[SPARK-7328] [MLLIB] [PYSPARK] Pyspark.mllib.linalg.Vectors: Missing items

Add
1. Class methods squared_dist
3. parse
4. norm
5. numNonzeros
6. copy

I made a few vectorizations wrt squared_dist and dot as well. I have added support for SparseMatrix serialization in a separate PR (apache#5775) and plan to complete support for Matrices in another PR.

Author: MechCoder <[email protected]>

Closes apache#5872 from MechCoder/local_linalg_api and squashes the following commits:

a8ff1e0 [MechCoder] minor
ce3e53e [MechCoder] Add error message for parser
1bd3c04 [MechCoder] Robust parser and removed unnecessary methods
f779561 [MechCoder] [SPARK-7328] Pyspark.mllib.linalg.Vectors: Missing items

Author: MechCoder

python/pyspark/mllib/linalg.py

@@ -208,9 +208,46 @@ def __init__(self, ar):
             ar = ar.astype(np.float64)
         self.array = ar
 
+    @staticmethod
+    def parse(s):
+        """
+        Parse string representation back into the DenseVector.
+
+        >>> DenseVector.parse(' [ 0.0,1.0,2.0,  3.0]')
+        DenseVector([0.0, 1.0, 2.0, 3.0])
+        """
+        start = s.find('[')
+        if start == -1:
+            raise ValueError("Array should start with '['.")
+        end = s.find(']')
+        if end == -1:
+            raise ValueError("Array should end with ']'.")
+        s = s[start + 1: end]
+
+        try:
+            values = [float(val) for val in s.split(',')]
+        except ValueError:
+            raise ValueError("Unable to parse values from %s" % s)
+        return DenseVector(values)
+
     def __reduce__(self):
         return DenseVector, (self.array.tostring(),)
 
+    def numNonzeros(self):
+        return np.count_nonzero(self.array)
+
+    def norm(self, p):
+        """
+        Calculte the norm of a DenseVector.
+
+        >>> a = DenseVector([0, -1, 2, -3])
+        >>> a.norm(2)
+        3.7...
+        >>> a.norm(1)
+        6.0
+        """
+        return np.linalg.norm(self.array, p)
+
     def dot(self, other):
         """
         Compute the dot product of two Vectors. We support
@@ -387,8 +424,74 @@ def __init__(self, size, *args):
                 if self.indices[i] >= self.indices[i + 1]:
                     raise TypeError("indices array must be sorted")
 
+    def numNonzeros(self):
+        return np.count_nonzero(self.values)
+
+    def norm(self, p):
+        """
+        Calculte the norm of a SparseVector.
+
+        >>> a = SparseVector(4, [0, 1], [3., -4.])
+        >>> a.norm(1)
+        7.0
+        >>> a.norm(2)
+        5.0
+        """
+        return np.linalg.norm(self.values, p)
+
     def __reduce__(self):
-        return (SparseVector, (self.size, self.indices.tostring(), self.values.tostring()))
+        return (
+            SparseVector,
+            (self.size, self.indices.tostring(), self.values.tostring()))
+
+    @staticmethod
+    def parse(s):
+        """
+        Parse string representation back into the DenseVector.
+
+        >>> SparseVector.parse(' (4, [0,1 ],[ 4.0,5.0] )')
+        SparseVector(4, {0: 4.0, 1: 5.0})
+        """
+        start = s.find('(')
+        if start == -1:
+            raise ValueError("Tuple should start with '('")
+        end = s.find(')')
+        if start == -1:
+            raise ValueError("Tuple should end with ')'")
+        s = s[start + 1: end].strip()
+
+        size = s[: s.find(',')]
+        try:
+            size = int(size)
+        except ValueError:
+            raise ValueError("Cannot parse size %s." % size)
+
+        ind_start = s.find('[')
+        if ind_start == -1:
+            raise ValueError("Indices array should start with '['.")
+        ind_end = s.find(']')
+        if ind_end == -1:
+            raise ValueError("Indices array should end with ']'")
+        new_s = s[ind_start + 1: ind_end]
+        ind_list = new_s.split(',')
+        try:
+            indices = [int(ind) for ind in ind_list]
+        except ValueError:
+            raise ValueError("Unable to parse indices from %s." % new_s)
+        s = s[ind_end + 1:].strip()
+
+        val_start = s.find('[')
+        if val_start == -1:
+            raise ValueError("Values array should start with '['.")
+        val_end = s.find(']')
+        if val_end == -1:
+            raise ValueError("Values array should end with ']'.")
+        val_list = s[val_start + 1: val_end].split(',')
+        try:
+            values = [float(val) for val in val_list]
+        except ValueError:
+            raise ValueError("Unable to parse values from %s." % s)
+        return SparseVector(size, indices, values)
 
     def dot(self, other):
         """
@@ -633,6 +736,49 @@ def stringify(vector):
         """
         return str(vector)
 
+    @staticmethod
+    def squared_distance(v1, v2):
+        """
+        Squared distance between two vectors.
+        a and b can be of type SparseVector, DenseVector, np.ndarray
+        or array.array.
+
+        >>> a = Vectors.sparse(4, [(0, 1), (3, 4)])
+        >>> b = Vectors.dense([2, 5, 4, 1])
+        >>> a.squared_distance(b)
+        51.0
+        """
+        v1, v2 = _convert_to_vector(v1), _convert_to_vector(v2)
+        return v1.squared_distance(v2)
+
+    @staticmethod
+    def norm(vector, p):
+        """
+        Find norm of the given vector.
+        """
+        return _convert_to_vector(vector).norm(p)
+
+    @staticmethod
+    def parse(s):
+        """Parse a string representation back into the Vector.
+
+        >>> Vectors.parse('[2,1,2 ]')
+        DenseVector([2.0, 1.0, 2.0])
+        >>> Vectors.parse(' ( 100,  [0],  [2])')
+        SparseVector(100, {0: 2.0})
+        """
+        if s.find('(') == -1 and s.find('[') != -1:
+            return DenseVector.parse(s)
+        elif s.find('(') != -1:
+            return SparseVector.parse(s)
+        else:
+            raise ValueError(
+                "Cannot find tokens '[' or '(' from the input string.")
+
+    @staticmethod
+    def zeros(size):
+        return DenseVector(np.zeros(size))
+
 
 class Matrix(object):
     """

python/pyspark/mllib/tests.py

@@ -24,7 +24,7 @@
 import tempfile
 import array as pyarray
 
-from numpy import array, array_equal, zeros
+from numpy import array, array_equal, zeros, inf
 from py4j.protocol import Py4JJavaError
 
 if sys.version_info[:2] <= (2, 6):
@@ -220,6 +220,29 @@ def test_dense_matrix_is_transposed(self):
         self.assertTrue(array_equal(sm.colPtrs, [0, 2, 5]))
         self.assertTrue(array_equal(sm.values, [1, 3, 4, 6, 9]))
 
+    def test_parse_vector(self):
+        a = DenseVector([3, 4, 6, 7])
+        self.assertTrue(str(a), '[3.0,4.0,6.0,7.0]')
+        self.assertTrue(Vectors.parse(str(a)), a)
+        a = SparseVector(4, [0, 2], [3, 4])
+        self.assertTrue(str(a), '(4,[0,2],[3.0,4.0])')
+        self.assertTrue(Vectors.parse(str(a)), a)
+        a = SparseVector(10, [0, 1], [4, 5])
+        self.assertTrue(SparseVector.parse(' (10, [0,1 ],[ 4.0,5.0] )'), a)
+
+    def test_norms(self):
+        a = DenseVector([0, 2, 3, -1])
+        self.assertAlmostEqual(a.norm(2), 3.742, 3)
+        self.assertTrue(a.norm(1), 6)
+        self.assertTrue(a.norm(inf), 3)
+        a = SparseVector(4, [0, 2], [3, -4])
+        self.assertAlmostEqual(a.norm(2), 5)
+        self.assertTrue(a.norm(1), 7)
+        self.assertTrue(a.norm(inf), 4)
+
+        tmp = SparseVector(4, [0, 2], [3, 0])
+        self.assertEqual(tmp.numNonzeros(), 1)
+
 
 class ListTests(MLlibTestCase):