{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Train Domain classifier on the [semantic scholar dataset](https://api.semanticscholar.org/corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "import pandas as pd\n",
        "from sklearn import metrics, set_config\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "from great_ai.utilities.clean import clean\n",
        "from great_ai.utilities.parallel_map import parallel_map\n",
        "from great_ai.utilities.language import is_english, predict_language\n",
        "from great_ai import save_model, configure, LargeFile\n",
        "\n",
        "from preprocess import preprocess"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "PREFIX = \"domain-\"\n",
        "DATASET_KEY = \"data\"\n",
        "MAX_FILE_COUNT = 5\n",
        "MODEL_KEY = \"small-domain-prediction-v2\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "configure()\n",
        "corpus_path = LargeFile(DATASET_KEY).get()\n",
        "\n",
        "set_config(display=\"diagram\")\n",
        "plt.rcParams[\"figure.figsize\"] = (30, 15)\n",
        "plt.rcParams[\"figure.facecolor\"] = \"white\"\n",
        "plt.rcParams[\"font.size\"] = 12\n",
        "plt.rcParams[\"axes.xmargin\"] = 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def clean_file(p: Path) -> None:\n",
        "    try:\n",
        "        processed_path = p.with_name(f\"{PREFIX}{p.stem}{p.suffix}\")\n",
        "\n",
        "        if processed_path.exists():\n",
        "            return\n",
        "\n",
        "        with open(p) as f:\n",
        "            content = json.load(f)\n",
        "\n",
        "        result = {\n",
        "            preprocess(\n",
        "                clean(f'{c[\"title\"]} {c[\"abstract\"]}', convert_to_ascii=True)\n",
        "            ): c[\"domain\"]\n",
        "            for c in content\n",
        "            if (\n",
        "                c[\"domain\"]\n",
        "                and c[\"abstract\"]\n",
        "                and is_english(predict_language(c[\"abstract\"]))\n",
        "            )\n",
        "        }\n",
        "\n",
        "        with open(processed_path, \"w\") as f:\n",
        "            json.dump(result, f)\n",
        "    except Exception as e:\n",
        "        print(f\"Error ({e}) processing {p}\")\n",
        "\n",
        "\n",
        "parallel_map(\n",
        "    clean_file,\n",
        "    list(corpus_path.glob(\"s2-corpus-*.json\"))[:MAX_FILE_COUNT],\n",
        "    chunk_size=1,\n",
        ")\n",
        "None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "corpora = list(corpus_path.glob(f\"{PREFIX}*.json\"))[:MAX_FILE_COUNT]\n",
        "print(f\"Found {len(corpora)} files\")\n",
        "\n",
        "data = []\n",
        "for p in corpora:\n",
        "    with open(p) as f:\n",
        "        data.extend(json.load(f).items())\n",
        "\n",
        "print(f\"Found {len(data)} documents\")\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    [d[0] for d in data], [d[1] for d in data], test_size=0.1, random_state=1\n",
        ")\n",
        "\n",
        "X_train = [x for x, y in zip(X_train, y_train) for domain in y]\n",
        "y_train = [domain for x, y in zip(X_train, y_train) for domain in y]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "classifier = GridSearchCV(\n",
        "    Pipeline(steps=[(\"vectorizer\", TfidfVectorizer(token_pattern=r\"[^ ]+\")), (\"classifier\", MultinomialNB())]),\n",
        "    {\n",
        "        \"vectorizer__max_df\": [0.05, 0.1, 0.3],\n",
        "        \"vectorizer__min_df\": [5, 10, 30],\n",
        "        \"vectorizer__sublinear_tf\": [True, False],\n",
        "        \"classifier__alpha\": [0.1, 0.25, 0.5, 0.75, 1],\n",
        "        \"classifier__fit_prior\": [True, False],\n",
        "    },\n",
        "    scoring=\"f1_macro\",\n",
        "    cv=3,\n",
        "    n_jobs=4,\n",
        "    verbose=1,\n",
        ")\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "results = pd.DataFrame(classifier.cv_results_)\n",
        "results.sort_values(\"rank_test_score\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "classifier = Pipeline(\n",
        "    steps=[\n",
        "        (\"vectorizer\", TfidfVectorizer(min_df=10, max_df=0.05, sublinear_tf=True, token_pattern=r\"[^ ]+\")),\n",
        "        (\"classifier\", MultinomialNB(alpha=0.5, fit_prior=False)),\n",
        "    ]\n",
        ")\n",
        "\n",
        "classifier.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "predicted = classifier.predict(X_test)\n",
        "\n",
        "y_test_aligned = [p if p in y else y[0] for p, y in zip(predicted, y_test)]\n",
        "\n",
        "print(metrics.classification_report(y_test_aligned, predicted))\n",
        "metrics.ConfusionMatrixDisplay.from_predictions(\n",
        "    y_true=y_test_aligned,\n",
        "    y_pred=predicted,\n",
        "    xticks_rotation=\"vertical\",\n",
        "    normalize=\"pred\",\n",
        "    values_format=\".2f\",\n",
        ")\n",
        "None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "save_model(classifier, key=MODEL_KEY, keep_last_n=1)"
      ]
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "acc70e949538f42041ccc57bc4df2261507e3fd7d6b9ce5dcc28e3bcf9d48274"
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    "kernelspec": {
      "display_name": "Python 3.8.5 ('.env': venv)",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
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      "file_extension": ".py",
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