<jupyter_start><jupyter_text>Keras for Text Classification**Learning Objectives**1. Learn how to create a text classification datasets using BigQuery1. Learn how to tokenize and integerize a corpus of text for training in Keras1. Learn how to do one-hot-encodings in Keras1. Learn how to use embedding layers to represent words in Keras1. Learn about the bag-of-word representation for sentences1. Learn how to use DNN/CNN/RNN model to classify text in keras IntroductionIn this notebook, we will implement text models to recognize the probable source (GitHub, TechCrunch, or The New York Times) of the titles we have in the title dataset we constructed in the first task of the lab.In the next step, we will load and pre-process the texts and labels so that they are suitable to be fed to a Keras model. For the texts of the titles we will learn how to split them into a list of tokens, and then how to map each token to an integer using the Keras Tokenizer class. What will be fed to our Keras models will be batches of padded list of integers representing the text. For the labels, we will learn how to one-hot-encode each of the 3 classes into a 3 dimensional basis vector.Then we will explore a few possible models to do the title classification. All models will be fed padded list of integers, and all models will start with a Keras Embedding layer that transforms the integer representing the words into dense vectors.The first model will be a simple bag-of-word DNN model that averages up the word vectors and feeds the tensor that results to further dense layers. Doing so means that we forget the word order (and hence that we consider sentences as a “bag-of-words”). In the second and in the third model we will keep the information about the word order using a simple RNN and a simple CNN allowing us to achieve the same performance as with the DNN model but in much fewer epochs.<jupyter_code>import os

import pandas as pd
from google.cloud import bigquery
%load_ext google.cloud.bigquery<jupyter_output><empty_output><jupyter_text>Replace the variable values in the cell below:<jupyter_code>PROJECT = !(gcloud config get-value core/project)
PROJECT = PROJECT[0]
%env PROJECT = {PROJECT}
%env BUCKET = {PROJECT}
%env REGION = "us-central1"
SEED = 0<jupyter_output><empty_output><jupyter_text>Create a Dataset from BigQuery Hacker news headlines are available as a BigQuery public dataset. The [dataset](https://bigquery.cloud.google.com/table/bigquery-public-data:hacker_news.stories?tab=details) contains all headlines from the sites inception in October 2006 until October 2015. Here is a sample of the dataset:<jupyter_code>%%bigquery --project $PROJECT

SELECT
    url, title, score
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    LENGTH(title) > 10
    AND score > 10
    AND LENGTH(url) > 0
LIMIT 10<jupyter_output><empty_output><jupyter_text>Let's do some regular expression parsing in BigQuery to get the source of the newspaper article from the URL. For example, if the url is http://mobile.nytimes.com/...., I want to be left with nytimes<jupyter_code>%%bigquery --project $PROJECT

SELECT
    ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.'))[OFFSET(1)] AS source,
    COUNT(title) AS num_articles
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    REGEXP_CONTAINS(REGEXP_EXTRACT(url, '.*://(.[^/]+)/'), '.com$')
    AND LENGTH(title) > 10
GROUP BY
    source
ORDER BY num_articles DESC
  LIMIT 100<jupyter_output><empty_output><jupyter_text>Now that we have good parsing of the URL to get the source, let's put together a dataset of source and titles. This will be our labeled dataset for machine learning.<jupyter_code>regex = ".*://(.[^/]+)/"


sub_query = """
SELECT
    title,
    ARRAY_REVERSE(SPLIT(REGEXP_EXTRACT(url, '{0}'), '.'))[OFFSET(1)] AS source
    
FROM
    `bigquery-public-data.hacker_news.stories`
WHERE
    REGEXP_CONTAINS(REGEXP_EXTRACT(url, '{0}'), '.com$')
    AND LENGTH(title) > 10
""".format(
    regex
)


query = """
SELECT 
    LOWER(REGEXP_REPLACE(title, '[^a-zA-Z0-9 $.-]', ' ')) AS title,
    source
FROM
  ({sub_query})
WHERE (source = 'github' OR source = 'nytimes' OR source = 'techcrunch')
""".format(
    sub_query=sub_query
)

print(query)<jupyter_output><empty_output><jupyter_text>For ML training, we usually need to split our dataset into training and evaluation datasets (and perhaps an independent test dataset if we are going to do model or feature selection based on the evaluation dataset). AutoML however figures out on its own how to create these splits, so we won't need to do that here.<jupyter_code>bq = bigquery.Client(project=PROJECT)
title_dataset = bq.query(query).to_dataframe()
title_dataset.head()<jupyter_output><empty_output><jupyter_text>AutoML for text classification requires that* the dataset be in csv form with * the first column being the texts to classify or a GCS path to the text * the last colum to be the text labelsThe dataset we pulled from BiqQuery satisfies these requirements.<jupyter_code>print(f"The full dataset contains {len(title_dataset)} titles")<jupyter_output><empty_output><jupyter_text>Let's make sure we have roughly the same number of labels for each of our three labels:<jupyter_code>title_dataset.source.value_counts()<jupyter_output><empty_output><jupyter_text>Finally we will save our data, which is currently in-memory, to disk.We will create a csv file containing the full dataset and another containing only 1000 articles for development.**Note:** It may take a long time to train AutoML on the full dataset, so we recommend to use the sample dataset for the purpose of learning the tool.<jupyter_code>DATADIR = "./data/"

if not os.path.exists(DATADIR):
    os.makedirs(DATADIR)
FULL_DATASET_NAME = "titles_full.csv"
FULL_DATASET_PATH = os.path.join(DATADIR, FULL_DATASET_NAME)

# Let's shuffle the data before writing it to disk.
title_dataset = title_dataset.sample(n=len(title_dataset))

title_dataset.to_csv(
    FULL_DATASET_PATH, header=False, index=False, encoding="utf-8"
)<jupyter_output><empty_output><jupyter_text>Now let's sample 1000 articles from the full dataset and make sure we have enough examples for each label in our sample dataset (see [here](https://cloud.google.com/natural-language/automl/docs/beginners-guide) for further details on how to prepare data for AutoML).<jupyter_code>sample_title_dataset = title_dataset.sample(n=1000)
sample_title_dataset.source.value_counts()<jupyter_output><empty_output><jupyter_text>Let's write the sample datatset to disk.<jupyter_code>SAMPLE_DATASET_NAME = "titles_sample.csv"
SAMPLE_DATASET_PATH = os.path.join(DATADIR, SAMPLE_DATASET_NAME)

sample_title_dataset.to_csv(
    SAMPLE_DATASET_PATH, header=False, index=False, encoding="utf-8"
)
sample_title_dataset.head()
import os
import shutil

import pandas as pd
import tensorflow as tf
from tensorflow.keras.callbacks import EarlyStopping, TensorBoard
from tensorflow.keras.layers import (
    GRU,
    Conv1D,
    Dense,
    Embedding,
    Flatten,
    Lambda,
)
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical

print(tf.__version__)
%matplotlib inline<jupyter_output><empty_output><jupyter_text>Let's start by specifying where the information about the trained models will be saved as well as where our dataset is located:<jupyter_code>LOGDIR = "./text_models"
DATA_DIR = "./data"<jupyter_output><empty_output><jupyter_text>Loading the dataset Our dataset consists of titles of articles along with the label indicating from which source these articles have been taken from (GitHub, TechCrunch, or The New York Times).<jupyter_code>DATASET_NAME = "titles_full.csv"
TITLE_SAMPLE_PATH = os.path.join(DATA_DIR, DATASET_NAME)
COLUMNS = ["title", "source"]

titles_df = pd.read_csv(TITLE_SAMPLE_PATH, header=None, names=COLUMNS)
titles_df.head()<jupyter_output><empty_output><jupyter_text>Integerize the texts The first thing we need to do is to find how many words we have in our dataset (`VOCAB_SIZE`), how many titles we have (`DATASET_SIZE`), and what the maximum length of the titles we have (`MAX_LEN`) is. Keras offers the `Tokenizer` class in its `keras.preprocessing.text` module to help us with that:<jupyter_code>tokenizer = Tokenizer()
tokenizer.fit_on_texts(titles_df.title)
integerized_titles = tokenizer.texts_to_sequences(titles_df.title)
integerized_titles[:3]
VOCAB_SIZE = len(tokenizer.index_word)
VOCAB_SIZE
DATASET_SIZE = tokenizer.document_count
DATASET_SIZE
MAX_LEN = max(len(sequence) for sequence in integerized_titles)
MAX_LEN<jupyter_output><empty_output><jupyter_text>Let's now implement a function `create_sequence` that will * take as input our titles as well as the maximum sentence length and * returns a list of the integers corresponding to our tokens padded to the sentence maximum lengthKeras has the helper functions `pad_sequence` for that on the top of the tokenizer methods.<jupyter_code># TODO 1
def create_sequences(texts, max_len=MAX_LEN):
    sequences = tokenizer.texts_to_sequences(texts)
    padded_sequences = pad_sequences(sequences, max_len, padding="post")
    return padded_sequences
sequences = create_sequences(titles_df.title[:3])
sequences
titles_df.source[:4]<jupyter_output><empty_output><jupyter_text>We now need to write a function that * takes a title source and* returns the corresponding one-hot encoded vectorKeras `to_categorical` is handy for that.<jupyter_code>CLASSES = {"github": 0, "nytimes": 1, "techcrunch": 2}
N_CLASSES = len(CLASSES)
# TODO 2
def encode_labels(sources):
    classes = [CLASSES[source] for source in sources]
    one_hots = to_categorical(classes)
    return one_hots
encode_labels(titles_df.source[:4])<jupyter_output><empty_output><jupyter_text>Preparing the train/test splits Let's split our data into train and test splits:<jupyter_code>N_TRAIN = int(DATASET_SIZE * 0.80)

titles_train, sources_train = (
    titles_df.title[:N_TRAIN],
    titles_df.source[:N_TRAIN],
)

titles_valid, sources_valid = (
    titles_df.title[N_TRAIN:],
    titles_df.source[N_TRAIN:],
)<jupyter_output><empty_output><jupyter_text>To be on the safe side, we verify that the train and test splitshave roughly the same number of examples per classes.Since it is the case, accuracy will be a good metric to use to measurethe performance of our models.<jupyter_code>sources_train.value_counts()
sources_valid.value_counts()<jupyter_output><empty_output><jupyter_text>Using `create_sequence` and `encode_labels`, we can now prepare thetraining and validation data to feed our models.The features will bepadded list of integers and the labels will be one-hot-encoded 3D vectors.<jupyter_code>X_train, Y_train = create_sequences(titles_train), encode_labels(sources_train)
X_valid, Y_valid = create_sequences(titles_valid), encode_labels(sources_valid)
X_train[:3]
Y_train[:3]<jupyter_output><empty_output><jupyter_text>Building a DNN model The build_dnn_model function below returns a compiled Keras model that implements a simple embedding layer transforming the word integers into dense vectors, followed by a Dense softmax layer that returns the probabilities for each class.Note that we need to put a custom Keras Lambda layer in between the Embedding layer and the Dense softmax layer to do an average of the word vectors returned by the embedding layer. This is the average that's fed to the dense softmax layer. By doing so, we create a model that is simple but that loses information about the word order, creating a model that sees sentences as "bag-of-words".<jupyter_code>def build_dnn_model(embed_dim):

    model = Sequential(
        [
            Embedding(
                VOCAB_SIZE + 1, embed_dim, input_shape=[MAX_LEN]
            ),  # TODO 3
            Lambda(lambda x: tf.reduce_mean(x, axis=1)),  # TODO 4
            Dense(N_CLASSES, activation="softmax"),  # TODO 5
        ]
    )

    model.compile(
        optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
    )
    return model<jupyter_output><empty_output><jupyter_text>Below we train the model on 100 epochs but adding an `EarlyStopping` callback that will stop the training as soon as the validation loss has not improved after a number of steps specified by `PATIENCE` . Note that we also give the `model.fit` method a Tensorboard callback so that we can later compare all the models using TensorBoard.<jupyter_code>%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, "dnn")
shutil.rmtree(MODEL_DIR, ignore_errors=True)

BATCH_SIZE = 300
EPOCHS = 100
EMBED_DIM = 10
PATIENCE = 5

dnn_model = build_dnn_model(embed_dim=EMBED_DIM)

dnn_history = dnn_model.fit(
    X_train,
    Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(dnn_history.history)[["loss", "val_loss"]].plot()
pd.DataFrame(dnn_history.history)[["accuracy", "val_accuracy"]].plot()

dnn_model.summary()<jupyter_output><empty_output><jupyter_text>Building a RNN model The `build_dnn_model` function below returns a compiled Keras model that implements a simple RNN model with a single `GRU` layer, which now takes into account the word order in the sentence.The first and last layers are the same as for the simple DNN model.Note that we set `mask_zero=True` in the `Embedding` layer so that the padded words (represented by a zero) are ignored by this and the subsequent layers.<jupyter_code>def build_rnn_model(embed_dim, units):

    model = Sequential(
        [
            Embedding(
                VOCAB_SIZE + 1, embed_dim, input_shape=[MAX_LEN], mask_zero=True
            ),  # TODO 3
            GRU(units),  # TODO 5
            Dense(N_CLASSES, activation="softmax"),
        ]
    )

    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
        loss="categorical_crossentropy",
        metrics=["accuracy"],
    )
    return model<jupyter_output><empty_output><jupyter_text>Let's train the model with early stoping as above. Observe that we obtain the same type of accuracy as with the DNN model, but in less epochs (~3 v.s. ~20 epochs):<jupyter_code>%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, "rnn")
shutil.rmtree(MODEL_DIR, ignore_errors=True)

EPOCHS = 100
BATCH_SIZE = 300
EMBED_DIM = 10
UNITS = 16
PATIENCE = 2

rnn_model = build_rnn_model(embed_dim=EMBED_DIM, units=UNITS)

history = rnn_model.fit(
    X_train,
    Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(history.history)[["loss", "val_loss"]].plot()
pd.DataFrame(history.history)[["accuracy", "val_accuracy"]].plot()

rnn_model.summary()<jupyter_output><empty_output><jupyter_text>Build a CNN model The `build_dnn_model` function below returns a compiled Keras model that implements a simple CNN model with a single `Conv1D` layer, which now takes into account the word order in the sentence.The first and last layers are the same as for the simple DNN model, but we need to add a `Flatten` layer betwen the convolution and the softmax layer.Note that we set `mask_zero=True` in the `Embedding` layer so that the padded words (represented by a zero) are ignored by this and the subsequent layers.<jupyter_code>def build_cnn_model(embed_dim, filters, ksize, strides):

    model = Sequential(
        [
            Embedding(
                VOCAB_SIZE + 1, embed_dim, input_shape=[MAX_LEN], mask_zero=True
            ),  # TODO 3
            Conv1D(  # TODO 5
                filters=filters,
                kernel_size=ksize,
                strides=strides,
                activation="relu",
            ),
            Flatten(),  # TODO 5
            Dense(N_CLASSES, activation="softmax"),
        ]
    )

    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
        loss="categorical_crossentropy",
        metrics=["accuracy"],
    )
    return model<jupyter_output><empty_output><jupyter_text>Let's train the model. Again we observe that we get the same kind of accuracy as with the DNN model but in many fewer steps.<jupyter_code>%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, "cnn")
shutil.rmtree(MODEL_DIR, ignore_errors=True)

EPOCHS = 100
BATCH_SIZE = 300
EMBED_DIM = 5
FILTERS = 200
STRIDES = 2
KSIZE = 3
PATIENCE = 2


cnn_model = build_cnn_model(
    embed_dim=EMBED_DIM,
    filters=FILTERS,
    strides=STRIDES,
    ksize=KSIZE,
)

cnn_history = cnn_model.fit(
    X_train,
    Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(cnn_history.history)[["loss", "val_loss"]].plot()
pd.DataFrame(cnn_history.history)[["accuracy", "val_accuracy"]].plot()

cnn_model.summary()<jupyter_output><empty_output><jupyter_text>(Optional) Using the Keras Text Preprocessing Layer Thanks to the new Keras preprocessing layer, we can also include the preprocessing of the text (i.e., the tokenization followed by the integer representation of the tokens) within the model itself as a standard Keras layer. Let us first import this text preprocessing layer:<jupyter_code>from keras.layers import TextVectorization<jupyter_output><empty_output><jupyter_text>At instanciation, we can specify the maximum length of the sequence output as well as the maximum number of tokens to be considered:<jupyter_code>MAX_LEN = 26
MAX_TOKENS = 20000
preprocessing_layer = TextVectorization(
    output_sequence_length=MAX_LEN, max_tokens=MAX_TOKENS
)<jupyter_output><empty_output><jupyter_text>Before using this layer in our model, we need to adapt it to our data so that it generates a token-to-integer mapping. Remeber our dataset looks like the following:<jupyter_code>titles_df.head()<jupyter_output><empty_output><jupyter_text>We can directly use the Pandas Series corresponding to the titles in our dataset to adapt the data using the `adapt` method:<jupyter_code>preprocessing_layer.adapt(titles_df.title)<jupyter_output><empty_output><jupyter_text>At this point, the preprocessing layer can create the integer representation of our input text if we simply apply the layer to it:<jupyter_code>X_train, X_valid = titles_train, titles_valid
X_train[:5]
integers = preprocessing_layer(X_train[:5])
integers<jupyter_output><empty_output><jupyter_text>**Exercise:** In the following cell, implement a function`build_model_with_text_preprocessing(embed_dim, units)` that returns a text model with the following sequential structure:1. the `preprocessing_layer` we defined above folowed by1. an embedding layer with `embed_dim` dimension for the output vectors followed by1. a GRU layer with `units` number of neurons followed by1. a final dense layer for classification<jupyter_code>def build_model_with_text_preprocessing(embed_dim, units):

    model = Sequential(
        [
            preprocessing_layer,
            Embedding(
                VOCAB_SIZE + 1, embed_dim, input_shape=[MAX_LEN], mask_zero=True
            ),  # TODO 3
            GRU(units),
            Dense(N_CLASSES, activation="softmax"),
        ]
    )

    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),
        loss="categorical_crossentropy",
        metrics=["accuracy"],
    )
    return model<jupyter_output><empty_output><jupyter_text>Our model is now able to cosume text directly as input! Again, consider the following text sample:<jupyter_code>X_train[:5]<jupyter_output><empty_output><jupyter_text>Then we can have our model directly predict on this input:<jupyter_code>model = build_model_with_text_preprocessing(embed_dim=EMBED_DIM, units=UNITS)
model.predict(X_train[:5])<jupyter_output><empty_output><jupyter_text>Of course the model above has not yet been trained, so its prediction are meaningless so far. Let us train it now on our dataset as before:<jupyter_code>%%time

tf.random.set_seed(33)

MODEL_DIR = os.path.join(LOGDIR, "rnn")
shutil.rmtree(MODEL_DIR, ignore_errors=True)

EPOCHS = 100
BATCH_SIZE = 300
EMBED_DIM = 10
UNITS = 16
PATIENCE = 2

model = build_model_with_text_preprocessing(embed_dim=EMBED_DIM, units=UNITS)

history = model.fit(
    X_train,
    Y_train,
    epochs=EPOCHS,
    batch_size=BATCH_SIZE,
    validation_data=(X_valid, Y_valid),
    callbacks=[EarlyStopping(patience=PATIENCE), TensorBoard(MODEL_DIR)],
)

pd.DataFrame(history.history)[["loss", "val_loss"]].plot()
pd.DataFrame(history.history)[["accuracy", "val_accuracy"]].plot()

model.summary()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.08552220888355343, 'fraction_numerical': 0.008307322929171669, 'mean_word_length': 4.986202931876976, 'pattern_counts': {'":': 3, '<': 137, '<?xml version=': 0, '>': 142, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '34', 'dir': 'jupyter-structured-clean-dedup', 'id': '76464', 'license': 'Apache-2.0', 'n_tokens_mistral': 6774, 'n_tokens_neox': 6491, 'n_words': 2087, 'path': 'notebooks/text_models/solutions/keras_for_text_classification.ipynb', 'repo_name': 'Jonathanpro/asl-ml-immersion', 'size': '20825'}