set random seed programwide in python

Ensuring Reproducibility: Setting a Program-Wide Random Seed in Python

Learn how to effectively set a global random seed in Python to ensure consistent and reproducible results across different runs of your programs, crucial for scientific computing, machine learning, and testing.

Randomness is a fundamental concept in many computational tasks, from simulations and statistical sampling to machine learning model initialization and cryptographic applications. Python's random module provides tools for generating pseudo-random numbers. However, for debugging, testing, or ensuring scientific reproducibility, it's often necessary to make these 'random' sequences predictable. This is achieved by setting a 'seed'.

Understanding Random Seeds

A random seed is an initial value that kickstarts a pseudo-random number generator (PRNG). PRNGs are deterministic algorithms that produce sequences of numbers that appear random but are entirely determined by their initial seed. If you use the same seed, the PRNG will produce the exact same sequence of 'random' numbers every time. This property is invaluable for debugging, where you need to reproduce a specific bug that only occurs with a particular random sequence, or in scientific research, where results must be verifiable.

flowchart TD
    A[Start Program] --> B{Is a seed set?}
    B -- No --> C[Default Seed (e.g., system time)]
    B -- Yes --> D[User-defined Seed]
    C --> E[Generate Random Sequence]
    D --> E
    E --> F[Reproducible Results (if seed is fixed)]
    E --> G[Non-Reproducible Results (if seed changes)]

Flowchart illustrating the impact of setting a random seed on reproducibility.

Setting the Seed for Python's Built-in random Module

Python's standard library random module is the primary source for pseudo-random numbers. To set a program-wide seed for this module, you simply call random.seed() with an integer argument at the beginning of your script. It's crucial to do this before any calls to random functions that generate numbers.

import random

# Set a program-wide seed for the 'random' module
random.seed(42)

print(f"First random number: {random.random()}")
print(f"Second random integer: {random.randint(1, 100)}")

# If you run this script again with seed(42), you'll get the same output.
# If you comment out random.seed(42), the output will change each run.

Example of setting a seed for Python's random module.

Handling Other Random Number Generators (NumPy, TensorFlow, PyTorch)

Many scientific computing and machine learning libraries in Python, such as NumPy, TensorFlow, and PyTorch, have their own random number generators. Setting the seed for Python's built-in random module does not automatically seed these external libraries. You must set their seeds separately to ensure full reproducibility across your entire application.

import random
import numpy as np
import torch
import os

# 1. Python's built-in random module
random.seed(42)
print(f"Python random: {random.random()}")

# 2. NumPy
np.random.seed(42)
print(f"NumPy random: {np.random.rand(1)}")

# 3. PyTorch (CPU and GPU)
torch.manual_seed(42)
print(f"PyTorch CPU random: {torch.rand(1)}")

if torch.cuda.is_available():
    torch.cuda.manual_seed(42)
    torch.cuda.manual_seed_all(42) # For multi-GPU setups
    print(f"PyTorch GPU random: {torch.rand(1, device='cuda')}")

# 4. TensorFlow (requires specific order for older versions or graph mode)
# For TensorFlow 2.x eager execution, tf.random.set_seed is usually sufficient.
# For older versions or graph mode, you might need to set Python and NumPy seeds first.
import tensorflow as tf
tf.random.set_seed(42)
print(f"TensorFlow random: {tf.random.uniform([1])}")

# 5. Environment variables for reproducibility (e.g., for some libraries)
os.environ['PYTHONHASHSEED'] = str(42)
# Note: PYTHONHASHSEED affects hash randomization, not general random numbers.
# It should be set BEFORE the Python interpreter starts for full effect.

print("\nAll seeds set to 42. Running again should yield identical results.")

Setting seeds for Python's random, NumPy, PyTorch, and TensorFlow.

Best Practices for Program-Wide Seeding

To ensure robust reproducibility, consider these best practices:

1. Centralize Seed Setting

Create a dedicated function or block at the start of your main script to set all necessary seeds (Python random, NumPy, TensorFlow, PyTorch, etc.). This makes it easy to find and modify the seed.

2. Use a Consistent Seed Value

Choose a fixed integer (e.g., 42, 0, or any other number) and use it consistently across all random number generators you intend to control. This simplifies debugging and verification.

3. Document Your Seeding Strategy

Clearly document which seeds are set and why. If you're running experiments, record the seed used for each experiment to ensure results can be replicated.

4. Be Aware of Library-Specific Seeding

Always consult the documentation for any third-party library you use that involves randomness (e.g., scikit-learn, XGBoost) as they often have their own random_state parameters or seeding mechanisms.

5. Consider Environment Variables

For some specific cases, environment variables like PYTHONHASHSEED might be relevant, but understand their specific impact. PYTHONHASHSEED primarily affects hash randomization, not general PRNGs.