labels)\n",
    "\n",
    "test = [d[\"text\"] for d in test_docs]\n",
    "test_labels = reuters.get_labels(test_docs)\n",
    "y_test = MultiLabelBinarizer().fit_transform(test_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vectorize Texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "vectorizer = TfidfVectorizer()\n",
    "vectorizer.fit(train)\n",
    "\n",
    "X_train = vectorizer.transform(train)\n",
    "X_test = vectorizer.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "top_ten_ids, top_ten_names = reuters.top_n(n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Logistic Regression\n",
    "\n",
    "This is usually one of the first models to try. Simple, robust, fast, elegant. One of the best baseline methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        earn       0.99      0.97      0.98      1087\n",
      "         acq       0.98      0.92      0.95       719\n",
      "    money-fx       0.78      0.51      0.62       179\n",
      "       grain       0.99      0.60      0.75       149\n",
      "       crude       0.96      0.57      0.72       189\n",
      "       trade       0.93      0.54      0.68       117\n",
      "    interest       0.91      0.47      0.62       131\n",
      "        ship       1.00      0.13      0.24        89\n",
      "       wheat       0.97      0.51      0.67        71\n",
      "        corn       0.95      0.32      0.48        56\n",
      "\n",
      "   micro avg       0.97      0.79      0.87      2787\n",
      "   macro avg       0.95      0.56      0.67      2787\n",
      "weighted avg       0.97      0.79      0.85      2787\n",
      " samples avg       0.70      0.69      0.69      2787\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#|notest\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# model = OneVsRestClassifier(LogisticRegression(C=100, solver=\"liblinear\", multi_class=\"ovr\"))\n",
    "model = OneVsRestClassifier(LogisticRegression(solver=\"liblinear\", multi_class=\"ovr\"))\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=top_ten_names, labels=top_ten_ids, zero_division=0.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        earn      0.991     0.980     0.985      1087\n",
      "         acq      0.984     0.950     0.967       719\n",
      "    money-fx      0.810     0.788     0.799       179\n",
      "       grain      0.975     0.799     0.878       149\n",
      "       crude      0.906     0.868     0.886       189\n",
      "       trade      0.830     0.709     0.765       117\n",
      "    interest      0.870     0.664     0.753       131\n",
      "        ship      0.924     0.685     0.787        89\n",
      "       wheat      0.929     0.732     0.819        71\n",
      "        corn      0.955     0.750     0.840        56\n",
      "\n",
      "   micro avg      0.956     0.896     0.925      2787\n",
      "   macro avg      0.917     0.793     0.848      2787\n",
      "weighted avg      0.954     0.896     0.922      2787\n",
      " samples avg      0.771     0.769     0.767      2787\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#|notest\n",
    "from sklearn.svm import LinearSVC\n",
    "model = OneVsRestClassifier(LinearSVC(dual=True))\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "print(classification_report(y_test, y_pred, target_names=top_ten_names, labels=top_ten_ids, digits=3, zero_division=0.0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Precision/Recall-Curve\n",
    "\n",
    "One way to visualize the performance of a classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7770, 9) (3019, 9)\n"
     ]
    }
   ],
   "source": [
    "#|notest\n",
    "df_train = df.query(\"modapte == 'train'\")\n",
    "df_test = df.query(\"modapte == 'test'\")\n",
    "\n",
    "mlb = MultiLabelBinarizer()\n",
    "y_train = mlb.fit_transform(df_train.label)\n",
    "y_test = mlb.transform(df_test.label)\n",
    "print(df_train.shape, df_test.shape)\n",
    "\n",
    "cache_dir = reuters_dir / \"cache\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "\n",
    "\n",
    "class EmptyFitMixin:\n",
    "    def fit(self, x, y=None):\n",
    "        return self\n",
    "\n",
    "\n",
    "class TextStats(BaseEstimator, EmptyFitMixin, TransformerMixin):\n",
    "    \"\"\"Extract features from each document\"\"\"\n",
    "\n",
    "    def transform(self, col):\n",
    "        tc = col.str\n",
    "        features = [\n",
    "            tc.len(),  # character count\n",
    "            tc.count(r\"\\n\"),  # line count\n",
    "            tc.count(r\"\\.\"),  # sentence count\n",
    "            tc.split().apply(lambda x: len(x) if x is not None else 0),  # word count\n",
    "        ]\n",
    "        features = np.concatenate([f.values.reshape(-1, 1) for f in features], axis=1)\n",
    "        where_are_NaNs = np.isnan(features)\n",
    "        features[where_are_NaNs] = 0\n",
    "        return features.astype(np.float64)\n",
    "\n",
    "\n",
    "class TextFromPandasColumns(EmptyFitMixin, BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Extract the text from a list of columns in a single pass.\n",
    "\n",
    "    Takes a pandas dataframe and produces a series of texts\n",
    "    from joined columns defined in `text_cols`.\n",
    "    \"\"\"\n",
    "    text_cols = [\"title\", \"body\"]\n",
    "\n",
    "    def transform(self, df):\n",
    "        def join(items, axis=None):\n",
    "            return \" \".join([str(item) for item in items])\n",
    "\n",
    "        data = df[self.text_cols].apply(lambda x: \"\" if x.iloc[0] is None else x, axis=1)\n",
    "        texts = data.apply(join, axis=1)\n",
    "        return texts\n",
    "\n",
    "\n",
    "class ColumnSelector(EmptyFitMixin, BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, column, filter_none=True):\n",
    "        self.column = column\n",
    "        self.filter_none = filter_none\n",
    "\n",
    "    def transform(self, df):\n",
    "        col = df[self.column]\n",
    "        if self.filter_none:\n",
    "            col = col.apply(lambda x: \"\" if x is None else x)\n",
    "        return col\n",
    "\n",
    "\n",
    "pipeline = Pipeline(\n",
    "    memory=str(cache_dir),\n",
    "    steps=[\n",
    "        (\"union\", FeatureUnion(n_jobs=1, transformer_list=[\n",
    "            (\"title_stats\", Pipeline([\n",
    "                (\"column\", ColumnSelector(\"title\")),\n",
    "                (\"stats\", TextStats()),\n",
    "                (\"scaled\", StandardScaler()),\n",
    "            ])),\n",
    "            (\"body_stats\", Pipeline([\n",
    "                (\"column\", ColumnSelector(\"body\")),\n",
    "                (\"stats\", TextStats()),\n",
    "                (\"scaled\", StandardScaler()),\n",
    "            ])),\n",
    "            (\"combined_text\", Pipeline([\n",
    "                (\"column\", TextFromPandasColumns()),\n",
    "                (\"tfidf\", TfidfVectorizer()),\n",
    "            ])),\n",
    "        ])),\n",
    "        (\"clf\", OneVsRestClassifier(LinearSVC(C=1, dual=True, max_iter=20000))),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "pipeline.fit(df_train, y_train)\n",
    "y_pred = pipeline.predict(df_test)\n",
    "print(classification_report(y_test, y_pred, target_names=top_ten_names, labels=top_ten_ids, digits=3, zero_division=0.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "y_score = pipeline.decision_function(df_test)\n",
    "\n",
    "from sklearn.preprocessing import label_binarize\n",
    "\n",
    "y_test_bin = label_binarize(y_test, classes=list(range(y_score.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "\n",
    "# For each class\n",
    "precision, recall, average_precision = {}, {}, {}\n",
    "for i in range(y_score.shape[1]):\n",
    "    precision[i], recall[i], _ = precision_recall_curve(y_test_bin[:, i], y_score[:, i])\n",
    "    average_precision[i] = average_precision_score(y_test_bin[:, i], y_score[:, i])\n",
    "\n",
    "# A \"micro-average\": quantifying score on all classes jointly\n",
    "precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(y_test_bin.ravel(), y_score.ravel())\n",
    "average_precision[\"micro\"] = average_precision_score(y_test_bin, y_score, average=\"micro\")\n",
    "print('Average precision score, micro-averaged over all classes: {0:0.2f}'\n",
    "      .format(average_precision[\"micro\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "# precision recall breakeven point\n",
    "prbp = None\n",
    "for num, (p, r) in enumerate(zip(precision[\"micro\"], recall[\"micro\"])):\n",
    "    if p == r:\n",
    "        print(num, p, r)\n",
    "        prbp = p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|notest\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall[\"micro\"], precision[\"micro\"], step='post', alpha=0.2,\n",
    "                 color='b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title(\n",
    "    'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n",
    "    .format(average_precision[\"micro\"]))"
   ]
  }
 ],
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