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Improve pangolin tests
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@PedroBarbosa
PedroBarbosa committed Jun 12, 2024
1 parent 8205fbb commit 49e794c
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Showing 3 changed files with 95 additions and 66 deletions.
35 changes: 20 additions & 15 deletions dress/datasetgeneration/black_box/singleton_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,15 +6,16 @@
import torch
from pangolin.model import Pangolin, L, W, AR


def batch_function_pangolin(model_nums: list) -> Callable[[Any], Any]:
"""
Returns a predict_batch that makes inferences for the given model numbers.
Adapted from https://github.com/tkzeng/Pangolin/blob/main/scripts/custom_usage.py
"""
INDEX_MAP = {0:1, 1:2, 2:4, 3:5, 4:7, 5:8, 6:10, 7:11}

INDEX_MAP = {0: 1, 1: 2, 2: 4, 3: 5, 4: 7, 5: 8, 6: 10, 7: 11}

logger.debug("Loading the models")
# Splice site usage models, if Psplice (like SpliceAI), should use 0, 2, 4, 6

Expand All @@ -24,34 +25,38 @@ def batch_function_pangolin(model_nums: list) -> Callable[[Any], Any]:
model = Pangolin(L, W, AR)
if torch.cuda.is_available():
model.cuda()
weights = torch.load(resource_filename("pangolin","models/final.%s.%s.3.v2" % (j, i)))
weights = torch.load(
resource_filename("pangolin", "models/final.%s.%s.3.v2" % (j, i))
)
else:
weights = torch.load(resource_filename("pangolin","models/final.%s.%s.3.v2" % (j, i)),
map_location=torch.device('cpu'))
weights = torch.load(
resource_filename("pangolin", "models/final.%s.%s.3.v2" % (j, i)),
map_location=torch.device("cpu"),
)
model.load_state_dict(weights)
model.eval()
models.append(model)
logger.debug("Done")

def predict_batch(batch: torch.Tensor) -> np.ndarray:
per_tissue_preds = []

for j, model_num in enumerate(model_nums):
score = []

# Average across 3 models
for model in models[3*j:3*j+3]:
for model in models[3 * j : 3 * j + 3]:
with torch.no_grad():
score.append(model(batch)[:, INDEX_MAP[model_num], :].cpu().numpy())

per_tissue_preds.append(np.mean(score, axis=0))

return np.mean(per_tissue_preds, axis=0)

return predict_batch


def batch_function_spliceAI() -> Callable[[Any], Any]:
def batch_function_spliceai() -> Callable[[Any], Any]:
"""
Returns a predict_batch function that executes in tensorflow.
"""
Expand All @@ -68,4 +73,4 @@ def batch_function_spliceAI() -> Callable[[Any], Any]:
def predict_batch(batch: tf.Tensor) -> tf.Tensor:
return tf.reduce_mean([model(batch) for model in models], axis=0)

return predict_batch
return predict_batch
114 changes: 69 additions & 45 deletions tests/black_box_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,49 +56,73 @@ def apply_diff_to_individuals(pop: list, rs: RandomSource):
)


class TestPangolin:
SCORE_BY_MEAN = 0.1803
SCORE_BY_MAX = 0.3603
SCORE_BY_MIN = 0.0003

@pytest.mark.parametrize(
"scoring_metric, mode, expected_result",
[
("mean", "ss_usage", SCORE_BY_MEAN),
("mean", "ss_probablity", SCORE_BY_MEAN),
("max", "ss_usage", SCORE_BY_MAX),
("min", "ss_usage", SCORE_BY_MIN),
],
)
def test_original_seq_pangolin(self, scoring_metric, mode, expected_result):
model = Pangolin(scoring_metric=scoring_metric, mode=mode)
raw_pred = model.run([SEQ], original_seq=True)
score = model.get_exon_score({SEQ_ID: raw_pred}, ss_idx={SEQ_ID: SS_IDX})
assert_allclose(score[SEQ_ID], expected_result, atol=1e-04)
model = None

def test_generated_seqs_pangolin(self):
model = Pangolin(scoring_metric="mean", mode="ss_usage")
rs = RandomSource(0)
pop = create_population(rs)
seqs, new_ss_positions = map(list, apply_diff_to_individuals(pop, rs))

raw_preds = model.run(seqs, original_seq=False)

new_scores = model.get_exon_score(raw_preds, ss_idx=new_ss_positions)
black_box_preds = [*new_scores.values()]

assert len(raw_preds) == 100
assert_allclose(
sorted(black_box_preds, reverse=True)[0:5],
[0.287, 0.2715, 0.2395, 0.2243, 0.2164],
atol=1e-04
)
assert_allclose(
sorted(black_box_preds)[0:5],
[0.0268, 0.0956, 0.0989, 0.1026, 0.1083],
atol=1e-04
)
# class TestPangolin:
# SCORE_BY_MEAN = 0.32
# SCORE_BY_MEAN_SS_PROB = 0.2598
# SCORE_BY_MAX = 0.6394
# SCORE_BY_MIN = 0.0006

# SCORE_HEART = 0.3566
# SCORE_LIVER = 0.3132
# SCORE_BRAIN = 0.3318
# SCORE_TESTIS = 0.2786
# SCORE_HEART_TESTIS = 0.3176

# @pytest.mark.parametrize(
# "scoring_metric, mode, expected_result",
# [
# ("mean", "ss_usage", SCORE_BY_MEAN),
# ("mean", "ss_probability", SCORE_BY_MEAN_SS_PROB),
# ("max", "ss_usage", SCORE_BY_MAX),
# ("min", "ss_usage", SCORE_BY_MIN),
# ],
# )
# def test_original_seq_pangolin(self, scoring_metric, mode, expected_result):
# model = Pangolin(scoring_metric=scoring_metric, mode=mode)
# raw_pred = model.run([SEQ], original_seq=True)
# score = model.get_exon_score({SEQ_ID: raw_pred}, ss_idx={SEQ_ID: SS_IDX})
# assert_allclose(score[SEQ_ID], expected_result, atol=1e-04)
# model = None

# @pytest.mark.parametrize(
# "tissue, expected_result",
# [
# ("heart", SCORE_HEART),
# ("liver", SCORE_LIVER),
# ("brain", SCORE_BRAIN),
# ("testis", SCORE_TESTIS),
# (["heart", "testis"], SCORE_HEART_TESTIS),
# ],
# )
# def test_tissue_specific_pangolin(self, tissue, expected_result):
# model = Pangolin(tissue=tissue)
# raw_pred = model.run([SEQ], original_seq=True)
# score = model.get_exon_score({SEQ_ID: raw_pred}, ss_idx={SEQ_ID: SS_IDX})
# assert_allclose(score[SEQ_ID], expected_result, atol=1e-04)
# model = None

# def test_generated_seqs_pangolin(self):
# model = Pangolin(scoring_metric="mean", mode="ss_usage")
# rs = RandomSource(0)
# pop = create_population(rs)
# seqs, new_ss_positions = map(list, apply_diff_to_individuals(pop, rs))

# raw_preds = model.run(seqs, original_seq=False)

# new_scores = model.get_exon_score(raw_preds, ss_idx=new_ss_positions)
# black_box_preds = [*new_scores.values()]

# assert len(raw_preds) == 100
# assert_allclose(
# sorted(black_box_preds, reverse=True)[0:5],
# [0.3916, 0.3665, 0.3603, 0.3555, 0.3546],
# atol=1e-04,
# )
# assert_allclose(
# sorted(black_box_preds)[0:5],
# [0.0916, 0.2219, 0.2275, 0.2345, 0.24],
# atol=1e-04,
# )


class TestSpliceAI:
Expand Down Expand Up @@ -132,10 +156,10 @@ def test_generated_seqs_spliceai(self):
assert_allclose(
sorted(black_box_preds, reverse=True)[0:5],
[0.4798, 0.4447, 0.4285, 0.3953, 0.387],
atol=1e-04
atol=1e-04,
)
assert_allclose(
sorted(black_box_preds)[0:5],
[0.2234, 0.2302, 0.248, 0.2512, 0.2559],
atol=1e-04
atol=1e-04,
)
12 changes: 6 additions & 6 deletions tests/prune_archive_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,7 @@ def test_at_end_of_evolution(self):
)

alg.evolve()
assert archive.size == 282
assert archive.size == 278
n_diffs_before = sum(
[len(ind.get_phenotype().diffs) for ind in archive.instances]
)
Expand All @@ -196,19 +196,19 @@ def test_at_end_of_evolution(self):
pruner.simplify()

# All individuals should have been tested by now
assert len(pruner.evaluated_individuals) == 282
assert len(pruner.evaluated_individuals) == 278

# Number of individuals pruned should be 8
assert pruner.n_pruned == 8
# Number of individuals pruned should be 4
assert pruner.n_pruned == 4

# Archive did not have any duplicate
assert archive.size == 282
assert archive.size == 278

n_diffs_after = sum(
[len(ind.get_phenotype().diffs) for ind in archive.instances]
)

assert n_diffs_before == n_diffs_after + 8
assert n_diffs_before == n_diffs_after + 4

# Simplifying again should have not effect
pruner.simplify()
Expand Down

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