[BUG] Can't Load A Pickled DebugPipeline With A Custom Log_callback

Mar 13, 2025 by ADMIN 68 views

Introduction

When working with machine learning pipelines, it's common to save and load them from files to reuse or share them with others. However, when trying to load a previously fitted and pickled DebugPipeline instance that uses a custom log_callback from a pickle file, you may encounter the following error:

AttributeError: 'Memory' object has no attribute '_'

This article will guide you through the steps to reproduce this error and provide a solution to fix it.

Reproducing the Error

To reproduce the error, you can use the following code snippet:

import joblib
import logging
from sklego.pipeline import make_debug_pipeline, default_log_callback
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

X, y = make_regression()
pipeline = make_debug_pipeline(StandardScaler(), LinearRegression(), log_callback=default_log_callback)
pipeline.fit(X, y)

joblib.dump(pipeline, './pipeline.pkl')
joblib.load('./pipeline.pkl')

This code creates a DebugPipeline instance with a custom log_callback and saves it to a pickle file using joblib.dump. Then, it tries to load the pipeline from the pickle file using joblib.load, which results in the AttributeError.

Understanding the Error

The error message indicates that the Memory object has no attribute _. This is because the Memory object is a special object used by joblib to store the pickled data. When joblib loads the pickle file, it tries to access the _ attribute of the Memory object, which does not exist.

Solution

To fix this issue, you need to use the joblib.load function with the mmap_mode parameter set to 'r'. This tells joblib to read the pickle file in read-only mode, which prevents it from trying to access the _ attribute of the Memory object.

Here's the corrected code:

import joblib
import logging
from sklego.pipeline import make_debug_pipeline, default_log_callback
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

X, y = make_regression()
pipeline = make_debug_pipeline(StandardScaler(), LinearRegression(), log_callback=default_log_callback)
pipeline.fit(X, y)

joblib.dump(pipeline, './pipeline.pkl')
loaded_pipeline = joblib.load('./pipeline.pkl', mmap_mode='r')

By setting mmap_mode to 'r', you ensure that joblib loads the pickle file in read-only mode, which fixes the AttributeError.

Conclusion

In this article, we've reproduced the error of loading a pickled DebugPipeline instance with a custom log_callback and provided a solution to fix it. By using the mmap_mode parameter with joblib.load, you can load the pickle file in read-only mode and avoid the AttributeError. This solution should help you to successfully load and reuse your machine learning pipelines.

Additional Tips

When working with machine learning pipelines, it's essential to save and load them from files to reuse or share them with others.
Use the joblib library to save and load pickle files, as it provides a convenient and efficient way to store and retrieve machine learning models.
When loading a pickle file, use the mmap_mode parameter to specify the mode in which the file should be loaded. This can help prevent errors like the one described in this article.

Related Resources

sklego documentation: The official documentation for the sklego library, which provides a set of tools for machine learning pipelines.
joblib documentation: The official documentation for the joblib library, which provides a set of tools for efficient data processing and machine learning.
Pickle documentation: The official documentation for the pickle module in Python, which provides a way to serialize and deserialize Python objects.
Q&A: Debugging Pickled DebugPipeline with Custom Log_Callback ================================================================

Q: What is the cause of the `AttributeError` when loading a pickled `DebugPipeline` instance with a custom `log_callback`?

A: The AttributeError is caused by the joblib library trying to access the _ attribute of the Memory object, which does not exist. This happens because the Memory object is a special object used by joblib to store the pickled data.

Q: How can I reproduce the error?

A: You can reproduce the error by using the following code snippet:

import joblib
import logging
from sklego.pipeline import make_debug_pipeline, default_log_callback
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

X, y = make_regression()
pipeline = make_debug_pipeline(StandardScaler(), LinearRegression(), log_callback=default_log_callback)
pipeline.fit(X, y)

joblib.dump(pipeline, './pipeline.pkl')
joblib.load('./pipeline.pkl')

Q: How can I fix the error?

A: To fix the error, you need to use the joblib.load function with the mmap_mode parameter set to 'r'. This tells joblib to read the pickle file in read-only mode, which prevents it from trying to access the _ attribute of the Memory object.

Here's the corrected code:

import joblib
import logging
from sklego.pipeline import make_debug_pipeline, default_log_callback
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.datasets import make_regression

X, y = make_regression()
pipeline = make_debug_pipeline(StandardScaler(), LinearRegression(), log_callback=default_log_callback)
pipeline.fit(X, y)

joblib.dump(pipeline, './pipeline.pkl')
loaded_pipeline = joblib.load('./pipeline.pkl', mmap_mode='r')

By setting mmap_mode to 'r', you ensure that joblib loads the pickle file in read-only mode, which fixes the AttributeError.

Q: What are some additional tips for working with machine learning pipelines?

A: Here are some additional tips for working with machine learning pipelines:

Use the joblib library to save and load pickle files, as it provides a convenient and efficient way to store and retrieve machine learning models.
When loading a pickle file, use the mmap_mode parameter to specify the mode in which the file should be loaded. This can help prevent errors like the one described in this article.
Make sure to save and load pickle files in the same environment, as the joblib library may not work correctly across different environments.

Q: What are some related resources for learning more about machine learning pipelines?

A: Here are some related resources for learning more about machine learning pipelines:

sklego documentation: The official documentation for the sklego library, which provides a set of tools for machine learning pipelines.
joblib documentation: The official documentation for the joblib library, which provides a set of tools for efficient data processing and machine learning.
Pickle documentation: The official documentation for the pickle module in Python, which provides a way to serialize and deserialize Python objects.

Q: Can I use this solution for other types of machine learning models?

A: Yes, this solution can be used for other types of machine learning models that use pickle files to store their data. However, you may need to modify the code to accommodate the specific requirements of your model.

Q: Are there any other ways to fix the error?

A: Yes, there are other ways to fix the error. One way is to use the pickle module directly to load the pickle file, rather than using the joblib library. However, this may not be as efficient as using joblib, and may require more manual effort to implement.

Q: Can I use this solution in a production environment?

A: Yes, this solution can be used in a production environment. However, you should make sure to test it thoroughly to ensure that it works correctly and does not introduce any errors or bugs. Additionally, you may need to modify the code to accommodate the specific requirements of your production environment.