def testBuildAnalysisTable(self):
model_location = self._exportEvalSavedModel(
linear_classifier.simple_linear_classifier)
eval_shared_model = types.EvalSharedModel(model_path=model_location)
example1 = self._makeExample(age=3.0,
language='english',
label=1.0,
slice_key='first_slice')
with beam.Pipeline() as pipeline:
result = (
pipeline
| 'CreateInput' >> beam.Create([example1.SerializeToString()])
| 'BuildTable' >> contrib.BuildAnalysisTable(
eval_shared_model=eval_shared_model))
def check_result(got):
self.assertEqual(1, len(got), 'got: %s' % got)
extracts = got[0]
# Values of type MaterializedColumn are emitted to signal to
# downstream sink components to output the data to file.
materialized_dict = dict(
(k, v) for k, v in extracts.items()
if isinstance(v, types.MaterializedColumn))
self._assertMaterializedColumns(
materialized_dict,
{
# Slice key
'features__slice_key':
types.MaterializedColumn(name='features__slice_key',
value=[b'first_slice']),
# Features
'features__language':
types.MaterializedColumn(name='features__language',
value=[b'english']),
'features__age':
types.MaterializedColumn(name='features__age',
value=np.array(
[3.], dtype=np.float32)),
# Label
'features__label':
types.MaterializedColumn(name='features__label',
value=np.array(
[1.], dtype=np.float32)),
'labels':
types.MaterializedColumn(name='labels',
value=np.array(
[1.], dtype=np.float32)),
})
self._assertMaterializedColumnsExist(materialized_dict, [
'predictions__logits', 'predictions__probabilities',
'predictions__classes', 'predictions__logistic',
'predictions__class_ids', constants.SLICE_KEYS_KEY
])
util.assert_that(result[constants.ANALYSIS_KEY], check_result)
def testBuildAnalysisTableWithSlices(self):
model_location = self._exportEvalSavedModel(
linear_classifier.simple_linear_classifier)
eval_shared_model = model_eval_lib.default_eval_shared_model(
eval_saved_model_path=model_location)
example1 = self._makeExample(age=3.0,
language='english',
label=1.0,
slice_key='first_slice')
slice_spec = [
slicer.SingleSliceSpec(columns=['age']),
slicer.SingleSliceSpec(features=[('age', 3)]),
slicer.SingleSliceSpec(columns=['age'],
features=[('language', 'english')])
]
with beam.Pipeline() as pipeline:
result = (
pipeline
| 'CreateInput' >> beam.Create([example1.SerializeToString()])
| 'BuildTable' >> contrib.BuildAnalysisTable(
eval_shared_model, slice_spec))
def check_result(got):
self.assertEqual(1, len(got), 'got: %s' % got)
extracts = got[0]
# Values of type MaterializedColumn are emitted to signal to
# downstream sink components to output the data to file.
materialized_dict = dict(
(k, v) for k, v in extracts.items()
if isinstance(v, types.MaterializedColumn))
self._assertMaterializedColumns(
materialized_dict, {
constants.SLICE_KEYS_KEY:
types.MaterializedColumn(
name=constants.SLICE_KEYS_KEY,
value=[
b'age:3.0', b'age:3',
b'age_X_language:3.0_X_english'
])
})
self._assertMaterializedColumnsExist(materialized_dict, [
'predictions__logits', 'predictions__probabilities',
'predictions__classes', 'predictions__logistic',
'predictions__class_ids'
])
util.assert_that(result[constants.ANALYSIS_KEY], check_result)