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add dbscan tests, pom file changes, pip changes
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jameswillis
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Sep 17, 2024
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@@ -10,6 +10,8 @@ jupyter="*" | |
| mkdocs="*" | ||
| pytest-cov = "*" | ||
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| scikit-learn = "*" | ||
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| [packages] | ||
| pandas="<=1.5.3" | ||
| numpy="<2" | ||
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| @@ -0,0 +1,214 @@ | ||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you under the Apache License, Version 2.0 (the | ||
| # "License"); you may not use this file except in compliance | ||
| # with the License. You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, | ||
| # software distributed under the License is distributed on an | ||
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| # KIND, either express or implied. See the License for the | ||
| # specific language governing permissions and limitations | ||
| # under the License. | ||
|
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| import pyspark.sql.functions as f | ||
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| from sedona.sql.st_constructors import ST_MakePoint | ||
| from sedona.sql.st_functions import ST_Buffer | ||
| from sklearn.cluster import DBSCAN as sklearnDBSCAN | ||
| from sedona.stats.clustering.dbscan import dbscan | ||
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| from tests.test_base import TestBase | ||
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| class TestDBScan(TestBase): | ||
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| def get_data(self): | ||
| return [ | ||
| {"id": 1, "x": 1.0, "y": 2.0}, | ||
| {"id": 2, "x": 3.0, "y": 4.0}, | ||
| {"id": 3, "x": 2.5, "y": 4.0}, | ||
| {"id": 4, "x": 1.5, "y": 2.5}, | ||
| {"id": 5, "x": 3.0, "y": 5.0}, | ||
| {"id": 6, "x": 12.8, "y": 4.5}, | ||
| {"id": 7, "x": 2.5, "y": 4.5}, | ||
| {"id": 8, "x": 1.2, "y": 2.5}, | ||
| {"id": 9, "x": 1.0, "y": 3.0}, | ||
| {"id": 10, "x": 1.0, "y": 5.0}, | ||
| {"id": 11, "x": 1.0, "y": 2.5}, | ||
| {"id": 12, "x": 5.0, "y": 6.0}, | ||
| {"id": 13, "x": 4.0, "y": 3.0}, | ||
| ] | ||
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| def create_sample_dataframe(self): | ||
| return self.spark.createDataFrame(self.get_data()).select( | ||
| ST_MakePoint("x", "y").alias("arealandmark"), "id" | ||
| ).repartition(9) | ||
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| def get_expected_result(self, input_data, epsilon, min_pts, include_outliers=True): | ||
| labels = ( | ||
| sklearnDBSCAN(eps=epsilon, min_samples=min_pts) | ||
| .fit([[datum["x"], datum["y"]] for datum in input_data]) | ||
| .labels_ | ||
| ) | ||
| expected = [(x[0] + 1, x[1]) for x in list(enumerate(labels))] | ||
| clusters = [x for x in set(labels) if (x != -1 or include_outliers)] | ||
| cluster_members = { | ||
| frozenset([y[0] for y in expected if y[1] == x]) for x in clusters | ||
| } | ||
| return cluster_members | ||
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| def get_actual_results( | ||
| self, | ||
| input_data, | ||
| epsilon, | ||
| min_pts, | ||
| geometry=None, | ||
| id=None, | ||
| include_outliers=True, | ||
| ): | ||
| result = dbscan( | ||
| input_data, epsilon, min_pts, geometry, include_outliers=include_outliers | ||
| ) | ||
| id = id or "id" | ||
| clusters_members = [ | ||
| (x[id], x.cluster) | ||
| for x in result.collect() | ||
| if x.cluster != -1 or include_outliers | ||
| ] | ||
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| clusters = { | ||
| frozenset([y[0] for y in clusters_members if y[1] == x]) | ||
| for x in set([y[1] for y in clusters_members]) | ||
| } | ||
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| return clusters | ||
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| def test_dbscan_valid_parameters(self): | ||
| # repeated broadcast joins with this small data size use a lot of RAM on broadcast references | ||
| prior_join_threshold = self.spark.conf.get("sedona.join.autoBroadcastJoinThreshold", None) | ||
| self.spark.conf.set( | ||
| "sedona.join.autoBroadcastJoinThreshold", -1 | ||
| ) | ||
| df = self.create_sample_dataframe() | ||
| for epsilon in [0.6, 0.7, 0.8]: | ||
| for min_pts in [3, 4, 5]: | ||
| assert self.get_expected_result( | ||
| self.get_data(), epsilon, min_pts | ||
| ) == self.get_actual_results(df, epsilon, min_pts) | ||
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| if prior_join_threshold is None: | ||
| self.spark.conf.unset("sedona.join.autoBroadcastJoinThreshold") | ||
| else: | ||
| self.spark.conf.set("sedona.join.autoBroadcastJoinThreshold", prior_join_threshold) | ||
|
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| def test_dbscan_valid_parameters_default_column_name(self): | ||
| df = self.create_sample_dataframe().select( | ||
| "id", f.col("arealandmark").alias("geometryFieldName") | ||
| ) | ||
| epsilon = 0.6 | ||
| min_pts = 4 | ||
|
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| assert self.get_expected_result( | ||
| self.get_data(), epsilon, min_pts | ||
| ) == self.get_actual_results(df, epsilon, min_pts) | ||
|
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| def test_dbscan_valid_parameters_polygons(self): | ||
| df = self.create_sample_dataframe().select( | ||
| "id", ST_Buffer(f.col("arealandmark"), 0.000001).alias("geometryFieldName") | ||
| ) | ||
| epsilon = 0.6 | ||
| min_pts = 4 | ||
|
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| assert self.get_expected_result( | ||
| self.get_data(), epsilon, min_pts | ||
| ) == self.get_actual_results(df, epsilon, min_pts) | ||
|
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| def test_dbscan_supports_other_distance_function(self): | ||
| df = self.create_sample_dataframe().select( | ||
| "id", ST_Buffer(f.col("arealandmark"), 0.000001).alias("geometryFieldName") | ||
| ) | ||
| epsilon = 0.6 | ||
| min_pts = 4 | ||
|
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| dbscan( | ||
| df, | ||
| epsilon, | ||
| min_pts, | ||
| "geometryFieldName", | ||
| use_spheroid=True, | ||
| ) | ||
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| def test_dbscan_invalid_epsilon(self): | ||
| df = self.create_sample_dataframe() | ||
|
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| try: | ||
| dbscan(df, -0.1, 5, "arealandmark") | ||
| assert False | ||
| except Exception: | ||
| assert True | ||
|
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| def test_dbscan_invalid_min_pts(self): | ||
| df = self.create_sample_dataframe() | ||
|
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| try: | ||
| dbscan(df, 0.1, -5, "arealandmark") | ||
| assert False | ||
| except Exception: | ||
| assert True | ||
|
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| def test_dbscan_invalid_geometry_column(self): | ||
| df = self.create_sample_dataframe() | ||
|
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| try: | ||
| dbscan(df, 0.1, 5, "invalid_column") | ||
| assert False | ||
| except Exception: | ||
| assert True | ||
|
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| def test_return_empty_df_when_no_clusters(self): | ||
| df = self.create_sample_dataframe() | ||
| epsilon = 0.1 | ||
| min_pts = 10000 | ||
|
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| assert dbscan(df, epsilon, min_pts, "arealandmark", include_outliers = False).count() == 0 | ||
| # picked some coefficient we know yields clusters and thus hit the happy case | ||
| assert ( | ||
| dbscan(df, epsilon, min_pts, "arealandmark", include_outliers = False).schema | ||
| == dbscan(df, 0.6, 3, "arealandmark").schema | ||
| ) | ||
|
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| def test_dbscan_doesnt_duplicate_border_points_in_two_clusters(self): | ||
| input_df = self.spark.createDataFrame( | ||
| [ | ||
| {"id": 10, "x": 1.0, "y": 1.8}, | ||
| {"id": 11, "x": 1.0, "y": 1.9}, | ||
| {"id": 12, "x": 1.0, "y": 2.0}, | ||
| {"id": 13, "x": 1.0, "y": 2.1}, | ||
| {"id": 14, "x": 2.0, "y": 2.0}, | ||
| {"id": 15, "x": 3.0, "y": 1.9}, | ||
| {"id": 16, "x": 3.0, "y": 2.0}, | ||
| {"id": 17, "x": 3.0, "y": 2.1}, | ||
| {"id": 18, "x": 3.0, "y": 2.2}, | ||
| ] | ||
| ).select(ST_MakePoint("x", "y").alias("geometry"), "id") | ||
|
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| # make sure no id occurs more than once | ||
| output_df = dbscan(input_df, 1.0, 4) | ||
|
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| assert output_df.count() == 9 | ||
| assert output_df.select("cluster").distinct().count() == 2 | ||
|
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| def test_return_outliers_false_doesnt_return_outliers(self): | ||
| df = self.create_sample_dataframe() | ||
| for epsilon in [0.6, 0.7, 0.8]: | ||
| for min_pts in [3, 4, 5]: | ||
| assert self.get_expected_result( | ||
| self.get_data(), epsilon, min_pts, include_outliers=False | ||
| ) == self.get_actual_results( | ||
| df, epsilon, min_pts, include_outliers=False | ||
| ) |
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