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An efficient way to assign user_ids to huge dataset under certain conditions

use python dictionary as a lookup table to store node_ids and their corresponding user_ids. Retrieve tx_id, node_id list ordered by tx_id, and if a node_id appeared with two tx_ids, the tx which comes later will find that the node_id already stored in python dictionary and get the user_id from dict.

This is union-find partitioning problem, the question is how to unite sets(tx in your case) if they have a common node_id.

Categories : Algorithm

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CountVectorizer() in scikit-learn Python gives Memory error when feeding big Dataset. Same code with Smaller dataset works fine, what am I missing?
IIRC the max_features is only applied after the whole dictionary is computed. The easiest way out is to use the HashingVectorizer that does not compute a dictionary. You will lose the ability to get the corresponding token for a feature, but you shouldn't run into memory issues any more.

Categories : Python
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Categories : C#
Get Inserted Row after .Net Dataset Row Added
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