spark-instrumented-optimizer/python/pyspark/ml/tests/test_evaluation.py
zero323 31a16fbb40 [SPARK-32714][PYTHON] Initial pyspark-stubs port
### What changes were proposed in this pull request?

This PR proposes migration of [`pyspark-stubs`](https://github.com/zero323/pyspark-stubs) into Spark codebase.

### Why are the changes needed?

### Does this PR introduce _any_ user-facing change?

Yes. This PR adds type annotations directly to Spark source.

This can impact interaction with development tools for users, which haven't used `pyspark-stubs`.

### How was this patch tested?

- [x] MyPy tests of the PySpark source
    ```
    mypy --no-incremental --config python/mypy.ini python/pyspark
    ```
- [x] MyPy tests of Spark examples
    ```
   MYPYPATH=python/ mypy --no-incremental --config python/mypy.ini examples/src/main/python/ml examples/src/main/python/sql examples/src/main/python/sql/streaming
    ```
- [x] Existing Flake8 linter

- [x] Existing unit tests

Tested against:

- `mypy==0.790+dev.e959952d9001e9713d329a2f9b196705b028f894`
- `mypy==0.782`

Closes #29591 from zero323/SPARK-32681.

Authored-by: zero323 <mszymkiewicz@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2020-09-24 14:15:36 +09:00

64 lines
2.7 KiB
Python

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import unittest
import numpy as np
from pyspark.ml.evaluation import ClusteringEvaluator, RegressionEvaluator
from pyspark.ml.linalg import Vectors
from pyspark.sql import Row
from pyspark.testing.mlutils import SparkSessionTestCase
class EvaluatorTests(SparkSessionTestCase):
def test_java_params(self):
"""
This tests a bug fixed by SPARK-18274 which causes multiple copies
of a Params instance in Python to be linked to the same Java instance.
"""
evaluator = RegressionEvaluator(metricName="r2")
df = self.spark.createDataFrame([Row(label=1.0, prediction=1.1)])
evaluator.evaluate(df)
self.assertEqual(evaluator._java_obj.getMetricName(), "r2")
evaluatorCopy = evaluator.copy({evaluator.metricName: "mae"})
evaluator.evaluate(df)
evaluatorCopy.evaluate(df)
self.assertEqual(evaluator._java_obj.getMetricName(), "r2")
self.assertEqual(evaluatorCopy._java_obj.getMetricName(), "mae")
def test_clustering_evaluator_with_cosine_distance(self):
featureAndPredictions = map(lambda x: (Vectors.dense(x[0]), x[1]),
[([1.0, 1.0], 1.0), ([10.0, 10.0], 1.0), ([1.0, 0.5], 2.0),
([10.0, 4.4], 2.0), ([-1.0, 1.0], 3.0), ([-100.0, 90.0], 3.0)])
dataset = self.spark.createDataFrame(featureAndPredictions, ["features", "prediction"])
evaluator = ClusteringEvaluator(predictionCol="prediction", distanceMeasure="cosine")
self.assertEqual(evaluator.getDistanceMeasure(), "cosine")
self.assertTrue(np.isclose(evaluator.evaluate(dataset), 0.992671213, atol=1e-5))
if __name__ == "__main__":
from pyspark.ml.tests.test_evaluation import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)