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feat(gold_standard): add traitFromSourceMappedId to schema #924

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merged 6 commits into from
Nov 27, 2024
Merged

feat(gold_standard): add traitFromSourceMappedId to schema #924

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c99b354
feat(gold_standard): add traitFromSourceMappedId to schema
ireneisdoomed Nov 22, 2024
c8a5f5b
Merge branch 'dev' of https://github.com/opentargets/gentropy into il…
ireneisdoomed Nov 22, 2024
24febad
chore: adapt tests
ireneisdoomed Nov 22, 2024
0bf2d3d
feat(feature_matrix): consider `traitFromSourceMappedId` a static column
ireneisdoomed Nov 22, 2024
bde32a7
feat(feature_matrix): consider `traitFromSourceMappedId` an optional …
ireneisdoomed Nov 22, 2024
56e079a
Merge branch 'dev' into il-gold-standard-schema-simple
ireneisdoomed Nov 27, 2024
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6 changes: 6 additions & 0 deletions src/gentropy/assets/schemas/l2g_gold_standard.json
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Expand Up @@ -25,6 +25,12 @@
"nullable": false,
"metadata": {}
},
{
"name": "traitFromSourceMappedId",
"type": "string",
"nullable": true,
"metadata": {}
},
{
"name": "goldStandardSet",
"type": "string",
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2 changes: 1 addition & 1 deletion src/gentropy/dataset/l2g_feature_matrix.py
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Expand Up @@ -36,7 +36,7 @@ def __init__(
with_gold_standard (bool): Whether to include the gold standard set in the feature matrix.
"""
self.with_gold_standard = with_gold_standard
self.fixed_cols = ["studyLocusId", "geneId"]
self.fixed_cols = ["studyLocusId", "geneId", "traitFromSourceMappedId"]
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Feature matrix doesn't have a schema.
But I need to define that this column is expected as one of the fixed columns to pivot on.

if self.with_gold_standard:
self.fixed_cols.append("goldStandardSet")

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2 changes: 1 addition & 1 deletion tests/gentropy/dataset/test_l2g_feature_matrix.py
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Expand Up @@ -87,7 +87,7 @@ def _setup(self: TestFromFeaturesList, spark: SparkSession) -> None:
"""Setup fixture."""
self.sample_gold_standard = L2GGoldStandard(
_df=spark.createDataFrame(
[(1, "var1", "gwas1", "g1", "positive", ["a_source"])],
[(1, "var1", "gwas1", "g1", "efo1", "positive", ["a_source"])],
L2GGoldStandard.get_schema(),
),
_schema=L2GGoldStandard.get_schema(),
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