Configure KNeighborsRegressor "n_jobs" Parameter

The n_jobs parameter in KNeighborsRegressor specifies the number of parallel jobs to run for neighbors search.

KNeighborsRegressor is a non-parametric method used for regression that predicts the target based on the k-nearest neighbors in the feature space.

The n_jobs parameter allows leveraging multiple CPU cores to speed up the computation, especially useful for large datasets.

The default value for n_jobs is None, which means 1 job. Setting n_jobs=-1 uses all available CPU cores.

import time
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error

# Generate synthetic dataset
X, y = make_regression(n_samples=1000, n_features=10, 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)

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

for n in n_jobs_values:
    knr = KNeighborsRegressor(n_jobs=n)
    start_time = time.time()
    knr.fit(X_train, y_train)
    y_pred = knr.predict(X_test)
    end_time = time.time()
    error = mean_squared_error(y_test, y_pred)
    elapsed_time = end_time - start_time
    errors.append(error)
    times.append(elapsed_time)
    print(f"n_jobs={n}, Mean Squared Error: {error:.3f}, Time: {elapsed_time:.3f} seconds")

Running the example gives an output like:

n_jobs=1, Mean Squared Error: 3728.344, Time: 0.003 seconds
n_jobs=2, Mean Squared Error: 3728.344, Time: 0.026 seconds
n_jobs=4, Mean Squared Error: 3728.344, Time: 0.014 seconds
n_jobs=-1, Mean Squared Error: 3728.344, Time: 0.016 seconds

The key steps in this example are:

Generate a synthetic regression dataset.
Split the data into train and test sets.
Train KNeighborsRegressor models with different n_jobs values.
Measure and evaluate both the mean squared error and the execution time of each model on the test set.

Some tips and heuristics for setting n_jobs:

Use n_jobs=-1 to utilize all available CPU cores for maximum parallelism.
Start with n_jobs=1 and then switch to n_jobs=-1 to compare the performance improvements.
Be aware of the system’s CPU capabilities and workload to avoid overloading the system.

Issues to consider:

Using multiple jobs can speed up computation but also increases memory usage.
On smaller datasets, the benefit of parallelism might be negligible.
Be cautious of the overhead introduced by managing multiple parallel jobs, especially on systems with fewer cores.

See Also