Solving MemoryError: Unable to Allocate 14.6 GiB for One-Hot Encoding

The error 'MemoryError: Unable to allocate 14.6 GiB for an array with shape (62649, 62649) and data type float32' occurs when the system is unable to allocate enough memory to create an array of the specified shape and data type. This often happens during one-hot encoding of labels in machine learning, particularly with large datasets.

Here are some solutions to address this memory issue:

1. Reduce the number of labels:

   • Remove rare labels: Identify and remove labels that appear very infrequently in your dataset.
   • Combine similar labels: Group labels that have similar meaning or characteristics.

2. Use a different data type for encoding:

   • uint8 or int16: These data types require less memory than float32, making them suitable for encoding labels.

3. Optimize training parameters:

   • Reduce batch size: Training with smaller batches can lower memory usage.
   • Use a smaller model architecture: Simpler models require less memory to operate.

4. Utilize memory-efficient libraries:

   • Sparse matrices: Libraries like scikit-learn offer sparse matrices to represent data with many zero values, resulting in significant memory savings.

By implementing these strategies, you can effectively manage memory usage and overcome the 'MemoryError' during one-hot encoding, allowing you to process large datasets efficiently.