Learning Objectives
In this notebook, we will implement text models to recognize the probable source (Github, Tech-Crunch, or The New-York Times) of the titles we have in the title dataset.
First, we will load and pre-process the texts and labels so that they are suitable to be fed to sequential Keras models with first layer being TF-hub pre-trained modules. Thanks to this first layer, we won't need to tokenize and integerize the text before passing it to our models. The pre-trained layer will take care of that for us, and consume directly raw text. However, we will still have to one-hot-encode each of the 3 classes into a 3 dimensional basis vector.
Then we will build, train and compare simple models starting with different pre-trained TF-Hub layers.
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# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
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import os
from google.cloud import bigquery
import pandas as pd
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%load_ext google.cloud.bigquery
Replace the variable values in the cell below:
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PROJECT = "cloud-training-demos" # Replace with your PROJECT
BUCKET = PROJECT # defaults to PROJECT
REGION = "us-central1" # Replace with your REGION
SEED = 0
Hacker news headlines are available as a BigQuery public dataset. The dataset contains all headlines from the sites inception in October 2006 until October 2015.
Here is a sample of the dataset:
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%%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
Let's do some regular expression parsing in BigQuery to get the source of the newspaper article from the URL.
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%%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
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.
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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)
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.
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bq = bigquery.Client(project=PROJECT)
title_dataset = bq.query(query).to_dataframe()
title_dataset.head()
AutoML for text classification requires that
The dataset we pulled from BiqQuery satisfies these requirements.
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print("The full dataset contains {n} titles".format(n=len(title_dataset)))
Let's make sure we have roughly the same number of labels for each of our three labels:
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title_dataset.source.value_counts()
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.
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DATADIR = './data/'
if not os.path.exists(DATADIR):
os.makedirs(DATADIR)
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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')
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 for further details on how to prepare data for AutoML).
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sample_title_dataset = title_dataset.sample(n=1000)
sample_title_dataset.source.value_counts()
Let's write the sample datatset to disk.
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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')
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sample_title_dataset.head()
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import datetime
import os
import shutil
import pandas as pd
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard, EarlyStopping
from tensorflow_hub import KerasLayer
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
print(tf.__version__)
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%matplotlib inline
Let's start by specifying where the information about the trained models will be saved as well as where our dataset is located:
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MODEL_DIR = "./text_models"
DATA_DIR = "./data"
As in the previous labs, our dataset consists of titles of articles along with the label indicating from which source these articles have been taken from (GitHub, Tech-Crunch, or the New-York Times):
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ls ./data/
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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()
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Let's look again at the number of examples per label to make sure we have a well-balanced dataset:
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titles_df.source.value_counts()
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In this lab, we will use pre-trained TF-Hub embeddings modules for english for the first layer of our models. One immediate advantage of doing so is that the TF-Hub embedding module will take care for us of processing the raw text. This also means that our model will be able to consume text directly instead of sequences of integers representing the words.
However, as before, we still need to preprocess the labels into one-hot-encoded vectors:
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CLASSES = {
'github': 0,
'nytimes': 1,
'techcrunch': 2
}
N_CLASSES = len(CLASSES)
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def encode_labels(sources):
classes = [CLASSES[source] for source in sources]
one_hots = to_categorical(classes, num_classes=N_CLASSES)
return one_hots
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encode_labels(titles_df.source[:4])
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Let's split our data into train and test splits:
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N_TRAIN = int(len(titles_df) * 0.95)
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:])
To be on the safe side, we verify that the train and test splits have roughly the same number of examples per class.
Since it is the case, accuracy will be a good metric to use to measure the performance of our models.
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sources_train.value_counts()
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sources_valid.value_counts()
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Now let's create the features and labels we will feed our models with:
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X_train, Y_train = titles_train.values, encode_labels(sources_train)
X_valid, Y_valid = titles_valid.values, encode_labels(sources_valid)
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X_train[:3]
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Y_train[:3]
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We will first try a word embedding pre-trained using a Neural Probabilistic Language Model. TF-Hub has a 50-dimensional one called nnlm-en-dim50-with-normalization, which also normalizes the vectors produced.
Once loaded from its url, the TF-hub module can be used as a normal Keras layer in a sequential or functional model. Since we have enough data to fine-tune the parameters of the pre-trained embedding itself, we will set trainable=True in the KerasLayer that loads the pre-trained embedding:
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NNLM = "https://tfhub.dev/google/nnlm-en-dim50/2"
nnlm_module = KerasLayer(
NNLM, output_shape=[50], input_shape=[], dtype=tf.string, trainable=True)
Note that this TF-Hub embedding produces a single 50-dimensional vector when passed a sentence:
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nnlm_module(tf.constant(["The dog is happy to see people in the street."]))
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Let's write a function that
KerasLayer (i.e. the nnlm_module we constructed above) as well as the name of the model (say nnlm)
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def build_model(hub_module, name):
model = Sequential([
hub_module, # TODO
Dense(16, activation='relu'),
Dense(N_CLASSES, activation='softmax')
], name=name)
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
return model
Let's also wrap the training code into a train_and_evaluate function that
batch_sizehistory object, which will help us to plot the learning curves
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def train_and_evaluate(train_data, val_data, model, batch_size=5000):
X_train, Y_train = train_data
tf.random.set_seed(33)
model_dir = os.path.join(MODEL_DIR, model.name)
if tf.io.gfile.exists(model_dir):
tf.io.gfile.rmtree(model_dir)
history = model.fit(
X_train, Y_train,
epochs=100,
batch_size=batch_size,
validation_data=val_data,
callbacks=[EarlyStopping(), TensorBoard(model_dir)],
)
return history
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data = (X_train, Y_train)
val_data = (X_valid, Y_valid)
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nnlm_model = build_model(nnlm_module, 'nnlm')
nnlm_history = train_and_evaluate(data, val_data, nnlm_model)
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history = nnlm_history
pd.DataFrame(history.history)[['loss', 'val_loss']].plot()
pd.DataFrame(history.history)[['accuracy', 'val_accuracy']].plot()
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Try to beat the best model by modifying the model architecture, changing the TF-Hub embedding, and tweaking the training parameters.
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