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feat: restrict criteria for human exclusion ML-265 #39

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Merged
pleary merged 1 commit into from
Jul 16, 2025
Merged

feat: restrict criteria for human exclusion ML-265 #39

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2 changes: 1 addition & 1 deletion lib/inat_inferrer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -640,7 +640,7 @@ def limit_leaf_scores_that_include_humans(self, leaf_scores, strategy=None):
return top_results.head(1)

# if requesting a more limited approach to human exclusion
if strategy == "limited" and self.taxonomy.mammals_taxon is not None:
if self.taxonomy.mammals_taxon is not None:
mammals_results = top_results.query(
f"left > {self.taxonomy.mammals_taxon['left']} and "
f"right < {self.taxonomy.mammals_taxon['right']}"
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4 changes: 4 additions & 0 deletions tests/fixtures/taxonomy.csv
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Original file line number Diff line number Diff line change
Expand Up @@ -24,3 +24,7 @@ parent_taxon_id,taxon_id,rank_level,leaf_class_id,iconic_class_id,spatial_class_
43367,43575,30,,,23,Hominidae
43575,43583,20,,,24,Homo
43583,43584,10,,,25,Homo sapiens
40151,43698,40,,,26,Rodentia
43698,44185,30,,,27,Muridae
44185,44678,20,,,28,Mus
44678,44705,10,,,29,Mus musculus
58 changes: 58 additions & 0 deletions tests/test_inat_inferrer.py
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Expand Up @@ -112,6 +112,8 @@ def test_results_are_unchanged_if_they_dont_include_humans(self, inatInferrer):
for n in range(20):
results.append({
"taxon_id": n,
"left": 0,
"right": 0,
"combined_score": 1.0 - (n * 0.01)
})
results = pd.DataFrame(results)
Expand All @@ -120,46 +122,98 @@ def test_results_are_unchanged_if_they_dont_include_humans(self, inatInferrer):
def test_results_are_unchanged_if_they_dont_include_humans_in_top_10(self, inatInferrer):
assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584

results = []
for n in range(20):
results.append({
"taxon_id": n,
"left": 0,
"right": 0,
"combined_score": 1.0 - (n * 0.01)
})
results.append({
"taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
"left": inatInferrer.taxonomy.human_taxon["left"],
"right": inatInferrer.taxonomy.human_taxon["right"],
"combined_score": 0.001
})
results = pd.DataFrame(results)
assert results.equals(inatInferrer.limit_leaf_scores_that_include_humans(results))

def test_results_are_empty_if_humans_are_in_top_10_but_not_first(self, inatInferrer):
# ... and the results contain another mammal
assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584
mouse_taxon = inatInferrer.taxonomy.df.query("name == 'Mus musculus'").iloc[0]

results = []
for n in range(5):
results.append({
"taxon_id": n,
"left": 0,
"right": 0,
"combined_score": 1.0 - (n * 0.01)
})
results.append({
"taxon_id": mouse_taxon["taxon_id"],
"left": mouse_taxon["left"],
"right": mouse_taxon["right"],
"combined_score": 0.002
})
results.append({
"taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
"left": inatInferrer.taxonomy.human_taxon["left"],
"right": inatInferrer.taxonomy.human_taxon["right"],
"combined_score": 0.001
})
results = pd.DataFrame(results)
assert inatInferrer.limit_leaf_scores_that_include_humans(results).empty

def test_humans_excluded_if_humans_are_in_top_10_but_not_first(self, inatInferrer):
assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584

results = []
for n in range(5):
results.append({
"taxon_id": n,
"left": 0,
"right": 0,
"combined_score": 1.0 - (n * 0.01)
})
results.append({
"taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
"left": inatInferrer.taxonomy.human_taxon["left"],
"right": inatInferrer.taxonomy.human_taxon["right"],
"combined_score": 0.001
})
results = pd.DataFrame(results)
assert results.head(5).equals(inatInferrer.limit_leaf_scores_that_include_humans(results))

def test_results_are_empty_if_humans_are_first_by_small_margin(self, inatInferrer):
# ... and the results contain another mammal
assert inatInferrer.taxonomy.human_taxon["name"] == "Homo sapiens"
assert inatInferrer.taxonomy.human_taxon["taxon_id"] == 43584
mouse_taxon = inatInferrer.taxonomy.df.query("name == 'Mus musculus'").iloc[0]

results = []
results.append({
"taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
"left": inatInferrer.taxonomy.human_taxon["left"],
"right": inatInferrer.taxonomy.human_taxon["right"],
"combined_score": 1.0
})
results.append({
"taxon_id": mouse_taxon["taxon_id"],
"left": mouse_taxon["left"],
"right": mouse_taxon["right"],
"combined_score": 0.95
})
for n in range(5):
results.append({
"taxon_id": n,
"left": 0,
"right": 0,
"combined_score": 0.9 - (n * 0.01)
})
results = pd.DataFrame(results)
Expand All @@ -171,12 +225,16 @@ def test_only_humans_returned_if_first_by_large_margin(self, inatInferrer):
results = []
human_result = {
"taxon_id": inatInferrer.taxonomy.human_taxon["taxon_id"],
"left": inatInferrer.taxonomy.human_taxon["left"],
"right": inatInferrer.taxonomy.human_taxon["right"],
"combined_score": 1.0
}
results.append(human_result)
for n in range(5):
results.append({
"taxon_id": n,
"left": 0,
"right": 0,
"combined_score": 0.5 - (n * 0.01)
})
results = pd.DataFrame(results)
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8 changes: 4 additions & 4 deletions tests/test_model_taxonomy_dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,15 +41,15 @@ def test_human_taxon(self, capsys, taxonomy):
def test_print(self, capsys, taxonomy):
ModelTaxonomyDataframe.print(taxonomy.df)
captured = capsys.readouterr()
assert "├──Animalia :: 0:33" in captured.out
assert "│ └──Chordata :: 1:32" in captured.out
assert "├──Animalia :: 0:41" in captured.out
assert "│ └──Chordata :: 1:40" in captured.out

def test_print_with_aggregated_combined_score(self, capsys, taxonomy):
taxonomy.df["aggregated_combined_score"] = 1
ModelTaxonomyDataframe.print(taxonomy.df)
captured = capsys.readouterr()
assert "├──Animalia :: 0:33" in captured.out
assert "│ └──Chordata :: 1:32" in captured.out
assert "├──Animalia :: 0:41" in captured.out
assert "│ └──Chordata :: 1:40" in captured.out

def test_print_with_lambda(self, capsys, taxonomy):
ModelTaxonomyDataframe.print(taxonomy.df, display_taxon_lambda=(
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