tutorial_notebook_part2.ipynb

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    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from pathlib import Path\n",
    "import json\n",
    "from sklearn.metrics import classification_report\n",
    "from spacy.cli.train import train"
   ]
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   "source": [
    "import spacy\n",
    "from spacy.tokens import DocBin\n",
    "from spacy.lang.en import English\n",
    "nlp = spacy.blank(\"en\")"
   ]
  },
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    "# some people may need this \"fix\" for installing packages in colab\n",
    "# source: https://stackoverflow.com/questions/56081324/why-are-google-colab-shell-commands-not-working\n",
    "import locale\n",
    "def getpreferredencoding(do_setlocale = True):\n",
    "    return \"UTF-8\"\n",
    "locale.getpreferredencoding = getpreferredencoding"
   ]
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  {
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   "source": [
    "from IPython.display import HTML, display\n",
    "# only need this for collab to enable output text wrapping\n",
    "# fix from: https://stackoverflow.com/questions/58890109/line-wrapping-in-collaboratory-google-results\n",
    "def set_css():\n",
    "  display(HTML('''\n",
    "  <style>\n",
    "    pre {\n",
    "        white-space: pre-wrap;\n",
    "    }\n",
    "  </style>\n",
    "  '''))\n",
    "get_ipython().events.register('pre_run_cell', set_css)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Bagging to BERT: A tour of applied NLP\n",
    "### Part 2: Beyond bagging\n",
    "### Table of Contents\n",
    "* [Data processing](#data)\n",
    "* [CNN](#cnn)\n",
    "* [BERT](#bert)\n",
    "\n"
   ]
  },
  {
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   "source": [
    "### Data processing <a class=\"anchor\" id=\"data\"></a>\n",
    "\n",
    "Copied from part 1\n",
    "\n",
    "You'll either need to download the [imdb review data](https://ai.stanford.edu/~amaas/data/sentiment/) and save it to this directory OR download the [processed data](https://drive.google.com/file/d/1oN_fO91IBkDHD_u6WXiUCvhhyNexQDJq/view?usp=sharinghttps://drive.google.com/file/d/1oN_fO91IBkDHD_u6WXiUCvhhyNexQDJq/view?usp=sharing)."
   ]
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   "source": [
    "# # processing the original data into DataFrame\n",
    "# # here for reference, don't need to run this if you're using reviews.pkl.gz\n",
    "# source_path = Path('./aclImdb/')\n",
    "# #neg_files = source_path.glob('./*/neg/*.txt')\n",
    "# #pos_files = source_path.glob('./*/pos/*.txt')\n",
    "# all_files = []\n",
    "# for f in source_path.glob('./*/*/*.txt'):\n",
    "#     filename = f.as_posix()\n",
    "#     if 'unsup' not in filename:\n",
    "#         # split up into useful components\n",
    "#         _, split, sent, idx = filename.split('/')\n",
    "#         idx = int(idx.split('_')[0])\n",
    "#         all_files.append([idx, split, sent, f.read_text()])\n",
    "# review_df = pd.DataFrame(all_files)\n",
    "# review_df.columns = ['idx', 'split', 'label', 'text']\n",
    "# # some minor html cruft is in here\n",
    "# review_df['text'] = review_df['text'].str.replace('<br /><br />', '')\n",
    "# review_df = review_df.to_pickle('reviews.pkl.gz')"
   ]
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   "source": [
    "# restart your Colab runtime after this\n",
    "!pip install spacy-transformers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f522858-8b4a-4b2a-a78c-17bc900f8cca",
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   "source": [
    "In the live tutorial, I will load some resources from my GDrive so I don't need to wait on the training.  I'll keep these resources available publicly for a bit, feel free to copy them to your Drive.  Access the directory [here](https://drive.google.com/drive/folders/1Xqn66AIm19icDfFtsG1NUQVeIwPEVhML?usp=share_link).  \n",
    "\n",
    "The path to use to load these resources is dependent on where you store the files."
   ]
  },
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   "source": [
    "# mounting GDrive to the colab instance\n",
    "from google.colab import drive\n",
    "drive.mount('/content/drive/')"
   ]
  },
  {
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   "source": [
    "!git clone -b revised_2023 https://github.com/bpben/bagging_to_bert"
   ]
  },
  {
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   "source": [
    "def preprocess(text, labels, name):\n",
    "    # preprocessing utility for saving a serialized corpus for spaCy\n",
    "    # initialize spaCy's DocBin format (easier use with spaCy pipeline)\n",
    "    db = DocBin()\n",
    "    output_path = f'{name}.spacy'\n",
    "    data_tuples = zip(text, labels)\n",
    "    # pipe is slightly faster than individually processing each\n",
    "    for doc, label in nlp.pipe(data_tuples, as_tuples=True):\n",
    "        # store the labels in the document's .cat attribute\n",
    "        if label == 'pos':\n",
    "            doc.cats['pos'] = True\n",
    "            doc.cats['neg'] = False\n",
    "        else:\n",
    "            doc.cats['pos'] = False\n",
    "            doc.cats['neg'] = True\n",
    "        db.add(doc)\n",
    "    # save the DocBin\n",
    "    db.to_disk(output_path)"
   ]
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  {
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   "source": [
    "# if you are using gdrive - you can change that directory here\n",
    "data_dir = '/content/drive/MyDrive/talks/odsc_2023/'\n",
    "# otherwise, you can just use\n",
    "data_dir = './'"
   ]
  },
  {
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   "source": [
    "# read in reviews pickle file - refer to part 1 for how this is created\n",
    "review_df = pd.read_pickle(\n",
    "    f'{data_dir}reviews.pkl.gz')\n",
    "# copied from part 1: want to use the same train/test split\n",
    "seed = 37\n",
    "np.random.seed(seed)\n",
    "pct_train = 0.7\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    review_df['text'],\n",
    "    review_df['label'], train_size=pct_train)"
   ]
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  {
   "cell_type": "code",
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   "id": "w4kXX9W06JaS",
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   "source": [
    "# running the preprocessing on each split\n",
    "preprocess(X_train, y_train, 'train')\n",
    "preprocess(X_test, y_test, 'test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5262c0f-113f-4e1a-884d-5a5925e712c1",
   "metadata": {},
   "source": [
    "### CNN <a class=\"anchor\" id=\"cnn\"></a>"
   ]
  },
  {
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   "metadata": {
    "colab": {
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    },
    "executionInfo": {
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   "source": [
    "# can override config info with overrides\n",
    "# the tutorial config file doesn't have the paths for train/dev corpora\n",
    "# going to just run this for a few epochs, see how it works\n",
    "train(\"./bagging_to_bert/spacy_materials/config.cfg\",\n",
    "      output_path='cnn_model',\n",
    "      overrides={\"paths.train\": \"train.spacy\", \n",
    "                 \"paths.dev\": \"test.spacy\",\n",
    "                 \"training.max_epochs\": 1},\n",
    "      use_gpu = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "qdmGkvsPQoEu",
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    },
    "executionInfo": {
     "elapsed": 10118,
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   "outputs": [],
   "source": [
    "# load the best version of the model from my GDrive, your directory structure will be different\n",
    "m = spacy.load(f'{data_dir}trained_cnn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "# looking at the outputs from running a simple example\n",
    "m('This movie is great').cats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cUaNYxfHAECZ",
   "metadata": {
    "id": "cUaNYxfHAECZ"
   },
   "source": [
    "SpaCy has its own evaluation capabilities, but for comparison's sake, let's use the same evaluation approach we did with out other applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Ih7n0wGxABGa",
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    "id": "Ih7n0wGxABGa"
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   "source": [
    "# if you're interested in trying out spacy's own evaluate\n",
    "#from spacy.cli.evaluate import evaluate\n",
    "#evaluate(model='./cnn_model/model-best/', data_path='test.spacy')"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# get the predicted category from the model\n",
    "pred = np.array([max(d.cats, key=d.cats.get) for d in m.pipe(X_test)])"
   ]
  },
  {
   "cell_type": "code",
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    },
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    "outputId": "b1544196-86ad-43d6-ea6a-7042a09eb39b"
   },
   "outputs": [],
   "source": [
    "print(f'accuracy: {np.where(pred == y_test)[0].shape[0]/y_test.shape[0]}')\n",
    "print(\n",
    "    classification_report(y_pred=pred,\n",
    "                          y_true=y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d9536ef-ae86-406f-92f2-8ef3fb07db18",
   "metadata": {
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   "source": [
    "### BERT <a class=\"anchor\" id=\"bert\"></a>\n",
    "From [HF tutorials](https://huggingface.co/blog/sentiment-analysis-python).  The sentiment analysis pipeline packages together the tokenizer and the BERT model with a classification layer.  The default pipeline uses this [distilBERT model](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english). "
   ]
  },
  {
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   "source": [
    "# this will need to be run if you don't already have this package\n",
    "!pip install transformers"
   ]
  },
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   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "sentiment_pipeline = pipeline(\"sentiment-analysis\", truncation=True, padding=True)"
   ]
  },
  {
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   "source": [
    "# some manipulations for speed and to play nice with BERT\n",
    "bert_pred = sentiment_pipeline(X_test.apply(lambda x: x).head(n=50).tolist())\n",
    "bert_pred = ['pos' if p['label']=='POSITIVE' else 'neg' for p in bert_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25afb1f9-1e29-463d-b4f9-50dcdd0c2af8",
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   "source": [
    "np.where(bert_pred == y_test[:50])\n",
    "print(f'accuracy: {np.where(bert_pred == y_test[:50])[0].shape[0]/50}')\n",
    "print(\n",
    "    classification_report(y_pred=bert_pred,\n",
    "                          y_true=y_test[:50]))"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "This is pretty good! But with some minor modifications, we can use our spacy configuration with a transformer model"
   ]
  },
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    "# can override config info with overrides\n",
    "# the tutorial config file doesn't have the paths for train/dev corpora\n",
    "# going to just run this for a few epochs, see how it works\n",
    "train(\"./bagging_to_bert/spacy_materials/config_trf.cfg\",\n",
    "      output_path='example_model',\n",
    "      overrides={\"paths.train\": \"train.spacy\", \n",
    "                 \"paths.dev\": \"test.spacy\",\n",
    "                 \"training.max_epochs\": 1},\n",
    "      use_gpu = 0)"
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  {
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   "source": [
    "# load the best version of the model from my GDrive, your directory structure will be different\n",
    "m = spacy.load(f'{data_dir}trained_trf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "952309ec-2b71-431d-b5ca-27ef329be660",
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   "outputs": [],
   "source": [
    "# looking at the outputs from running a simple example\n",
    "m('This movie is great').cats"
   ]
  },
  {
   "cell_type": "code",
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   "id": "5bc2a97f-f54d-423a-bdca-f349172ec8dd",
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   "source": [
    "# if you're interested in trying out spacy's own evaluate\n",
    "# warning - this may bump up against RAM limits for free versions of colab\n",
    "from spacy.cli.evaluate import evaluate\n",
    "evaluate(model='./example_model/model-best/', data_path='test.spacy')"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "# get the predicted category from the model\n",
    "# again - may run into RAM limits on free colab\n",
    "# for the sake of just running this, you can subset the text set\n",
    "sample_set_size = 500\n",
    "pred = np.array([max(d.cats, key=d.cats.get) for d in m.pipe(X_test[:sample_set_size])])"
   ]
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   "source": [
    "accuracy = np.where(\n",
    "    pred == y_test[:sample_set_size])[0].shape[0]/y_test[:sample_set_size.shape[0]\n",
    "print(f'accuracy: {accuracy}')\n",
    "print(\n",
    "    classification_report(y_pred=pred,\n",
    "                          y_true=y_test[:sample_set_size]))"
   ]
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