Add a groupby operator #1123
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Add a groupby operator #1123
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HenryL27
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Jan 30, 2025
| def init_embedding(row): | ||
| doc = Document.from_row(row) | ||
| return {"vector": doc.embedding, "cluster": -1} | ||
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can you add a "assert self.context.exec_mode == ExecMode.RAY" in here?
| return context.read.document(doc_list) | ||
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| def test_groupby_count(self, fruits_docset): | ||
| aggregated = fruits_docset.groupby("text_representation").count() |
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What do the Documents in aggregated look like at this point?
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| def init(embeddings, K, init_mode): | ||
| if init_mode == "random": |
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supernit: init_mode could be an Enum but str is fine too. I guess would be nice to have the list of known init_modes in the exception?
HenryL27
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| from ray.data._internal.aggregate import Count | ||
| from ray.data.aggregate import AggregateFn |
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from typing import TYPE_CHECKING
if TYPE_CHECKING:
<ray imports>
HenryL27
reviewed
Jan 31, 2025
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| return DocSet(self._docset.context, DatasetScan(serialized)) | ||
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| def count(self) -> DocSet: |
HenryL27
reviewed
Jan 31, 2025
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| @staticmethod | ||
| def update(embeddings, centroids, iterations, epsilon): | ||
| i = 0 |
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This includes generally three steps: 1. materialize a document's embedding 2. initialize centroids randomly 2. iterate the kmeans process until converge, this is based on ray dataset map group and aggregate operators. The result centroids could be used for downstream work.
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Implement groupby based on ray dataset groupby and show how a general entity clustering could be used together with kmeans clustering.
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Implement groupby based on ray dataset groupby and show how a general
entity clustering could be used together with kmeans clustering.