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Self-Supervised Learning Methods

This section explains the self-supervised learning methods implemented in Tabular SSL.

Overview

Self-supervised learning (SSL) is a machine learning paradigm where models learn from unlabeled data by creating their own supervision signals. In Tabular SSL, we implement several SSL methods:

  1. Masked Feature Prediction
  2. Contrastive Learning
  3. Feature Reconstruction

Masked Feature Prediction

How It Works

  1. Feature Masking
  2. Randomly mask a portion of features
  3. Use a masking ratio (default: 0.15)

  4. Preserve feature relationships

  5. Prediction Task

  6. Predict masked features
  7. Use surrounding features as context
  8. Learn feature dependencies

Implementation

from tabular_ssl import TabularSSL

model = TabularSSL(
    input_dim=10,
    mask_ratio=0.15  # 15% of features masked
)

# Train with masked feature prediction
history = model.train(
    data=train_data,
    ssl_method='masked_prediction'
)

Contrastive Learning

How It Works

  1. Data Augmentation
  2. Create positive pairs
  3. Apply transformations

  4. Generate negative samples

  5. Contrastive Loss

  6. Maximize similarity of positive pairs
  7. Minimize similarity of negative pairs
  8. Learn robust representations

Implementation

from tabular_ssl import TabularSSL

model = TabularSSL(
    input_dim=10,
    ssl_method='contrastive'
)

# Train with contrastive learning
history = model.train(
    data=train_data,
    temperature=0.07,  # Temperature parameter
    queue_size=65536   # Size of memory queue
)

Feature Reconstruction

How It Works

  1. Autoencoder Architecture
  2. Encode input features
  3. Decode to reconstruct

  4. Learn feature representations

  5. Reconstruction Loss

  6. Minimize reconstruction error
  7. Learn feature relationships
  8. Capture data structure

Implementation

from tabular_ssl import TabularSSL

model = TabularSSL(
    input_dim=10,
    ssl_method='reconstruction'
)

# Train with feature reconstruction
history = model.train(
    data=train_data,
    reconstruction_weight=1.0
)

Combining Methods

Multi-Task Learning

from tabular_ssl import TabularSSL

model = TabularSSL(
    input_dim=10,
    ssl_methods=['masked_prediction', 'contrastive']
)

# Train with multiple SSL methods
history = model.train(
    data=train_data,
    method_weights={
        'masked_prediction': 0.5,
        'contrastive': 0.5
    }
)

Method Selection

When to Use Each Method

  1. Masked Feature Prediction
  2. When feature relationships are important
  3. For structured tabular data

  4. When interpretability is needed

  5. Contrastive Learning

  6. For robust representations
  7. When data augmentation is possible

  8. For transfer learning

  9. Feature Reconstruction

  10. For simple feature learning
  11. When computational efficiency is important
  12. For basic representation learning

Best Practices

Method Selection

  1. Data Characteristics
  2. Consider data structure
  3. Evaluate feature relationships

  4. Assess data quality

  5. Task Requirements

  6. Define learning objectives
  7. Consider downstream tasks

  8. Evaluate computational needs

  9. Resource Constraints

  10. Consider memory usage
  11. Evaluate training time
  12. Assess hardware requirements

Implementation Tips

  1. Hyperparameter Tuning
  2. Masking ratio
  3. Temperature parameter

  4. Loss weights

  5. Training Strategy

  6. Learning rate scheduling
  7. Batch size selection

  8. Early stopping

  9. Evaluation

  10. Monitor SSL metrics
  11. Evaluate downstream performance
  12. Compare methods