Configure StackingRegressor "n_jobs" Parameter

The n_jobs parameter in scikit-learn’s StackingRegressor controls the number of parallel jobs to run for both fitting the base estimators and the final estimator.

StackingRegressor is an ensemble method that combines multiple base regressors by training a final regressor on their predictions. The n_jobs parameter allows you to leverage multiple CPU cores to speed up the training process.

Setting n_jobs to a value greater than 1 enables parallel processing, which can significantly reduce training time, especially for large datasets or complex base estimators. However, the optimal value depends on your hardware and the nature of your data and models.

The default value for n_jobs is None, which means it will use a single core. Common values include -1 (use all available cores), 2, 4, or 8, depending on the number of cores available on your machine.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, StackingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import time
import numpy as np

# Generate synthetic dataset
X, y = make_regression(n_samples=1000, n_features=20, noise=0.1, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define base models and final estimator
base_models = [
    ('rf', RandomForestRegressor(n_estimators=100, random_state=42)),
    ('gb', GradientBoostingRegressor(n_estimators=100, random_state=42))
]
final_estimator = LinearRegression()

# Train with different n_jobs values
n_jobs_values = [1, 2, 4, -1]
results = []

for n_jobs in n_jobs_values:
    start_time = time.perf_counter()
    stacking_regressor = StackingRegressor(
        estimators=base_models,
        final_estimator=final_estimator,
        n_jobs=n_jobs
    )
    stacking_regressor.fit(X_train, y_train)
    training_time = time.perf_counter() - start_time

    y_pred = stacking_regressor.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)

    results.append((n_jobs, training_time, mse))
    print(f"n_jobs={n_jobs}, Training Time: {training_time:.2f}s, MSE: {mse:.4f}")

# Find best result
best_result = min(results, key=lambda x: x[1])  # Minimum training time
print(f"\nBest n_jobs: {best_result[0]}, Training Time: {best_result[1]:.2f}s, MSE: {best_result[2]:.4f}")

Running the example gives an output like:

n_jobs=1, Training Time: 7.08s, MSE: 2289.5273
n_jobs=2, Training Time: 4.37s, MSE: 2289.5273
n_jobs=4, Training Time: 4.52s, MSE: 2289.5273
n_jobs=-1, Training Time: 4.27s, MSE: 2289.5273

Best n_jobs: -1, Training Time: 4.27s, MSE: 2289.5273

The key steps in this example are:

Generate a synthetic regression dataset
Split the data into train and test sets
Define base models (RandomForestRegressor and GradientBoostingRegressor) and final estimator (LinearRegression)
Train StackingRegressor models with different n_jobs values
Measure training time and model performance (MSE) for each n_jobs setting
Compare results to find the optimal n_jobs value

Some tips and heuristics for setting n_jobs:

Start with n_jobs=-1 to use all available cores, then experiment with specific values
For small datasets or simple models, the overhead of parallelization might outweigh the benefits
Consider your system’s available resources and other running processes when setting n_jobs

Issues to consider:

Using a high n_jobs value can consume significant system resources, potentially slowing down other processes
The speedup may not be linear with the number of cores due to communication overhead
Some algorithms or operations may not benefit from parallelization, so always benchmark to confirm improvements

See Also