本文整理汇总了Python中tests.__file__方法的典型用法代码示例。如果您正苦于以下问题:Python tests.__file__方法的具体用法?Python tests.__file__怎么用?Python tests.__file__使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tests
的用法示例。
在下文中一共展示了tests.__file__方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setup_complex_po
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def setup_complex_po(self):
import tests
from tests.factories import StoreDBFactory
from pootle_translationproject.models import TranslationProject
po_file = os.path.join(
os.path.dirname(tests.__file__), *("data", "po", "complex.po")
)
with open(po_file, "rb") as f:
ttk = getclass(f)(f.read())
tp = TranslationProject.objects.get(
project__code="project0", language__code="language0"
)
store = StoreDBFactory(
parent=tp.directory, translation_project=tp, name="complex.po"
)
store.update(ttk)
示例2: test_add_to_model_adds_specified_kwargs_to_mlmodel_configuration
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def test_add_to_model_adds_specified_kwargs_to_mlmodel_configuration():
custom_kwargs = {
"key1": "value1",
"key2": 20,
"key3": range(10),
}
model_config = Model()
mlflow.pyfunc.add_to_model(model=model_config,
loader_module=os.path.basename(__file__)[:-3],
data="data",
code="code",
env=None,
**custom_kwargs)
assert mlflow.pyfunc.FLAVOR_NAME in model_config.flavors
assert all([item in model_config.flavors[mlflow.pyfunc.FLAVOR_NAME] for item in custom_kwargs])
示例3: read_file
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def read_file(filename):
"""Read the contents of a file in the tests directory."""
root_dir = os.path.dirname(os.path.realpath(tests.__file__))
with open(os.path.join(root_dir, filename), "r") as f:
return f.read()
示例4: test_spark_udf
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def test_spark_udf(spark, model_path):
mlflow.pyfunc.save_model(
path=model_path,
loader_module=__name__,
code_path=[os.path.dirname(tests.__file__)],
)
reloaded_pyfunc_model = mlflow.pyfunc.load_pyfunc(model_path)
pandas_df = pd.DataFrame(data=np.ones((10, 10)), columns=[str(i) for i in range(10)])
spark_df = spark.createDataFrame(pandas_df)
# Test all supported return types
type_map = {"float": (FloatType(), np.number),
"int": (IntegerType(), np.int32),
"double": (DoubleType(), np.number),
"long": (LongType(), np.int),
"string": (StringType(), None)}
for tname, tdef in type_map.items():
spark_type, np_type = tdef
prediction_df = reloaded_pyfunc_model.predict(pandas_df)
for is_array in [True, False]:
t = ArrayType(spark_type) if is_array else spark_type
if tname == "string":
expected = prediction_df.applymap(str)
else:
expected = prediction_df.select_dtypes(np_type)
if tname == "float":
expected = expected.astype(np.float32)
expected = [list(row[1]) if is_array else row[1][0] for row in expected.iterrows()]
pyfunc_udf = spark_udf(spark, model_path, result_type=t)
new_df = spark_df.withColumn("prediction", pyfunc_udf(*pandas_df.columns))
actual = list(new_df.select("prediction").toPandas()['prediction'])
assert expected == actual
if not is_array:
pyfunc_udf = spark_udf(spark, model_path, result_type=tname)
new_df = spark_df.withColumn("prediction", pyfunc_udf(*pandas_df.columns))
actual = list(new_df.select("prediction").toPandas()['prediction'])
assert expected == actual
示例5: test_model_cache
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def test_model_cache(spark, model_path):
mlflow.pyfunc.save_model(
path=model_path,
loader_module=__name__,
code_path=[os.path.dirname(tests.__file__)],
)
archive_path = SparkModelCache.add_local_model(spark, model_path)
assert archive_path != model_path
# Ensure we can use the model locally.
local_model = SparkModelCache.get_or_load(archive_path)
assert isinstance(local_model, PyFuncModel)
assert isinstance(local_model._model_impl, ConstantPyfuncWrapper)
# Define the model class name as a string so that each Spark executor can reference it
# without attempting to resolve ConstantPyfuncWrapper, which is only available on the driver.
constant_model_name = ConstantPyfuncWrapper.__name__
# Request the model on all executors, and see how many times we got cache hits.
def get_model(_):
model = SparkModelCache.get_or_load(archive_path)
assert (isinstance(model, PyFuncModel))
# NB: Can not use instanceof test as remote does not know about ConstantPyfuncWrapper class.
assert type(model._model_impl).__name__ == constant_model_name
return SparkModelCache._cache_hits
# This will run 30 distinct tasks, and we expect most to reuse an already-loaded model.
# Note that we can't necessarily expect an even split, or even that there were only
# exactly 2 python processes launched, due to Spark and its mysterious ways, but we do
# expect significant reuse.
results = spark.sparkContext.parallelize(range(0, 100), 30).map(get_model).collect()
# TODO(tomas): Looks like spark does not reuse python workers with python==3.x
assert sys.version[0] == '3' or max(results) > 10
# Running again should see no newly-loaded models.
results2 = spark.sparkContext.parallelize(range(0, 100), 30).map(get_model).collect()
assert sys.version[0] == '3' or min(results2) > 0
示例6: test_pyfunc_model_serving_without_conda_env_activation_succeeds_with_module_scoped_class
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def test_pyfunc_model_serving_without_conda_env_activation_succeeds_with_module_scoped_class(
sklearn_knn_model, iris_data, tmpdir):
sklearn_model_path = os.path.join(str(tmpdir), "sklearn_model")
mlflow.sklearn.save_model(sk_model=sklearn_knn_model, path=sklearn_model_path)
def test_predict(sk_model, model_input):
return sk_model.predict(model_input) * 2
pyfunc_model_path = os.path.join(str(tmpdir), "pyfunc_model")
mlflow.pyfunc.save_model(path=pyfunc_model_path,
artifacts={
"sk_model": sklearn_model_path
},
python_model=ModuleScopedSklearnModel(test_predict),
code_path=[os.path.dirname(tests.__file__)],
conda_env=_conda_env())
loaded_pyfunc_model = mlflow.pyfunc.load_pyfunc(model_uri=pyfunc_model_path)
sample_input = pd.DataFrame(iris_data[0])
scoring_response = pyfunc_serve_and_score_model(
model_uri=pyfunc_model_path,
data=sample_input,
content_type=pyfunc_scoring_server.CONTENT_TYPE_JSON_SPLIT_ORIENTED,
extra_args=["--no-conda"])
assert scoring_response.status_code == 200
np.testing.assert_array_equal(
np.array(json.loads(scoring_response.text)),
loaded_pyfunc_model.predict(sample_input))
示例7: spec_fixture
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def spec_fixture():
"""Generates plugin spec for testing, using tests/example plugin dir. """
plugin_dir = path.join(path.abspath(path.dirname(tests.__file__)),
'example')
test_plugin = plugins.InfraredPlugin(plugin_dir=plugin_dir)
from infrared.api import InfraredPluginsSpec
spec = InfraredPluginsSpec(test_plugin)
yield spec
示例8: test_execute_main_role_path
# 需要导入模块: import tests [as 别名]
# 或者: from tests import __file__ [as 别名]
def test_execute_main_role_path(spec_fixture, workspace_manager_fixture, # noqa
test_workspace, input_value, input_roles):
"""Verify execution runs the main.yml playbook when roles_path is set.
Workflow is the same as in the test_execute_main test, however, the plugin
used here has config.roles_path set.
Verifies that ANSIBLE_ROLES_PATH is set before plugin's main.yml execution
and it's restored to the original value after the plugin execution is over.
"""
input_string = ['example']
# get the plugin with role_path defined
role_path_plugin = 'example/plugins/plugin_with_role_path/infrared/plugin'
plugin_dir = path.join(path.abspath(path.dirname(tests.__file__)),
role_path_plugin)
test_plugin = plugins.InfraredPlugin(plugin_dir=plugin_dir)
from infrared.api import InfraredPluginsSpec
spec = InfraredPluginsSpec(test_plugin)
spec_manager = api.SpecManager()
spec_manager.register_spec(spec)
inventory_dir = test_workspace.path
output_file = "output.example"
environ['ANSIBLE_ROLES_PATH'] = input_value
assert not path.exists(path.join(inventory_dir, output_file))
assert not path.exists(path.join(inventory_dir, "role_" + output_file))
workspace_manager_fixture.activate(test_workspace.name)
return_value = spec_manager.run_specs(args=input_string)
out_file = open(path.join(inventory_dir, output_file), "r")
expected_resp = 'ANSIBLE_ROLES_PATH=' + input_roles
expected_resp += path.join(plugin_dir, test_plugin.roles_path + '../')
assert return_value == 0
assert environ.get('ANSIBLE_ROLES_PATH', '') == input_value
assert path.exists(path.join(inventory_dir, output_file))
assert out_file.read() == expected_resp