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How to solve a problem on Kaggle with TF-Hub

View on TensorFlow.org

TF-Hub is a platform to share machine learning expertise packaged in reusable resources, notably pre-trained modules. In this tutorial, we will use a TF-Hub text embedding module to train a simple sentiment classifier with a reasonable baseline accuracy. We will then submit the predictions to Kaggle.

For more detailed tutorial on text classification with TF-Hub and further steps for improving the accuracy, take a look at Text classification with TF-Hub.

Setup

pip install -q kaggle

import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import zipfile

from sklearn import model_selection

2023-12-08 13:55:28.089603: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2023-12-08 13:55:28.089651: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2023-12-08 13:55:28.091170: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

Since this tutorial will be using a dataset from Kaggle, it requires creating an API Token for your Kaggle account, and uploading it to the Colab environment.

import os
import pathlib

# Upload the API token.
def get_kaggle():
  try:
    import kaggle
    return kaggle
  except OSError:
    pass

  token_file = pathlib.Path("~/.kaggle/kaggle.json").expanduser()
  token_file.parent.mkdir(exist_ok=True, parents=True)

  try:
    from google.colab import files
  except ImportError:
    raise ValueError("Could not find kaggle token.")

  uploaded = files.upload()
  token_content = uploaded.get('kaggle.json', None)
  if token_content:
    token_file.write_bytes(token_content)
    token_file.chmod(0o600)
  else:
    raise ValueError('Need a file named "kaggle.json"')

  import kaggle
  return kaggle


kaggle = get_kaggle()

Getting started

Data

We will try to solve the Sentiment Analysis on Movie Reviews task from Kaggle. The dataset consists of syntactic subphrases of the Rotten Tomatoes movie reviews. The task is to label the phrases as negative or positive on the scale from 1 to 5.

You must accept the competition rules before you can use the API to download the data.

SENTIMENT_LABELS = [
    "negative", "somewhat negative", "neutral", "somewhat positive", "positive"
]

# Add a column with readable values representing the sentiment.
def add_readable_labels_column(df, sentiment_value_column):
  df["SentimentLabel"] = df[sentiment_value_column].replace(
      range(5), SENTIMENT_LABELS)

# Download data from Kaggle and create a DataFrame.
def load_data_from_zip(path):
  with zipfile.ZipFile(path, "r") as zip_ref:
    name = zip_ref.namelist()[0]
    with zip_ref.open(name) as zf:
      return pd.read_csv(zf, sep="\t", index_col=0)


# The data does not come with a validation set so we'll create one from the
# training set.
def get_data(competition, train_file, test_file, validation_set_ratio=0.1):
  data_path = pathlib.Path("data")
  kaggle.api.competition_download_files(competition, data_path)
  competition_path = (data_path/competition)
  competition_path.mkdir(exist_ok=True, parents=True)
  competition_zip_path = competition_path.with_suffix(".zip")

  with zipfile.ZipFile(competition_zip_path, "r") as zip_ref:
    zip_ref.extractall(competition_path)

  train_df = load_data_from_zip(competition_path/train_file)
  test_df = load_data_from_zip(competition_path/test_file)

  # Add a human readable label.
  add_readable_labels_column(train_df, "Sentiment")

  # We split by sentence ids, because we don't want to have phrases belonging
  # to the same sentence in both training and validation set.
  train_indices, validation_indices = model_selection.train_test_split(
      np.unique(train_df["SentenceId"]),
      test_size=validation_set_ratio,
      random_state=0)

  validation_df = train_df[train_df["SentenceId"].isin(validation_indices)]
  train_df = train_df[train_df["SentenceId"].isin(train_indices)]
  print("Split the training data into %d training and %d validation examples." %
        (len(train_df), len(validation_df)))

  return train_df, validation_df, test_df


train_df, validation_df, test_df = get_data(
    "sentiment-analysis-on-movie-reviews",
    "train.tsv.zip", "test.tsv.zip")

Split the training data into 140315 training and 15745 validation examples.

train_df.head(20)

Training an Model

class MyModel(tf.keras.Model):
  def __init__(self, hub_url):
    super().__init__()
    self.hub_url = hub_url
    self.embed = hub.load(self.hub_url).signatures['default']
    self.sequential = tf.keras.Sequential([
      tf.keras.layers.Dense(500),
      tf.keras.layers.Dense(100),
      tf.keras.layers.Dense(5),
    ])

  def call(self, inputs):
    phrases = inputs['Phrase'][:,0]
    embedding = 5*self.embed(phrases)['default']
    return self.sequential(embedding)

  def get_config(self):
    return {"hub_url":self.hub_url}

model = MyModel("https://tfhub.dev/google/nnlm-en-dim128/1")
model.compile(
    loss = tf.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=tf.optimizers.Adam(), 
    metrics = [tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")])

2023-12-08 13:55:32.728028: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:274] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected

history = model.fit(x=dict(train_df), y=train_df['Sentiment'],
          validation_data=(dict(validation_df), validation_df['Sentiment']),
          epochs = 25)

Epoch 1/25
4385/4385 [==============================] - 15s 3ms/step - loss: 1.0232 - accuracy: 0.5853 - val_loss: 0.9968 - val_accuracy: 0.5964
Epoch 2/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9998 - accuracy: 0.5950 - val_loss: 0.9812 - val_accuracy: 0.5966
Epoch 3/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9954 - accuracy: 0.5957 - val_loss: 0.9867 - val_accuracy: 0.5933
Epoch 4/25
4385/4385 [==============================] - 14s 3ms/step - loss: 0.9925 - accuracy: 0.5982 - val_loss: 0.9881 - val_accuracy: 0.5927
Epoch 5/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9910 - accuracy: 0.5982 - val_loss: 0.9897 - val_accuracy: 0.5995
Epoch 6/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9902 - accuracy: 0.5991 - val_loss: 0.9802 - val_accuracy: 0.5947
Epoch 7/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9894 - accuracy: 0.5988 - val_loss: 0.9945 - val_accuracy: 0.5867
Epoch 8/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9890 - accuracy: 0.5984 - val_loss: 0.9895 - val_accuracy: 0.5972
Epoch 9/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9882 - accuracy: 0.5988 - val_loss: 0.9825 - val_accuracy: 0.5947
Epoch 10/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9882 - accuracy: 0.5988 - val_loss: 0.9818 - val_accuracy: 0.5921
Epoch 11/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9878 - accuracy: 0.5992 - val_loss: 0.9842 - val_accuracy: 0.5924
Epoch 12/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9879 - accuracy: 0.5991 - val_loss: 0.9813 - val_accuracy: 0.5927
Epoch 13/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9874 - accuracy: 0.6002 - val_loss: 0.9831 - val_accuracy: 0.5933
Epoch 14/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9872 - accuracy: 0.5998 - val_loss: 0.9795 - val_accuracy: 0.5966
Epoch 15/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9868 - accuracy: 0.5996 - val_loss: 0.9812 - val_accuracy: 0.5943
Epoch 16/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9868 - accuracy: 0.5980 - val_loss: 0.9757 - val_accuracy: 0.5956
Epoch 17/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9865 - accuracy: 0.6003 - val_loss: 0.9762 - val_accuracy: 0.5986
Epoch 18/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9867 - accuracy: 0.5994 - val_loss: 0.9804 - val_accuracy: 0.6006
Epoch 19/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9864 - accuracy: 0.5989 - val_loss: 0.9773 - val_accuracy: 0.6024
Epoch 20/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9864 - accuracy: 0.6000 - val_loss: 0.9788 - val_accuracy: 0.5946
Epoch 21/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9860 - accuracy: 0.6010 - val_loss: 0.9779 - val_accuracy: 0.5943
Epoch 22/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9859 - accuracy: 0.5994 - val_loss: 0.9948 - val_accuracy: 0.5968
Epoch 23/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9857 - accuracy: 0.6000 - val_loss: 0.9745 - val_accuracy: 0.5980
Epoch 24/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9859 - accuracy: 0.5995 - val_loss: 0.9768 - val_accuracy: 0.5964
Epoch 25/25
4385/4385 [==============================] - 13s 3ms/step - loss: 0.9858 - accuracy: 0.6005 - val_loss: 0.9764 - val_accuracy: 0.5989

Prediction

Run predictions for the validation set and training set.

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])

[<matplotlib.lines.Line2D at 0x7f4a7f30a4c0>]

png

train_eval_result = model.evaluate(dict(train_df), train_df['Sentiment'])
validation_eval_result = model.evaluate(dict(validation_df), validation_df['Sentiment'])

print(f"Training set accuracy: {train_eval_result[1]}")
print(f"Validation set accuracy: {validation_eval_result[1]}")

4385/4385 [==============================] - 13s 3ms/step - loss: 0.9833 - accuracy: 0.6026
493/493 [==============================] - 1s 1ms/step - loss: 0.9764 - accuracy: 0.5989
Training set accuracy: 0.6026440262794495
Validation set accuracy: 0.5988568067550659

Confusion matrix

Another very interesting statistic, especially for multiclass problems, is the confusion matrix. The confusion matrix allows visualization of the proportion of correctly and incorrectly labelled examples. We can easily see how much our classifier is biased and whether the distribution of labels makes sense. Ideally the largest fraction of predictions should be distributed along the diagonal.

predictions = model.predict(dict(validation_df))
predictions = tf.argmax(predictions, axis=-1)
predictions

493/493 [==============================] - 1s 1ms/step
<tf.Tensor: shape=(15745,), dtype=int64, numpy=array([1, 1, 2, ..., 2, 2, 2])>

cm = tf.math.confusion_matrix(validation_df['Sentiment'], predictions)
cm = cm/cm.numpy().sum(axis=1)[:, tf.newaxis]

sns.heatmap(
    cm, annot=True,
    xticklabels=SENTIMENT_LABELS,
    yticklabels=SENTIMENT_LABELS)
plt.xlabel("Predicted")
plt.ylabel("True")

Text(50.72222222222221, 0.5, 'True')

png

We can easily submit the predictions back to Kaggle by pasting the following code to a code cell and executing it:

test_predictions = model.predict(dict(test_df))
test_predictions = np.argmax(test_predictions, axis=-1)

result_df = test_df.copy()

result_df["Predictions"] = test_predictions

result_df.to_csv(
    "predictions.csv",
    columns=["Predictions"],
    header=["Sentiment"])
kaggle.api.competition_submit("predictions.csv", "Submitted from Colab",
                              "sentiment-analysis-on-movie-reviews")

After submitting, check the leaderboard to see how you did.