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added majority as default stat for zonal operations with numpy and cupy #844

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Merged
brendancol merged 1 commit into from
Jan 25, 2026
Merged

added majority as default stat for zonal operations with numpy and cupy #844

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110 changes: 104 additions & 6 deletions xrspatial/tests/test_zonal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,23 @@ def data_values_3d(backend):

@pytest.fixture
def result_default_stats():
expected_result = {
'zone': [0, 1, 2, 3],
'mean': [0, 1, 2, 2.4],
'max': [0, 1, 2, 3],
'min': [0, 1, 2, 0],
'sum': [0, 6, 8, 12],
'std': [0, 0, 0, 1.2],
'var': [0, 0, 0, 1.44],
'count': [5, 6, 4, 5],
'majority': [0, 1, 2, 3]
}
return expected_result


@pytest.fixture
def result_default_stats_no_majority():
"""Expected result for dask backend which doesn't support majority."""
expected_result = {
'zone': [0, 1, 2, 3],
'mean': [0, 1, 2, 2.4],
Expand Down Expand Up @@ -102,13 +119,35 @@ def result_default_stats_dataarray():

[[5., 5., 6., 6., 4., 4., 5., 5.],
[5., 5., 6., 6., 4., 4., 5., 5.],
[5., 5., 6., 6., 4., np.nan, 5., 5.]]]
[5., 5., 6., 6., 4., np.nan, 5., 5.]],

[[0., 0., 1., 1., 2., 2., 3., 3.],
[0., 0., 1., 1., 2., 2., 3., 3.],
[0., 0., 1., 1., 2., np.nan, 3., 3.]]]
)
return expected_result


@pytest.fixture
def result_zone_ids_stats():
zone_ids = [0, 3]
expected_result = {
'zone': [0, 3],
'mean': [0, 2.4],
'max': [0, 3],
'min': [0, 0],
'sum': [0, 12],
'std': [0, 1.2],
'var': [0, 1.44],
'count': [5, 5],
'majority': [0, 3]
}
return zone_ids, expected_result


@pytest.fixture
def result_zone_ids_stats_no_majority():
"""Expected result for dask backend which doesn't support majority."""
zone_ids = [0, 3]
expected_result = {
'zone': [0, 3],
Expand Down Expand Up @@ -153,7 +192,11 @@ def result_zone_ids_stats_dataarray():

[[5., 5., np.nan, np.nan, np.nan, np.nan, 5., 5.],
[5., 5., np.nan, np.nan, np.nan, np.nan, 5., 5.],
[5., 5., np.nan, np.nan, np.nan, np.nan, 5., 5.]]])
[5., 5., np.nan, np.nan, np.nan, np.nan, 5., 5.]],

[[0., 0., np.nan, np.nan, np.nan, np.nan, 3., 3.],
[0., 0., np.nan, np.nan, np.nan, np.nan, 3., 3.],
[0., 0., np.nan, np.nan, np.nan, np.nan, 3., 3.]]])

return zone_ids, expected_result

Expand Down Expand Up @@ -362,7 +405,8 @@ def check_results(


@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy', 'cupy'])
def test_default_stats(backend, data_zones, data_values_2d, result_default_stats):
def test_default_stats(backend, data_zones, data_values_2d, result_default_stats,
result_default_stats_no_majority):
if backend == 'cupy' and not has_cuda_and_cupy():
pytest.skip("Requires CUDA and CuPy")

Expand All @@ -374,7 +418,9 @@ def test_default_stats(backend, data_zones, data_values_2d, result_default_stats
copied_data_values_2d = copy.deepcopy(data_values_2d)

df_result = stats(zones=data_zones, values=data_values_2d)
check_results(backend, df_result, result_default_stats)
# dask doesn't support majority stat (can't be computed block-by-block)
expected_result = result_default_stats_no_majority if 'dask' in backend else result_default_stats
check_results(backend, df_result, expected_result)

assert_input_data_unmodified(data_zones, copied_data_zones)
assert_input_data_unmodified(data_values_2d, copied_data_values_2d)
Expand Down Expand Up @@ -403,7 +449,8 @@ def test_default_stats_dataarray(


@pytest.mark.parametrize("backend", ['numpy', 'dask+numpy', 'cupy'])
def test_zone_ids_stats(backend, data_zones, data_values_2d, result_zone_ids_stats):
def test_zone_ids_stats(backend, data_zones, data_values_2d, result_zone_ids_stats,
result_zone_ids_stats_no_majority):
if backend == 'cupy' and not has_cuda_and_cupy():
pytest.skip("Requires CUDA and CuPy")

Expand All @@ -414,7 +461,11 @@ def test_zone_ids_stats(backend, data_zones, data_values_2d, result_zone_ids_sta
copied_data_zones = copy.deepcopy(data_zones)
copied_data_values_2d = copy.deepcopy(data_values_2d)

zone_ids, expected_result = result_zone_ids_stats
# dask doesn't support majority stat (can't be computed block-by-block)
if 'dask' in backend:
zone_ids, expected_result = result_zone_ids_stats_no_majority
else:
zone_ids, expected_result = result_zone_ids_stats
df_result = stats(zones=data_zones, values=data_values_2d,
zone_ids=zone_ids)
check_results(backend, df_result, expected_result)
Expand Down Expand Up @@ -491,6 +542,53 @@ def test_custom_stats_dataarray(backend, data_zones, data_values_2d, result_cust
assert_input_data_unmodified(data_values_2d, copied_data_values_2d)


@pytest.mark.parametrize("backend", ['numpy', 'cupy'])
def test_majority_stats(backend, data_zones, data_values_2d):
"""Test that majority stat returns the most frequent value in each zone."""
if backend == 'cupy' and not has_cuda_and_cupy():
pytest.skip("Requires CUDA and CuPy")

# copy input data to verify they're unchanged after running the function
copied_data_zones = copy.deepcopy(data_zones)
copied_data_values_2d = copy.deepcopy(data_values_2d)

df_result = stats(zones=data_zones, values=data_values_2d, stats_funcs=['majority'])
expected_result = {
'zone': [0, 1, 2, 3],
'majority': [0, 1, 2, 3]
}
check_results(backend, df_result, expected_result)
assert_input_data_unmodified(data_zones, copied_data_zones)
assert_input_data_unmodified(data_values_2d, copied_data_values_2d)


@pytest.mark.parametrize("backend", ['numpy', 'cupy'])
def test_majority_with_ties(backend):
"""Test majority when there are ties - should return the smallest value."""
if backend == 'cupy' and not has_cuda_and_cupy():
pytest.skip("Requires CUDA and CuPy")

# Create test data with ties
zones_data = np.array([[1, 1, 1, 1],
[1, 1, 2, 2],
[2, 2, 2, 2]])
values_data = np.array([[1, 1, 2, 2], # zone 1 has two 1s and two 2s - tie
[3, 3, 5, 5], # zone 1 also has two 3s, zone 2 has two 5s
[5, 5, 6, 6]]) # zone 2 has two more 5s and two 6s

zones = create_test_raster(zones_data, backend)
values = create_test_raster(values_data, backend)

df_result = stats(zones=zones, values=values, stats_funcs=['majority'])
# Zone 1: values [1, 1, 2, 2, 3, 3] - three values with count 2, majority is 1 (smallest)
# Zone 2: values [5, 5, 5, 5, 6, 6] - majority is 5 (count 4)
expected_result = {
'zone': [1, 2],
'majority': [1, 5]
}
check_results(backend, df_result, expected_result)


def test_zonal_stats_against_qgis(elevation_raster_no_nans, raster, qgis_zonal_stats):
stats_funcs = list(set(qgis_zonal_stats.keys()) - set(['zone']))
zones_agg = create_test_raster(raster)
Expand Down
24 changes: 21 additions & 3 deletions xrspatial/zonal.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,21 @@ def _stats_count(data):
return stats_count


def _stats_majority(data):
if isinstance(data, np.ndarray):
# numpy case
values, counts = np.unique(data, return_counts=True)
return values[np.argmax(counts)]
elif isinstance(data, cupy.ndarray):
# cupy case
values, counts = cupy.unique(data, return_counts=True)
return values[cupy.argmax(counts)]
else:
# dask case
values, counts = da.unique(data, return_counts=True)
return values[da.argmax(counts)]


_DEFAULT_STATS = dict(
mean=lambda z: z.mean(),
max=lambda z: z.max(),
Expand All @@ -61,6 +76,7 @@ def _stats_count(data):
std=lambda z: z.std(),
var=lambda z: z.var(),
count=lambda z: _stats_count(z),
majority=lambda z: _stats_majority(z),
)


Expand Down Expand Up @@ -246,8 +262,9 @@ def _stats_dask_numpy(
stats_df = dd.concat([dd.from_dask_array(s) for s in stats_dict.values()], axis=1)
# name columns
stats_df.columns = stats_dict.keys()
# select columns
stats_df = stats_df[['zone'] + list(stats_funcs.keys())]
# select columns (only include stats that were actually computed)
computed_stats = [s for s in stats_funcs.keys() if s in stats_dict]
stats_df = stats_df[['zone'] + computed_stats]

if not select_all_zones:
# only return zones specified in `zone_ids`
Expand Down Expand Up @@ -414,6 +431,7 @@ def stats(
"std",
"var",
"count",
"majority",
],
nodata_values: Union[int, float] = None,
return_type: str = 'pandas.DataFrame',
Expand Down Expand Up @@ -449,7 +467,7 @@ def stats(
all zones will be used.

stats_funcs : dict, or list of strings, default=['mean', 'max', 'min',
'sum', 'std', 'var', 'count']
'sum', 'std', 'var', 'count', 'majority']
The statistics to calculate for each zone. If a list, possible
choices are subsets of the default options.
In the dictionary case, all of its values must be
Expand Down