Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’

Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’

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Introduction: A Developer’s Unexpected Roadblock

In the world of Python and machine learning development, nothing halts progress faster than an unfamiliar error message. One such error, often encountered while working with scikit-learn (sklearn) or its extended libraries, is:

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Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’

At first glance, this might look cryptic or intimidating, especially for developers integrating custom classes with sklearn components. But fear not—this error has clear causes and even clearer solutions once we delve deeper. This article aims to unpack this error, explain why it occurs, and offer actionable solutions and best practices to prevent it in your ML development process.

What Is the Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’?

This specific AttributeError occurs when a Python class that is supposed to interact with sklearn’s internal system attempts to access the __sklearn_tags__ method from its superclass—but that superclass doesn’t implement it.

This happens most frequently in custom model or transformer classes that try to inherit or extend sklearn base classes improperly.

The Role of Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’ in scikit-learn

In recent versions of sklearn, the Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’ method plays an important internal role. It provides metadata or “tags” about the estimator. These tags help sklearn pipelines and tools determine capabilities of a model, such as:

  • Whether it handles NaN values

  • If it supports sparse input

  • Whether it’s stateless or requires fitting

The method looks something like this in a compliant sklearn estimator:

python
def _more_tags(self):
return {'requires_fit': True}

This tag system is used internally by sklearn utilities like check_estimator and others that automate testing and validation of estimators.

Root Causes of This Error

You might encounter the Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’ due to several reasons:

  • Using an outdated sklearn version that doesn’t support __sklearn_tags__

  • Incorrectly inheriting from a base class that doesn’t define __sklearn_tags__

  • Not defining _more_tags() in your custom class

  • Calling super().__sklearn_tags__() when the parent class doesn’t have that method

Real-World Example Causing the Error

Here’s an example of code that triggers this AttributeError:

python
from sklearn.base import BaseEstimator

class MyModel(BaseEstimator):
def __sklearn_tags__(self):
tags = super().__sklearn_tags__()
tags.update({'requires_fit': True})
return tags

Problem: The BaseEstimator class doesn’t define __sklearn_tags__, so calling super().__sklearn_tags__() raises the error.

Resolving the AttributeError Step-by-Step

To resolve the issue, you should:

  1. Ensure you are using the correct method: It’s _more_tags, not __sklearn_tags__, that’s meant to be overridden.

  2. Avoid calling non-existent super().__sklearn_tags__().

  3. Override _more_tags(self) instead, which sklearn internally calls to gather tags.

Here’s the corrected version:

python
from sklearn.base import BaseEstimator

class MyModel(BaseEstimator):
def _more_tags(self):
return {'requires_fit': True}

Fixing the Error by Overriding _more_tags Properly

Let’s walk through a proper fix using a transformer example:

python
from sklearn.base import BaseEstimator, TransformerMixin

class MyTransformer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
return self

def transform(self, X):
return X

def _more_tags(self):
return {'allow_nan': True}

This avoids touching __sklearn_tags__ directly and instead allows sklearn to collect metadata via _more_tags.

Best Practices for Extending sklearn Classes

When you create a custom estimator or transformer:

  • Always inherit from BaseEstimator and possibly TransformerMixin or ClassifierMixin

  • Implement fit() and transform() or predict() as needed

  • Only override _more_tags() when specific metadata is needed

  • Do not call super().__sklearn_tags__() unless you’re certain the parent class implements it

Checking sklearn Version Compatibility

Some older versions of sklearn didn’t support certain tag-related functionality.

  • Always make sure you’re using sklearn >= 0.22, where tags were first introduced.

  • Use pip show scikit-learn or sklearn.__version__ in your Python shell to verify.

If you’re on a very old version, update via:

bash
pip install --upgrade scikit-learn

Avoiding Silent Failures in Custom Estimators

Custom estimators can be tricky. Sklearn tries to validate them through utilities like check_estimator. If your class fails silently, consider using:

python
from sklearn.utils.estimator_checks import check_estimator

check_estimator(MyModel)

This test will explicitly fail if _more_tags or other required methods are improperly implemented.

Tips for Debugging sklearn Attribute Errors

Here are smart debugging habits to follow:

  • Use dir(your_object) to see available attributes

  • Add print statements inside _more_tags() to ensure it’s being called

  • Check parent classes with MyModel.__mro__ to understand the inheritance chain

Common Misconceptions About the Error

Misconception 1:

“I need to define __sklearn_tags__ myself.”

Truth: You never need to manually define or call __sklearn_tags__. Just define _more_tags().

Misconception 2:

“BaseEstimator already handles all tagging.”

Truth: It provides minimal implementation. You must supply additional tags manually if needed.

Misconception 3:

“This is a bug in sklearn.”

Truth: This is usually a misuse of the sklearn API, not a library bug.

Summary of Fixes

Issue Solution
Calling super().__sklearn_tags__() Replace with _more_tags()
Missing tags Define them in _more_tags()
Using outdated sklearn Upgrade sklearn to >= 0.22
Inheriting from wrong class Use BaseEstimator or appropriate mixin
Confused by naming Remember: _more_tags() is the method to override

Frequently Asked Questions (FAQs)

Q1: What is the meaning of Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’?

Answer: It means you’re trying to call __sklearn_tags__() from a superclass that doesn’t implement it, usually due to a misunderstanding of sklearn’s internal API.

Q2: How do I fix the error without removing metadata functionality?

Answer: Use _more_tags(self) instead of __sklearn_tags__(). This is the standard way to declare metadata in sklearn.

Q3: Which sklearn versions support _more_tags()?

Answer: sklearn version 0.22 and above support _more_tags() for user-defined estimators and transformers.

Q4: Should I ever define __sklearn_tags__ myself?

Answer: No. sklearn will generate it internally. You only need to override _more_tags() when necessary.

Q5: I extended a sklearn class but still see the error. Why?

Answer: It’s likely you’ve called super().__sklearn_tags__() assuming the parent has it. Replace this with a self-contained _more_tags() method.

Q6: Can this error occur even if I didn’t write __sklearn_tags__?

Answer: Yes, if a third-party library or tool you’re using tries to invoke super().__sklearn_tags__() on a base class that doesn’t define it.

Final Thoughts

The Attributeerror: ‘super’ object has no attribute ‘__sklearn_tags__’ might seem like an unexpected wall in your coding journey, but it’s ultimately a teachable moment about the internals of sklearn. By understanding how metadata tagging works and how to extend sklearn classes properly, you can avoid such pitfalls and build more robust, testable, and pipeline-ready machine learning components.

The key takeaway: Avoid overriding or calling __sklearn_tags__ directly. Stick with _more_tags() and test thoroughly.

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