BlockBootstrap Bug: Samples Excluded From Training/Test Sets
Hey guys! Let's dive into a tricky bug in BlockBootstrap that can cause some samples to be excluded from both training and test sets. This can lead to unexpected behavior, especially when you're working with time series data or other sequential data where sample order matters. So, let's break down the issue, how to reproduce it, and potential fixes.
Understanding the BlockBootstrap Bug
When using BlockBootstrap, if your total number of samples isn't perfectly divisible by the block length you've specified, a sneaky issue arises. The BlockBootstrap method, in certain scenarios, drops the initial samples. This exclusion means these samples won't appear in any training or test set during the resampling process. This is problematic because it effectively throws away data, which can skew your model and predictions. This is crucial for maintaining the integrity of your data and the reliability of your machine learning models.
The warning message, which suggests increasing the number of resamplings, doesn't actually address the core problem. The root cause is the way BlockBootstrap handles remainders when the total number of samples doesn't divide evenly by the block size. This misdirection can lead to frustration as users attempt the suggested fix without resolving the underlying issue. To effectively address this bug, it's essential to understand the mechanics behind the resampling process and how it handles the edge case of non-divisible sample sizes. In practical terms, this means that users may need to implement custom solutions or modify the BlockBootstrap implementation to ensure all data points are appropriately included in the resampling process.
The core of the problem lies in the potential for biased sampling. When initial samples are systematically excluded, the resulting training and test sets may not accurately represent the original data distribution. This bias can have significant implications for model performance, particularly in scenarios where the excluded samples contain critical information or represent a specific data pattern. Therefore, it's vital to ensure that all samples have an equal opportunity to be included in the resampling process. This equitable distribution is a cornerstone of robust statistical analysis and model building. By addressing this issue, we can help maintain the validity and reliability of the models built using the BlockBootstrap method.
How to Reproduce the Bug
To really grasp what's happening, let's look at how to reproduce this bug. Here's a Python code snippet using numpy, mapie, and scikit-learn that demonstrates the issue:
import numpy as np
from mapie.regression import TimeSeriesRegressor
from mapie.subsample import BlockBootstrap
from sklearn.ensemble import RandomForestRegressor
random_state = 42
number_of_samples = 11
X_train = np.random.rand(number_of_samples, 2)
y_train = np.random.rand(number_of_samples)
cross_validation = BlockBootstrap(
n_resamplings=6,
n_blocks=3,
overlapping=False,
random_state=random_state,
)
train_indices_present_in_every_split = set(np.arange(X_train.shape[0])) # start with all indices
test_indices_present_across_all_splits = set() # start with no indices
for train_indices, test_indices in cross_validation.split(X_train):
# Remove indices not present in the current train set
train_indices_present_in_every_split = train_indices_present_in_every_split.intersection(train_indices)
# Add indices present in the current test set
test_indices_present_across_all_splits = test_indices_present_across_all_splits.union(set(test_indices))
print(f"train indices: {train_indices}, test indices: {test_indices}")
print(f"There are {len(train_indices_present_in_every_split)} indices included in every training set: {train_indices_present_in_every_split}")
print(f"There are {len(test_indices_present_across_all_splits)} indices included across all test sets: {test_indices_present_across_all_splits}")
model = TimeSeriesRegressor(
estimator=RandomForestRegressor(random_state=random_state),
method="enbpi",
cv=cross_validation,
)
model.fit(X_train, y_train)
When you run this code, you'll notice that some indices are completely missing from the training sets. Specifically, when number_of_samples is 11 and n_blocks is 3, the first few samples (0 and 1 in this case) are dropped. This happens because 11 isn't neatly divisible by 3, leaving a remainder that the current implementation doesn't handle correctly. The output clearly demonstrates this issue, showing that certain indices are absent from all training sets, which is a red flag.
This issue is further compounded by the warning message generated during the fitting process, which, as previously mentioned, provides misleading guidance. The warning suggests increasing the number of resamplings, but this won't resolve the problem of dropped samples. It's like trying to fix a leaky faucet by tightening a different pipe – it just won't work. The disconnect between the warning and the actual problem can lead to confusion and wasted effort in troubleshooting. Therefore, a more accurate and targeted warning message would be beneficial, one that explicitly mentions the issue of non-divisible sample sizes and suggests alternative solutions.
Expected Behavior vs. Actual Behavior
Ideally, we'd expect all indices not in a given training set to be included in the corresponding test set. In our example, indices 0 and 1 should be part of every test set since they don't appear in any training set. This ensures that no data is discarded and that the model has the opportunity to be evaluated on all available samples. The current behavior deviates from this expectation, leading to a potential underestimation of model uncertainty and a less robust evaluation.
The warning message also contributes to the discrepancy between expected and actual behavior. Users might reasonably expect the warning to accurately describe the issue and provide a viable solution. However, the suggestion to increase the number of resamplings is a red herring in this case. A more helpful warning would directly address the issue of sample exclusion due to non-divisible block sizes. This would help users quickly identify the root cause and implement appropriate solutions, such as adjusting the number of samples or modifying the BlockBootstrap parameters. Ultimately, aligning the actual behavior with the expected behavior is crucial for the reliability and usability of the BlockBootstrap method.
Diving into the Code: The Root Cause
The culprit lies in line 204 of subsample.py within the MAPIE library. Specifically, this line:
indices = indices[(n % length):]
Here, indices gets overwritten, potentially excluding the first few indices if n % length is not zero. This is where the initial samples get dropped, preventing them from being included in any subsequent training or test sets. To fix this, we need to ensure that all samples, even those that don't neatly fit into a block, are considered for inclusion in the test sets.
To truly understand the impact of this line of code, it's essential to visualize the resampling process. Imagine you have 11 data points and you're dividing them into blocks of 3. The first nine points can be neatly divided into three blocks. However, the remaining two points don't form a complete block. The current implementation effectively discards these two points, which can lead to a biased representation of the data. This bias is particularly problematic when dealing with time series data, where the order of observations matters. Dropping initial data points can distort the temporal dependencies and lead to inaccurate predictions.
Proposed Solution: Keeping Track of Original Indices
One potential fix is to keep a copy of the original indices. Then, in line 221, we can sample from these original indices to ensure any samples not part of the training set are included in the test set. This way, even if some samples don't belong to a block, they still have a chance to be part of the test set. This approach ensures that no data is discarded, and the model has the opportunity to be evaluated on all available samples. This is consistent with the principles of sound statistical practice and helps maintain the integrity of the analysis.
Specifically, the suggested fix involves modifying the code to retain a reference to the original indices and then using these original indices when constructing the test sets. This would ensure that any samples that are excluded from the training sets due to the block division process are still included in the test sets. This approach not only addresses the immediate issue of sample exclusion but also contributes to a more robust and reliable implementation of the BlockBootstrap method. By preserving all available data, we can improve the accuracy and generalizability of the models built using this technique.
Call to Action: Contributing to MAPIE
If this sounds like a good approach to you, I encourage you to submit a pull request to the MAPIE library! Open-source projects thrive on community contributions, and your help can make a real difference in improving the reliability and usability of this valuable tool. By working together, we can ensure that MAPIE remains a robust and dependable resource for uncertainty estimation in machine learning.
Before submitting a pull request, it's always a good idea to discuss your proposed changes with the project maintainers. This can help ensure that your changes align with the project's goals and coding standards. It's also a good opportunity to get feedback on your approach and identify any potential issues. By engaging in this collaborative process, we can collectively contribute to the improvement of MAPIE and make it an even more valuable tool for the machine learning community. So, let's roll up our sleeves and get coding!
Conclusion
This BlockBootstrap bug highlights the importance of careful attention to detail when working with resampling methods. Seemingly small issues, like how remainders are handled, can have significant impacts on the validity of your results. By understanding the underlying mechanics of these methods and actively contributing to open-source projects, we can build more robust and reliable tools for the machine learning community. Keep an eye out for these edge cases, and happy coding, everyone!
Remember, bugs are a natural part of software development, but by identifying and addressing them collaboratively, we can create better tools for everyone. The MAPIE library is a valuable resource, and by contributing to its improvement, we can help ensure that it remains a reliable and effective tool for uncertainty estimation. So, don't hesitate to get involved – your contributions can make a real difference!