def test_anchor_max_two(anchor_schema_max_two: AnchorSchema,
block_item: BlockAggregate) -> None:
anchor = Anchor(anchor_schema_max_two)
assert anchor.evaluate_anchor(block_item, EvaluationContext()) is True
anchor.add_condition_met()
assert anchor.evaluate_anchor(block_item, EvaluationContext()) is True
anchor.add_condition_met()
assert anchor.evaluate_anchor(block_item, EvaluationContext()) is False
def test_anchor_max_not_specified(anchor_schema_max_one: AnchorSchema,
block_item: BlockAggregate) -> None:
anchor_schema_max_one.max = None
anchor = Anchor(anchor_schema_max_one)
assert anchor.evaluate_anchor(block_item, EvaluationContext()) is True
anchor.add_condition_met()
assert anchor.evaluate_anchor(block_item, EvaluationContext()) is True
anchor.add_condition_met()
assert anchor.evaluate_anchor(block_item, EvaluationContext()) is True
def test_anchor_condition(anchor_schema_max_one: AnchorSchema,
block_item: BlockAggregate) -> None:
anchor_schema_max_one.condition = Expression('session.events > 3')
eval_context = EvaluationContext()
anchor = Anchor(anchor_schema_max_one)
eval_context.local_context.add(block_item._schema.fully_qualified_name,
block_item)
assert anchor.evaluate_anchor(block_item, eval_context) is False
block_item.run_restore({
'events':
4,
'_start_time':
datetime(2018, 3, 7, 22, 36, 31, 0, timezone.utc).isoformat(),
'_end_time':
datetime(2018, 3, 7, 22, 37, 31, 0, timezone.utc).isoformat()
})
assert anchor.evaluate_anchor(block_item, eval_context) is True
anchor.add_condition_met()
assert anchor.evaluate_anchor(block_item, eval_context) is False
def __init__(self, schema: WindowTransformerSchema, identity: str,
context: Context) -> None:
super().__init__(schema, identity)
self._evaluation_context.merge(EvaluationContext(context))
self._anchor = Anchor(schema.anchor)
class WindowTransformer(Transformer):
"""
The Window DTC transformer that performs window operations on pre-aggregated
block data.
"""
def __init__(self, schema: WindowTransformerSchema, identity: str,
context: Context) -> None:
super().__init__(schema, identity)
self._evaluation_context.merge(EvaluationContext(context))
self._anchor = Anchor(schema.anchor)
def run_evaluate(self, block: BlockAggregate) -> bool:
"""
Evaluates the anchor condition against the specified block.
:param block: Block to run the anchor condition against.
:return: True, if the anchor condition is met, otherwise, False.
"""
if self._anchor.evaluate_anchor(block, self._evaluation_context):
try:
self.run_reset()
self._evaluation_context.global_add('anchor', block)
self._evaluate()
self._anchor.add_condition_met()
return True
finally:
self._evaluation_context.global_remove('anchor')
return False
def _evaluate(self):
if 'anchor' not in self._evaluation_context.global_context or self._anchor.anchor_block is None:
raise AnchorBlockNotDefinedError()
if not self._needs_evaluation:
return
for item in self._nested_items.values():
if isinstance(item, WindowAggregate):
item._prepare_window(self._anchor.anchor_block._start_time)
super().run_evaluate()
@property
def run_flattened_snapshot(self) -> Dict:
"""
Generates a flattened snapshot where the final key for a field is <aggregate_name>.<field_name>.
:return: The flattened snapshot.
"""
snapshot_dict = super()._snapshot
# Flatten to feature dict
return self._flatten_snapshot(None, snapshot_dict)
def _flatten_snapshot(self, prefix: Optional[str], value: Dict) -> Dict:
flattened_dict = {}
for k, v in value.items():
if isinstance(v, dict):
flattened_dict.update(self._flatten_snapshot(k, v))
else:
flattened_dict[prefix + '.' +
k if prefix is not None else k] = v
return flattened_dict