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When using sklearn, I've seen a lot of folks just pickle the model and use that as the interchange format. I like the human-readable interchange format you are

When using sklearn, I've seen a lot of folks just pickle the model and use that as the interchange format. I like the human-readable interchange format you are using better. I assume you just rolled your own. Why not something like PMML?

cf 10y ago
PMML is fairly verbose and limited to a particular set of models. It's often easier to pickle the models and then keep tagged versions. I think a human readable format could be created, but since most models are just a pile of numbers it's unclear what is gained.
- angusb 10y ago
  For Logistic Regression we find human readable config makes a lot of sense. It's pretty intuitive if there aren't too many features - if the model starts behaving weirdly, we can sometimes track it down to a change in a single feature using this (especially when viewing recent git diffs).
  - cf 10y ago
    Sure. I tend to keep my postprocessing of a model under version control. In particular, what features were most helpful for predictions.
    - angusb 10y ago
      Can't really talk about features on here :(
deleted 10y ago
[deleted]
angusb 10y ago
Yep, we made our own. I haven't heard of PMML before - quite cool! What we've made is a bit more readable for what we're using it for though, IMO. Looks like this: { "intercept": 1.0, "features": { "feature_1": { "coefficient": 1.0, "range": [0.1, 10.0], "mean_feature_score": 1.0, "imputation_value": 1.0 }, { .... } } }
- sandGorgon 10y ago
  Is this open source? We were looking for something like this.
  - angusb 10y ago
    Sadly not. I'd be totally up for open sourcing if there's clear demand. If you can find it, send me an email at angus@{company_I_work_at}.com Note that it's very tied down to our use case right now: only compatible with Logistic Regression, and currently it assumes fixed hyperparameters (will change this in future though), assumes a production pipeline of min-max scaling, imputation, then classification.

cf 10y ago

PMML is fairly verbose and limited to a particular set of models. It's often easier to pickle the models and then keep tagged versions. I think a human readable format could be created, but since most models are just a pile of numbers it's unclear what is gained.

angusb 10y ago
For Logistic Regression we find human readable config makes a lot of sense. It's pretty intuitive if there aren't too many features - if the model starts behaving weirdly, we can sometimes track it down to a change in a single feature using this (especially when viewing recent git diffs).
- cf 10y ago
  Sure. I tend to keep my postprocessing of a model under version control. In particular, what features were most helpful for predictions.
  - angusb 10y ago
    Can't really talk about features on here :(

deleted 10y ago

[deleted]

angusb 10y ago

Yep, we made our own. I haven't heard of PMML before - quite cool! What we've made is a bit more readable for what we're using it for though, IMO. Looks like this: { "intercept": 1.0, "features": { "feature_1": { "coefficient": 1.0, "range": [0.1, 10.0], "mean_feature_score": 1.0, "imputation_value": 1.0 }, { .... } } }

sandGorgon 10y ago
Is this open source? We were looking for something like this.
- angusb 10y ago
  Sadly not. I'd be totally up for open sourcing if there's clear demand. If you can find it, send me an email at angus@{company_I_work_at}.com Note that it's very tied down to our use case right now: only compatible with Logistic Regression, and currently it assumes fixed hyperparameters (will change this in future though), assumes a production pipeline of min-max scaling, imputation, then classification.