src-d/wmd-relax

Classification use-case

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#16 opened on 2017年6月11日

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 (2 comments) (0 reactions) (1 assignee)Python (456 stars) (83 forks)batch import
enhancementhelp wanted

説明

This project doesn't currently allow for the predicting the type of an input, as there is no sense of knowing to what type an input value maps.

Normally when using a classifier, there is a two stage process. 1 - fit(X, y), using training input and output data 2 - predict(X), using unknown data, and returning the estimated

It would be good if this project presented a similar interface.

I would suggest creating a class, wmd_classifier, which implements these two models.

fit, which would:

  • take in an array of documents and break them down into bows
  • create a WMD instance
  • cache centroids

predict, which would:

  • take in a document
  • break it into a bow
  • calculate its centroid
  • call nearest_neighbours
  • calculate the output type, based on the k nearest neighbours, weighted by their closeness

コントリビューターガイド

技術スタック
python
領域
machine learning
Issue 種別
feature
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
3
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
1-2 days
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
mostly clear
前提条件
Basic understanding of Word Mover's DistancePythonNumPyK nearest neighbors concept
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
40
調査方針
Review the existing WMD implementation in the repository to understand the centroid and nearest neighbors functions. The issue asks to create a class `wmd classifier` with `fit` and `predict` methods. In `fit`, convert documents to bag of words, create a WMD instance, and cache centroids. In `predict`, break input document to bow, calculate its centroid, call nearest neighbors, and compute output type based on weighted k nearest neighbors. Check comments for any additional clarifications or constraints.