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added from_dict() method to LTRModelConfig #806

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added from_dict() method to LTRModelConfig #806

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58 changes: 58 additions & 0 deletions eland/ml/ltr/ltr_model_config.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,3 +154,61 @@ def feature_index(self, feature_name: str) -> int:
"Returns the index of the feature in the feature lists."

return self.feature_names.index(feature_name)

@classmethod
def from_dict(cls, d: Mapping[str, Any]) -> "LTRModelConfig":
"""
Create an LTRModelConfig from a dict.

Parameters
----------
d: Mapping[str, Any]
Dict representing the LTR model config.

Examples
--------
>>> from eland.ml.ltr import LTRModelConfig

>>> ltr_model_config_dict = {
... "learning_to_rank": {
... "feature_extractors": [
... {
... "query_extractor": {
... "feature_name": "title_bm25",
... "query": { "match": { "title": "{{query}}" } }
... }
... },
... {
... "query_extractor": {
... "feature_name": "description_bm25",
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Copilot AI Sep 24, 2025

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There's a spelling error in the example - 'description_bm25' should match the variable name 'descritption_bm25' used in the PR description, or the PR description should be corrected to use 'description_bm25'.

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... "query": { "match": { "description": "{{query}}" } }
... }
... }
... ]
... }
... }

>>> ltr_model_config = LTRModelConfig.from_dict(ltr_model_config_dict)
"""
if TYPE_LEARNING_TO_RANK not in d:
raise ValueError(
f"Invalid LTR model config, missing '{TYPE_LEARNING_TO_RANK}' key"
)

feature_extractors = []
for feature_extractor in d[TYPE_LEARNING_TO_RANK]["feature_extractors"]:
if "query_extractor" in feature_extractor:
Comment on lines +198 to +200
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Copilot AI Sep 24, 2025

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Missing validation for the 'feature_extractors' key. If the dictionary doesn't contain this key, a KeyError will be raised. Add a check to ensure 'feature_extractors' exists in the nested dictionary.

Suggested change
feature_extractors = []
for feature_extractor in d[TYPE_LEARNING_TO_RANK]["feature_extractors"]:
if "query_extractor" in feature_extractor:
if "feature_extractors" not in d[TYPE_LEARNING_TO_RANK]:
raise ValueError(
f"Invalid LTR model config, missing 'feature_extractors' key in '{TYPE_LEARNING_TO_RANK}'"
)
feature_extractors = []
for feature_extractor in d[TYPE_LEARNING_TO_RANK]["feature_extractors"]:

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fe = feature_extractor["query_extractor"]
feature_extractors.append(
QueryFeatureExtractor(
feature_name=fe["feature_name"],
query=fe["query"],
default_score=fe.get("default_score"),
)
)
else:
raise ValueError(
f"Unknown feature extractor type: {list(feature_extractor.keys())}"
)

return cls(feature_extractors=feature_extractors)