scikit-learn-contrib/category_encoders

Add argument for alternative indexing of OrdinalEncoder

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#291 创建于 2021年2月11日

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描述

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

贡献者指南

研究方向
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.
技术栈
pythonpandasnumpy
领域
datamachine learning
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Pythonpandasbasic understanding of category encoders
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