wbo4958 commented on code in PR #49547:
URL: https://github.com/apache/spark/pull/49547#discussion_r1920289915
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python/pyspark/ml/tests/test_evaluation.py:
##########
@@ -14,18 +14,368 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
-
+import tempfile
import unittest
import numpy as np
-from pyspark.ml.evaluation import ClusteringEvaluator, RegressionEvaluator
+from pyspark.ml.evaluation import (
+ ClusteringEvaluator,
+ RegressionEvaluator,
+ BinaryClassificationEvaluator,
+ MulticlassClassificationEvaluator,
+ MultilabelClassificationEvaluator,
+ RankingEvaluator,
+)
from pyspark.ml.linalg import Vectors
-from pyspark.sql import Row
-from pyspark.testing.mlutils import SparkSessionTestCase
+from pyspark.sql import Row, SparkSession
+
+
+class EvaluatorTestsMixin:
+ def test_ranking_evaluator(self):
+ scoreAndLabels = [
+ ([1.0, 6.0, 2.0, 7.0, 8.0, 3.0, 9.0, 10.0, 4.0, 5.0], [1.0, 2.0,
3.0, 4.0, 5.0]),
+ ([4.0, 1.0, 5.0, 6.0, 2.0, 7.0, 3.0, 8.0, 9.0, 10.0], [1.0, 2.0,
3.0]),
+ ([1.0, 2.0, 3.0, 4.0, 5.0], []),
+ ]
+ dataset = self.spark.createDataFrame(scoreAndLabels, ["prediction",
"label"])
+
+ # Initialize RankingEvaluator
+ evaluator = RankingEvaluator().setPredictionCol("prediction")
+
+ # Evaluate the dataset using the default metric (mean average
precision)
+ mean_average_precision = evaluator.evaluate(dataset)
+ self.assertTrue(np.allclose(mean_average_precision, 0.3550, atol=1e-4))
+
+ # Evaluate the dataset using precisionAtK for k=2
+ precision_at_k = evaluator.evaluate(
+ dataset, {evaluator.metricName: "precisionAtK", evaluator.k: 2}
+ )
+ self.assertTrue(np.allclose(precision_at_k, 0.3333, atol=1e-4))
+
+ # read/write
+ with tempfile.TemporaryDirectory(prefix="save") as tmp_dir:
+ # Save the evaluator
+ ranke_path = tmp_dir + "/ranke"
Review Comment:
DOne
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