diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/mllib/linalg.py | 73 | ||||
| -rw-r--r-- | python/pyspark/mllib/tests.py | 6 |
2 files changed, 53 insertions, 26 deletions
diff --git a/python/pyspark/mllib/linalg.py b/python/pyspark/mllib/linalg.py index 537b176578..f7aa2b0cb0 100644 --- a/python/pyspark/mllib/linalg.py +++ b/python/pyspark/mllib/linalg.py @@ -30,7 +30,7 @@ import copy_reg import numpy as np from pyspark.sql import UserDefinedType, StructField, StructType, ArrayType, DoubleType, \ - IntegerType, ByteType, Row + IntegerType, ByteType __all__ = ['Vector', 'DenseVector', 'SparseVector', 'Vectors', 'DenseMatrix', 'Matrices'] @@ -173,12 +173,16 @@ class DenseVector(Vector): A dense vector represented by a value array. """ def __init__(self, ar): - if not isinstance(ar, array.array): - ar = array.array('d', ar) + if isinstance(ar, basestring): + ar = np.frombuffer(ar, dtype=np.float64) + elif not isinstance(ar, np.ndarray): + ar = np.array(ar, dtype=np.float64) + if ar.dtype != np.float64: + ar.astype(np.float64) self.array = ar def __reduce__(self): - return DenseVector, (self.array,) + return DenseVector, (self.array.tostring(),) def dot(self, other): """ @@ -207,9 +211,10 @@ class DenseVector(Vector): ... AssertionError: dimension mismatch """ - if type(other) == np.ndarray and other.ndim > 1: - assert len(self) == other.shape[0], "dimension mismatch" - return np.dot(self.toArray(), other) + if type(other) == np.ndarray: + if other.ndim > 1: + assert len(self) == other.shape[0], "dimension mismatch" + return np.dot(self.array, other) elif _have_scipy and scipy.sparse.issparse(other): assert len(self) == other.shape[0], "dimension mismatch" return other.transpose().dot(self.toArray()) @@ -261,7 +266,7 @@ class DenseVector(Vector): return np.dot(diff, diff) def toArray(self): - return np.array(self.array) + return self.array def __getitem__(self, item): return self.array[item] @@ -276,7 +281,7 @@ class DenseVector(Vector): return "DenseVector([%s])" % (', '.join(_format_float(i) for i in self.array)) def __eq__(self, other): - return isinstance(other, DenseVector) and self.array == other.array + return isinstance(other, DenseVector) and np.array_equal(self.array, other.array) def __ne__(self, other): return not self == other @@ -314,18 +319,28 @@ class SparseVector(Vector): if type(pairs) == dict: pairs = pairs.items() pairs = sorted(pairs) - self.indices = array.array('i', [p[0] for p in pairs]) - self.values = array.array('d', [p[1] for p in pairs]) + self.indices = np.array([p[0] for p in pairs], dtype=np.int32) + self.values = np.array([p[1] for p in pairs], dtype=np.float64) else: - assert len(args[0]) == len(args[1]), "index and value arrays not same length" - self.indices = array.array('i', args[0]) - self.values = array.array('d', args[1]) + if isinstance(args[0], basestring): + assert isinstance(args[1], str), "values should be string too" + if args[0]: + self.indices = np.frombuffer(args[0], np.int32) + self.values = np.frombuffer(args[1], np.float64) + else: + # np.frombuffer() doesn't work well with empty string in older version + self.indices = np.array([], dtype=np.int32) + self.values = np.array([], dtype=np.float64) + else: + self.indices = np.array(args[0], dtype=np.int32) + self.values = np.array(args[1], dtype=np.float64) + assert len(self.indices) == len(self.values), "index and value arrays not same length" for i in xrange(len(self.indices) - 1): if self.indices[i] >= self.indices[i + 1]: raise TypeError("indices array must be sorted") def __reduce__(self): - return (SparseVector, (self.size, self.indices, self.values)) + return (SparseVector, (self.size, self.indices.tostring(), self.values.tostring())) def dot(self, other): """ @@ -461,8 +476,7 @@ class SparseVector(Vector): Returns a copy of this SparseVector as a 1-dimensional NumPy array. """ arr = np.zeros((self.size,), dtype=np.float64) - for i in xrange(len(self.indices)): - arr[self.indices[i]] = self.values[i] + arr[self.indices] = self.values return arr def __len__(self): @@ -493,8 +507,8 @@ class SparseVector(Vector): """ return (isinstance(other, self.__class__) and other.size == self.size - and other.indices == self.indices - and other.values == self.values) + and np.array_equal(other.indices, self.indices) + and np.array_equal(other.values, self.values)) def __ne__(self, other): return not self.__eq__(other) @@ -577,25 +591,34 @@ class DenseMatrix(Matrix): """ def __init__(self, numRows, numCols, values): Matrix.__init__(self, numRows, numCols) + if isinstance(values, basestring): + values = np.frombuffer(values, dtype=np.float64) + elif not isinstance(values, np.ndarray): + values = np.array(values, dtype=np.float64) assert len(values) == numRows * numCols - if not isinstance(values, array.array): - values = array.array('d', values) + if values.dtype != np.float64: + values.astype(np.float64) self.values = values def __reduce__(self): - return DenseMatrix, (self.numRows, self.numCols, self.values) + return DenseMatrix, (self.numRows, self.numCols, self.values.tostring()) def toArray(self): """ Return an numpy.ndarray - >>> arr = array.array('d', [float(i) for i in range(4)]) - >>> m = DenseMatrix(2, 2, arr) + >>> m = DenseMatrix(2, 2, range(4)) >>> m.toArray() array([[ 0., 2.], [ 1., 3.]]) """ - return np.reshape(self.values, (self.numRows, self.numCols), order='F') + return self.values.reshape((self.numRows, self.numCols), order='F') + + def __eq__(self, other): + return (isinstance(other, DenseMatrix) and + self.numRows == other.numRows and + self.numCols == other.numCols and + all(self.values == other.values)) class Matrices(object): diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 9fa4d6f6a2..8332f8e061 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -33,7 +33,8 @@ if sys.version_info[:2] <= (2, 6): else: import unittest -from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector +from pyspark.mllib.linalg import Vector, SparseVector, DenseVector, VectorUDT, _convert_to_vector,\ + DenseMatrix from pyspark.mllib.regression import LabeledPoint from pyspark.mllib.random import RandomRDDs from pyspark.mllib.stat import Statistics @@ -62,6 +63,7 @@ def _squared_distance(a, b): class VectorTests(PySparkTestCase): def _test_serialize(self, v): + self.assertEqual(v, ser.loads(ser.dumps(v))) jvec = self.sc._jvm.SerDe.loads(bytearray(ser.dumps(v))) nv = ser.loads(str(self.sc._jvm.SerDe.dumps(jvec))) self.assertEqual(v, nv) @@ -75,6 +77,8 @@ class VectorTests(PySparkTestCase): self._test_serialize(DenseVector(array([1., 2., 3., 4.]))) self._test_serialize(DenseVector(pyarray.array('d', range(10)))) self._test_serialize(SparseVector(4, {1: 1, 3: 2})) + self._test_serialize(SparseVector(3, {})) + self._test_serialize(DenseMatrix(2, 3, range(6))) def test_dot(self): sv = SparseVector(4, {1: 1, 3: 2}) |