How to Pickle Functions from within __main__

While trying to load a pickled classification model within a Flask app, I received a missing attribute error:

AttributeError: Can't get attribute 'function' on <module 'flask.__main__'
from '~/anaconda3/envs/folder/lib/python3.12/site-packages/flask/__main__.py'>

I learned this was due to the way Pickle serializes Python objects and that I had saved my model in __main__, so let’s start with what serialization is.

What is Serialization

Serialization is the process of converting an object’s state into a format that can be stored or transmitted and reconstructed later. We serialize objects to facilitate data storage, transmission over a network, or for saving program state across sessions. Python’s pickle library is one common way of serializing and deserializing objects, enabling them to be saved to a file or sent over a network and later reconstructed back into the original object.

Why the Error

The missing attribute error I encountered while trying to load a pickled classification model within a Flask app was due to how the pickle library handles object serialization and deserialization, especially concerning the Python environment and namespaces.

When you pickle an object in Python, pickle stores the object’s state and information about how to reconstruct the object, including references to functions and classes. If your model depends on certain functions or classes defined in the script where the model was pickled (the example below relies on tokenize()), these dependencies are pickled by reference, not by value. This means pickle stores the path to the function or class, not the actual code.

def load_data():
    ''' Load database from SQL db '''

    engine = create_engine('sqlite:///../data/databes_name.db')
    table = 'table_name'
    df = pd.read_sql_table(table, engine)

    X = df[[features]]
    y = df[target]

    return X, y

def tokenize(text):
    ''' Tokenizes input text '''

    # split text strings into tokens
    tokens = wordpunct_tokenize(text.lower().strip())

    # Remove stopwords
    rm = set(stopwords.words("english"))
    tokens = list(set(tokens) - rm)

    # stem tokens
    tokens =  [PorterStemmer().stem(w) for w in tokens]

    return tokens

def save_model(model):
    ''' Save trained classification model to pickle file '''

    with open('../models/message_classifier.pkl', "wb") as f:
        pickle.dump(model, f)

if __name__ == '__main__':

        X, y = load_data()

        X_train, X_test, Y_train, Y_test = train_test_split(X, y)

        model = Pipeline([
            ('vect', CountVectorizer(tokenizer=tokenize)),
            ('tfidf', TfidfTransformer()),
            ('clf', MultiOutputClassifier(KNeighborsClassifier()))
        ])

        model.fit(X_train, Y_train)

        save_model(model)

Upon deserialization (loading the pickled file), pickle attempts to locate these functions or classes using their stored paths. If these functions or classes were defined in the script’s global namespace (typically referred to as __main__ when the script is run directly), pickle expects to find them in the same namespace during loading. However, in a different environment, like the Flask app, the __main__ namespace is different, and pickle cannot find the required functions or classes, leading to the AttributeError.

The error occurs because pickle is trying to reconstruct the model in an environment where it cannot correctly resolve all references to the necessary functions or classes due to the change in the __main__ namespace or the absence of those definitions in the current environment.

How to Pickle Functions from within __main__

There are several strategies to address the challenges presented earlier based on our attribute error and ensure seamless serialization and deserialization of Python objects.

Using the Dill Library

dill extends pickle’s capabilities by being able to serialize a wider range of Python object types, including those defined in the __main__ module. Switching to dill for serialization can bypass some of the limitations associated with pickle’s handling of the __main__ namespace.

Here’s how you can fix the problem by serializing with dill:

1. Install dill if you haven’t already. You can do this using pip or conda, depending on how you manage your Python environments:

pip install dill
# or
conda install dill

2. Use dill to serialize your classification model:

import dill

# Assuming 'model' is your classification model
with open('model.dill', 'wb') as file:
    dill.dump(model, file)

3. When you need to load the model (such as in a flask application), use dill to deserialize it:

import dill

with open('model.dill', 'rb') as file:
    model = dill.load(file)

By using dill for serialization, you might avoid the missing argument error since dill can handle more complex Python objects. This approach is particularly useful if your classification model or any associated preprocessing functions involve components that pickle struggles with. The downside of dill is that it can be slower than pickle.

Modularization

By refactoring functions and classes out of the __main__ module and into separate modules, you can avoid the namespace mismatch issue. When functions are bound to the __main__ namespace, they can cause issues during deserialization with pickle. When these entities are imported from dedicated environments, their references become consistent and stable across different environments, facilitating pickle’s ability to locate and reconstruct them upon loading.

Here’s how to organize your project:

1. Separate Functions into a Module:

   • Create a new Python file (e.g., model_utils.py) in your project directory.
   • Move all the relevant functions, including any preprocessing or postprocessing functions associated with your classification model, into this new file.

2. Import Functions in Your Main Application and Scripts:

   • In your Flask app or any script where you need to use these functions, import them from the module you created. For example:

   from model_utils import my_preprocessing_function, my_postprocessing_function

3. Pickle the Model:

   • When pickling your classification model, ensure it only relies on functions imported from the separate module (model_utils.py) and not on any defined in the __main__ script.
   • Serialize the model as usual using pickle:

   import pickle
   
   with open('model.pkl', 'wb') as file:
       pickle.dump(model, file)

4. Load the Model:

   • Ensure that model_utils.py is accessible from your Flask app, and then deserialize the model using pickle:

   import pickle
   import sys
   sys.path.append('/home/user/directories/model_utils_directory')
   from model_utils import my_preprocessing_function, my_postprocessing_function
   
   with open('model.pkl', 'rb') as file:
       model = pickle.load(file)

By structuring your project this way, the functions associated with your model are part of a defined module rather than being transiently defined in __main__ when the script runs. This ensures that when pickle loads the model, it can correctly resolve all function references, thereby avoiding the missing argument error.

Conclusion

In conclusion, while the pickle library in Python is a powerful tool for object serialization, it poses challenges when dealing with objects that reference functions or classes defined in the __main__ module, especially when moving serialized objects across different execution contexts.

Solutions such as modularizing code and using the dill library for its enhanced serialization capabilities can effectively address these issues. Adopting these approaches facilitates seamless serialization and deserialization processes, ensuring that objects maintain their integrity and functionality across diverse environments.