diff options
Diffstat (limited to 'python/pyspark/ml/tests.py')
| -rw-r--r-- | python/pyspark/ml/tests.py | 105 |
1 files changed, 73 insertions, 32 deletions
diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py index ba6478dcd5..10fe0ef8db 100644 --- a/python/pyspark/ml/tests.py +++ b/python/pyspark/ml/tests.py @@ -31,10 +31,12 @@ else: import unittest from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase -from pyspark.sql import DataFrame -from pyspark.ml.param import Param +from pyspark.sql import DataFrame, SQLContext +from pyspark.ml.param import Param, Params from pyspark.ml.param.shared import HasMaxIter, HasInputCol -from pyspark.ml.pipeline import Estimator, Model, Pipeline, Transformer +from pyspark.ml import Estimator, Model, Pipeline, Transformer +from pyspark.ml.feature import * +from pyspark.mllib.linalg import DenseVector class MockDataset(DataFrame): @@ -43,44 +45,43 @@ class MockDataset(DataFrame): self.index = 0 -class MockTransformer(Transformer): +class HasFake(Params): + + def __init__(self): + super(HasFake, self).__init__() + self.fake = Param(self, "fake", "fake param") + + def getFake(self): + return self.getOrDefault(self.fake) + + +class MockTransformer(Transformer, HasFake): def __init__(self): super(MockTransformer, self).__init__() - self.fake = Param(self, "fake", "fake") self.dataset_index = None - self.fake_param_value = None - def transform(self, dataset, params={}): + def _transform(self, dataset): self.dataset_index = dataset.index - if self.fake in params: - self.fake_param_value = params[self.fake] dataset.index += 1 return dataset -class MockEstimator(Estimator): +class MockEstimator(Estimator, HasFake): def __init__(self): super(MockEstimator, self).__init__() - self.fake = Param(self, "fake", "fake") self.dataset_index = None - self.fake_param_value = None - self.model = None - def fit(self, dataset, params={}): + def _fit(self, dataset): self.dataset_index = dataset.index - if self.fake in params: - self.fake_param_value = params[self.fake] model = MockModel() - self.model = model + self._copyValues(model) return model -class MockModel(MockTransformer, Model): - - def __init__(self): - super(MockModel, self).__init__() +class MockModel(MockTransformer, Model, HasFake): + pass class PipelineTests(PySparkTestCase): @@ -91,19 +92,17 @@ class PipelineTests(PySparkTestCase): transformer1 = MockTransformer() estimator2 = MockEstimator() transformer3 = MockTransformer() - pipeline = Pipeline() \ - .setStages([estimator0, transformer1, estimator2, transformer3]) + pipeline = Pipeline(stages=[estimator0, transformer1, estimator2, transformer3]) pipeline_model = pipeline.fit(dataset, {estimator0.fake: 0, transformer1.fake: 1}) - self.assertEqual(0, estimator0.dataset_index) - self.assertEqual(0, estimator0.fake_param_value) - model0 = estimator0.model + model0, transformer1, model2, transformer3 = pipeline_model.stages self.assertEqual(0, model0.dataset_index) + self.assertEqual(0, model0.getFake()) self.assertEqual(1, transformer1.dataset_index) - self.assertEqual(1, transformer1.fake_param_value) - self.assertEqual(2, estimator2.dataset_index) - model2 = estimator2.model - self.assertIsNone(model2.dataset_index, "The model produced by the last estimator should " - "not be called during fit.") + self.assertEqual(1, transformer1.getFake()) + self.assertEqual(2, dataset.index) + self.assertIsNone(model2.dataset_index, "The last model shouldn't be called in fit.") + self.assertIsNone(transformer3.dataset_index, + "The last transformer shouldn't be called in fit.") dataset = pipeline_model.transform(dataset) self.assertEqual(2, model0.dataset_index) self.assertEqual(3, transformer1.dataset_index) @@ -129,7 +128,7 @@ class ParamTests(PySparkTestCase): maxIter = testParams.maxIter self.assertEqual(maxIter.name, "maxIter") self.assertEqual(maxIter.doc, "max number of iterations (>= 0)") - self.assertTrue(maxIter.parent is testParams) + self.assertTrue(maxIter.parent == testParams.uid) def test_params(self): testParams = TestParams() @@ -139,6 +138,7 @@ class ParamTests(PySparkTestCase): params = testParams.params self.assertEqual(params, [inputCol, maxIter]) + self.assertTrue(testParams.hasParam(maxIter)) self.assertTrue(testParams.hasDefault(maxIter)) self.assertFalse(testParams.isSet(maxIter)) self.assertTrue(testParams.isDefined(maxIter)) @@ -147,6 +147,7 @@ class ParamTests(PySparkTestCase): self.assertTrue(testParams.isSet(maxIter)) self.assertEquals(testParams.getMaxIter(), 100) + self.assertTrue(testParams.hasParam(inputCol)) self.assertFalse(testParams.hasDefault(inputCol)) self.assertFalse(testParams.isSet(inputCol)) self.assertFalse(testParams.isDefined(inputCol)) @@ -159,5 +160,45 @@ class ParamTests(PySparkTestCase): "maxIter: max number of iterations (>= 0) (default: 10, current: 100)"])) +class FeatureTests(PySparkTestCase): + + def test_binarizer(self): + b0 = Binarizer() + self.assertListEqual(b0.params, [b0.inputCol, b0.outputCol, b0.threshold]) + self.assertTrue(all([~b0.isSet(p) for p in b0.params])) + self.assertTrue(b0.hasDefault(b0.threshold)) + self.assertEqual(b0.getThreshold(), 0.0) + b0.setParams(inputCol="input", outputCol="output").setThreshold(1.0) + self.assertTrue(all([b0.isSet(p) for p in b0.params])) + self.assertEqual(b0.getThreshold(), 1.0) + self.assertEqual(b0.getInputCol(), "input") + self.assertEqual(b0.getOutputCol(), "output") + + b0c = b0.copy({b0.threshold: 2.0}) + self.assertEqual(b0c.uid, b0.uid) + self.assertListEqual(b0c.params, b0.params) + self.assertEqual(b0c.getThreshold(), 2.0) + + b1 = Binarizer(threshold=2.0, inputCol="input", outputCol="output") + self.assertNotEqual(b1.uid, b0.uid) + self.assertEqual(b1.getThreshold(), 2.0) + self.assertEqual(b1.getInputCol(), "input") + self.assertEqual(b1.getOutputCol(), "output") + + def test_idf(self): + sqlContext = SQLContext(self.sc) + dataset = sqlContext.createDataFrame([ + (DenseVector([1.0, 2.0]),), + (DenseVector([0.0, 1.0]),), + (DenseVector([3.0, 0.2]),)], ["tf"]) + idf0 = IDF(inputCol="tf") + self.assertListEqual(idf0.params, [idf0.inputCol, idf0.minDocFreq, idf0.outputCol]) + idf0m = idf0.fit(dataset, {idf0.outputCol: "idf"}) + self.assertEqual(idf0m.uid, idf0.uid, + "Model should inherit the UID from its parent estimator.") + output = idf0m.transform(dataset) + self.assertIsNotNone(output.head().idf) + + if __name__ == "__main__": unittest.main() |