def test_group_by_key(self): def gen_data(N, step): for i in range(1, N + 1, step): for j in range(i): yield (i, [j]) def gen_gs(N, step=1): return shuffle.GroupByKey(gen_data(N, step)) self.assertEqual(1, len(list(gen_gs(1)))) self.assertEqual(2, len(list(gen_gs(2)))) self.assertEqual(100, len(list(gen_gs(100)))) self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)]) self.assertTrue( all(list(range(k)) == list(vs) for k, vs in gen_gs(100))) for k, vs in gen_gs(50002, 10000): self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs)) ser = PickleSerializer() l = ser.loads(ser.dumps(list(gen_gs(50002, 30000)))) for k, vs in l: self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs))
def test_group_by_key(self): def gen_data(N, step): for i in range(1, N + 1, step): for j in range(i): yield (i, [j]) def gen_gs(N, step=1): return shuffle.GroupByKey(gen_data(N, step)) self.assertEqual(1, len(list(gen_gs(1)))) self.assertEqual(2, len(list(gen_gs(2)))) self.assertEqual(100, len(list(gen_gs(100)))) self.assertEqual(list(range(1, 101)), [k for k, _ in gen_gs(100)]) self.assertTrue(all(list(range(k)) == list(vs) for k, vs in gen_gs(100))) for k, vs in gen_gs(50002, 10000): self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs)) ser = PickleSerializer() l = ser.loads(ser.dumps(list(gen_gs(50002, 30000)))) for k, vs in l: self.assertEqual(k, len(vs)) self.assertEqual(list(range(k)), list(vs))
def corr(x, y=None, method=None): """ Compute the correlation (matrix) for the input RDD(s) using the specified method. Methods currently supported: I{pearson (default), spearman}. If a single RDD of Vectors is passed in, a correlation matrix comparing the columns in the input RDD is returned. Use C{method=} to specify the method to be used for single RDD inout. If two RDDs of floats are passed in, a single float is returned. >>> x = sc.parallelize([1.0, 0.0, -2.0], 2) >>> y = sc.parallelize([4.0, 5.0, 3.0], 2) >>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2) >>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7 True >>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson") True >>> Statistics.corr(x, y, "spearman") 0.5 >>> from math import isnan >>> isnan(Statistics.corr(x, zeros)) True >>> from pyspark.mllib.linalg import Vectors >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]), ... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])]) >>> pearsonCorr = Statistics.corr(rdd) >>> print str(pearsonCorr).replace('nan', 'NaN') [[ 1. 0.05564149 NaN 0.40047142] [ 0.05564149 1. NaN 0.91359586] [ NaN NaN 1. NaN] [ 0.40047142 0.91359586 NaN 1. ]] >>> spearmanCorr = Statistics.corr(rdd, method="spearman") >>> print str(spearmanCorr).replace('nan', 'NaN') [[ 1. 0.10540926 NaN 0.4 ] [ 0.10540926 1. NaN 0.9486833 ] [ NaN NaN 1. NaN] [ 0.4 0.9486833 NaN 1. ]] >>> try: ... Statistics.corr(rdd, "spearman") ... print "Method name as second argument without 'method=' shouldn't be allowed." ... except TypeError: ... pass """ sc = x.ctx # Check inputs to determine whether a single value or a matrix is needed for output. # Since it's legal for users to use the method name as the second argument, we need to # check if y is used to specify the method name instead. if type(y) == str: raise TypeError("Use 'method=' to specify method name.") jx = _to_java_object_rdd(x) if not y: resultMat = sc._jvm.PythonMLLibAPI().corr(jx, method) bytes = sc._jvm.SerDe.dumps(resultMat) ser = PickleSerializer() return ser.loads(str(bytes)).toArray() else: jy = _to_java_object_rdd(y) return sc._jvm.PythonMLLibAPI().corr(jx, jy, method)
def corr(x, y=None, method=None): """ Compute the correlation (matrix) for the input RDD(s) using the specified method. Methods currently supported: I{pearson (default), spearman}. If a single RDD of Vectors is passed in, a correlation matrix comparing the columns in the input RDD is returned. Use C{method=} to specify the method to be used for single RDD inout. If two RDDs of floats are passed in, a single float is returned. >>> x = sc.parallelize([1.0, 0.0, -2.0], 2) >>> y = sc.parallelize([4.0, 5.0, 3.0], 2) >>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2) >>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7 True >>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson") True >>> Statistics.corr(x, y, "spearman") 0.5 >>> from math import isnan >>> isnan(Statistics.corr(x, zeros)) True >>> from pyspark.mllib.linalg import Vectors >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]), ... Vectors.dense([6, 7, 0, 8]), Vectors.dense([9, 0, 0, 1])]) >>> pearsonCorr = Statistics.corr(rdd) >>> print str(pearsonCorr).replace('nan', 'NaN') [[ 1. 0.05564149 NaN 0.40047142] [ 0.05564149 1. NaN 0.91359586] [ NaN NaN 1. NaN] [ 0.40047142 0.91359586 NaN 1. ]] >>> spearmanCorr = Statistics.corr(rdd, method="spearman") >>> print str(spearmanCorr).replace('nan', 'NaN') [[ 1. 0.10540926 NaN 0.4 ] [ 0.10540926 1. NaN 0.9486833 ] [ NaN NaN 1. NaN] [ 0.4 0.9486833 NaN 1. ]] >>> try: ... Statistics.corr(rdd, "spearman") ... print "Method name as second argument without 'method=' shouldn't be allowed." ... except TypeError: ... pass """ sc = x.ctx # Check inputs to determine whether a single value or a matrix is needed for output. # Since it's legal for users to use the method name as the second argument, we need to # check if y is used to specify the method name instead. if type(y) == str: raise TypeError("Use 'method=' to specify method name.") jx = _to_java_object_rdd(x) if not y: resultMat = sc._jvm.PythonMLLibAPI().corr(jx, method) bytes = sc._jvm.SerDe.dumps(resultMat) ser = PickleSerializer() return ser.loads(str(bytes)).toArray() else: jy = _to_java_object_rdd(y) return sc._jvm.PythonMLLibAPI().corr(jx, jy, method)