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  from shutil import rmtree
  import tempfile
  import unittest
  
  import numpy as np
  
  from pyspark.ml.classification import DecisionTreeClassifier, LogisticRegression, \
      MultilayerPerceptronClassifier, OneVsRest
  from pyspark.ml.clustering import DistributedLDAModel, KMeans, LocalLDAModel, LDA, LDAModel
  from pyspark.ml.fpm import FPGrowth
  from pyspark.ml.linalg import Matrices, Vectors
  from pyspark.ml.recommendation import ALS
  from pyspark.ml.regression import GeneralizedLinearRegression, LinearRegression
  from pyspark.sql import Row
  from pyspark.testing.mlutils import SparkSessionTestCase
  
  
  class LogisticRegressionTest(SparkSessionTestCase):
  
      def test_binomial_logistic_regression_with_bound(self):
  
          df = self.spark.createDataFrame(
              [(1.0, 1.0, Vectors.dense(0.0, 5.0)),
               (0.0, 2.0, Vectors.dense(1.0, 2.0)),
               (1.0, 3.0, Vectors.dense(2.0, 1.0)),
               (0.0, 4.0, Vectors.dense(3.0, 3.0)), ], ["label", "weight", "features"])
  
          lor = LogisticRegression(regParam=0.01, weightCol="weight",
                                   lowerBoundsOnCoefficients=Matrices.dense(1, 2, [-1.0, -1.0]),
                                   upperBoundsOnIntercepts=Vectors.dense(0.0))
          model = lor.fit(df)
          self.assertTrue(
              np.allclose(model.coefficients.toArray(), [-0.2944, -0.0484], atol=1E-4))
          self.assertTrue(np.isclose(model.intercept, 0.0, atol=1E-4))
  
      def test_multinomial_logistic_regression_with_bound(self):
  
          data_path = "data/mllib/sample_multiclass_classification_data.txt"
          df = self.spark.read.format("libsvm").load(data_path)
  
          lor = LogisticRegression(regParam=0.01,
                                   lowerBoundsOnCoefficients=Matrices.dense(3, 4, range(12)),
                                   upperBoundsOnIntercepts=Vectors.dense(0.0, 0.0, 0.0))
          model = lor.fit(df)
          expected = [[4.593, 4.5516, 9.0099, 12.2904],
                      [1.0, 8.1093, 7.0, 10.0],
                      [3.041, 5.0, 8.0, 11.0]]
          for i in range(0, len(expected)):
              self.assertTrue(
                  np.allclose(model.coefficientMatrix.toArray()[i], expected[i], atol=1E-4))
          self.assertTrue(
              np.allclose(model.interceptVector.toArray(), [-0.9057, -1.1392, -0.0033], atol=1E-4))
  
  
  class MultilayerPerceptronClassifierTest(SparkSessionTestCase):
  
      def test_raw_and_probability_prediction(self):
  
          data_path = "data/mllib/sample_multiclass_classification_data.txt"
          df = self.spark.read.format("libsvm").load(data_path)
  
          mlp = MultilayerPerceptronClassifier(maxIter=100, layers=[4, 5, 4, 3],
                                               blockSize=128, seed=123)
          model = mlp.fit(df)
          test = self.sc.parallelize([Row(features=Vectors.dense(0.1, 0.1, 0.25, 0.25))]).toDF()
          result = model.transform(test).head()
          expected_prediction = 2.0
          expected_probability = [0.0, 0.0, 1.0]
          expected_rawPrediction = [-11.6081922998, -8.15827998691, 22.17757045]
          self.assertTrue(result.prediction, expected_prediction)
          self.assertTrue(np.allclose(result.probability, expected_probability, atol=1E-4))
          self.assertTrue(np.allclose(result.rawPrediction, expected_rawPrediction, atol=1E-4))
  
  
  class OneVsRestTests(SparkSessionTestCase):
  
      def test_copy(self):
          df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
                                           (1.0, Vectors.sparse(2, [], [])),
                                           (2.0, Vectors.dense(0.5, 0.5))],
                                          ["label", "features"])
          lr = LogisticRegression(maxIter=5, regParam=0.01)
          ovr = OneVsRest(classifier=lr)
          ovr1 = ovr.copy({lr.maxIter: 10})
          self.assertEqual(ovr.getClassifier().getMaxIter(), 5)
          self.assertEqual(ovr1.getClassifier().getMaxIter(), 10)
          model = ovr.fit(df)
          model1 = model.copy({model.predictionCol: "indexed"})
          self.assertEqual(model1.getPredictionCol(), "indexed")
  
      def test_output_columns(self):
          df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
                                           (1.0, Vectors.sparse(2, [], [])),
                                           (2.0, Vectors.dense(0.5, 0.5))],
                                          ["label", "features"])
          lr = LogisticRegression(maxIter=5, regParam=0.01)
          ovr = OneVsRest(classifier=lr, parallelism=1)
          model = ovr.fit(df)
          output = model.transform(df)
          self.assertEqual(output.columns, ["label", "features", "rawPrediction", "prediction"])
  
      def test_parallelism_doesnt_change_output(self):
          df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8)),
                                           (1.0, Vectors.sparse(2, [], [])),
                                           (2.0, Vectors.dense(0.5, 0.5))],
                                          ["label", "features"])
          ovrPar1 = OneVsRest(classifier=LogisticRegression(maxIter=5, regParam=.01), parallelism=1)
          modelPar1 = ovrPar1.fit(df)
          ovrPar2 = OneVsRest(classifier=LogisticRegression(maxIter=5, regParam=.01), parallelism=2)
          modelPar2 = ovrPar2.fit(df)
          for i, model in enumerate(modelPar1.models):
              self.assertTrue(np.allclose(model.coefficients.toArray(),
                                          modelPar2.models[i].coefficients.toArray(), atol=1E-4))
              self.assertTrue(np.allclose(model.intercept, modelPar2.models[i].intercept, atol=1E-4))
  
      def test_support_for_weightCol(self):
          df = self.spark.createDataFrame([(0.0, Vectors.dense(1.0, 0.8), 1.0),
                                           (1.0, Vectors.sparse(2, [], []), 1.0),
                                           (2.0, Vectors.dense(0.5, 0.5), 1.0)],
                                          ["label", "features", "weight"])
          # classifier inherits hasWeightCol
          lr = LogisticRegression(maxIter=5, regParam=0.01)
          ovr = OneVsRest(classifier=lr, weightCol="weight")
          self.assertIsNotNone(ovr.fit(df))
          # classifier doesn't inherit hasWeightCol
          dt = DecisionTreeClassifier()
          ovr2 = OneVsRest(classifier=dt, weightCol="weight")
          self.assertIsNotNone(ovr2.fit(df))
  
  
  class KMeansTests(SparkSessionTestCase):
  
      def test_kmeans_cosine_distance(self):
          data = [(Vectors.dense([1.0, 1.0]),), (Vectors.dense([10.0, 10.0]),),
                  (Vectors.dense([1.0, 0.5]),), (Vectors.dense([10.0, 4.4]),),
                  (Vectors.dense([-1.0, 1.0]),), (Vectors.dense([-100.0, 90.0]),)]
          df = self.spark.createDataFrame(data, ["features"])
          kmeans = KMeans(k=3, seed=1, distanceMeasure="cosine")
          model = kmeans.fit(df)
          result = model.transform(df).collect()
          self.assertTrue(result[0].prediction == result[1].prediction)
          self.assertTrue(result[2].prediction == result[3].prediction)
          self.assertTrue(result[4].prediction == result[5].prediction)
  
  
  class LDATest(SparkSessionTestCase):
  
      def _compare(self, m1, m2):
          """
          Temp method for comparing instances.
          TODO: Replace with generic implementation once SPARK-14706 is merged.
          """
          self.assertEqual(m1.uid, m2.uid)
          self.assertEqual(type(m1), type(m2))
          self.assertEqual(len(m1.params), len(m2.params))
          for p in m1.params:
              if m1.isDefined(p):
                  self.assertEqual(m1.getOrDefault(p), m2.getOrDefault(p))
                  self.assertEqual(p.parent, m2.getParam(p.name).parent)
          if isinstance(m1, LDAModel):
              self.assertEqual(m1.vocabSize(), m2.vocabSize())
              self.assertEqual(m1.topicsMatrix(), m2.topicsMatrix())
  
      def test_persistence(self):
          # Test save/load for LDA, LocalLDAModel, DistributedLDAModel.
          df = self.spark.createDataFrame([
              [1, Vectors.dense([0.0, 1.0])],
              [2, Vectors.sparse(2, {0: 1.0})],
          ], ["id", "features"])
          # Fit model
          lda = LDA(k=2, seed=1, optimizer="em")
          distributedModel = lda.fit(df)
          self.assertTrue(distributedModel.isDistributed())
          localModel = distributedModel.toLocal()
          self.assertFalse(localModel.isDistributed())
          # Define paths
          path = tempfile.mkdtemp()
          lda_path = path + "/lda"
          dist_model_path = path + "/distLDAModel"
          local_model_path = path + "/localLDAModel"
          # Test LDA
          lda.save(lda_path)
          lda2 = LDA.load(lda_path)
          self._compare(lda, lda2)
          # Test DistributedLDAModel
          distributedModel.save(dist_model_path)
          distributedModel2 = DistributedLDAModel.load(dist_model_path)
          self._compare(distributedModel, distributedModel2)
          # Test LocalLDAModel
          localModel.save(local_model_path)
          localModel2 = LocalLDAModel.load(local_model_path)
          self._compare(localModel, localModel2)
          # Clean up
          try:
              rmtree(path)
          except OSError:
              pass
  
  
  class FPGrowthTests(SparkSessionTestCase):
      def setUp(self):
          super(FPGrowthTests, self).setUp()
          self.data = self.spark.createDataFrame(
              [([1, 2], ), ([1, 2], ), ([1, 2, 3], ), ([1, 3], )],
              ["items"])
  
      def test_association_rules(self):
          fp = FPGrowth()
          fpm = fp.fit(self.data)
  
          expected_association_rules = self.spark.createDataFrame(
              [([3], [1], 1.0, 1.0), ([2], [1], 1.0, 1.0)],
              ["antecedent", "consequent", "confidence", "lift"]
          )
          actual_association_rules = fpm.associationRules
  
          self.assertEqual(actual_association_rules.subtract(expected_association_rules).count(), 0)
          self.assertEqual(expected_association_rules.subtract(actual_association_rules).count(), 0)
  
      def test_freq_itemsets(self):
          fp = FPGrowth()
          fpm = fp.fit(self.data)
  
          expected_freq_itemsets = self.spark.createDataFrame(
              [([1], 4), ([2], 3), ([2, 1], 3), ([3], 2), ([3, 1], 2)],
              ["items", "freq"]
          )
          actual_freq_itemsets = fpm.freqItemsets
  
          self.assertEqual(actual_freq_itemsets.subtract(expected_freq_itemsets).count(), 0)
          self.assertEqual(expected_freq_itemsets.subtract(actual_freq_itemsets).count(), 0)
  
      def tearDown(self):
          del self.data
  
  
  class ALSTest(SparkSessionTestCase):
  
      def test_storage_levels(self):
          df = self.spark.createDataFrame(
              [(0, 0, 4.0), (0, 1, 2.0), (1, 1, 3.0), (1, 2, 4.0), (2, 1, 1.0), (2, 2, 5.0)],
              ["user", "item", "rating"])
          als = ALS().setMaxIter(1).setRank(1)
          # test default params
          als.fit(df)
          self.assertEqual(als.getIntermediateStorageLevel(), "MEMORY_AND_DISK")
          self.assertEqual(als._java_obj.getIntermediateStorageLevel(), "MEMORY_AND_DISK")
          self.assertEqual(als.getFinalStorageLevel(), "MEMORY_AND_DISK")
          self.assertEqual(als._java_obj.getFinalStorageLevel(), "MEMORY_AND_DISK")
          # test non-default params
          als.setIntermediateStorageLevel("MEMORY_ONLY_2")
          als.setFinalStorageLevel("DISK_ONLY")
          als.fit(df)
          self.assertEqual(als.getIntermediateStorageLevel(), "MEMORY_ONLY_2")
          self.assertEqual(als._java_obj.getIntermediateStorageLevel(), "MEMORY_ONLY_2")
          self.assertEqual(als.getFinalStorageLevel(), "DISK_ONLY")
          self.assertEqual(als._java_obj.getFinalStorageLevel(), "DISK_ONLY")
  
  
  class GeneralizedLinearRegressionTest(SparkSessionTestCase):
  
      def test_tweedie_distribution(self):
  
          df = self.spark.createDataFrame(
              [(1.0, Vectors.dense(0.0, 0.0)),
               (1.0, Vectors.dense(1.0, 2.0)),
               (2.0, Vectors.dense(0.0, 0.0)),
               (2.0, Vectors.dense(1.0, 1.0)), ], ["label", "features"])
  
          glr = GeneralizedLinearRegression(family="tweedie", variancePower=1.6)
          model = glr.fit(df)
          self.assertTrue(np.allclose(model.coefficients.toArray(), [-0.4645, 0.3402], atol=1E-4))
          self.assertTrue(np.isclose(model.intercept, 0.7841, atol=1E-4))
  
          model2 = glr.setLinkPower(-1.0).fit(df)
          self.assertTrue(np.allclose(model2.coefficients.toArray(), [-0.6667, 0.5], atol=1E-4))
          self.assertTrue(np.isclose(model2.intercept, 0.6667, atol=1E-4))
  
      def test_offset(self):
  
          df = self.spark.createDataFrame(
              [(0.2, 1.0, 2.0, Vectors.dense(0.0, 5.0)),
               (0.5, 2.1, 0.5, Vectors.dense(1.0, 2.0)),
               (0.9, 0.4, 1.0, Vectors.dense(2.0, 1.0)),
               (0.7, 0.7, 0.0, Vectors.dense(3.0, 3.0))], ["label", "weight", "offset", "features"])
  
          glr = GeneralizedLinearRegression(family="poisson", weightCol="weight", offsetCol="offset")
          model = glr.fit(df)
          self.assertTrue(np.allclose(model.coefficients.toArray(), [0.664647, -0.3192581],
                                      atol=1E-4))
          self.assertTrue(np.isclose(model.intercept, -1.561613, atol=1E-4))
  
  
  class LinearRegressionTest(SparkSessionTestCase):
  
      def test_linear_regression_with_huber_loss(self):
  
          data_path = "data/mllib/sample_linear_regression_data.txt"
          df = self.spark.read.format("libsvm").load(data_path)
  
          lir = LinearRegression(loss="huber", epsilon=2.0)
          model = lir.fit(df)
  
          expectedCoefficients = [0.136, 0.7648, -0.7761, 2.4236, 0.537,
                                  1.2612, -0.333, -0.5694, -0.6311, 0.6053]
          expectedIntercept = 0.1607
          expectedScale = 9.758
  
          self.assertTrue(
              np.allclose(model.coefficients.toArray(), expectedCoefficients, atol=1E-3))
          self.assertTrue(np.isclose(model.intercept, expectedIntercept, atol=1E-3))
          self.assertTrue(np.isclose(model.scale, expectedScale, atol=1E-3))
  
  
  if __name__ == "__main__":
      from pyspark.ml.tests.test_algorithms import *
  
      try:
          import xmlrunner
          testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
      except ImportError:
          testRunner = None
      unittest.main(testRunner=testRunner, verbosity=2)
  