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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 *

    try:
        import xmlrunner
        testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
    except ImportError:
        testRunner = None
    unittest.main(testRunner=testRunner, verbosity=2)