custom_script_demos/keras_nlp/Headline Classifier Local.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# End-to-End NLP: News Headline Classifier (Local Version)\n",
    "\n",
    "_**Train a Keras-based model to classify news headlines between four domains**_\n",
    "\n",
    "> *This notebook was tested with the `Python 3 (ipykernel)` kernel on SageMaker Studio Distribution v1.9*\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "In this version, the model is trained and evaluated here on the notebook instance itself. We'll show in the follow-on notebook how to take advantage of Amazon SageMaker to separate these infrastructure needs.\n",
    "\n",
    "The SageMaker kernel specified above has most of the libraries we need, but we'll first check the SageMaker Python SDK version is up-to-date and install IPyWidgets to power our interactive classifier widget later on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install \"ipywidgets>=8,<9\" \"sagemaker>2.207,<3\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download News Aggregator Dataset\n",
    "\n",
    "We will download **FastAI AG News** dataset from the [Registry of Open Data on AWS](https://registry.opendata.aws/fast-ai-nlp/) public repository. This dataset contains a table of news headlines and their corresponding classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "local_dir = \"data\"\n",
    "# Download the AG News data from the Registry of Open Data on AWS.\n",
    "!mkdir -p {local_dir}\n",
    "!aws s3 cp s3://fast-ai-nlp/ag_news_csv.tgz {local_dir} --no-sign-request\n",
    "\n",
    "# Un-tar the AG News data.\n",
    "!tar zxf {local_dir}/ag_news_csv.tgz -C {local_dir}/ --strip-components=1 --no-same-owner\n",
    "print(\"Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's visualize the dataset\n",
    "\n",
    "We will load the ag_news_csv/train.csv file to a Pandas dataframe for our data processing work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import re\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import util.preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "column_names = [\"CATEGORY\", \"TITLE\", \"CONTENT\"]\n",
    "# we use the train.csv only\n",
    "df = pd.read_csv(f\"{local_dir}/train.csv\", names=column_names, header=None, delimiter=\",\")\n",
    "# shuffle the DataFrame rows\n",
    "df = df.sample(frac=1, random_state=1337)\n",
    "# make the category classes more readable\n",
    "mapping = {1: \"World\", 2: \"Sports\", 3: \"Business\", 4: \"Sci/Tech\"}\n",
    "df = df.replace({\"CATEGORY\": mapping})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this exercise we'll **only use**:\n",
    "\n",
    "- The **title** (Headline) of the news story, as our input\n",
    "- The **category**, as our target variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"CATEGORY\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset has **four article categories** with equal weighting:\n",
    "\n",
    "- Business\n",
    "- Sci/Tech\n",
    "- Sports\n",
    "- World\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Natural Language Pre-Processing\n",
    "\n",
    "We'll do some basic processing of the text data to convert it into numerical form that the algorithm will be able to consume to create a model.\n",
    "\n",
    "We will do typical pre processing for NLP workloads such as: dummy encoding the labels, tokenizing the documents and set fixed sequence lengths for input feature dimension, padding documents to have fixed length input vectors.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Encode the Labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "encoded_y, labels = util.preprocessing.dummy_encode_labels(df, \"CATEGORY\")\n",
    "print(labels)\n",
    "print(encoded_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example, looking at the first record in our (shuffled) dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"CATEGORY\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "encoded_y[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenize and Set Fixed Sequence Lengths\n",
    "\n",
    "We want to describe our inputs at the more meaningful word level (rather than individual characters), and ensure a fixed length of the input feature dimension.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_cell_guid": "7bcf422f-0e75-4d49-b3b1-12553fcaf4ff",
    "_uuid": "46b7fc9aef5a519f96a295e980ba15deee781e97",
    "tags": []
   },
   "outputs": [],
   "source": [
    "processed_docs, tokenizer = util.preprocessing.tokenize_and_pad_docs(df, \"TITLE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df[\"TITLE\"].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "processed_docs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Word Embeddings\n",
    "\n",
    "To represent our words in numeric form, we'll use pre-trained vector representations for each word in the vocabulary: In this case we'll be using [pre-trained word embeddings from FastText](https://fasttext.cc/docs/en/crawl-vectors.html), which are also available for a broad range of languages other than English.\n",
    "\n",
    "You could also explore training custom, domain-specific word embeddings using SageMaker's built-in [BlazingText algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/blazingtext.html). See the official [blazingtext_word2vec_text8 sample](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/introduction_to_amazon_algorithms/blazingtext_word2vec_text8) for an example notebook showing how.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "embedding_matrix = util.preprocessing.get_word_embeddings(tokenizer, f\"{local_dir}/embeddings\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.save(\n",
    "    file=f\"{local_dir}/embeddings/docs-embedding-matrix\",\n",
    "    arr=embedding_matrix,\n",
    "    allow_pickle=False,\n",
    ")\n",
    "vocab_size = embedding_matrix.shape[0]\n",
    "print(embedding_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Train and Test Sets\n",
    "\n",
    "Finally we need to divide our data into model training and evaluation sets:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    processed_docs,\n",
    "    encoded_y,\n",
    "    test_size=0.2,\n",
    "    random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Do you always remember to save your datasets for traceability when experimenting locally? ;-)\n",
    "os.makedirs(f\"{local_dir}/train\", exist_ok=True)\n",
    "np.save(f\"{local_dir}/train/train_X.npy\", X_train)\n",
    "np.save(f\"{local_dir}/train/train_Y.npy\", y_train)\n",
    "os.makedirs(f\"{local_dir}/test\", exist_ok=True)\n",
    "np.save(f\"{local_dir}/test/test_X.npy\", X_test)\n",
    "np.save(f\"{local_dir}/test/test_Y.npy\", y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import Conv1D, Dense, Dropout, Embedding, Flatten, MaxPooling1D\n",
    "from tensorflow.keras.models import Sequential\n",
    "\n",
    "seed = 42\n",
    "np.random.seed(seed)\n",
    "num_classes = len(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(\n",
    "    Embedding(\n",
    "        embedding_matrix.shape[0],  # Final vocabulary size\n",
    "        embedding_matrix.shape[1],  # Word vector dimensions\n",
    "        weights=[embedding_matrix],\n",
    "        input_length=40,\n",
    "        trainable=False,\n",
    "        name=\"embed\",\n",
    "    )\n",
    ")\n",
    "model.add(Conv1D(filters=128, kernel_size=3, activation=\"relu\", name=\"conv_1\"))\n",
    "model.add(MaxPooling1D(pool_size=5, name=\"maxpool_1\"))\n",
    "model.add(Flatten(name=\"flat_1\"))\n",
    "model.add(Dropout(0.3, name=\"dropout_1\"))\n",
    "model.add(Dense(128, activation=\"relu\", name=\"dense_1\"))\n",
    "model.add(Dense(num_classes, activation=\"softmax\", name=\"out_1\"))\n",
    "\n",
    "# Compile the model\n",
    "optimizer = tf.keras.optimizers.RMSprop(learning_rate=0.001)\n",
    "model.compile(optimizer=optimizer, loss=\"binary_crossentropy\", metrics=[\"acc\"])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit (Train) and Evaluate the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# fit the model here in the notebook:\n",
    "print(\"Training model\")\n",
    "model.fit(X_train, y_train, batch_size=16, epochs=5, verbose=1)\n",
    "print(\"Evaluating model\")\n",
    "# TODO: Better differentiate train vs val loss in logs\n",
    "scores = model.evaluate(X_test, y_test, verbose=2)\n",
    "print(\n",
    "    \"Validation results: \"\n",
    "    + \"; \".join(\n",
    "        map(lambda i: f\"{model.metrics_names[i]}={scores[i]:.5f}\", range(len(model.metrics_names)))\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the Model (Locally)\n",
    "\n",
    "Let's evaluate our model with some example headlines...\n",
    "\n",
    "If you struggle with the widget, you can always simply call the `classify()` function from Python. You can be creative with your headlines!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython import display\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "\n",
    "def classify(text):\n",
    "    \"\"\"Classify a headline and print the results\"\"\"\n",
    "    encoded_example = tokenizer.texts_to_sequences([text])\n",
    "    # Pad documents to a max length of 40 words\n",
    "    max_length = 40\n",
    "    padded_example = pad_sequences(encoded_example, maxlen=max_length, padding=\"post\")\n",
    "    result = model.predict(padded_example)\n",
    "    print(result)\n",
    "    ix = np.argmax(result)\n",
    "    print(f\"Predicted class: '{labels[ix]}' with confidence {result[0][ix]:.2%}\")\n",
    "\n",
    "\n",
    "interaction = widgets.interact_manual(\n",
    "    classify,\n",
    "    text=widgets.Text(\n",
    "        value=\"The markets were bullish after news of the merger\",\n",
    "        placeholder=\"Type a news headline...\",\n",
    "        description=\"Headline:\",\n",
    "        layout=widgets.Layout(width=\"99%\"),\n",
    "    ),\n",
    ")\n",
    "interaction.widget.children[1].description = \"Classify!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Or just use the function to classify your own headline:\n",
    "classify(\"Retailers are expanding after the recent economic growth\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review\n",
    "\n",
    "In this notebook we pre-processed publicly downloadable data and trained a neural news headline classifier model: As a data scientist might normally do when working on a local machine.\n",
    "\n",
    "...But can we use the cloud more effectively to allocate high-performance resources; and easily deploy our trained models for use by other applications?\n",
    "\n",
    "Head on over to the next notebook, [Headline Classifier SageMaker.ipynb](Headline%20Classifier%20SageMaker.ipynb), where we'll show how the same model can be trained and then deployed on specific target infrastructure with Amazon SageMaker.\n"
   ]
  }
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