Return array of counts for each feature of input

Question

I have an array of integer labels and I would like to determine how many of each label is present and store those values in an array of the same size as the input. This can

Answer 1

Approach #1

Here's one using np.unique -

_, tags, count = np.unique(labels, return_counts=1, return_inverse=1)
sizes = count[tags]

Approach #2

With positive numbers in labels, simpler and more efficient way with np.bincount -

sizes = np.bincount(labels)[labels]

---

Runtime test

Setup with 60,000 unique positive numbers and two such sets of lengths 100,000 and 1000,000 are timed.

Set #1 :

In [192]: np.random.seed(0)
     ...: labels = np.random.randint(0,60000,(100000))

In [193]: %%timeit
     ...: sizes = np.zeros(labels.shape)
     ...: for num in np.unique(labels):
     ...:     mask = labels == num
     ...:     sizes[mask] = np.count_nonzero(mask)
1 loop, best of 3: 2.32 s per loop

In [194]: %timeit np.bincount(labels)[labels]
1000 loops, best of 3: 376 µs per loop

In [195]: 2320/0.376 # Speedup figure
Out[195]: 6170.212765957447

Set #2 :

In [196]: np.random.seed(0)
     ...: labels = np.random.randint(0,60000,(1000000))

In [197]: %%timeit
     ...: sizes = np.zeros(labels.shape)
     ...: for num in np.unique(labels):
     ...:     mask = labels == num
     ...:     sizes[mask] = np.count_nonzero(mask)
1 loop, best of 3: 43.6 s per loop

In [198]: %timeit np.bincount(labels)[labels]
100 loops, best of 3: 5.15 ms per loop

In [199]: 43600/5.15 # Speedup figure
Out[199]: 8466.019417475727