diff --git a/helpers/generate_histograms.py b/helpers/generate_histograms.py
index 8d4f83b515d3ad2f0ff51fff382fa43234529556..57c9f342258674b91caedae6bb82d2cda5931da9 100644
--- a/helpers/generate_histograms.py
+++ b/helpers/generate_histograms.py
@@ -299,9 +299,7 @@ def main(
if not output_dir.exists():
output_dir.mkdir(parents=True, exist_ok=True)
- output_file = (
- output_dir / f"histograms{model_name}_{k}_{fold_name}.json"
- )
+ output_file = output_dir / f"histograms{model_name}_{k}_{fold_name}.json"
output_json_data = {}
diff --git a/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian/datamodule.py b/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian/datamodule.py
index b09fa338731907c6a5fcd0196eccff6d57013c2b..2f6caeac988d8b27639ed573dd7a63e96708226e 100644
--- a/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian/datamodule.py
+++ b/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian/datamodule.py
@@ -11,8 +11,8 @@ from mednet.libs.common.data.datamodule import ConcatDataModule
from mednet.libs.common.data.split import make_split
from ..indian.datamodule import CONFIGURATION_KEY_DATADIR as INDIAN_KEY_DATADIR
-from ..indian.datamodule import DataModule as IndianDataModule
from ..indian.datamodule import ClassificationRawDataLoader as IndianLoader
+from ..indian.datamodule import DataModule as IndianDataModule
from ..montgomery.datamodule import (
ClassificationRawDataLoader as MontgomeryLoader,
)
diff --git a/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian_tbx11k/datamodule.py b/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian_tbx11k/datamodule.py
index 532945277debb3558fae1a97c0a26d434690cea4..c83680ecb89efe2ab7b51accaa21ab9e476ceb5f 100644
--- a/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian_tbx11k/datamodule.py
+++ b/src/mednet/libs/classification/config/data/montgomery_shenzhen_indian_tbx11k/datamodule.py
@@ -11,8 +11,8 @@ from mednet.libs.common.data.datamodule import ConcatDataModule
from mednet.libs.common.data.split import make_split
from ..indian.datamodule import CONFIGURATION_KEY_DATADIR as INDIAN_KEY_DATADIR
-from ..indian.datamodule import DataModule as IndianDataModule
from ..indian.datamodule import ClassificationRawDataLoader as IndianLoader
+from ..indian.datamodule import DataModule as IndianDataModule
from ..montgomery.datamodule import (
ClassificationRawDataLoader as MontgomeryLoader,
)
diff --git a/src/mednet/libs/classification/config/data/nih_cxr14_padchest/datamodule.py b/src/mednet/libs/classification/config/data/nih_cxr14_padchest/datamodule.py
index d5a44ce64e5060f69083f95824a5995f63246e05..e1907a35947bebc9f278266ee8bb6a3f16cc1876 100644
--- a/src/mednet/libs/classification/config/data/nih_cxr14_padchest/datamodule.py
+++ b/src/mednet/libs/classification/config/data/nih_cxr14_padchest/datamodule.py
@@ -11,6 +11,7 @@ from mednet.libs.common.data.split import make_split
from ..nih_cxr14.datamodule import ClassificationRawDataLoader as CXR14Loader
from ..padchest.datamodule import ClassificationRawDataLoader as PadchestLoader
+
class DataModule(ConcatDataModule):
"""Aggregated dataset composed of NIH CXR14 relabeld and PadChest
(normalized) datasets.
diff --git a/src/mednet/libs/classification/scripts/cli.py b/src/mednet/libs/classification/scripts/cli.py
index b7839d0dd1bf78eae8eb81e4fdbd8078cc9bec0d..34ba1ff38d08568dd96f3df61e85b2f70669d079 100644
--- a/src/mednet/libs/classification/scripts/cli.py
+++ b/src/mednet/libs/classification/scripts/cli.py
@@ -17,18 +17,14 @@ def classification():
pass
-classification.add_command(
- importlib.import_module("..config", package=__name__).config
-)
+classification.add_command(importlib.import_module("..config", package=__name__).config)
classification.add_command(
importlib.import_module("..database", package=__name__).database,
)
classification.add_command(
importlib.import_module("..predict", package=__name__).predict
)
-classification.add_command(
- importlib.import_module("..train", package=__name__).train
-)
+classification.add_command(importlib.import_module("..train", package=__name__).train)
classification.add_command(
importlib.import_module(
"mednet.libs.common.scripts.train_analysis",
diff --git a/src/mednet/libs/classification/tests/conftest.py b/src/mednet/libs/classification/tests/conftest.py
index ecd74256dbc8824d70e3962140498a88a313e827..40e5f9d0bde84d4cdc24707a0563cc03e97d2722 100644
--- a/src/mednet/libs/classification/tests/conftest.py
+++ b/src/mednet/libs/classification/tests/conftest.py
@@ -70,8 +70,7 @@ def pytest_runtest_setup(item):
# iterates over all markers for the item being examined, get the first
# argument and accumulate these names
rc_names = [
- mark.args[0]
- for mark in item.iter_markers(name="skip_if_rc_var_not_set")
+ mark.args[0] for mark in item.iter_markers(name="skip_if_rc_var_not_set")
]
# checks all names mentioned are set in ~/.config/mednet.libs.classification.toml, otherwise,
@@ -197,10 +196,7 @@ class DatabaseCheckers:
assert "name" in batch[1]
assert all(
- [
- any([k.startswith(j) for j in prefixes])
- for k in batch[1]["name"]
- ],
+ [any([k.startswith(j) for j in prefixes]) for k in batch[1]["name"]],
)
# use the code below to view generated images
@@ -227,9 +223,7 @@ class DatabaseCheckers:
for idx, rs in enumerate(raw_samples):
raw_samples_indices[rs[0]] = idx
- for ref_hist_path, ref_hist_data in ref_histogram_splits[
- split_name
- ]:
+ for ref_hist_path, ref_hist_data in ref_histogram_splits[split_name]:
# Get index in the dataset that will return the data
# corresponding to the specified sample name
dataset_sample_index = raw_samples_indices[ref_hist_path]
@@ -257,9 +251,7 @@ class DatabaseCheckers:
# reference and check the similarity within a certain
# threshold
pearson_coeffs = numpy.corrcoef(histogram, ref_hist_data)
- assert (
- 1 - pearson_coeff_threshold <= pearson_coeffs[0][1] <= 1
- )
+ assert 1 - pearson_coeff_threshold <= pearson_coeffs[0][1] <= 1
else:
assert histogram == ref_hist_data
diff --git a/src/mednet/libs/classification/tests/test_cli_classification.py b/src/mednet/libs/classification/tests/test_cli_classification.py
index 8c76cda2b0f8146d5a1e5c9a2024e25dc2a8857a..8eb8a93c69488d1d4e37bf0650e21e1c84e12ecf 100644
--- a/src/mednet/libs/classification/tests/test_cli_classification.py
+++ b/src/mednet/libs/classification/tests/test_cli_classification.py
@@ -441,9 +441,7 @@ def test_experiment(temporary_basedir):
_assert_exit_0(result)
assert (output_folder / "model" / "meta.json").exists()
- assert (
- output_folder / "model" / f"model-at-epoch={num_epochs-1}.ckpt"
- ).exists()
+ assert (output_folder / "model" / f"model-at-epoch={num_epochs-1}.ckpt").exists()
assert (output_folder / "predictions" / "predictions.json").exists()
assert (output_folder / "predictions" / "predictions.meta.json").exists()
diff --git a/src/mednet/libs/common/data/datamodule.py b/src/mednet/libs/common/data/datamodule.py
index 32e40ca0eaf919e501089eddbfda1768a5a450eb..dff5a24f0b0a95b2c9c1b36e1f962da14da32e4c 100644
--- a/src/mednet/libs/common/data/datamodule.py
+++ b/src/mednet/libs/common/data/datamodule.py
@@ -139,9 +139,7 @@ class _DelayedLoadingDataset(Dataset):
def __getitem__(self, key: int) -> Sample:
tensor, metadata = self.loader.sample(self.raw_dataset[key])
- return self.transform(tensor), transform_tvtensors(
- metadata, self.transform
- )
+ return self.transform(tensor), transform_tvtensors(metadata, self.transform)
def __len__(self):
return len(self.raw_dataset)
@@ -177,9 +175,7 @@ def _apply_loader_and_transforms(
"""
sample = load(info)
- return model_transform(sample[0]), transform_tvtensors(
- sample[1], model_transform
- )
+ return model_transform(sample[0]), transform_tvtensors(sample[1], model_transform)
class _CachedDataset(Dataset):
@@ -306,145 +302,6 @@ class _ConcatDataset(Dataset):
yield from dataset
-def _make_balanced_random_sampler(
- dataset: Dataset,
- target: str = "target",
-) -> torch.utils.data.WeightedRandomSampler:
- """Generate a pytorch sampler that samples according to class
- probabilities.
-
- This function takes as input a torch Dataset, and computes the weights to
- balance each class in the dataset, and the datasets themselves if one
- passes a :py:class:`torch.utils.data.ConcatDataset`.
-
- In this implementation, we balance **both** class and dataset-origin
- probabilities, what you expect for a truly *equitable* random sampler.
-
- Take this example for illustration:
-
- * Dataset 1: N = 10 samples, 9 samples with target=0, 1 sample with target=1
- * Dataset 2: N = 6 samples, 3 samples with target=0, 3 samples with target=1
-
- So:
-
- | Dataset | Target | Samples | Weight | Normalised weight |
- +---------+--------+---------+--------+-------------------+
- | 1 | 0 | 9 | 1/9 | 1/36 |
- | 1 | 1 | 1 | 1/1 | 1/4 |
- | 2 | 0 | 3 | 1/3 | 1/12 |
- | 2 | 1 | 3 | 1/3 | 1/12 |
-
- Legend:
-
- * Weight: the weights computed by this method
- * Normalised weight: the weight per sample used by the random sampler,
- after normalising the weights by the sum of all weights in the
- concatenated dataset, such that the sum of all normalized weights times
- the number of samples is 1.
-
- The properties of this algorithm are as follows:
-
- 1. The probability of picking a sample from any target is the same (0.5 in
- this case). To verify this, notice that the probability of picking a
- sample with ``target=0`` is :math:`1/4 x 1 + 1/12 x 3 = 0.5`.
- 2. The probability of picking a sample with ``target=0`` from Dataset 2 is
- 3 times higher than those from Dataset 1. As there are 3 times fewer
- samples in Dataset 2 with ``target=0``, this makes choosing samples from
- Dataset 1 proportionally less likely.
- 3. The probability of picking a sample with ``target=1`` from Dataset 2 is
- 3 times lower than those from Dataset 1. As there are 3 times fewer
- samples in Dataset 1 with ``target=1``, this makes choosing samples from
- Dataset 2 proportionally less likely.
-
- This function assumes targets are stored on a dictionary entry named
- ``target`` inside the metadata information for the
- :py:data:`.typing.Sample`, and that its value is an integer.
-
- We then instantiate a pytorch sampler using the inverse probabilities (the
- more samples in a class, the less likely it becomes to be sampled.
-
- Parameters
- ----------
- dataset
- An instance of torch Dataset.
- :py:class:`torch.utils.data.ConcatDataset` are supported.
- target
- The name of a metadata key pointing to an integer property that allows
- balancing the dataset.
-
- Returns
- -------
- A sampler, to be used in a dataloader equipped with the same dataset
- used to calculate the relative sample weights.
-
- Raises
- ------
- RuntimeError
- If requested to balance a dataset (single, not-concatenated) without an
- existing target.
- """
-
- def _calculate_weights(targets: list[int]) -> list[float]:
- counts = collections.Counter(targets)
- weights = {k: 1.0 / v for k, v in counts.items()}
- return [weights[k] for k in targets]
-
- if isinstance(dataset, torch.utils.data.ConcatDataset):
- # There are two possible cases: targets/no-targets
- metadata_example = dataset.datasets[0][0][1]
- if target in metadata_example and isinstance(
- metadata_example[target],
- int,
- ):
- # there are integer targets, let's balance with those
- logger.info(
- f"Balancing sample selection probabilities **and** "
- f"concatenated-datasets using metadata targets `{target}`",
- )
- targets = [
- k
- for ds in dataset.datasets
- for k in typing.cast(Dataset, ds).targets()
- ]
- weights = _calculate_weights(targets) # type: ignore
- else:
- logger.warning(
- f"Balancing samples **and** concatenated-datasets "
- f"by using dataset totals as `{target}: int` is not true",
- )
- weights = [
- k
- for ds in dataset.datasets
- for k in len(typing.cast(typing.Sized, ds))
- * [1.0 / len(typing.cast(typing.Sized, ds))]
- ]
-
- pass
-
- else:
- metadata_example = dataset[0][1]
- if target in metadata_example and isinstance(
- metadata_example[target],
- int,
- ):
- logger.info(
- f"Balancing samples from dataset using metadata "
- f"targets `{target}`",
- )
- weights = _calculate_weights(dataset.targets()) # type: ignore
- else:
- raise RuntimeError(
- f"Cannot balance samples with multiple class targets "
- f"({target}: list[int]) or without metadata targets `{target}`",
- )
-
- return torch.utils.data.WeightedRandomSampler(
- weights,
- len(weights),
- replacement=True,
- )
-
-
class ConcatDataModule(lightning.LightningDataModule):
"""A conveninent DataModule with dictionary split loading, mini- batching,
parallelisation and caching, all in one.
diff --git a/src/mednet/libs/common/models/normalizer.py b/src/mednet/libs/common/models/normalizer.py
index 603de9c132d0a51a1c2cd56958574fdc874bb56f..2eccf0511239d1557557ce6ad16b888291040f2a 100644
--- a/src/mednet/libs/common/models/normalizer.py
+++ b/src/mednet/libs/common/models/normalizer.py
@@ -40,9 +40,7 @@ def make_z_normalizer(
try:
target = batch[1]["target"][0].item()
if not isinstance(target, int):
- logger.info(
- "Targets are not Integer type, skipping z-normalization."
- )
+ logger.info("Targets are not Integer type, skipping z-normalization.")
return None
except RuntimeError:
logger.info("Targets are not Integer type, skipping z-normalization.")
diff --git a/src/mednet/libs/common/scripts/config.py b/src/mednet/libs/common/scripts/config.py
index a813e90f717e48eb3258a7557750627ab0c1c0e4..d4a8062b60c9962c5284817b24444e4dc2232220 100644
--- a/src/mednet/libs/common/scripts/config.py
+++ b/src/mednet/libs/common/scripts/config.py
@@ -93,9 +93,7 @@ def describe(name, entry_point_group, verbose) -> None: # numpydoc ignore=PR01
click.echo(inspect.getdoc(mod))
-def copy(
- source, destination, entry_point_group
-) -> None: # numpydoc ignore=PR01
+def copy(source, destination, entry_point_group) -> None: # numpydoc ignore=PR01
"""Copy a specific configuration resource so it can be modified locally."""
import shutil
diff --git a/src/mednet/libs/common/scripts/database.py b/src/mednet/libs/common/scripts/database.py
index 63f1dde1c17c28778288151d0db06fa76ab7a378..07c6f1f8d63fbfafa082f7a9cba2e834b2158f99 100644
--- a/src/mednet/libs/common/scripts/database.py
+++ b/src/mednet/libs/common/scripts/database.py
@@ -42,9 +42,7 @@ def check(entry_point_group, fold, limit): # numpydoc ignore=PR01
click.secho(f"Checking fold `{fold}`...", fg="yellow")
try:
- module = importlib.metadata.entry_points(group=entry_point_group)[
- fold
- ].module
+ module = importlib.metadata.entry_points(group=entry_point_group)[fold].module
except KeyError:
raise Exception(f"Could not find database fold `{fold}`")
diff --git a/src/mednet/libs/common/tests/conftest.py b/src/mednet/libs/common/tests/conftest.py
index 07dbdce9d5e028a9335bfaa6080e07991f91612a..e9c23ab2fa4c458078c42f5d0c5cdd3045cc0d9a 100644
--- a/src/mednet/libs/common/tests/conftest.py
+++ b/src/mednet/libs/common/tests/conftest.py
@@ -2,7 +2,6 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
-import json
import pathlib
import pytest
diff --git a/src/mednet/libs/common/utils/gitlab.py b/src/mednet/libs/common/utils/gitlab.py
index d0b111ddcf1f54ec995ffb0544c0199aa3d4f7b4..0baeba83012905eff8b43e504404c52dd9584749 100644
--- a/src/mednet/libs/common/utils/gitlab.py
+++ b/src/mednet/libs/common/utils/gitlab.py
@@ -70,9 +70,7 @@ def sanitize_filename(tmpdir: pathlib.Path, path: pathlib.Path) -> pathlib.Path:
return path
absolute_sanitized_filename = tmpdir / sanitized_filename
- logger.info(
- f"Sanitazing filename `{path}` -> `{absolute_sanitized_filename}`"
- )
+ logger.info(f"Sanitazing filename `{path}` -> `{absolute_sanitized_filename}`")
shutil.copy2(path, absolute_sanitized_filename)
return absolute_sanitized_filename
diff --git a/src/mednet/libs/segmentation/engine/evaluator.py b/src/mednet/libs/segmentation/engine/evaluator.py
index 4cf5ecf02a29a486cb834f5d76c935ac36431f66..019171043df6577cb75463bc98126838ebccd761 100644
--- a/src/mednet/libs/segmentation/engine/evaluator.py
+++ b/src/mednet/libs/segmentation/engine/evaluator.py
@@ -144,8 +144,7 @@ def _sample_measures(
step_size = 1.0 / steps
data = [
- (index, threshold)
- + sample_measures_for_threshold(pred, gt, mask, threshold)
+ (index, threshold) + sample_measures_for_threshold(pred, gt, mask, threshold)
for index, threshold in enumerate(numpy.arange(0.0, 1.0, step_size))
]
@@ -499,9 +498,7 @@ def run(
output_folder.mkdir(parents=True, exist_ok=True)
measures_path = output_folder / f"{name}.csv"
- logger.info(
- f"Saving measures over all input images at {measures_path}..."
- )
+ logger.info(f"Saving measures over all input images at {measures_path}...")
measures.to_csv(measures_path)
return maxf1_threshold
diff --git a/src/mednet/libs/segmentation/models/lwnet.py b/src/mednet/libs/segmentation/models/lwnet.py
index 8bae2956f333216f75a21e4109fdf63fc1a93851..b9519e734331788443d0205dde13844713eb9d2c 100644
--- a/src/mednet/libs/segmentation/models/lwnet.py
+++ b/src/mednet/libs/segmentation/models/lwnet.py
@@ -66,15 +66,11 @@ class ConvBlock(torch.nn.Module):
else:
self.pool = False
- block.append(
- torch.nn.Conv2d(in_c, out_c, kernel_size=k_sz, padding=pad)
- )
+ block.append(torch.nn.Conv2d(in_c, out_c, kernel_size=k_sz, padding=pad))
block.append(torch.nn.ReLU())
block.append(torch.nn.BatchNorm2d(out_c))
- block.append(
- torch.nn.Conv2d(out_c, out_c, kernel_size=k_sz, padding=pad)
- )
+ block.append(torch.nn.Conv2d(out_c, out_c, kernel_size=k_sz, padding=pad))
block.append(torch.nn.ReLU())
block.append(torch.nn.BatchNorm2d(out_c))
@@ -106,14 +102,10 @@ class UpsampleBlock(torch.nn.Module):
super().__init__()
block = []
if up_mode == "transp_conv":
- block.append(
- torch.nn.ConvTranspose2d(in_c, out_c, kernel_size=2, stride=2)
- )
+ block.append(torch.nn.ConvTranspose2d(in_c, out_c, kernel_size=2, stride=2))
elif up_mode == "up_conv":
block.append(
- torch.nn.Upsample(
- mode="bilinear", scale_factor=2, align_corners=False
- )
+ torch.nn.Upsample(mode="bilinear", scale_factor=2, align_corners=False)
)
block.append(torch.nn.Conv2d(in_c, out_c, kernel_size=1))
else:
@@ -141,9 +133,7 @@ class ConvBridgeBlock(torch.nn.Module):
pad = (k_sz - 1) // 2
block = []
- block.append(
- torch.nn.Conv2d(channels, channels, kernel_size=k_sz, padding=pad)
- )
+ block.append(torch.nn.Conv2d(channels, channels, kernel_size=k_sz, padding=pad))
block.append(torch.nn.ReLU())
block.append(torch.nn.BatchNorm2d(channels))
@@ -384,6 +374,4 @@ class LittleWNet(Model):
return separate((probabilities, batch[1]))
def configure_optimizers(self):
- return self._optimizer_type(
- self.parameters(), **self._optimizer_arguments
- )
+ return self._optimizer_type(self.parameters(), **self._optimizer_arguments)
diff --git a/src/mednet/libs/segmentation/models/separate.py b/src/mednet/libs/segmentation/models/separate.py
index 716addc67de95b60978526cc7c9a6bf92b408a81..4f2628f8b41d4b1fea238b8cbda441200cfe109e 100644
--- a/src/mednet/libs/segmentation/models/separate.py
+++ b/src/mednet/libs/segmentation/models/separate.py
@@ -53,8 +53,7 @@ def separate(batch: Sample) -> list[SegmentationPrediction]:
# as of now, this is really simple - to be made more complex upon need.
metadata = [
- {key: value[i] for key, value in batch[1].items()}
- for i in range(len(batch[0]))
+ {key: value[i] for key, value in batch[1].items()} for i in range(len(batch[0]))
]
return _as_predictions(zip(batch[0], metadata))
diff --git a/src/mednet/libs/segmentation/scripts/evaluate.py b/src/mednet/libs/segmentation/scripts/evaluate.py
index 84ba542c6dff4a4547846c80513be9735201b386..0fdddc9adea3a9cabe4a0d70e08fb27414214372 100644
--- a/src/mednet/libs/segmentation/scripts/evaluate.py
+++ b/src/mednet/libs/segmentation/scripts/evaluate.py
@@ -176,9 +176,7 @@ def evaluate(
# we try to convert it to float first
threshold = float(threshold)
if threshold < 0.0 or threshold > 1.0:
- raise ValueError(
- "Float thresholds must be within range [0.0, 1.0]"
- )
+ raise ValueError("Float thresholds must be within range [0.0, 1.0]")
except ValueError:
if threshold not in splits:
raise ValueError(
@@ -205,9 +203,7 @@ def evaluate(
# Compute threshold on specified split
if isinstance(threshold, str):
logger.info(f"Evaluating threshold on '{threshold}' split")
- threshold = run(
- threshold, predict_data[threshold], output_folder, steps=steps
- )
+ threshold = run(threshold, predict_data[threshold], output_folder, steps=steps)
logger.info(f"Set --threshold={threshold:.5f}")
for split_name, predictions in predict_data.items():
diff --git a/src/mednet/libs/segmentation/scripts/experiment.py b/src/mednet/libs/segmentation/scripts/experiment.py
index b909e26f3eb9ed28d01d2fbfc89080dc4e06f2f0..4c906c023f6e2d6e4e63f3973bb7c676b73040f6 100644
--- a/src/mednet/libs/segmentation/scripts/experiment.py
+++ b/src/mednet/libs/segmentation/scripts/experiment.py
@@ -92,9 +92,7 @@ def experiment(
train_stop_timestamp = datetime.now()
logger.info(f"Ended training in {train_stop_timestamp}")
- logger.info(
- f"Training runtime: {train_stop_timestamp-train_start_timestamp}"
- )
+ logger.info(f"Training runtime: {train_stop_timestamp-train_start_timestamp}")
logger.info("Started train analysis")
from mednet.libs.common.scripts.train_analysis import train_analysis
@@ -128,9 +126,7 @@ def experiment(
predict_stop_timestamp = datetime.now()
logger.info(f"Ended prediction in {predict_stop_timestamp}")
- logger.info(
- f"Prediction runtime: {predict_stop_timestamp-predict_start_timestamp}"
- )
+ logger.info(f"Prediction runtime: {predict_stop_timestamp-predict_start_timestamp}")
evaluation_start_timestamp = datetime.now()
logger.info(f"Started evaluation at {evaluation_start_timestamp}")
diff --git a/src/mednet/libs/segmentation/tests/conftest.py b/src/mednet/libs/segmentation/tests/conftest.py
index 648d96740cb362bf0999230cb2cfd6c694223979..5f8efecc9ba6cd913b9de9d92453d7ced866e0de 100644
--- a/src/mednet/libs/segmentation/tests/conftest.py
+++ b/src/mednet/libs/segmentation/tests/conftest.py
@@ -70,8 +70,7 @@ def pytest_runtest_setup(item):
# iterates over all markers for the item being examined, get the first
# argument and accumulate these names
rc_names = [
- mark.args[0]
- for mark in item.iter_markers(name="skip_if_rc_var_not_set")
+ mark.args[0] for mark in item.iter_markers(name="skip_if_rc_var_not_set")
]
# checks all names mentioned are set in ~/.config/mednet.libs.segmentation.toml, otherwise,
@@ -182,25 +181,18 @@ class DatabaseCheckers:
assert len(batch[1]) in [2, 3] # target, Optional(mask), name
assert "target" in batch[1]
- assert all(
- [isinstance(target, torch.Tensor) for target in batch[1]["target"]]
- )
+ assert all([isinstance(target, torch.Tensor) for target in batch[1]["target"]])
if expected_num_targets:
assert len(batch[1]["target"]) == expected_num_targets
if "mask" in batch[1]:
- assert all(
- [isinstance(mask, torch.Tensor) for mask in batch[1]["mask"]]
- )
+ assert all([isinstance(mask, torch.Tensor) for mask in batch[1]["mask"]])
assert "name" in batch[1]
if prefixes is not None:
assert all(
- [
- any([k.startswith(j) for j in prefixes])
- for k in batch[1]["name"]
- ],
+ [any([k.startswith(j) for j in prefixes]) for k in batch[1]["name"]],
)
@staticmethod
@@ -222,9 +214,7 @@ class DatabaseCheckers:
for idx, rs in enumerate(raw_samples):
raw_samples_indices[rs[0]] = idx
- for ref_hist_path, ref_hist_data in ref_histogram_splits[
- split_name
- ]:
+ for ref_hist_path, ref_hist_data in ref_histogram_splits[split_name]:
# Get index in the dataset that will return the data
# corresponding to the specified sample name
dataset_sample_index = raw_samples_indices[ref_hist_path]
@@ -252,9 +242,7 @@ class DatabaseCheckers:
# reference and check the similarity within a certain
# threshold
pearson_coeffs = numpy.corrcoef(histogram, ref_hist_data)
- assert (
- 1 - pearson_coeff_threshold <= pearson_coeffs[0][1] <= 1
- )
+ assert 1 - pearson_coeff_threshold <= pearson_coeffs[0][1] <= 1
else:
assert histogram == ref_hist_data
diff --git a/src/mednet/libs/segmentation/tests/test_measures.py b/src/mednet/libs/segmentation/tests/test_measures.py
index 5d1b40699250368664a2dc1ec1c2f0fe25db05fb..a9fdceb02a077a7dcd3b59bba91f271f6a45dcb0 100644
--- a/src/mednet/libs/segmentation/tests/test_measures.py
+++ b/src/mednet/libs/segmentation/tests/test_measures.py
@@ -54,9 +54,7 @@ class TestFrequentist(unittest.TestCase):
def test_f1(self):
p, r, s, a, j, f1 = base_measures(self.tp, self.fp, self.tn, self.fn)
- self.assertEqual(
- (2.0 * self.tp) / (2.0 * self.tp + self.fp + self.fn), f1
- )
+ self.assertEqual((2.0 * self.tp) / (2.0 * self.tp + self.fp + self.fn), f1)
self.assertAlmostEqual((2 * p * r) / (p + r), f1) # base definition
@@ -133,27 +131,17 @@ class TestBayesian:
fn = random.randint(100000, 1000000)
_prec, _rec, _spec, _acc, _jac, _f1 = base_measures(tp, fp, tn, fn)
- prec, rec, spec, acc, jac, f1 = bayesian_measures(
- tp, fp, tn, fn, 0.5, 0.95
- )
+ prec, rec, spec, acc, jac, f1 = bayesian_measures(tp, fp, tn, fn, 0.5, 0.95)
# Notice that for very large k and l, the base frequentist measures
# should be approximately the same as the bayesian mean and mode
# extracted from the beta posterior. We test that here.
- assert numpy.isclose(
- _prec, prec[0]
- ), f"freq: {_prec} <> bays: {prec[0]}"
- assert numpy.isclose(
- _prec, prec[1]
- ), f"freq: {_prec} <> bays: {prec[1]}"
+ assert numpy.isclose(_prec, prec[0]), f"freq: {_prec} <> bays: {prec[0]}"
+ assert numpy.isclose(_prec, prec[1]), f"freq: {_prec} <> bays: {prec[1]}"
assert numpy.isclose(_rec, rec[0]), f"freq: {_rec} <> bays: {rec[0]}"
assert numpy.isclose(_rec, rec[1]), f"freq: {_rec} <> bays: {rec[1]}"
- assert numpy.isclose(
- _spec, spec[0]
- ), f"freq: {_spec} <> bays: {spec[0]}"
- assert numpy.isclose(
- _spec, spec[1]
- ), f"freq: {_spec} <> bays: {spec[1]}"
+ assert numpy.isclose(_spec, spec[0]), f"freq: {_spec} <> bays: {spec[0]}"
+ assert numpy.isclose(_spec, spec[1]), f"freq: {_spec} <> bays: {spec[1]}"
assert numpy.isclose(_acc, acc[0]), f"freq: {_acc} <> bays: {acc[0]}"
assert numpy.isclose(_acc, acc[1]), f"freq: {_acc} <> bays: {acc[1]}"
assert numpy.isclose(_jac, jac[0]), f"freq: {_jac} <> bays: {jac[0]}"
@@ -186,21 +174,11 @@ class TestBayesian:
def test_auc():
# basic tests
assert math.isclose(auc([0.0, 0.5, 1.0], [1.0, 1.0, 1.0]), 1.0)
- assert math.isclose(
- auc([0.0, 0.5, 1.0], [1.0, 0.5, 0.0]), 0.5, rel_tol=0.001
- )
- assert math.isclose(
- auc([0.0, 0.5, 1.0], [0.0, 0.0, 0.0]), 0.0, rel_tol=0.001
- )
- assert math.isclose(
- auc([0.0, 0.5, 1.0], [0.0, 1.0, 0.0]), 0.5, rel_tol=0.001
- )
- assert math.isclose(
- auc([0.0, 0.5, 1.0], [0.0, 0.5, 0.0]), 0.25, rel_tol=0.001
- )
- assert math.isclose(
- auc([0.0, 0.5, 1.0], [0.0, 0.5, 0.0]), 0.25, rel_tol=0.001
- )
+ assert math.isclose(auc([0.0, 0.5, 1.0], [1.0, 0.5, 0.0]), 0.5, rel_tol=0.001)
+ assert math.isclose(auc([0.0, 0.5, 1.0], [0.0, 0.0, 0.0]), 0.0, rel_tol=0.001)
+ assert math.isclose(auc([0.0, 0.5, 1.0], [0.0, 1.0, 0.0]), 0.5, rel_tol=0.001)
+ assert math.isclose(auc([0.0, 0.5, 1.0], [0.0, 0.5, 0.0]), 0.25, rel_tol=0.001)
+ assert math.isclose(auc([0.0, 0.5, 1.0], [0.0, 0.5, 0.0]), 0.25, rel_tol=0.001)
# reversing tht is also true
assert math.isclose(auc([0.0, 0.5, 1.0][::-1], [1.0, 1.0, 1.0][::-1]), 1.0)
@@ -222,9 +200,7 @@ def test_auc():
def test_auc_raises_value_error():
- with pytest.raises(
- ValueError, match=r".*neither increasing nor decreasing.*"
- ):
+ with pytest.raises(ValueError, match=r".*neither increasing nor decreasing.*"):
# x is **not** monotonically increasing or decreasing
assert math.isclose(auc([0.0, 0.5, 0.0], [1.0, 1.0, 1.0]), 1.0)
diff --git a/src/mednet/libs/segmentation/utils/measure.py b/src/mednet/libs/segmentation/utils/measure.py
index 1b56f31520c098d9c68096ec93d35a87dac55591..8cbd22fbd7135c638b8f1c614b14104e62528f34 100644
--- a/src/mednet/libs/segmentation/utils/measure.py
+++ b/src/mednet/libs/segmentation/utils/measure.py
@@ -370,9 +370,7 @@ def auc(x: numpy.ndarray, y: numpy.ndarray):
x = x[::-1]
y = y[::-1]
else:
- raise ValueError(
- "x is neither increasing nor decreasing " f": {x}."
- )
+ raise ValueError("x is neither increasing nor decreasing " f": {x}.")
y_interp = numpy.interp(
numpy.arange(0, 1, 0.001),