[SPARK-9793] [MLlib] [PySpark] PySpark DenseVector, SparseVector implement __eq__ and __hash__ correctly

python/pyspark/mllib/linalg/__init__.py

@@ -25,6 +25,7 @@
 
 import sys
 import array
+import struct
 
 if sys.version >= '3':
     basestring = str
@@ -122,6 +123,15 @@ def _format_float_list(l):
     return [_format_float(x) for x in l]
 
 
+def _double_to_long_bits(value):
+    if value != value:
+        # value is NaN, standardize to canonical non-signaling NaN
+        return 0x7ff8000000000000
+    else:
+        # pack double into 64 bits, then unpack as long int
+        return struct.unpack('Q', struct.pack('d', value))[0]
+
+
 class VectorUDT(UserDefinedType):
     """
     SQL user-defined type (UDT) for Vector.
@@ -404,11 +414,31 @@ def __repr__(self):
         return "DenseVector([%s])" % (', '.join(_format_float(i) for i in self.array))
 
     def __eq__(self, other):
-        return isinstance(other, DenseVector) and np.array_equal(self.array, other.array)
+        if isinstance(other, DenseVector):
+            return np.array_equal(self.array, other.array)
+        elif isinstance(other, SparseVector):
+            if len(self) != other.size:
+                return False
+            return Vectors._equals(list(xrange(len(self))), self.array, other.indices, other.values)
+        return False
 
     def __ne__(self, other):
         return not self == other
 
+    def __hash__(self):
+        size = len(self)
+        result = 31 + size
+        nnz = 0
+        i = 0
+        while i < size and nnz < 128:
+            if self.array[i] != 0:
+                result = 31 * result + i
+                bits = _double_to_long_bits(self.array[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
     def __getattr__(self, item):
         return getattr(self.array, item)
 
@@ -704,20 +734,14 @@ def __repr__(self):
         return "SparseVector({0}, {{{1}}})".format(self.size, entries)
 
     def __eq__(self, other):
-        """
-        Test SparseVectors for equality.
-
-        >>> v1 = SparseVector(4, [(1, 1.0), (3, 5.5)])
-        >>> v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
-        >>> v1 == v2
-        True
-        >>> v1 != v2
-        False
-        """
-        return (isinstance(other, self.__class__)
-                and other.size == self.size
-                and np.array_equal(other.indices, self.indices)
-                and np.array_equal(other.values, self.values))
+        if isinstance(other, SparseVector):
+            return other.size == self.size and np.array_equal(other.indices, self.indices) \
+                and np.array_equal(other.values, self.values)
+        elif isinstance(other, DenseVector):
+            if self.size != len(other):
+                return False
+            return Vectors._equals(self.indices, self.values, list(xrange(len(other))), other.array)
+        return False
 
     def __getitem__(self, index):
         inds = self.indices
@@ -739,6 +763,19 @@ def __getitem__(self, index):
     def __ne__(self, other):
         return not self.__eq__(other)
 
+    def __hash__(self):
+        result = 31 + self.size
+        nnz = 0
+        i = 0
+        while i < len(self.values) and nnz < 128:
+            if self.values[i] != 0:
+                result = 31 * result + int(self.indices[i])
+                bits = _double_to_long_bits(self.values[i])
+                result = 31 * result + (bits ^ (bits >> 32))
+                nnz += 1
+            i += 1
+        return result
+
 
 class Vectors(object):
 
@@ -841,6 +878,31 @@ def parse(s):
     def zeros(size):
         return DenseVector(np.zeros(size))
 
+    @staticmethod
+    def _equals(v1_indices, v1_values, v2_indices, v2_values):
+        """
+        Check equality between sparse/dense vectors,
+        v1_indices and v2_indices assume to be strictly increasing.
+        """
+        v1_size = len(v1_values)
+        v2_size = len(v2_values)
+        k1 = 0
+        k2 = 0
+        all_equal = True
+        while all_equal:
+            while k1 < v1_size and v1_values[k1] == 0:
+                k1 += 1
+            while k2 < v2_size and v2_values[k2] == 0:
+                k2 += 1
+
+            if k1 >= v1_size or k2 >= v2_size:
+                return k1 >= v1_size and k2 >= v2_size
+
+            all_equal = v1_indices[k1] == v2_indices[k2] and v1_values[k1] == v2_values[k2]
+            k1 += 1
+            k2 += 1
+        return all_equal
+
 
 class Matrix(object):
 

python/pyspark/mllib/tests.py

@@ -194,6 +194,37 @@ def test_squared_distance(self):
         self.assertEquals(3.0, _squared_distance(sv, arr))
         self.assertEquals(3.0, _squared_distance(sv, narr))
 
+    def test_hash(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([1.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertTrue(hash(v1) == hash(v2))
+        self.assertFalse(hash(v1) == hash(v3))
+        self.assertFalse(hash(v2) == hash(v3))
+        self.assertFalse(hash(v2) == hash(v4))
+
+    def test_eq(self):
+        v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
+        v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
+        v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
+        v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
+        v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
+        self.assertTrue(v1 == v2)
+        self.assertTrue(v1 == v3)
+        self.assertFalse(v2 == v4)
+        self.assertFalse(v1 == v5)
+        self.assertFalse(v1 == v6)
+
+    def test_equals(self):
+        indices = [1, 2, 4]
+        values = [1., 3., 2.]
+        self.assertTrue(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 1., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 0., 2.]))
+        self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 2., 2.]))
+
     def test_conversion(self):
         # numpy arrays should be automatically upcast to float64
         # tests for fix of [SPARK-5089]