numpy condition function for 2-D data

Question

I have a synthetic dataset consisting of features (X) and labels (y) which is used for KMeans clustering using Python 3.8 and sklearn 0.22.2 and numpy 1.19.

X.shape, y.shape
# ((100, 2), (100,))

kmeans = KMeans(n_clusters = 3, init = 'random', n_init = 10, max_iter = 300)

# Train model on scaled features-
kmeans.fit(X)

After training KMeans on 'X', I want to replace the unique (continuous) values of 'X' with the cluster centers (discreet) obtained using KMeans.

for i in range(3):
    print("cluster number {0} has center = {1}".format(i + 1, kmeans.cluster_centers_[i, :]))
'''
cluster number 1 has center = [-0.7869159   1.14173859]
cluster number 2 has center = [ 1.28010442 -1.04663318]
cluster number 3 has center = [-0.54654735  0.0054752 ]
'''


set(kmeans.labels_)
# {0, 1, 2}

One way I have of doing it is:

X[np.where(clustered_labels == 0)] = val[0,:]
X[np.where(clustered_labels == 1)] = val[1,:]
X[np.where(clustered_labels == 2)] = val[2,:]

Can I do it using np.select()?

cond = [clustered_labels == i for i in range(3)]
val = kmeans.cluster_centers_[:,:]

But on executing the code:

np.select(cond, val)  

             

I get the following error:

--------------------------------------------------------------------------- ValueError Traceback (most recent call last) in ----> 1 np.select(cond, val)

<array_function internals> in select(*args, **kwargs)

~/.local/lib/python3.8/site-packages/numpy/lib/function_base.py in select(condlist, choicelist, default) 693 result_shape = condlist[0].shape 694 else: --> 695 result_shape = np.broadcast_arrays(condlist[0], choicelist[0])[0].shape 696 697 result = np.full(result_shape, choicelist[-1], dtype)

<array_function internals> in broadcast_arrays(*args, **kwargs)

~/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py in broadcast_arrays(subok, *args) 256 args = [np.array(_m, copy=False, subok=subok) for _m in args] 257 --> 258 shape = _broadcast_shape(*args) 259 260 if all(array.shape == shape for array in args):

~/.local/lib/python3.8/site-packages/numpy/lib/stride_tricks.py in _broadcast_shape(*args) 187 # use the old-iterator because np.nditer does not handle size 0 arrays 188 # consistently --> 189 b = np.broadcast(*args[:32]) 190 # unfortunately, it cannot handle 32 or more arguments directly 191 for pos in range(32, len(args), 31):

ValueError: shape mismatch: objects cannot be broadcast to a single shape

Suggestions?

Thanks!

Answer 1

Somewhat cleaner way to do it (but very similar to your way) will be the following. Here's a simple example:

from sklearn.cluster import KMeans
import numpy as np

x1 = np.random.normal(0, 2, 100)
y1 = np.random.normal(0, 1, 100)
label1 = np.ones(100)
d1 = np.column_stack([x1, y1, label1])

x2 = np.random.normal(3, 1, 100)
y2 = np.random.normal(1, 2, 100)
label2 = np.ones(100) * 2
d2 = np.column_stack([x2, y2, label2])

x3 = np.random.normal(-3, 0.5, 100)
y3 = np.random.normal(0.5, 0.25, 100)
label3 = np.ones(100) * 3
d3 = np.column_stack([x3, y3, label3])

D = np.row_stack([d1, d2, d3])
np.random.shuffle(D)
X = D[:, :2]
y = D[:, 2]

print(f'X.shape = {X.shape}, y.shape = {y.shape}')
# X.shape = (300, 2), y.shape = (300,)

kmeans = KMeans(n_clusters = 3, init = 'random', n_init = 10, max_iter = 300)

# Train model on scaled features-
kmeans.fit(X)

preds = kmeans.predict(X)
X[preds==0] = kmeans.cluster_centers_[0]
X[preds==1] = kmeans.cluster_centers_[1]
X[preds==2] = kmeans.cluster_centers_[2]

Yet another way to accomplish the task is to use the np.put method instead of the assignment as follows:

np.put(X, preds==0, kmeans.cluster_centers_[0])
np.put(X, preds==1, kmeans.cluster_centers_[1])
np.put(X, preds==2, kmeans.cluster_centers_[2])

Frankly, I don't see a way to accomplish the task by the means of the np.select function, and I guess the way you do it is the best way, based on this answer.

Cheers.