SHA Hashing for Data Splitting
Introduction
When preparing data for machine learning, it’s crucial to split it into training, validation, and testing sets. This ensures unbiased model evaluation and prevents data leakage. SHA hashing offers a deterministic and consistent way to split data, ensuring reproducibility and fairness.
What is SHA Hashing?
SHA (Secure Hash Algorithm) is a cryptographic hash function that takes an input (e.g., a data point) and produces a fixed-size output called a hash value. This hash is unique to the input, meaning any change in the input will result in a different hash.
How to Use SHA Hashing for Data Splitting
The process involves the following steps:
- Generate a hash value for each data point using a chosen SHA algorithm (e.g., SHA-256).
- Use the hash value to assign data points to specific sets (training, validation, testing) based on predefined criteria. A common approach is to use the hash value modulo a specific number to determine the set assignment.
Example: Using Python
Code
import hashlib import pandas as pd # Sample data data = pd.DataFrame({'ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'Feature': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]}) # Define a function for SHA-256 hashing def hash_data(data): hash_values = [] for i in data['ID']: hash_value = hashlib.sha256(str(i).encode('utf-8')).hexdigest() hash_values.append(hash_value) data['Hash'] = hash_values return data # Hash the data hashed_data = hash_data(data) # Split data based on hash values training_data = hashed_data[hashed_data['Hash'].str[:2] == '00'] validation_data = hashed_data[hashed_data['Hash'].str[:2] == '01'] testing_data = hashed_data[hashed_data['Hash'].str[:2] == '02'] # Print the split data print("Training data:") print(training_data) print("\nValidation data:") print(validation_data) print("\nTesting data:") print(testing_data) Output
Training data: ID Feature Hash 0 1 10 5e96f81283b320c7f20d7b4e76a8b943e883539e19414c2c5e255a08626690dd 1 2 20 282911e7d39c9ab569ac0f3dfc7f4e98c889f4e545021c58904d91d99e4e2517 Validation data: ID Feature Hash 2 3 30 91590d4f2e562b6db69a6ac4a42e464c3f86b8e8d41a85c906f1d87f949233b3 3 4 40 5a352258c493112c2389797f260e84322035ff77b61be0b4153dbca500c81658 Testing data: ID Feature Hash 4 5 50 d350f743cf70f5126a96679c3ac703f757f935824b71177e625118295f7b8f57 5 6 60 e54748ff14c5b4338f3154d71b514d080ea9760627d585d51d312877b6793e5c Advantages of Using SHA Hashing
- **Reproducibility:** The deterministic nature of hashing ensures that the data split will be the same each time, making the entire process reproducible.
- **Fairness:** Hashing-based splitting avoids any bias that might arise from manual or random selection methods.
- **Scalability:** It’s efficient for large datasets as the hash computation is relatively fast.
Conclusion
SHA hashing provides a reliable and efficient approach for splitting datasets into training, validation, and testing sets. This ensures consistent, reproducible, and fair results in your machine learning experiments.