def test_joined_hartigan_type_error():
"""Test that joining hartigan to something which isn't query like raises a type error."""
cd = CallDays(
SubscriberLocations("2016-01-01",
"2016-01-04",
spatial_unit=make_spatial_unit("versioned-site")))
hartigan = HartiganCluster(calldays=cd, radius=50)
with pytest.raises(TypeError):
hartigan.join_to_cluster_components("banana")
def test_joined_hartigan_cluster_bad_query_column_names_raises_error():
"""
Test that joining a HartiganCluster to a query without 'site_id' and 'version' columns raises an error.
"""
cd = CallDays(
SubscriberLocations("2016-01-01",
"2016-01-04",
spatial_unit=make_spatial_unit("versioned-site")))
hartigan = HartiganCluster(calldays=cd, radius=50)
es = EventScore(start="2016-01-01",
stop="2016-01-04",
spatial_unit=make_spatial_unit("lon-lat"))
with pytest.raises(ValueError):
hartigan.join_to_cluster_components(es)
def test_join_returns_the_same_clusters():
"""
Test whether joining to another table for which the start and stop time are the same yields the same clusters.
"""
cd = CallDays(
SubscriberLocations("2016-01-01",
"2016-01-04",
spatial_unit=make_spatial_unit("versioned-site")))
hartigan = HartiganCluster(calldays=cd, radius=50)
har_df = hartigan.to_geopandas()
es = EventScore(
start="2016-01-01",
stop="2016-01-04",
spatial_unit=make_spatial_unit("versioned-site"),
)
joined = (
hartigan.join_to_cluster_components(es).to_geopandas().sort_values(
["subscriber", "rank", "calldays"]))
joined.reset_index(inplace=True, drop=True)
har_df.sort_values(["subscriber", "rank", "calldays"], inplace=True)
har_df.reset_index(inplace=True, drop=True)
cols = ["subscriber", "geometry", "rank", "calldays"]
compare = joined[cols] == har_df[cols]
assert all(compare.all())
def test_joined_hartigan_column_names():
"""Test that Hartigan has correct column_names property."""
cd = CallDays("2016-01-01", "2016-01-04", level="versioned-site")
hartigan = HartiganCluster(cd, 50)
es = EventScore(start="2016-01-01",
stop="2016-01-05",
level="versioned-site")
joined = hartigan.join_to_cluster_components(es)
assert joined.head(0).columns.tolist() == joined.column_names
def test_joined_hartigan_column_names(get_column_names_from_run):
"""Test that Hartigan has correct column_names property."""
cd = CallDays(
subscriber_locations("2016-01-01",
"2016-01-04",
level="versioned-site"))
hartigan = HartiganCluster(calldays=cd, radius=50)
es = EventScore(start="2016-01-01",
stop="2016-01-05",
level="versioned-site")
joined = hartigan.join_to_cluster_components(es)
assert get_column_names_from_run(joined) == joined.column_names
class TestHartiganCluster(TestCase):
def setUp(self):
self.cd = CallDays("2016-01-01", "2016-01-04", level="versioned-site")
self.cd_df = self.cd.get_dataframe()
self.hartigan = HartiganCluster(self.cd, 50)
self.har_df = self.hartigan.to_geopandas()
def tearDown(self):
[cd.invalidate_db_cache() for cd in CallDays.get_stored()]
def test_returns_dataframe(self):
"""
Tests that it returns a dataframe with the correct columns
"""
self.assertIsInstance(self.har_df, pd.DataFrame)
self.assertEquals(
set(self.har_df.columns),
set([
"subscriber",
"geometry",
"rank",
"calldays",
"site_id",
"version",
"centroid",
]),
)
def test_cluster_is_within_envelope(self):
"""
Test that all the clusters are within the enveloped formed by all the towers in the cluster.
"""
sites = Sites().to_geopandas()
self.har_df["towers"] = self.har_df.apply(
lambda x: get_geom_point(x, sites), 1)
s = self.har_df.apply(
lambda x: x.geometry.intersects(box(*x.towers.bounds)), axis=1)
self.assertTrue(all(s))
def test_first_call_day_in_first_cluster(self):
"""
Test that the first ranked call day of each subscriber is in the first cluster of each subscriber.
"""
cd_first = (self.cd_df[["subscriber", "site_id",
"version"]].groupby("subscriber").first())
har_first = (self.har_df[["subscriber", "site_id",
"version"]].groupby("subscriber").first())
joined = cd_first.join(har_first, lsuffix="_cd", rsuffix="_har")
s = joined.apply(
lambda x: (x.site_id_cd in x.site_id_har) and
(x.version_cd in x.version_har),
axis=1,
)
self.assertTrue(all(s))
def test_bigger_radius_yields_fewer_clusters(self):
"""
Test whether bigger radius yields fewer clusters per subscriber
"""
radius = [1, 2, 5, 10, 50]
h = HartiganCluster(self.cd, radius[0]).get_dataframe()
nclusters_small_radius = h.groupby("subscriber").size()
for r in radius[1:]:
h = HartiganCluster(self.cd, r).get_dataframe()
nclusters_big_radius = h.groupby("subscriber").size()
self.assertTrue(
all(nclusters_small_radius >= nclusters_big_radius))
nclusters_small_radius = nclusters_big_radius
def test_different_call_days_format(self):
"""
Test whether we can pass different call days format such as table name, SQL query and CallDays class.
"""
cd = CallDays("2016-01-01", "2016-01-04", level="versioned-site")
har = HartiganCluster(cd, 50).get_dataframe()
self.assertIsInstance(har, pd.DataFrame)
cd.store().result()
har = HartiganCluster(Table(cd.table_name), 50).get_dataframe()
self.assertIsInstance(har, pd.DataFrame)
cd_query = cd.get_query()
har = HartiganCluster(CustomQuery(cd_query), 50).get_dataframe()
self.assertIsInstance(har, pd.DataFrame)
def test_call_threshold_works(self):
"""
Test whether a call threshold above 1 limits the number of clusters.
"""
self.assertTrue(any(self.har_df.calldays == 1))
har = HartiganCluster(self.cd, 50, call_threshold=2).get_dataframe()
self.assertFalse(all(self.har_df.calldays > 1))
def test_buffered_hartigan(self):
"""
Test whether Hartigan produces buffered clusters when buffer is larger than 0.
"""
har = HartiganCluster(self.cd, 50, buffer=2).to_geopandas()
areas = har.geometry.area
# since the mock data does not have geom_area in the site table we either
# get the clusters with area equivalent to 2km2 (areas below are in degrees) or None.
min_area = areas.min()
max_area = areas.max()
self.assertAlmostEquals(min_area, 0.001, 3)
self.assertAlmostEquals(max_area, 0.001, 3)
def test_all_options_hartigan(self):
"""
Test whether Hartigan works when changing all options.
"""
har = HartiganCluster(self.cd, 50, buffer=2,
call_threshold=2).to_geopandas()
self.assertIsInstance(har, pd.DataFrame)
self.assertEquals(
set(har.columns),
set([
"subscriber",
"geometry",
"rank",
"calldays",
"site_id",
"version",
"centroid",
]),
)
def test_join_returns_the_same_clusters(self):
"""
Test whether joining to another table for which the start and stop time are the same yields the same clusters.
"""
es = EventScore(start="2016-01-01",
stop="2016-01-04",
level="versioned-site")
joined = (self.hartigan.join_to_cluster_components(
es).to_geopandas().sort_values(["subscriber", "rank", "calldays"]))
joined.reset_index(inplace=True, drop=True)
self.har_df.sort_values(["subscriber", "rank", "calldays"],
inplace=True)
self.har_df.reset_index(inplace=True, drop=True)
cols = ["subscriber", "geometry", "rank", "calldays"]
compare = joined[cols] == self.har_df[cols]
self.assertTrue(all(compare.all()))
def test_joined_hartigan_type_error():
"""Test that joining hartigan to something which isn't query like raises a type error."""
cd = CallDays("2016-01-01", "2016-01-04", level="versioned-site")
hartigan = HartiganCluster(cd, 50)
with pytest.raises(TypeError):
hartigan.join_to_cluster_components("banana")
