scikit-learn-contrib/category_encoders

Add argument for alternative indexing of OrdinalEncoder

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#291 opened on Feb 11, 2021

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Description

Expected Behavior

There are a variety of applications in which zero-indexing would be preferred for the OrdinalEncoder. One example is preparing features for a PyTorch model with categorical embeddings, in which case the ordinal label is used to slice dimensions of an embedding matrix. Note also that the sklearn OrdinalEncoder is zero-indexed.

One could possibly add an argument to init() that specifies the indexing (e.g., self.index_start), so that the ordinal_encoding() method can do something like:

data = pd.Series(index=index, data=range(self.index_start, len(index) + self.index_start))

Actual Behavior

The ordinal_encoding() method imposes one-indexing in this line: data = pd.Series(index=index, data=range(1, len(index) + 1))

Specifications

  • Version: 2.2.2

Contributor guide

Research direction
The issue requests adding an optional argument to OrdinalEncoder. init () to specify the starting index for encoding (default 1). Modify the ordinal encoding() method in category encoders/ordinal.py to use self.index start. Ensure backward compatibility. Add tests and update documentation. Refer to similar implementations in sklearn's OrdinalEncoder for guidance.
Tech stack
pythonpandasnumpy
Domain
datamachine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Pythonpandasbasic understanding of category encoders
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
65