def test_joined_modal_aggregate(self):
"""
Test join with modal aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
rog = SubscriberDegree("2016-01-01", "2016-01-04")
joined = mfl.join_aggregate(rog, method="mode")
rawus_mode = (rog.get_dataframe().set_index("subscriber").join(
mfl.get_dataframe().set_index("subscriber")).set_index(
"name").ix["Rasuwa"].degree.mode()[0])
self.assertAlmostEqual(
joined.get_dataframe().set_index("name").ix["Rasuwa"].degree,
rawus_mode)
def _unsampled_query_obj(self):
"""
Return the underlying flowmachine subscriber_degree object.
Returns
-------
Query
"""
return SubscriberDegree(
start=self.start,
stop=self.stop,
direction=self.direction,
subscriber_subset=self.subscriber_subset,
)
def test_joined_modal_aggregate(get_dataframe):
"""
Test join with modal aggregate.
"""
mfl = MostFrequentLocation("2016-01-01", "2016-01-04", level="admin3")
rog = SubscriberDegree("2016-01-01", "2016-01-04")
joined = mfl.join_aggregate(rog, method="mode")
rawus_mode = (
get_dataframe(rog)
.set_index("subscriber")
.join(get_dataframe(mfl).set_index("subscriber"))
.set_index("pcod")
.loc["524 2 05 29"]
.value.mode()[0]
)
assert (
pytest.approx(rawus_mode)
== get_dataframe(joined).set_index("pcod").loc["524 2 05 29"].value
)
def test_collects_metrics():
"""
Test that we can instantiate flowmachine.feature_collection with list of
objects.
"""
start, stop = "2016-01-01", "2016-01-03"
metrics = [
RadiusOfGyration(start, stop),
NocturnalEvents(start, stop),
SubscriberDegree(start, stop),
]
expected_columns = [
"subscriber",
"value_radiusofgyration_0",
"value_nocturnalevents_1",
"value_subscriberdegree_2",
]
fc = feature_collection(metrics)
column_names = fc.column_names
assert expected_columns == column_names