Understanding the ‘numpy.int’ Attribute Error
The “AttributeError: ‘numpy.int’ object has no attribute ‘…’ ” often arises when using skopt.BayesSearchCV in scikit-learn for hyperparameter tuning. This error signifies that your code is trying to access an attribute that isn’t present in a NumPy integer object. The issue typically stems from incompatibility between the data types used in the search space and the model’s expected parameter types.
Identifying the Culprit
1. Inspect Your Search Space
The culprit often lies in the search space definition. Ensure your search space values align with the types accepted by the model’s hyperparameters.
- For integer parameters, use `skopt.space.Integer`.
- For real-valued parameters, use `skopt.space.Real`.
- For categorical parameters, use `skopt.space.Categorical`.
2. Check Model Parameters
Examine the documentation of your chosen model to understand the expected types of its hyperparameters. Ensure that the search space’s data types match these expectations.
Resolving the Issue
Example: Support Vector Machine (SVM)
Let’s assume we’re tuning the SVM model with the following search space:
from sklearn.svm import SVC from skopt import BayesSearchCV from skopt.space import Real, Integer search_spaces = { 'C': Real(low=0.1, high=10.0, prior='uniform'), 'gamma': Real(low=1e-3, high=1.0, prior='uniform'), 'kernel': Categorical(['linear', 'rbf']), 'degree': Integer(low=2, high=5) } model = SVC() search = BayesSearchCV(model, search_spaces, n_iter=50, cv=5) # **Fix:** Ensure the search space types match model parameters # ... Correcting the Data Types
- Use `skopt.space.Integer` for parameters like `degree` that expect integers.
- Use `skopt.space.Real` for parameters like `C` and `gamma` that expect floating-point values.
Code Example with Correction
Here’s how to fix the issue with the corrected search space:
from skopt import space search_spaces = { 'C': space.Real(low=0.1, high=10.0, prior='uniform'), 'gamma': space.Real(low=1e-3, high=1.0, prior='uniform'), 'kernel': space.Categorical(['linear', 'rbf']), 'degree': space.Integer(low=2, high=5) } model = SVC() search = BayesSearchCV(model, search_spaces, n_iter=50, cv=5) # Train the search object as before... Common Mistakes
- Using `int` instead of `skopt.space.Integer` for integer parameters.
- Using `float` instead of `skopt.space.Real` for real-valued parameters.
- Using incorrect data types for categorical parameters.
Conclusion
By carefully defining your search space using appropriate data types, you can avoid the “AttributeError: ‘numpy.int’ object has no attribute ‘…’ ” error and successfully utilize skopt.BayesSearchCV for hyperparameter optimization in scikit-learn.