Configure ElasticNet "warm_start" Parameter

The warm_start parameter in ElasticNet controls whether the solution of a previous fit is reused as the initialization for the next call to fit.

ElasticNet is a linear regression model that combines L1 and L2 regularization. It is particularly useful when there are multiple correlated features.

warm_start can help in scenarios where the model is being retrained on similar data, potentially speeding up convergence.

The default value for warm_start is False.

Common values are True or False, depending on whether iterative fitting is needed.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_squared_error
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 initial train set and additional batch
X_train, X_new, y_train, y_new = train_test_split(X, y, test_size=0.2, random_state=42)

# Train with warm_start=False
en = ElasticNet(warm_start=False, random_state=42)
en.fit(X_train, y_train)
y_pred_false = en.predict(X_new)
score_false = mean_squared_error(y_new, y_pred_false)
print(f"MSE with warm_start=False: {score_false:.3f}")

# Train with warm_start=True
X_combined = np.concatenate((X_train, X_new))
y_combined = np.concatenate((y_train, y_new))

en.set_params(warm_start=True)
en.fit(X_combined, y_combined)
y_pred_true = en.predict(X_new)
score_true = mean_squared_error(y_new, y_pred_true)
print(f"MSE with warm_start=True: {score_true:.3f}")

Running the example gives an output like:

MSE with warm_start=False: 4638.839
MSE with warm_start=True: 4514.241

The key steps in this example are:

Generate a synthetic regression dataset with noise features
Split the data into train and test sets
Train ElasticNet model normally.
Update the ElasticNet model with new data by setting warm_start to True.

Some tips and heuristics for setting warm_start:

Use warm_start=True if you are iteratively fitting the model on data batches or in a cross-validation loop
warm_start=False is generally suitable for a single fit process, as it starts from scratch
Check for improvements in convergence time and model performance when using warm_start=True

Issues to consider:

Using warm_start=True can speed up training, but it might lead to suboptimal solutions if not managed properly
Monitor model performance and convergence to ensure that warm_start is beneficial for your specific use case
Iterative fitting with warm_start=True should be handled carefully to avoid cumulative errors

See Also