def setUpClass(cls) -> None:
print('Setting up.')
cls.manager, output = set_up_manager_and_out()
cls.correct_resources = os.path.join(
_project_root_directory, 'tests', 'resources',
'dynamic_loader_test_files_present')
cls.incorrect_resources = os.path.join(_project_root_directory,
'tests', 'resources',
'dynamic_loader_test_no_files')
cls.required_attributes = ['name', 'some_function']
def setUpClass(cls):
print('Setting up.')
manager, cls.output_dir = set_up_manager_and_out()
manager.output_filename = os.path.join(cls.output_dir,
'test_output.txt')
with open(
os.path.join(_project_root_directory, 'tests', 'resources',
'xgb_booster_poc.pickle.dat'),
'rb') as model_file:
cls.model = pickle.load(model_file)
def setUpClass(cls) -> None:
cls.manager, cls.output = set_up_manager_and_out()
cls.edge_cases = os.path.join(_project_root_directory, 'tests',
'resources', 'edge_cases_vep.tsv.gz')
cls.breakpoints = os.path.join(_project_root_directory, 'tests',
'resources', 'breakends_vep.tsv.gz')
cls.symbolic = os.path.join(_project_root_directory, 'tests',
'resources', 'symbolic_alleles_vep.tsv.gz')
with open(
os.path.join(_project_root_directory, 'tests', 'resources',
'xgb_booster_poc.pickle.dat'),
'rb') as model_file:
cls.model = pickle.load(model_file)
cls.main = set_up_predict()
def setUpClass(cls):
print('Setting up.')
manager, cls.output_path = set_up_manager_and_out()
cls.exporter = CapiceExporter(file_path=cls.output_path, output_given=True)
cls.prediction_output_dataframe = pd.DataFrame(
{
Column.chr_pos_ref_alt.value: [
'1_VeryUniqueCAPICESeparator_100'
'_VeryUniqueCAPICESeparator_A_VeryUniqueCAPICESeparator_C',
'2_VeryUniqueCAPICESeparator_200'
'_VeryUniqueCAPICESeparator_T_VeryUniqueCAPICESeparator_G'
],
Column.gene_name.value: ['foo', 'bar'],
Column.gene_id.value: [1000, 2000],
Column.id_source.value: ['foo', 'bar'],
Column.feature.value: ['TRANS_01', 'TRANS_02'],
Column.feature_type.value: ['Transcript', 'RegulatoryFeature'],
Column.score.value: [0.01, 0.998],
Column.suggested_class.value: ['VUS', 'VUS']
}
)
cls.expected_prediction_output_dataframe = pd.DataFrame(
{
Column.chr.value: ['1', '2'],
Column.pos.value: [100, 200],
Column.ref.value: ['A', 'T'],
Column.alt.value: ['C', 'G'],
Column.gene_name.value: ['foo', 'bar'],
Column.gene_id.value: [1000, 2000],
Column.id_source.value: ['foo', 'bar'],
Column.feature.value: ['TRANS_01', 'TRANS_02'],
Column.feature_type.value: ['Transcript', 'RegulatoryFeature'],
Column.score.value: [0.01, 0.998],
Column.suggested_class.value: ['VUS', 'VUS']
}
)
cls.export_dataset = pd.DataFrame(
{
'chr': [1, 2],
'pos': [100, 200],
'ref': ['A', 'A'],
'alt': ['C', 'G'],
'feature_1': [0.001, 0.2],
'feature_2': [0.02, 5.5]
}
)
def setUpClass(cls) -> None:
set_up_manager_and_out()
# Creating a dataframe of data that is tailored to test the
# return N append function within lookup.py.
cls.dataset = pd.DataFrame({
'chr': {
0: '1',
1: '1'
},
'pos': {
0: 1,
1: 10042538
},
'ref': {
0: 'C',
1: 'C'
},
'alt': {
0: 'T',
1: 'T'
},
'Consequence': {
0: 'missense_variant',
1: 'downstream_gene_variant'
},
'gene_name': {
0: 'NMNAT1',
1: 'NMNAT1'
},
'SourceID': {
0: 'HGNC',
1: 'HGNC'
},
'HGNC_ID': {
0: '17877',
1: '17877'
},
'FeatureID': {
0: 'ENST00000377205',
1: 'ENST00000403197'
},
'cDNA_position': {
0: '763/3781',
1: np.nan
},
'CDS_position': {
0: '619/840',
1: np.nan
},
'Protein_position': {
0: '207/279',
1: np.nan
},
'Amino_acids': {
0: 'R/W',
1: np.nan
},
'STRAND': {
0: 1,
1: 1
},
'SIFT': {
0: 0.04,
1: np.nan
},
'PolyPhen': {
0: 0.08,
1: np.nan
},
'Exon': {
0: '5/5',
1: np.nan
},
'Intron': {
0: np.nan,
1: np.nan
}
})
cls.annotator = ManualVEPProcessor()
def setUpClass(cls) -> None:
print('Setting up.')
set_up_manager_and_out()
def setUpClass(cls):
print('Setting up.')
manager, cls.output_dir = set_up_manager_and_out()
cls.output_filename = 'train_example_capice.pickle.dat'
manager.output_filename = cls.output_filename