Parallel Grid Search

Parallel Grid Search

Overview

Use joblib to parallelize expensive computations across multiple CPU cores, significantly speeding up grid search over hyperparameter combinations.

Installation

bashCopy code
pip install joblib scikit-learn

Basic Pattern

pythonCopy code
from joblib import Parallel, delayed
import itertools

def evaluate_hyperparams(hp_combination, data, evaluation_func):
    """Evaluate a single hyperparameter combination."""
    result = evaluation_func(hp_combination, data)
    return {**hp_combination, **result}

# Define hyperparameter grid
param_grid = {
    'min_samples': [3, 4, 5, 6, 7, 8, 9],
    'epsilon': [4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24],
    'shape_weight': [0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9]
}

# Generate all combinations
combinations = [
    {k: v for k, v in zip(param_grid.keys(), vals)}
    for vals in itertools.product(*param_grid.values())
]

# Parallel evaluation
n_jobs = -1  # Use all available cores
results = Parallel(n_jobs=n_jobs, verbose=10)(
    delayed(evaluate_hyperparams)(combo, data, eval_func)
    for combo in combinations
)

Advanced: Batching and Progress

pythonCopy code
from tqdm import tqdm

def parallel_grid_search_batched(param_grid, data, evaluation_func, n_jobs=-1):
    """
    Perform parallel grid search with progress tracking.

    Args:
        param_grid: Dictionary of parameter names to lists of values
        data: Dataset to evaluate on
        evaluation_func: Function that takes (hyperparams_dict, data) -> results_dict
        n_jobs: Number of parallel jobs (-1 = all cores)

    Returns:
        List of result dictionaries
    """
    # Generate all combinations
    combinations = [
        {k: v for k, v in zip(param_grid.keys(), vals)}
        for vals in itertools.product(*param_grid.values())
    ]

    # Parallel evaluation with progress bar
    results = Parallel(n_jobs=n_jobs)(
        delayed(evaluation_func)(combo, data)
        for combo in tqdm(combinations, desc="Grid Search", total=len(combinations))
    )

    return results

joblib Best Practices

Memory-Efficient Processing

pythonCopy code
# Use batch_size for memory efficiency
results = Parallel(n_jobs=-1, batch_size='auto')(
    delayed(expensive_function)(item)
    for item in data
)

Controlling Verbosity

pythonCopy code
# verbose=10 prints progress every 10 jobs
# verbose=0 is silent, verbose=1 prints at start/end
results = Parallel(n_jobs=-1, verbose=10)(
    delayed(task)(x) for x in items
)

Backend Selection

pythonCopy code
# Default is 'loky' (good for most tasks)
# 'threading' is lighter but can have GIL issues
# 'processes' spawns new processes

results = Parallel(n_jobs=-1, backend='loky')(
    delayed(task)(x) for x in items
)

Collecting Results into DataFrame

pythonCopy code
import pandas as pd

results = Parallel(n_jobs=-1)(
    delayed(evaluate_hyperparams)(combo, data, eval_func)
    for combo in combinations
)

# Convert to DataFrame
results_df = pd.DataFrame(results)

# Filter and sort
filtered = results_df[results_df['f1'] > 0.5].sort_values('f1', ascending=False)

Notes

• joblib handles pickling of functions and data automatically
• For very large datasets, consider passing data references rather than copies
• Use n_jobs=-1 to use all cores; use -2 to leave one core free
• Progress tracking requires tqdm package