Incompatibility Issue Between scikit-learn 0.24.1 and scikit-optimize 0.8.1
This article outlines a known incompatibility issue between scikit-learn version 0.24.1 and scikit-optimize version 0.8.1. This issue can lead to unexpected errors when using scikit-optimize for hyperparameter tuning of scikit-learn models.
The Problem
The primary cause of this incompatibility stems from changes made to scikit-learn’s API in version 0.24. Specifically, the fit() method of certain estimators, including GridSearchCV, now returns an object instead of modifying the estimator object in-place.
Scikit-optimize relies on the old behavior where fit() modified the estimator object directly. When using scikit-optimize with scikit-learn 0.24.1, this discrepancy leads to errors because scikit-optimize attempts to access modified attributes on the estimator object that are not present anymore.
Example Scenario
Consider the following code snippet, which demonstrates the issue using a simple example with a Random Forest classifier:
from sklearn.ensemble import RandomForestClassifier from skopt import BayesSearchCV from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Load Iris dataset iris = load_iris() X = iris.data y = iris.target # Split data 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 search space for hyperparameters search_space = { 'n_estimators': (10, 100), 'max_depth': (1, 10) } # Create BayesSearchCV object bayes_search = BayesSearchCV( estimator=RandomForestClassifier(), search_spaces=search_space, n_iter=50, cv=5 ) # Fit the search object to the training data bayes_search.fit(X_train, y_train)
When this code is run with scikit-learn 0.24.1 and scikit-optimize 0.8.1, the following error message is generated:
AttributeError: 'GridSearchCV' object has no attribute 'best_estimator_'
Solution
There are two primary ways to address this incompatibility:
1. Upgrade scikit-optimize
The most straightforward solution is to upgrade scikit-optimize to version 0.9.0 or later. These versions of scikit-optimize have been updated to be compatible with the changes in scikit-learn’s API.
2. Manually Update Code
If upgrading scikit-optimize is not feasible, the code can be manually updated to work with scikit-learn 0.24.1. This involves extracting the best estimator from the fitted GridSearchCV object. Here’s the modified code snippet:
# ... (previous code) # Fit the search object to the training data bayes_search.fit(X_train, y_train) # Extract the best estimator best_estimator = bayes_search.best_estimator_ # Access attributes of the best estimator print(best_estimator.n_estimators) print(best_estimator.max_depth)
Conclusion
The incompatibility issue between scikit-learn 0.24.1 and scikit-optimize 0.8.1 can be easily resolved by upgrading scikit-optimize or manually adapting your code. It’s essential to be aware of such API changes and ensure that your dependencies are compatible to avoid unexpected errors.