Github user feynmanliang commented on a diff in the pull request:
https://github.com/apache/spark/pull/7854#discussion_r37827836
--- Diff: python/pyspark/mllib/linalg/__init__.py ---
@@ -461,32 +461,41 @@ def __init__(self, size, *args):
self.size = int(size)
""" Size of the vector. """
assert 1 <= len(args) <= 2, "must pass either 2 or 3 arguments"
- if len(args) == 1:
- pairs = args[0]
- if type(pairs) == dict:
- pairs = pairs.items()
- pairs = sorted(pairs)
- self.indices = np.array([p[0] for p in pairs], dtype=np.int32)
- """ A list of indices corresponding to active entries. """
- self.values = np.array([p[1] for p in pairs], dtype=np.float64)
- """ A list of values corresponding to active entries. """
+ if isinstance(args[0], bytes):
+ assert isinstance(args[1], bytes), "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:
- if isinstance(args[0], bytes):
- assert isinstance(args[1], bytes), "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)
+ if len(args) == 1:
+ args = args[0]
+ if isinstance(args, dict):
+ args = args.items()
+ args = list(zip(*args))
+
+ # Handle empty args case.
+ if len(args) == 0:
+ indices = []
+ values = []
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")
--- End diff --
I'm not sure if we should expect users to supply sorted indices. There are
[tests](https://github.com/apache/spark/blob/a018b85716fd510ae95a3c66d676bbdb90f8d4e7/mllib/src/test/scala/org/apache/spark/mllib/linalg/VectorsSuite.scala#L53)
covering this use case.
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