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2021-11-27 Online Learning Python Library: river

Naka Masato
November 27, 2021

2021-11-27 Online Learning Python Library: river

Introduction of online learning Python library river.

- https://riverml.xyz/
- https://github.com/online-ml/river

Naka Masato

November 27, 2021

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  1. Online Learning Python Library:
    2021/11/27 Naka Masato

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  2. 自己紹介
    名前 那珂将人
    ● アルゴリズムエンジニアとしてレコメンドエンジン開発
    ● インフラ基盤整備
    Twitter: https://twitter.com/gymnstcs

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  3. Content: river

    Python library for online/streaming learning https://riverml.xyz/

    2.9k stars https://github.com/online-ml/river

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  4. Batch Learning
    Common steps:
    1. Loading (and preprocessing) the data
    2. Fitting a model to the data
    3. Computing the performance of the model on unseen data
    1. Requires a lot of memory if dataset is huge
    2. Can't elegantly learn from new data
    3. Not easy to respond to changes of available features
    Some solutions: learn the data in chuck or mini-batches (Dask and Spark's MLlib.)

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  5. Incremental learning (online/streaming learning)

    Learn a model for each observation.

    Feature scaling (running statistics).

    SGD (Stochastic Gradient Descent) to update weights.

    Model can be updated seamlessly.

    Concept drift can be detected.

    Quick response to recent actions.

    Performance might be not as good as batch learning.

    Systems become more complex.

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  6. Batch learning vs. Online Learning in Implementation
    Batch Learning:
    Online Learning:

    Pipeline needs to be different.
    Preprocess Train Predict
    Preprocess Train Preprocess Train Preprocess Train Preprocess Train
    Preprocess and train for each observation

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  7. Main components of river
    1. Estimator
    2. Pipeline
    a. Transformer
    b. Classifier/Regressor
    i. learn_one
    ii. predict_one
    3. datasets

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  8. Pipeline in river
    Pipelines allow you to chain different
    steps into a sequence.

    One or more transformers

    Final step: classifier or regressor
    for supervised learning

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  9. Train a model
    1. Define preprocesses and a model with Pipeline
    2. For each observation in the iteration for datasets
    a. Make a prediction.
    b. Update metrics.
    c. Update weight.

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  10. Progressive Validation
    1. Get next sample.
    2. Make a prediction.
    3. Update a running average of the error.
    4. Update the model.
    All samples can be used as a validation set.
    In some situation, leakage might happen.

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  11. Example: Prediction of taxi trip duration

    Obviously the duration of the trip, is only known once the taxi arrives at the
    desired destination.

    Instead of updating the model immediately after making a prediction, update it
    once the ground truth is available Delayed progressive validation



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  12. Delayed Progressive Validation
    1. Specify moment to get the
    timestamp for the
    2. Specify delay one of str, int,
    timedelta, or callable.
    Example: impression (moment)

    click or non-click (delayed)

    Model will be updated only
    when the delay has been passed.

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  13. Challenges
    1. Deployment for production. (Integration with ML platforms)
    2. Save and load model for production.

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