def test_meaningful_locations_od_results(get_dataframe):
"""
Test that OD on MeaningfulLocations returns expected results and counts clusters per subscriber correctly.
"""
# FIXME: Because of the nature of the test data, we can't actually test much for most admin levels because
# the counts will always be below 15, and hence get redacted!
mfl_a = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="unknown",
)
mfl_b = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-02",
stop="2016-01-03",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-02",
stop="2016-01-03",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="unknown",
)
mfl_od = MeaningfulLocationsOD(
meaningful_locations_a=mfl_a,
meaningful_locations_b=mfl_b,
spatial_unit=make_spatial_unit("admin", level=1),
)
mfl_od_df = get_dataframe(mfl_od)
# Aggregate should not include any counts below 15
assert all(mfl_od_df.total > 15)
# Smoke test one admin1 region gets the expected result
assert mfl_od_df[(mfl_od_df.pcod_from == "524 1") & (
mfl_od_df.pcod_to == "524 4")].total[0] == pytest.approx(16.490_807)
assert mfl_od_df.total.sum() == pytest.approx(350.806_012)
def test_column_names_meaningful_locations_od(exemplar_spatial_unit_param,
get_column_names_from_run):
""" Test that column_names property matches head(0) for an od matrix between meaningful locations"""
if not exemplar_spatial_unit_param.is_polygon:
pytest.xfail(
f"The spatial unit {exemplar_spatial_unit_param} is not supported as an aggregation unit for ODs between MeaningfulLocations."
)
mfl_a = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="evening",
)
mfl_b = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="unknown",
)
mfl_od = MeaningfulLocationsOD(
meaningful_locations_a=mfl_a,
meaningful_locations_b=mfl_b,
spatial_unit=exemplar_spatial_unit_param,
)
assert get_column_names_from_run(mfl_od) == mfl_od.column_names
def test_meaningful_locations_od_raises_for_bad_spatial_unit(
exemplar_spatial_unit_param, get_dataframe):
"""
Test that od on meaningful locations raises an InvalidSpatialUnitError for a bad spatial unit.
"""
mfl = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="evening",
)
with pytest.raises(InvalidSpatialUnitError):
mfl_od = MeaningfulLocationsOD(
meaningful_locations_a=mfl,
meaningful_locations_b=mfl,
spatial_unit=make_spatial_unit("lon-lat"),
)
def test_meaningful_locations_aggregation_results(exemplar_spatial_unit_param,
get_dataframe):
"""
Test that aggregating MeaningfulLocations returns expected results and redacts values below 15.
"""
if not exemplar_spatial_unit_param.is_polygon:
pytest.xfail(
f"The spatial unit {exemplar_spatial_unit_param} is not supported as an aggregation unit for MeaningfulLocations."
)
mfl = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="evening",
)
mfl_agg = MeaningfulLocationsAggregate(
meaningful_locations=mfl, spatial_unit=exemplar_spatial_unit_param)
mfl_df = get_dataframe(mfl)
mfl_agg_df = get_dataframe(mfl_agg)
# Aggregate should not include any counts below 15
assert all(mfl_agg_df.total > 15)
# Sum of aggregate should be less than the number of unique subscribers
assert mfl_agg_df.total.sum() < mfl_df.subscriber.nunique()
def test_meaningful_locations_results(label, expected_number_of_clusters,
get_dataframe):
"""
Test that MeaningfulLocations returns expected results and counts clusters per subscriber correctly.
"""
mfl = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label=label,
)
mfl_df = get_dataframe(mfl)
assert len(mfl_df) == expected_number_of_clusters
count_clusters = mfl_df.groupby(["subscriber", "label", "n_clusters"],
as_index=False).count()
# Check that query has correctly counted the number of clusters per subscriber
assert all(count_clusters.n_clusters == count_clusters.cluster)
def test_column_names_meaningful_locations_aggregate(
exemplar_level_param, get_column_names_from_run):
""" Test that column_names property matches head(0) for aggregated meaningful locations"""
if exemplar_level_param[
"level"] not in MeaningfulLocationsAggregate.allowed_levels:
pytest.xfail(
f'The level "{exemplar_level_param["level"]}" is not supported as an aggregation unit for MeaningfulLocations.'
)
mfl_agg = MeaningfulLocationsAggregate(
meaningful_locations=MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=subscriber_locations(
start="2016-01-01",
stop="2016-01-02",
level="versioned-site")),
radius=1,
),
scores=EventScore(start="2016-01-01",
stop="2016-01-02",
level="versioned-site"),
labels=labels,
label="evening",
),
**exemplar_level_param,
)
assert get_column_names_from_run(mfl_agg) == mfl_agg.column_names
def test_meaningful_locations_aggregation_results(exemplar_level_param,
get_dataframe):
"""
Test that aggregating MeaningfulLocations returns expected results and redacts values below 15.
"""
if exemplar_level_param[
"level"] not in MeaningfulLocationsAggregate.allowed_levels:
pytest.xfail(
f'The level "{exemplar_level_param["level"]}" is not supported as an aggregation unit for MeaningfulLocations.'
)
mfl = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=subscriber_locations(
start="2016-01-01", stop="2016-01-02",
level="versioned-site")),
radius=1,
),
scores=EventScore(start="2016-01-01",
stop="2016-01-02",
level="versioned-site"),
labels=labels,
label="evening",
)
mfl_agg = MeaningfulLocationsAggregate(meaningful_locations=mfl,
**exemplar_level_param)
mfl_df = get_dataframe(mfl)
mfl_agg_df = get_dataframe(mfl_agg)
# Aggregate should not include any counts below 15
assert all(mfl_agg_df.total > 15)
# Sum of aggregate should be less than the number of unique subscribers
assert mfl_agg_df.total.sum() < mfl_df.subscriber.nunique()
def test_column_names_meaningful_locations(get_column_names_from_run):
""" Test that column_names property matches head(0) for meaningfullocations"""
mfl = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=subscriber_locations(
start="2016-01-01", stop="2016-01-02",
level="versioned-site")),
radius=1,
),
scores=EventScore(start="2016-01-01",
stop="2016-01-02",
level="versioned-site"),
labels=labels,
label="evening",
)
assert get_column_names_from_run(mfl) == mfl.column_names
def test_meaningful_locations_aggregate_disallowed_level_raises():
""" Test that a bad level raises a BadLevelError"""
with pytest.raises(BadLevelError):
mfl_agg = MeaningfulLocationsAggregate(
meaningful_locations=MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(
subscriber_locations=subscriber_locations(
start="2016-01-01",
stop="2016-01-02",
level="versioned-site",
)),
radius=1,
),
scores=EventScore(start="2016-01-01",
stop="2016-01-02",
level="versioned-site"),
labels=labels,
label="evening",
),
level="NOT_A_LEVEL",
)
def test_meaningful_locations_od_raises_for_bad_level(exemplar_level_param,
get_dataframe):
"""
Test that od on meaningful locations raises a BadLevelError for a bad level.
"""
mfl = MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=subscriber_locations(
start="2016-01-01", stop="2016-01-02",
level="versioned-site")),
radius=1,
),
scores=EventScore(start="2016-01-01",
stop="2016-01-02",
level="versioned-site"),
labels=labels,
label="evening",
)
with pytest.raises(BadLevelError):
mfl_od = MeaningfulLocationsOD(meaningful_locations_a=mfl,
meaningful_locations_b=mfl,
level="NOT_A_LEVEL")
def test_meaningful_locations_aggregate_disallowed_spatial_unit_raises():
""" Test that a bad spatial unit raises an InvalidSpatialUnitError"""
with pytest.raises(InvalidSpatialUnitError):
mfl_agg = MeaningfulLocationsAggregate(
meaningful_locations=MeaningfulLocations(
clusters=HartiganCluster(
calldays=CallDays(subscriber_locations=SubscriberLocations(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
)),
radius=1,
),
scores=EventScore(
start="2016-01-01",
stop="2016-01-02",
spatial_unit=make_spatial_unit("versioned-site"),
),
labels=labels,
label="evening",
),
spatial_unit=make_spatial_unit("lon-lat"),
)
