def test_test_example(example, tmpdir):
"""kipoi test ..., add also output file writing
"""
if example in {"rbp", "non_bedinput_model", "iris_model_template"} \
and sys.version_info[0] == 2:
pytest.skip("example not supported on python 2 ")
example_dir = cp_tmpdir("example/models/{0}".format(example), tmpdir)
args = [
"python", "./kipoi/__main__.py", "test", "--batch_size=4", example_dir
]
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args)
assert returncode == 0
if example == 'pyt':
# python interface, write also the output file
output_file = os.path.join(example_dir, 'preds.h5')
kipoi.cli.main.cli_test("test", args[3:] + ["-o", output_file])
assert os.path.exists(output_file)
preds = HDF5Reader.load(output_file)
assert 'inputs' in preds
assert 'metadata' in preds
assert 'preds' in preds
def test_preproc_example(example, tmpdir):
"""kipoi preproc ...
"""
if example in {"rbp", "non_bedinput_model", "iris_model_template"
} and sys.version_info[0] == 2:
pytest.skip("example not supported on python 2 ")
if example in {"extended_coda", "kipoi_dataloader_decorator"}:
# extended_coda will anyway be tested in models
pytest.skip(
"randomly failing on circleci without any reason. Skipping this test."
)
example_dir = cp_tmpdir("example/models/{0}".format(example), tmpdir)
# example_dir = "example/models/{0}".format(example)
tmpfile = str(tmpdir.mkdir("output", ).join("out.h5"))
# run the
args = [
"python",
os.path.abspath("./kipoi/__main__.py"),
"preproc",
"../", # directory
"--source=dir",
"--batch_size=4",
"--num_workers=2",
"--dataloader_args=test.json",
"--output",
tmpfile
]
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args,
cwd=os.path.realpath(example_dir +
"/example_files"))
assert returncode == 0
assert os.path.exists(tmpfile)
data = HDF5Reader.load(tmpfile)
with open(example_dir + "/dataloader.yaml", "r") as f:
ex_descr = yaml.load(f)
if example not in {"pyt", "sklearn_iris"}:
assert data["inputs"].keys(
) == ex_descr["output_schema"]["inputs"].keys()
if example == 'pyt':
args[-1] = tmpfile + "2.h5"
with kipoi.utils.cd(os.path.join(example_dir, "example_files")):
kipoi.cli.main.cli_preproc("preproc", args[3:])
def test_predict_activation_example(example, tmpdir):
"""Kipoi predict --layer=x with a specific output layer specified
"""
if example in {"rbp", "non_bedinput_model", "iris_model_template"
} and sys.version_info[0] == 2:
pytest.skip("rbp example not supported on python 2 ")
if example in {'kipoi_dataloader_decorator'}:
pytest.skip(
"Automatically-dowloaded input files skipped for prediction")
example_dir = cp_tmpdir("example/models/{0}".format(example), tmpdir)
# example_dir = "example/models/{0}".format(example)
print(example)
print("tmpdir: {0}".format(tmpdir))
tmpfile = str(tmpdir.mkdir("output").join("out.h5"))
# run the
args = [
"python",
os.path.abspath("./kipoi/__main__.py"),
"predict",
"../", # directory
"--source=dir",
"--layer",
predict_activation_layers[example],
"--batch_size=4",
"--num_workers=2",
"--dataloader_args=test.json",
"--output",
tmpfile
]
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args,
cwd=os.path.realpath(example_dir +
"/example_files"))
assert returncode == 0
assert os.path.exists(tmpfile)
data = HDF5Reader.load(tmpfile)
assert {'metadata', 'preds'} <= set(data.keys())
if example == 'pyt':
args[-1] = tmpfile + "2.h5"
with kipoi.utils.cd(os.path.join(example_dir, "example_files")):
kipoi.cli.main.cli_predict("predict", args[3:])
def test_cli_test_expect(tmpdir):
"""kipoi test - check that the expected predictions also match
"""
example = 'pyt'
example_dir = cp_tmpdir("example/models/{0}".format(example), tmpdir)
# fail the test
args = [
"python", "./kipoi/__main__.py", "test", "--batch_size=4", "-e",
os.path.join(example_dir, "wrong.pred.h5"), example_dir
]
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args)
assert returncode == 1
# succeed
kipoi.cli.main.cli_test("test", [
"--batch_size=4", "-e",
os.path.join(example_dir, "expected.pred.h5"), example_dir
])
def test_decorator_env_loading(tmpdir):
mdir = cp_tmpdir("example/models/kipoi_dataloader_decorator", tmpdir)
assert merge_deps([mdir], source='dir') == \
Dependencies(conda=['python=2.7', 'scikit-learn'],
pip=['kipoi', 'scikit-learn', 'tqdm'],
conda_channels=['defaults'])
def test_predict_example(example, tmpdir):
"""kipoi predict ...
"""
# TODO - test -out
# Traceback (most recent call last):
# File "/home/avsec/projects-work/kipoi/kipoi/__main__.py", line 60, in <module>
# main()
# File "/home/avsec/projects-work/kipoi/kipoi/__main__.py", line 56, in main
# command_fn(args.command, sys.argv[2:])
# File "/home/avsec/bin/anaconda3/lib/python3.6/site-packages/kipoi/pipeline.py", line 273, in cli_predict
# pred_batch = model.predict_on_batch(batch['inputs'])
# File "/home/avsec/bin/anaconda3/lib/python3.6/site-packages/kipoi/model.py", line 22, in predict_on_batch
# raise NotImplementedError
# NotImplementedError
# _________________________
if example in {"rbp", "non_bedinput_model", "iris_model_template"
} and sys.version_info[0] == 2:
pytest.skip("rbp example not supported on python 2 ")
if example in {'kipoi_dataloader_decorator'}:
pytest.skip(
"Automatically-dowloaded input files skipped for prediction")
example_dir = cp_tmpdir("example/models/{0}".format(example), tmpdir)
# example_dir = "example/models/{0}".format(example)
if example == "rbp":
file_format = "tsv"
else:
file_format = "hdf5"
print(example)
print("tmpdir: {0}".format(tmpdir))
tmpfile = str(tmpdir.mkdir("output").join("out.{0}".format(file_format)))
# run the
args = [
"python",
os.path.abspath("./kipoi/__main__.py"),
"predict",
"../", # directory
"--source=dir",
"--batch_size=4",
"--num_workers=2",
"--dataloader_args=test.json",
"--output",
tmpfile
]
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args,
cwd=os.path.realpath(example_dir +
"/example_files"))
assert returncode == 0
assert os.path.exists(tmpfile)
if file_format == "hdf5":
data = HDF5Reader.load(tmpfile)
assert {'metadata', 'preds'} <= set(data.keys())
else:
data = pd.read_csv(tmpfile, sep="\t")
assert list(data.columns) == [
'metadata/ranges/chr', 'metadata/ranges/end', 'metadata/ranges/id',
'metadata/ranges/start', 'metadata/ranges/strand', 'preds/0'
]
if example == 'pyt':
args[-1] = tmpfile + "out2.{0}".format(file_format)
with kipoi.utils.cd(os.path.join(example_dir, "example_files")):
kipoi.cli.main.cli_predict("predict", args[3:])
def test_preproc_example(example, new_dataloader_kwargs_format, tmpdir):
"""kipoi preproc ...
"""
if example in {"rbp", "non_bedinput_model", "iris_model_template"
} and sys.version_info[0] == 2:
pytest.skip("example not supported on python 2 ")
if example in {"extended_coda", "kipoi_dataloader_decorator"}:
# extended_coda will anyway be tested in models
pytest.skip(
"randomly failing on circleci without any reason. Skipping this test."
)
example_dir = cp_tmpdir("example/models/{0}".format(example), tmpdir)
# example_dir = "example/models/{0}".format(example)
tmpfile = str(tmpdir.mkdir("output", ).join("out.h5"))
if example in {"rbp"} and new_dataloader_kwargs_format:
if example == "rbp":
dataloader_args = [
"intervals_file=intervals.tsv", "fasta_file=hg38_chr22.fa",
"preproc_transformer=../dataloader_files/encodeSplines.pkl",
"gtf_file=gencode_v25_chr22.gtf.pkl.gz",
"tarOget_file=targets.tsv"
]
elif example == "extended_coda":
dataloader_args = [
"intervals_file=intervals.tsv",
"input_data_sources={'H3K27AC_subsampled':'H3K27AC_subsampled.bw'}",
"batch_size=4"
]
# run the
args = [
"python",
os.path.abspath("./kipoi/__main__.py"),
"preproc",
"../", # directory
"--source=dir",
"--batch_size=4",
"--num_workers=2",
"--dataloader_args"
] + dataloader_args + ["--output", tmpfile]
else:
# run the
args = [
"python",
os.path.abspath("./kipoi/__main__.py"),
"preproc",
"../", # directory
"--source=dir",
"--batch_size=4",
"--num_workers=2",
"--dataloader_args=test.json",
"--output",
tmpfile
]
if INSTALL_FLAG:
args.append(INSTALL_FLAG)
returncode = subprocess.call(args=args,
cwd=os.path.realpath(example_dir +
"/example_files"))
assert returncode == 0
assert os.path.exists(tmpfile)
data = HDF5Reader.load(tmpfile)
with open(example_dir + "/dataloader.yaml", "r") as f:
ex_descr = yaml.load(f)
if example not in {"pyt", "sklearn_iris"}:
assert data["inputs"].keys(
) == ex_descr["output_schema"]["inputs"].keys()
if example == 'pyt':
args[-1] = tmpfile + "2.h5"
with kipoi_utils.utils.cd(os.path.join(example_dir, "example_files")):
kipoi.cli.main.cli_preproc("preproc", args[3:])