notebooks/models/Train CRF.ipynb

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   "source": [
    "CRF trainer using the sklearn_crfsuite package (Python wrapper for CRFSuite): https://sklearn-crfsuite.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "\n",
    "import sklearn_crfsuite\n",
    "from sklearn_crfsuite import metrics\n",
    "\n",
    "from presidio_evaluator import InputSample\n",
    "from presidio_evaluator.models.crf_model import CRFModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DATE = \"Jan-15-2022\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source a dataset to use for training / testing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_samples = InputSample.read_dataset_json(\n",
    "    \"../../data/train_{}.json\".format(DATA_DATE)\n",
    ")\n",
    "test_samples = InputSample.read_dataset_json(\n",
    "    \"../../data/test_{}.json\".format(DATA_DATE)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_tagged = [sample for sample in train_samples if len(sample.spans) > 0]\n",
    "print(\n",
    "    \"Kept {} train samples after removal of non-tagged samples\".format(\n",
    "        len(train_tagged)\n",
    "    )\n",
    ")\n",
    "train_data = InputSample.create_conll_dataset(train_tagged)\n",
    "\n",
    "test_data = InputSample.create_conll_dataset(test_samples)\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Turn every sentence into a list of lists (list of tokens + pos + label)\n",
    "test_sents = test_data.groupby(\"sentence\")[[\"text\", \"pos\", \"label\"]].apply(\n",
    "    lambda x: x.values.tolist()\n",
    ")\n",
    "train_sents = train_data.groupby(\"sentence\")[[\"text\", \"pos\", \"label\"]].apply(\n",
    "    lambda x: x.values.tolist()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create features for CRF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CRFModel.sent2features(train_sents[0])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "X_train = [CRFModel.sent2features(s) for s in train_sents]\n",
    "y_train = [CRFModel.sent2labels(s) for s in train_sents]\n",
    "\n",
    "X_test = [CRFModel.sent2features(s) for s in test_sents]\n",
    "y_test = [CRFModel.sent2labels(s) for s in test_sents]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "crf = sklearn_crfsuite.CRF(\n",
    "    algorithm=\"lbfgs\", c1=0.1, c2=0.1, max_iterations=100, all_possible_transitions=True\n",
    ")\n",
    "crf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save trained model to pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "\n",
    "os.makedirs(\"../../models/\", exist_ok=True)\n",
    "\n",
    "with open(\"../../models/crf.pickle\", \"wb\") as f:\n",
    "    pickle.dump(crf, f, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"../../models/crf.pickle\", \"rb\") as f:\n",
    "    crf = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract info and predictions from model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = list(crf.classes_)\n",
    "labels.remove(\"O\")\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = crf.predict(X_test)\n",
    "metrics.flat_f1_score(y_test, y_pred, average=\"weighted\", labels=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## predict one:\n",
    "y_5_pred = crf.predict([X_test[5]])\n",
    "y_5_pred[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group B and I results\n",
    "sorted_labels = sorted(labels, key=lambda name: (name[1:], name[0]))\n",
    "print(\n",
    "    metrics.flat_classification_report(y_test, y_pred, labels=sorted_labels, digits=3)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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