[ML][Inference] Adding classification_weights to ensemble models #50874
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classification_weights are a way to allow models to prefer specific classification results over others this might be advantageous if classification value probabilities are a known quantity and can improve model error rates.
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Pinging @elastic/ml-core (:ml) |
davidkyle
approved these changes
Jan 14, 2020
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| if (classificationLabels != null && probabilities.size() != classificationLabels.size()) { | ||
| throw ExceptionsHelper | ||
| .serverError( | ||
| "model returned classification probabilities of size [{}] which is not equal to classification labels size [{}]", | ||
| "model returned classification probabilities of size [{}] which is not equal to classification labels size {}", |
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I think it's better how it was before [{}]
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🤔 possibly? I might want to switch this around a bit. It is writing out the classification labels array not the size. Woops!
| @@ -156,11 +146,6 @@ public TargetType targetType() { | |||
| return TargetType.CLASSIFICATION; | |||
| } | |||
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| @Override | |||
| public List<String> classificationLabels() { | |||
| return LANGUAGE_NAMES; | |||
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Consider adding LANGUAGE_NAMES to toXContent and the parser. Tree includes the labels in its xcontent representation
...e/src/main/java/org/elasticsearch/xpack/core/ml/inference/trainedmodel/InferenceHelpers.java
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| /** | ||
| * @return List of featureNames expected by the model. In the order that they are expected | ||
| */ | ||
| List<String> getFeatureNames(); |
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Both these methods are just not called anywhere? Is that why they are removed? If so 👍
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Yes, I am cleaning up the interface. I added a bunch of stuff that I thought "all" models would need, and it turns out that that is not the case.
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@elasticmachine update branch |
benwtrent
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…stic#50874) * [ML][Inference] Adding classification_weights to ensemble models classification_weights are a way to allow models to prefer specific classification results over others this might be advantageous if classification value probabilities are a known quantity and can improve model error rates.
benwtrent
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) (#50994) * [ML][Inference] Adding classification_weights to ensemble models classification_weights are a way to allow models to prefer specific classification results over others this might be advantageous if classification value probabilities are a known quantity and can improve model error rates.
SivagurunathanV
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Jan 23, 2020
…stic#50874) * [ML][Inference] Adding classification_weights to ensemble models classification_weights are a way to allow models to prefer specific classification results over others this might be advantageous if classification value probabilities are a known quantity and can improve model error rates.
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classification_weights are a way to allow models to
prefer specific classification results over others
this might be advantageous if classification value
probabilities are a known quantity and can improve
model error rates.