5 Effective Methods to Split the Iliad Dataset into Training and Test Data Using TensorFlow in Python

by

💡 Problem Formulation: In the realm of machine learning, one often needs to divide a dataset into training and test sets to evaluate the performance of models. The Iliad dataset, a substantial text corpus, is no exception. The goal is to partition this dataset, ensuring a representative distribution of data while maximizing the efficacy of our models. For example, given the entire text, we want to split it in a way that the training set contains 80% of the data, and the test set contains the remaining 20%.

Method 1: Manual Split Using TensorFlow’s Data API

This method leverages TensorFlow’s Data API to manually split the dataset into training and test sets. By using the take() and skip() methods, one can precisely control the amount of data in each subset based on the desired ratio.

Here’s an example:

import tensorflow as tf

# Assume 'all_data' is a TensorFlow dataset containing the Iliad text
# Let's say we want 80% training, 20% testing
split_ratio = 0.8
dataset_size = len(list(all_data))
train_size = int(split_ratio * dataset_size)

train_data = all_data.take(train_size)
test_data = all_data.skip(train_size)

Output: Two datasets, one for training and another for testing.

This code snippet first converts the Iliad dataset into a TensorFlow dataset. It then computes the size of the training set based on the desired split ratio. Using the take() method, it creates the training subset, and with skip(), it creates the test subset.

Method 2: Use of TensorFlow’s shuffle() and Split

Randomly shuffling the data before splitting ensures that the training and test sets are representative of the overall dataset. TensorFlow’s shuffle() method can create such a randomized dataset, followed by a manual split.

Here’s an example:

import tensorflow as tf

# Seed for reproducibility
seed = 123
all_data = all_data.shuffle(buffer_size=dataset_size, seed=seed)
train_data = all_data.take(train_size)
test_data = all_data.skip(train_size)

Output: Randomly shuffled and then split datasets.

First, this snippet applies the shuffle() method to the entire dataset, using a defined seed for reproducibility. After shuffling, it proceeds with the same splitting process as Method 1.

Method 3: Automated Split Using tfds.Split

Using TensorFlow Datasets’ built-in split functionality, one can perform the split automatically by specifying the desired proportions for training and test sets using the tfds.Split API.

Here’s an example:

import tensorflow_datasets as tfds

# Define the split
split = tfds.Split.TRAIN.subsplit([tfds.percent[:80], tfds.percent[80:]])

# Load the Iliad dataset, pre-split
train_data, test_data = tfds.load('iliad', split=split)

Output: Pre-split datasets based on the specified percentages.

When using this method, tfds.load() is called with an argument that defines how to split the data. subsplit() allows specifying percentages for different subsets, simplifying the process.

Method 4: Stratified Sampling with TensorFlow

Stratified sampling is crucial when the dataset has imbalanced classes or requires representative sampling from subgroups. TensorFlow offers the ability to perform such stratified splits to maintain the distribution of classes across training and test sets.

Here’s an example:

# This method requires a more complex implementation,
# generally involving tf.data.experimental.sample_from_datasets
# and might involve creating explicit stratification logic based on labels.

Output: Training and test sets with stratified sampling.

This method, while not explicitly shown, involves using TensorFlow’s data API to ensure that each class is proportionally represented in the training and test sets. This method is more complex and generally requires explicit implementation based on the specific needs of the dataset.

Bonus One-Liner Method 5: Use TensorFlow’s split Attribute

TensorFlow datasets sometimes come with a split attribute that can be easily used to divide the dataset according to pre-defined splits. This is a one-liner approach when such predefined splits are available.

Here’s an example:

import tensorflow_datasets as tfds

train_data, test_data = tfds.load('iliad', split=['train', 'test'])

Output: Automatically split training and test sets if predefined.

This simple line of code loads the Iliad dataset and immediately splits it into training and test sets, which TensorFlow datasets’ maintainers have predefined.

Summary/Discussion

  • Method 1: Manual Split Using TensorFlow’s Data API. Strengths: Fine-grained control over the dataset split. Weaknesses: Can be cumbersome for large datasets.
  • Method 2: TensorFlow’s shuffle() and Split. Strengths: Ensures a random representation of data. Weaknesses: Requires enough memory to shuffle large datasets.
  • Method 3: Automated Split Using tfds.Split. Strengths: Convenient and clean code for splitting. Weaknesses: Dependent on the availability of the dataset within TensorFlow Datasets.
  • Method 4: Stratified Sampling with TensorFlow. Strengths: Maintains class distribution across sets. Weaknesses: Complex implementation for those unfamiliar with stratified sampling.
  • Method 5: TensorFlow’s split Attribute. Strengths: Extremely easy when usable. Weaknesses: Only works with predefined splits provided by the dataset creator.