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b/medacy/tests/data/test_dataset.py |
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import os |
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import shutil |
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import tempfile |
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import unittest |
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from collections import Counter |
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from pathlib import Path |
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import pkg_resources |
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from medacy.data.dataset import Dataset |
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from medacy.data.annotations import Annotations |
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from medacy.data.data_file import DataFile |
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from medacy.tests.sample_data import test_dir |
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class TestDataset(unittest.TestCase): |
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"""Unit tests for Dataset""" |
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@classmethod |
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def setUpClass(cls): |
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cls.dataset = Dataset(os.path.join(test_dir, 'sample_dataset_1')) |
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cls.prediction_directory = tempfile.mkdtemp() # Set up predict directory |
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cls.entities = cls.dataset.get_labels(as_list=True) |
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cls.ann_files = [] |
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# Fill directory of prediction files (only the text files) |
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for data_file in cls.dataset: |
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new_file_path = os.path.join(cls.prediction_directory, data_file.file_name + '.txt') |
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shutil.copyfile(data_file.txt_path, new_file_path) |
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# Fill a directory with just ann files |
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cls.ann_dir = tempfile.mkdtemp() |
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for data_file in cls.dataset: |
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new_ann_path = os.path.join(cls.ann_dir, data_file.file_name + '.ann') |
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shutil.copyfile(data_file.ann_path, new_ann_path) |
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@classmethod |
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def tearDownClass(cls): |
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pkg_resources.cleanup_resources() |
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for directory in [cls.prediction_directory, cls.ann_dir]: |
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shutil.rmtree(directory) |
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def test_init(self): |
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"""Tests initializing Datasets from different directories to see that they create accurate DataFiles""" |
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# Test both txt, ann, and metamapped |
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test_dir_path = Path(self.dataset.data_directory) |
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expected = [ |
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DataFile( |
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file_name="PMC1257590", |
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txt_path=test_dir_path / "PMC1257590.txt", |
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ann_path=test_dir_path / "PMC1257590.ann", |
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metamapped_path=test_dir_path / "metamapped" / "PMC1257590.metamapped" |
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), |
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DataFile( |
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file_name="PMC1314908", |
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txt_path=test_dir_path / "PMC1314908.txt", |
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ann_path=test_dir_path / "PMC1314908.ann", |
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metamapped_path=test_dir_path / "metamapped" / "PMC1314908.metamapped" |
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), |
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DataFile( |
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file_name="PMC1392236", |
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txt_path=test_dir_path / "PMC1392236.txt", |
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ann_path=test_dir_path / "PMC1392236.ann", |
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metamapped_path=test_dir_path / "metamapped" / "PMC1392236.metamapped" |
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) |
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] |
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expected.sort(key=lambda x: x.file_name) |
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actual = list(self.dataset) |
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self.assertListEqual(actual, expected) |
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# Test txt only |
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test_dir_path = Path(self.prediction_directory) |
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expected = [ |
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DataFile( |
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file_name="PMC1257590", |
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txt_path=test_dir_path / "PMC1257590.txt", |
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ann_path=None, |
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metamapped_path=None |
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), |
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DataFile( |
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file_name="PMC1314908", |
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txt_path=test_dir_path / "PMC1314908.txt", |
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ann_path=None, |
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metamapped_path=None |
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), |
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DataFile( |
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file_name="PMC1392236", |
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txt_path=test_dir_path / "PMC1392236.txt", |
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ann_path=None, |
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metamapped_path=None |
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) |
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] |
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expected.sort(key=lambda x: x.file_name) |
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actual = list(Dataset(self.prediction_directory)) |
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self.assertListEqual(actual, expected) |
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# Test ann only |
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test_dir_path = Path(self.ann_dir) |
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expected = [ |
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DataFile( |
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file_name="PMC1257590", |
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txt_path=None, |
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ann_path=test_dir_path / "PMC1257590.ann", |
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metamapped_path=None |
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), |
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DataFile( |
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file_name="PMC1314908", |
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txt_path=None, |
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ann_path=test_dir_path / "PMC1314908.ann", |
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metamapped_path=None, |
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), |
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DataFile( |
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file_name="PMC1392236", |
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txt_path=None, |
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ann_path=test_dir_path / "PMC1392236.ann", |
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metamapped_path=None |
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) |
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] |
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expected.sort(key=lambda x: x.file_name) |
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actual = list(Dataset(self.ann_dir)) |
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self.assertListEqual(actual, expected) |
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def test_init_with_data_limit(self): |
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"""Tests that initializing with a data limit works""" |
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dataset = Dataset(self.dataset.data_directory, data_limit=1) |
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self.assertEqual(len(list(dataset)), 1) |
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def test_generate_annotations(self): |
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"""Tests that generate_annotations() creates Annotations objects""" |
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for ann in self.dataset.generate_annotations(): |
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self.assertIsInstance(ann, Annotations) |
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def test_get_labels(self): |
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"""Tests that get_labels returns a set of the correct labels""" |
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expected = { |
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'DoseFrequency', 'SampleSize', 'TimeUnits', 'Vehicle', 'TestArticlePurity', 'Endpoint', 'TestArticle', |
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'GroupName', 'DoseDurationUnits', 'GroupSize', 'TimeAtFirstDose', 'Dose', 'DoseDuration', 'Species', |
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'DoseUnits', 'Sex', 'EndpointUnitOfMeasure', 'TimeEndpointAssessed', 'DoseRoute', 'CellLine', 'Strain' |
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} |
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actual = self.dataset.get_labels() |
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self.assertSetEqual(actual, expected) |
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def test_compute_counts(self): |
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"""Tests that compute_counts() returns a Counter containing counts for all labels""" |
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counts = self.dataset.compute_counts() |
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self.assertIsInstance(counts, Counter) |
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for label in self.dataset.get_labels(): |
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self.assertIn(label, counts.keys()) |
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def test_getitem(self): |
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"""Tests that some_dataset['filename'] returns an Annotations for 'filename.ann', or raises FileNotFoundError""" |
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some_file_name = self.dataset.data_files[0].file_name |
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result = self.dataset[some_file_name] |
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self.assertIsInstance(result, Annotations) |
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with self.assertRaises(FileNotFoundError): |
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ann = self.dataset['notafilepath'] |
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def test_valid_datafiles(self): |
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"""Tests that each DataFile in the Dataset is an existing file""" |
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for d in self.dataset: |
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self.assertTrue(os.path.isfile(d.txt_path)) |
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self.assertTrue(os.path.isfile(d.ann_path)) |
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self.assertTrue(os.path.isfile(d.metamapped_path)) |
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if __name__ == '__main__': |
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unittest.main() |