CI
cd0af18851
Build pipeline: vsh-ci-dev-k8tz4
Source commit: 2dbe3b7231
Source message: Fix pointers to test resources
142 lines
5.9 KiB
Python
142 lines
5.9 KiB
Python
import sys
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import os
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import pytest
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import re
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import mudata as mu
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import anndata as ad
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from openpipelinetestutils.asserters import assert_annotation_objects_equal
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import scvi
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import os
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## VIASH START
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meta = {
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"resources_dir": "resources_test"
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}
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## VIASH END
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input_file = f"{meta['resources_dir']}/pbmc_1k_protein_v3/pbmc_1k_protein_v3_mms.h5mu"
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reference_file = f"{meta['resources_dir']}/annotation_test_data/TS_Blood_filtered.h5mu"
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@pytest.fixture
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def create_scvi_model(random_path, tmp_path):
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def wrapper(input_file, reference_file):
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input_data = mu.read_h5mu(input_file)
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input_modality = input_data.mod["rna"]
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reference_data = mu.read_h5mu(reference_file)
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reference_modality = reference_data.mod["rna"]
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reference_data.var["gene_symbol"] = list(reference_data.var.index)
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reference_data.var.index = [re.sub("\\.[0-9]+$", "", s) for s in reference_data.var["ensemblid"]]
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reference_modality.var["gene_symbol"] = list(reference_modality.var.index)
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reference_modality.var.index = [re.sub("\\.[0-9]+$", "", s) for s in reference_modality.var["ensemblid"]]
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common_ens_ids = list(set(reference_modality.var.index).intersection(set(input_modality.var.index)))
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reference = reference_modality[:, common_ens_ids].copy()
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query = input_modality[:, common_ens_ids].copy()
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scvi.model.SCVI.setup_anndata(reference,
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labels_key="cell_ontology_class"
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)
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scvi_model = scvi.model.SCVI(
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reference,
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use_layer_norm="both",
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use_batch_norm="none",
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encode_covariates=True,
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dropout_rate=0.2,
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n_layers=1,
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)
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scvi_model.train(max_epochs=10)
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input_data.mod["rna"] = query
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reference_data.mod["rna"] = reference
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input_data_file = random_path(extension="h5mu")
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reference_file = random_path(extension="h5mu")
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scvi_model_file = tmp_path
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input_data.write_h5mu(input_data_file)
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reference_data.write_h5mu(reference_file)
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scvi_model.save(scvi_model_file, overwrite=True)
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return scvi_model_file, input_data_file, reference_file
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return wrapper
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def test_simple_execution(run_component, random_h5mu_path, create_scvi_model):
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scvi_model_file, input_file_scvi, reference_file_scvi = create_scvi_model(input_file, reference_file)
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output_file = random_h5mu_path()
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run_component([
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"--input", input_file_scvi,
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"--reference", reference_file_scvi,
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"--scvi_reference_model", scvi_model_file,
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"--reference_obs_label", "cell_ontology_class",
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"--reference_max_epochs", "10",
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"--query_max_epochs", "10",
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"--output", output_file
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])
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assert os.path.exists(output_file), "Output file does not exist"
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input_mudata = mu.read_h5mu(input_file_scvi)
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output_mudata = mu.read_h5mu(output_file)
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assert input_mudata.mod["rna"].n_obs == output_mudata.mod["rna"].n_obs, f"Number of observations changed"
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assert input_mudata.mod["rna"].n_vars == output_mudata.mod["rna"].n_vars, f"Number of variables changed"
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assert "scanvi_embedding" in output_mudata.mod["rna"].obsm.keys(), "Latent representation not added"
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assert "scanvi_pred" in output_mudata.mod["rna"].obs.keys(), "Predictions not added"
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assert "scanvi_probability" in output_mudata.mod["rna"].obs.keys(), "Probabilities not added"
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assert_annotation_objects_equal(input_mudata.mod["prot"],
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output_mudata.mod["prot"])
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def test_multiple_arguments(run_component, random_h5mu_path, create_scvi_model, tmp_path):
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scvi_model_file, input_file_scvi, reference_file_scvi = create_scvi_model(input_file, reference_file)
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output_file = random_h5mu_path()
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run_component([
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"--input", input_file_scvi,
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"--reference", reference_file_scvi,
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"--scvi_reference_model", scvi_model_file,
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"--reference_obs_label", "cell_ontology_class",
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"--output", output_file,
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"--reference_max_epochs", "10",
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"--reference_reduce_lr_on_plateau", "True",
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"--reference_lr_patience", "5",
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"--reference_lr_factor", "0.5",
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"--reference_train_size", "0.8",
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"--reference_early_stopping", "True",
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"--reference_early_stopping_patience", "5",
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"--reference_early_stopping_min_delta", "0.01",
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"--query_max_epochs", "10",
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"--query_reduce_lr_on_plateau", "True",
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"--query_lr_patience", "5",
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"--query_lr_factor", "0.5",
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"--query_train_size", "0.8",
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"--query_early_stopping", "True",
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"--query_early_stopping_patience", "5",
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"--query_early_stopping_min_delta", "0.01",
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"--output_obs_predictions", "scanvi_pred",
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"--output_obs_probabilities", "scanvi_probabilitity",
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"--output_compression", "gzip",
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"--output_model", tmp_path
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])
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assert os.path.exists(output_file), "Output file does not exist"
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assert os.path.exists(tmp_path / "model.pt"), "Model file does not exist"
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input_mudata = mu.read_h5mu(input_file_scvi)
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output_mudata = mu.read_h5mu(output_file)
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assert input_mudata.mod["rna"].n_obs == output_mudata.mod["rna"].n_obs, f"Number of observations changed"
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assert input_mudata.mod["rna"].n_vars == output_mudata.mod["rna"].n_vars, f"Number of variables changed"
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assert "scanvi_embedding" in output_mudata.mod["rna"].obsm.keys(), "Latent representation not added"
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assert "scanvi_pred" in output_mudata.mod["rna"].obs.keys(), "Predictions not added"
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assert "scanvi_probability" in output_mudata.mod["rna"].obs.keys(), "Probabilities not added"
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assert_annotation_objects_equal(input_mudata.mod["prot"],
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output_mudata.mod["prot"])
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if __name__ == '__main__':
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sys.exit(pytest.main([__file__])) |