Support for Score Ranges with an Unspecified Classification

src/mavedb/view_models/score_set.py

@@ -2,13 +2,12 @@
 from __future__ import annotations
 
 from datetime import date
-from typing import Any, Collection, Dict, Optional, Sequence
+from typing import Any, Collection, Dict, Optional, Sequence, Literal
 
 from humps import camelize
 from pydantic import root_validator
 
 from mavedb.lib.validation import urn_re
-from mavedb.lib.validation.constants.score_set import default_ranges
 from mavedb.lib.validation.exceptions import ValidationError
 from mavedb.lib.validation.utilities import inf_or_float, is_null
 from mavedb.models.enums.mapping_state import MappingState
@@ -55,23 +54,13 @@ class Config:
 class ScoreRange(BaseModel):
     label: str
     description: Optional[str]
-    classification: str
+    classification: Literal["normal", "abnormal", "not_specified"]
     # Purposefully vague type hint because of some odd JSON Schema generation behavior.
     # Typing this as tuple[Union[float, None], Union[float, None]] will generate an invalid
     # jsonschema, and fail all tests that access the schema. This may be fixed in pydantic v2,
     # but it's unclear. Even just typing it as Tuple[Any, Any] will generate an invalid schema!
     range: list[Any]  # really: tuple[Union[float, None], Union[float, None]]
 
-    @validator("classification")
-    def range_classification_value_is_accepted(cls, field_value: str):
-        classification = field_value.strip().lower()
-        if classification not in default_ranges:
-            raise ValidationError(
-                f"Unexpected classification value(s): {classification}. Permitted values: {default_ranges}"
-            )
-
-        return classification
-
     @validator("range")
     def ranges_are_not_backwards(cls, field_value: tuple[Any]):
         if len(field_value) != 2:
@@ -89,7 +78,7 @@ def ranges_are_not_backwards(cls, field_value: tuple[Any]):
 
 
 class ScoreRanges(BaseModel):
-    wt_score: float
+    wt_score: Optional[float]
     ranges: list[ScoreRange]  # type: ignore
 
 
@@ -209,17 +198,16 @@ def score_range_labels_must_be_unique(cls, field_value: Optional[ScoreRanges]):
         return field_value
 
     @validator("score_ranges")
-    def ranges_contain_normal_and_abnormal(cls, field_value: Optional[ScoreRanges]):
+    def score_range_normal_classification_exists_if_wild_type_score_provided(cls, field_value: Optional[ScoreRanges]):
         if field_value is None:
             return None
 
-        ranges = set([range_model.classification for range_model in field_value.ranges])
-        if not set(default_ranges).issubset(ranges):
-            raise ValidationError(
-                "Both `normal` and `abnormal` ranges must be provided.",
-                # Raise this error inside the first classification provided by the model.
-                custom_loc=["body", "scoreRanges", "ranges", 0, "classification"],
-            )
+        if field_value.wt_score is not None:
+            if not any([range_model.classification == "normal" for range_model in field_value.ranges]):
+                raise ValidationError(
+                    "A wild type score has been provided, but no normal classification range exists.",
+                    custom_loc=["body", "scoreRanges", "wtScore"],
+                )
 
         return field_value
 
@@ -264,6 +252,16 @@ def wild_type_score_in_normal_range(cls, field_value: Optional[ScoreRanges]):
         normal_ranges = [
             range_model.range for range_model in field_value.ranges if range_model.classification == "normal"
         ]
+
+        if normal_ranges and field_value.wt_score is None:
+            raise ValidationError(
+                "A normal range has been provided, but no wild type score has been provided.",
+                custom_loc=["body", "scoreRanges", "wtScore"],
+            )
+
+        if field_value.wt_score is None:
+            return field_value
+
         for range in normal_ranges:
             if field_value.wt_score >= inf_or_float(range[0], lower=True) and field_value.wt_score < inf_or_float(
                 range[1], lower=False

tests/view_models/test_score_set.py

@@ -1,7 +1,6 @@
 import pytest
 from fastapi.encoders import jsonable_encoder
 
-from mavedb.lib.validation.constants.score_set import default_ranges
 from mavedb.view_models.publication_identifier import PublicationIdentifierCreate
 from mavedb.view_models.score_set import ScoreSetCreate, ScoreSetModify
 from mavedb.view_models.target_gene import TargetGeneCreate
@@ -440,20 +439,35 @@ def test_cannot_create_score_set_with_wild_type_outside_normal_range():
     )
 
 
-@pytest.mark.parametrize("present_name", default_ranges)
-def test_cannot_create_score_set_without_default_range(present_name):
+def test_cannot_create_score_set_with_wild_type_score_and_no_normal_range():
+    wt_score = -0.5
     score_set_test = TEST_MINIMAL_SEQ_SCORESET.copy()
     score_set_test["score_ranges"] = {
-        "wt_score": -1.5,
+        "wt_score": wt_score,
         "ranges": [
-            {"label": "range_2", "classification": f"{present_name}", "range": (-3, -1)},
+            {"label": "range_1", "classification": "abnormal", "range": (-1, 0)},
         ],
     }
 
     with pytest.raises(ValueError) as exc_info:
         ScoreSetModify(**jsonable_encoder(score_set_test))
 
-    assert "Both `normal` and `abnormal` ranges must be provided." in str(exc_info.value)
+    assert "A wild type score has been provided, but no normal classification range exists." in str(exc_info.value)
+
+
+def test_cannot_create_score_set_with_normal_range_and_no_wild_type_score():
+    score_set_test = TEST_MINIMAL_SEQ_SCORESET.copy()
+    score_set_test["score_ranges"] = {
+        "wt_score": None,
+        "ranges": [
+            {"label": "range_1", "classification": "normal", "range": (-1, 0)},
+        ],
+    }
+
+    with pytest.raises(ValueError) as exc_info:
+        ScoreSetModify(**jsonable_encoder(score_set_test))
+
+    assert "A normal range has been provided, but no wild type score has been provided." in str(exc_info.value)
 
 
 def test_cannot_create_score_set_without_default_ranges():
@@ -468,4 +482,18 @@ def test_cannot_create_score_set_without_default_ranges():
     with pytest.raises(ValueError) as exc_info:
         ScoreSetModify(**jsonable_encoder(score_set_test))
 
-    assert "Unexpected classification value(s): other. Permitted values: ['normal', 'abnormal']" in str(exc_info.value)
+    assert "unexpected value; permitted: 'normal', 'abnormal', 'not_specified'" in str(exc_info.value)
+
+
+@pytest.mark.parametrize("classification", ["normal", "abnormal", "not_specified"])
+def test_can_create_score_set_with_any_range_classification(classification):
+    wt_score = -0.5 if classification == "normal" else None
+    score_set_test = TEST_MINIMAL_SEQ_SCORESET.copy()
+    score_set_test["score_ranges"] = {
+        "wt_score": wt_score,
+        "ranges": [
+            {"label": "range_1", "classification": classification, "range": (-1, 0)},
+        ],
+    }
+
+    ScoreSetModify(**jsonable_encoder(score_set_test))