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PERF: fix performance regression in overlay keep_geometry_type=True (in checking for GeometryCollection) (#2103)
This commit is contained in:
Joris Van den Bossche
@@ -575,6 +575,11 @@ def test_keep_geom_type_geometry_collection():
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df1 = read_file(os.path.join(DATA, "geom_type", "df1.geojson"))
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df2 = read_file(os.path.join(DATA, "geom_type", "df2.geojson"))
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with pytest.warns(UserWarning, match="`keep_geom_type=True` in overlay"):
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intersection = overlay(df1, df2, keep_geom_type=None)
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assert len(intersection) == 1
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assert (intersection.geom_type == "Polygon").all()
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intersection = overlay(df1, df2, keep_geom_type=True)
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assert len(intersection) == 1
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assert (intersection.geom_type == "Polygon").all()
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@@ -584,6 +589,44 @@ def test_keep_geom_type_geometry_collection():
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assert (intersection.geom_type == "GeometryCollection").all()
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def test_keep_geom_type_geometry_collection2():
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polys1 = [
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box(0, 0, 1, 1),
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box(1, 1, 3, 3).union(box(1, 3, 5, 5)),
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]
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polys2 = [
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box(0, 0, 1, 1),
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box(3, 1, 4, 2).union(box(4, 1, 5, 4)),
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]
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df1 = GeoDataFrame({"left": [0, 1], "geometry": polys1})
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df2 = GeoDataFrame({"right": [0, 1], "geometry": polys2})
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result1 = overlay(df1, df2, keep_geom_type=True)
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expected1 = GeoDataFrame(
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{
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"left": [0, 1],
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"right": [0, 1],
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"geometry": [box(0, 0, 1, 1), box(4, 3, 5, 4)],
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}
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)
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assert_geodataframe_equal(result1, expected1)
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result1 = overlay(df1, df2, keep_geom_type=False)
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expected1 = GeoDataFrame(
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{
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"left": [0, 1, 1],
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"right": [0, 0, 1],
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"geometry": [
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box(0, 0, 1, 1),
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Point(1, 1),
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GeometryCollection([box(4, 3, 5, 4), LineString([(3, 1), (3, 2)])]),
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],
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}
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)
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assert_geodataframe_equal(result1, expected1)
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@pytest.mark.parametrize("make_valid", [True, False])
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def test_overlap_make_valid(make_valid):
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bowtie = Polygon([(1, 1), (9, 9), (9, 1), (1, 9), (1, 1)])
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+52
-20
@@ -185,8 +185,8 @@ def overlay(df1, df2, how="intersection", keep_geom_type=None, make_valid=True):
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1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3....
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2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3....
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3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0....
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4 2.0 NaN MULTIPOLYGON (((4.00000 3.00000, 4.00000 2.000...
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5 NaN 1.0 MULTIPOLYGON (((3.00000 2.00000, 3.00000 1.000...
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4 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000...
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5 NaN 1.0 MULTIPOLYGON (((2.00000 2.00000, 3.00000 2.000...
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6 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5....
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>>> geopandas.overlay(df1, df2, how='intersection')
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@@ -198,8 +198,8 @@ def overlay(df1, df2, how="intersection", keep_geom_type=None, make_valid=True):
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>>> geopandas.overlay(df1, df2, how='symmetric_difference')
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df1_data df2_data geometry
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0 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0....
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1 2.0 NaN MULTIPOLYGON (((4.00000 3.00000, 4.00000 2.000...
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2 NaN 1.0 MULTIPOLYGON (((3.00000 2.00000, 3.00000 1.000...
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1 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000...
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2 NaN 1.0 MULTIPOLYGON (((2.00000 2.00000, 3.00000 2.000...
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3 NaN 2.0 POLYGON ((3.00000 5.00000, 5.00000 5.00000, 5....
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>>> geopandas.overlay(df1, df2, how='difference')
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@@ -213,7 +213,7 @@ def overlay(df1, df2, how="intersection", keep_geom_type=None, make_valid=True):
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1 2.0 1.0 POLYGON ((2.00000 2.00000, 2.00000 3.00000, 3....
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2 2.0 2.0 POLYGON ((4.00000 4.00000, 4.00000 3.00000, 3....
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3 1.0 NaN POLYGON ((2.00000 0.00000, 0.00000 0.00000, 0....
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4 2.0 NaN MULTIPOLYGON (((4.00000 3.00000, 4.00000 2.000...
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4 2.0 NaN MULTIPOLYGON (((3.00000 3.00000, 4.00000 3.000...
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See also
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--------
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@@ -317,32 +317,64 @@ def overlay(df1, df2, how="intersection", keep_geom_type=None, make_valid=True):
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result = dfunion[dfunion["__idx1"].notnull()].copy()
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if keep_geom_type:
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key_order = result.keys()
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exploded = result.reset_index(drop=True).explode()
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exploded = exploded.reset_index(level=0)
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orig_num_geoms = result.shape[0]
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geom_type = df1.geom_type.iloc[0]
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# First we filter the geometry types inside GeometryCollections objects
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# (e.g. GeometryCollection([polygon, point]) -> polygon)
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# we do this separately on only the relevant rows, as this is an expensive
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# operation (an expensive no-op for geometry types other than collections)
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is_collection = result.geom_type == "GeometryCollection"
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if is_collection.any():
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geom_col = result._geometry_column_name
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collections = result[[geom_col]][is_collection]
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exploded = collections.reset_index(drop=True).explode()
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exploded = exploded.reset_index(level=0)
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orig_num_geoms_exploded = exploded.shape[0]
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if geom_type in polys:
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exploded = exploded.loc[exploded.geom_type.isin(polys)]
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elif geom_type in lines:
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exploded = exploded.loc[exploded.geom_type.isin(lines)]
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elif geom_type in points:
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exploded = exploded.loc[exploded.geom_type.isin(points)]
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else:
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raise TypeError(
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"`keep_geom_type` does not support {}.".format(geom_type)
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)
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num_dropped_collection = orig_num_geoms_exploded - exploded.shape[0]
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# level_0 created with above reset_index operation
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# and represents the original geometry collections
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# TODO avoiding dissolve to call unary_union in this case could further
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# improve performance (we only need to collect geometries in their
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# respective Multi version)
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dissolved = exploded.dissolve(by="level_0")
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result.loc[is_collection, geom_col] = dissolved[geom_col].values
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else:
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num_dropped_collection = 0
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# Now we filter all geometries (in theory we don't need to do this
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# again for the rows handled above for GeometryCollections, but filtering
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# them out is probably more expensive as simply including them when this
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# is typically about only a few rows)
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orig_num_geoms = result.shape[0]
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if geom_type in polys:
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exploded = exploded.loc[exploded.geom_type.isin(polys)]
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result = result.loc[result.geom_type.isin(polys)]
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elif geom_type in lines:
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exploded = exploded.loc[exploded.geom_type.isin(lines)]
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result = result.loc[result.geom_type.isin(lines)]
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elif geom_type in points:
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exploded = exploded.loc[exploded.geom_type.isin(points)]
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result = result.loc[result.geom_type.isin(points)]
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else:
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raise TypeError("`keep_geom_type` does not support {}.".format(geom_type))
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num_dropped = orig_num_geoms - result.shape[0]
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# level_0 created with above reset_index operation
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# and represents the original geometry collections
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result = exploded.dissolve(by="level_0")[key_order]
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if (result.shape[0] != orig_num_geoms) and keep_geom_type_warning:
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num_dropped = orig_num_geoms - result.shape[0]
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if (num_dropped > 0 or num_dropped_collection > 0) and keep_geom_type_warning:
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warnings.warn(
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"`keep_geom_type=True` in overlay resulted in {} dropped "
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"geometries of different geometry types than df1 has. "
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"Set `keep_geom_type=False` to retain all "
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"geometries".format(num_dropped),
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"geometries".format(num_dropped + num_dropped_collection),
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UserWarning,
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stacklevel=2,
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)
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