Use Numpy.frompyfunc To Add Broadcasting To A Python Function With Argument

From an array like db (which will be approximately (1e6, 300)) and a mask = [1, 0, 1] vector, I define the target as a 1 in the first column. I want to create an out vector that co

Solution 1:

Converting vline to a numpy ufunc fundamentally doesn't make sense, since ufuncs are always applied to numpy arrays in an elementwise fashion. Because of this, the input arguments must either have the same shape, or must be broadcastable to the same shape. You are passing two arrays with incompatible shapes to your ufunc_vline function (db.shape == (6, 4) and mask.shape == (3,)), hence the ValueError you are seeing.

There are a couple of other issues with ufunc_vline:

• np.frompyfunc(vline, 2, 1) specifies that vline should return a single output argument, whereas vline actually returns nothing (but modifies out in place).

• You are passing db as the first argument to ufunc_vline, whereas vline expects the first argument to be idx, which is used as an index into the rows of db.

Also, bear in mind that creating a ufunc from a Python function using np.frompyfunc will not yield any noticeable performance benefit over a standard Python for loop. To see any serious improvement you would probably need to code the ufunc in a low-level language such as C (see this example in the documentation).

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Having said that, your vline function can be easily vectorized using standard boolean array operations:

def vline_vectorized(db, mask): 
    return db[:, 0] & np.all((mask & db[:, 1:]) == mask, axis=1)

For example:

db = np.array([       # out for mask = [1, 0, 1]
# target,  vector     #
  [1,      1, 0, 1],  # 1
  [0,      1, 1, 1],  # 0 (fit to mask but target == 0)
  [0,      0, 1, 0],  # 0
  [1,      1, 0, 1],  # 1
  [0,      1, 1, 0],  # 0
  [1,      0, 0, 0],  # 0
  ])

mask = np.array([1, 0, 1])

print(repr(vline_vectorized(db, mask)))
# array([1, 0, 0, 1, 0, 0])