I have two Numpy array whose size is 994 and 1000. As such I when I am doing the below operation:
X * Y
I get error that "ValueError: operands could not be broadcast together with shapes (994) (1000)"
Hence as per fix I am trying to pad extras / trailing zeros to the array which great size by below method:
padzero = 0
if(bw.size > w.size):
padzero = bw.size - w.size
w = np.pad(w,padzero, 'constant', constant_values=0)
if(bw.size < w.size):
padzero = w.size - bw.size
bw = np.pad(bw,padzero, 'constant', constant_values=0)
But now the issue comes that if the size difference is 6 then 12 0's are getting padded in the array - which exactly should be six in my case.
I tried many ways to achieve this but its not resulting to resolve the issue. If I try he below way:
bw = np.pad(bw,padzero/2, 'constant', constant_values=0)
ValueError: Unable to create correctly shaped tuple from 3.0
How can I fix the issue?
a = np.array([1, 2, 3])
To insert zeros front:
np.pad(a,(2,0),'constant', constant_values=0)
array([0, 0, 1, 2, 3])
To insert zeros back:
np.pad(a,(0,2),'constant', constant_values=0)
array([1, 2, 3, 0, 0])
Front and back:
np.pad(a,(1,1),'constant', constant_values=0)
array([0, 1, 2, 3, 0])
Related
I have a torch tensor edge_index of shape (2, N) that represents edges in a graph. For each (x, y) there is also a (y, x), where x and y are node IDs (ints). During the forward pass of my model I need to mask out certain edges. So, for example, I have:
n1 = [0, 3, 4] # list of node ids as x
n2 = [1, 2, 1] # list of node ids as y
edge_index = [[1, 2, 0, 1, 3, 4, 2, 3, 1, 4, 2, 4], # actual edges as (x, y) and (y, x)
[2, 1, 1, 0, 4, 3, 3, 2, 4, 1, 4, 2]]
# do something that efficiently removes (x, y) and (y, x) edges as formed by n1 and n2
Final edge_index should look like:
>>> edge_index
[[1, 2, 3, 4, 2, 4],
[2, 1, 4, 3, 4, 2]]
Preferably we need to efficiently make some kind of boolean mask that I can apply to edge index e.g. as edge_index[:, mask] or something like that.
Could also be done in numpy but I'd like to avoid converting back and forth.
Edit #1:
If that can't be done, then I can think of a way so that, instead of n1 and n2, I have access to the indices of the positions I need to exclude in one tensor e.g. _except=[2, 3, 6, 7, 8, 9] (by making a dict/index once in the beginning).
Is there a way to get the desired result by "telling" edge_index to drop the indices in except? edge_index[:, _except] gives me the ones I want to get rid of. I need its complement operation.
Edit #2:
I managed to do it like this:
mask = torch.ones(edge_index.shape[1], dtype=torch.bool)
for i in range(len(n1)):
mask = mask & ~(torch.tensor([n1[i], n2[i]], dtype=torch.long) == edge_index.T).all(dim=1) & ~(torch.tensor([n2[i], n1[i]], dtype=torch.long) == edge_index.T).all(dim=1)
edge_index[:, mask]
but it is too slow and I can't use it. How can I speed it up?
Edit #3: I managed to solve this Edit#1 efficiently with:
mask = torch.ones(edge_index.shape[1], dtype=torch.bool)
mask[_except] = False
edge_index[:, mask]
Still interested in solving the original problem if someone comes up with something...
If you're ok with the way you suggested at Edit#1,
you get the complement result by:
edge_index[:, [i for i in range(edge_index.shape[1]) if not (i in _except)]]
hope this is fast enough for your requirement.
Edit 1:
from functools import reduce
ids = torch.stack([torch.tensor(n1), torch.tensor(n2)], dim=1)
ids = torch.cat([ids, ids[:, [1,0]]], dim=0)
res = edge_index.unsqueeze(0).repeat(6, 1, 1) == ids.unsqueeze(2).repeat(1, 1, 12)
mask = ~reduce(lambda x, y: x | (reduce(lambda p, q: p & q, y)), res, reduce(lambda p, q: p & q, res[0]))
edge_index[:, mask]
Edit 2:
ids = torch.stack([torch.tensor(n1), torch.tensor(n2)], dim=1)
ids = torch.cat([ids, ids[:, [1,0]]], dim=0)
res = edge_index.unsqueeze(0).repeat(6, 1, 1) == ids.unsqueeze(2).repeat(1, 1, 12)
mask = ~(res.sum(1) // 2).sum(0).bool()
edge_index[:, mask]
My goal is to get joint probability (here we use count for example) matrix from data samples. Now I can get the expected result, but I'm wondering how to optimize it. Here is my implementation:
def Fill2DCountTable(arraysList):
'''
:param arraysList: List of arrays, length=2
each array is of shape (k, sampleSize),
k == 1 (or None. numpy will align it) if it's single variable
else k for a set of variables of size k
:return: xyJointCounts, xMarginalCounts, yMarginalCounts
'''
jointUniques, jointCounts = np.unique(np.vstack(arraysList), axis=1, return_counts=True)
_, xReverseIndexs = np.unique(jointUniques[[0]], axis=1, return_inverse=True) ###HIGHLIGHT###
_, yReverseIndexs = np.unique(jointUniques[[1]], axis=1, return_inverse=True)
xyJointCounts = np.zeros((xReverseIndexs.max() + 1, yReverseIndexs.max() + 1), dtype=np.int32)
xyJointCounts[tuple(np.vstack([xReverseIndexs, yReverseIndexs]))] = jointCounts
xMarginalCounts = np.sum(xyJointCounts, axis=1) ###HIGHLIGHT###
yMarginalCounts = np.sum(xyJointCounts, axis=0)
return xyJointCounts, xMarginalCounts, yMarginalCounts
def Fill3DCountTable(arraysList):
# :param arraysList: List of arrays, length=3
jointUniques, jointCounts = np.unique(np.vstack(arraysList), axis=1, return_counts=True)
_, xReverseIndexs = np.unique(jointUniques[[0]], axis=1, return_inverse=True)
_, yReverseIndexs = np.unique(jointUniques[[1]], axis=1, return_inverse=True)
_, SReverseIndexs = np.unique(jointUniques[2:], axis=1, return_inverse=True)
SxyJointCounts = np.zeros((SReverseIndexs.max() + 1, xReverseIndexs.max() + 1, yReverseIndexs.max() + 1), dtype=np.int32)
SxyJointCounts[tuple(np.vstack([SReverseIndexs, xReverseIndexs, yReverseIndexs]))] = jointCounts
SMarginalCounts = np.sum(SxyJointCounts, axis=(1, 2))
SxJointCounts = np.sum(SxyJointCounts, axis=2)
SyJointCounts = np.sum(SxyJointCounts, axis=1)
return SxyJointCounts, SMarginalCounts, SxJointCounts, SyJointCounts
My use scenario is to do conditional independence test over variables. SampleSize is usually quite big (~10k) and each variable's categorical cardinality is relatively small (~10). I still find the speed not satisfying.
How to best optimize this code, or even logic outside the code? I may have some thoughts:
The ###HIGHLIGHT### lines. On a single X I may calculate (X;Y1), (Y2;X), (X;Y3|S1)... for many times, so what if I save cache variable's (and conditional set's) {uniqueValue: reversedIndex} dictionary and its marginal count, and then directly get marginalCounts (no need to sum) and replace to get reverseIndexs (no need to unique).
How to further use matrix parallelization to do CITest in batch, i.e. calculate (X;Y|S1), (X;Y|S2), (X;Y|S3)... simultaneously?
Will torch be faster than numpy, on same CPU? Or on GPU?
It's an open question. Thank you for any possible ideas. Big thanks for your help :)
================== A test example is as follows ==================
xs = np.array( [2, 4, 2, 3, 3, 1, 3, 1, 2, 1] )
ys = np.array( [5, 5, 5, 4, 4, 4, 4, 4, 6, 5] )
Ss = np.array([ [1, 0, 0, 0, 1, 0, 0, 0, 1, 1],
[1, 1, 1, 0, 1, 0, 1, 0, 1, 0] ])
xyJointCounts, xMarginalCounts, yMarginalCounts = Fill2DCountTable([xs, ys])
SxyJointCounts, SMarginalCounts, SxJointCounts, SyJointCounts = Fill3DCountTable([xs, ys, Ss])
get 2D from (X;Y): xMarginalCounts=[3 3 3 1], yMarginalCounts=[5 4 1], and xyJointCounts (added axes name FYI):
xy| 4 5 6
--|-------
1 | 2 1 1
2 | 0 2 1
3 | 3 0 0
4 | 0 1 0
get 3D from (X;Y|{Z1,Z2}): SxyJointCounts is of shape 4x4x3, where the first 4 means the cardinality of {Z1,Z2} (00, 01, 10, 11 with respective SMarginalCounts=[3 3 1 3]). SxJointCounts is of shape 4x4 and SyJointCounts is of shape 4x3.
I have numpy array like this a = [-- -- -- 1.90 2.91 1.91 2.92]
I need to find % of values more than 2, so here it is 50%.
How to get the same in easy way? also, why len(a) gives 7 (instead of 4)?
Try this:
import numpy as np
import numpy.ma as ma
a = ma.array([0, 1, 2, 1.90, 2.91, 1.91, 2.92])
for i in range(3):
a[i] = ma.masked
print(a)
print(np.sum(a>2)/((len(a) - ma.count_masked(a))))
The last line prints 0.5 which is your 50%. It subtracted from the total length of your array (7) the number of masked elements (3) which you see as the three "--" in the output you posted.
Generally speaking, you can simply use
a = np.array([...])
threshold = 2.0
fraction_higher = (a > threshold).sum() / len(a) # in [0, 1)
percentage_higher = fraction_higher * 100
The array contains 7 elements, being 3 of them masked. This code emulates the test case, generating a masked array as well:
# generate the test case: a masked array
a = np.ma.array([-1, -1, -1, 1.90, 2.91, 1.91, 2.92], mask=[1, 1, 1, 0, 0, 0, 0])
# check its format
print(a)
[-- -- -- 1.9 2.91 1.91 2.92]
#print the output
print(a[a>2].count() / a.count())
0.5
I have a multidimensional array, and I need to get the top k elements from each row of the last dimension.
>>> x = np.random.random_integers(0, 100, size=(2,1,1,5))
>>> x
array([[[[99, 39, 10, 18, 68]]],
[[[22, 3, 13, 56, 2]]]])
I'm trying to get:
array([[[[ 99., 68.]]],
[[[ 18., 99.]]]])
I can get the indices using the following, but I'm not sure how to slice out the values.
>>> k = 2
>>> parts = np.flip(-1 - np.arange(k), 0)
>>> indices = np.flip(
... np.argpartition(x, parts, axis=-1)[..., -k:],
... axis=-1)
>>> indices
array([[[[0, 4]]],
[[[3, 0]]]])
This could solve your problem.
np.sort(x, axis=len(x.shape)-1)[...,-2:]
np.partition(x, 2)[..., -2:]
returns 2 largest elements from each row. E.g.,
x = np.random.random_integers(0, 100, size=(2,1,1,5))
print(x)
print(np.partition(x, 2)[..., -2:])
prints something like
[[[[79 34 90 80 56]]]
[[[78 11 24 20 42]]]]
[[[[80 90]]]
[[[78 42]]]]
I have two multi-dimensional tensors a and b. And I want to sort them by the values of a.
I found tf.nn.top_k is able to sort a tensor and return the indices which is used to sort the input. How can I use the returned indices from tf.nn.top_k(a, k=2) to sort b?
For example,
import tensorflow as tf
a = tf.reshape(tf.range(30), (2, 5, 3))
b = tf.reshape(tf.range(210), (2, 5, 3, 7))
k = 2
sorted_a, indices = tf.nn.top_k(a, k)
# How to sort b into
# sorted_b[0, 0, 0, :] = b[0, 0, indices[0, 0, 0], :]
# sorted_b[0, 0, 1, :] = b[0, 0, indices[0, 0, 1], :]
# sorted_b[0, 1, 0, :] = b[0, 1, indices[0, 1, 0], :]
# ...
Update
Combining tf.gather_nd with tf.meshgrid can be one solution. For example, the following code is tested on python 3.5 with tensorflow 1.0.0-rc0:
a = tf.reshape(tf.range(30), (2, 5, 3))
b = tf.reshape(tf.range(210), (2, 5, 3, 7))
k = 2
sorted_a, indices = tf.nn.top_k(a, k)
shape_a = tf.shape(a)
auxiliary_indices = tf.meshgrid(*[tf.range(d) for d in (tf.unstack(shape_a[:(a.get_shape().ndims - 1)]) + [k])], indexing='ij')
sorted_b = tf.gather_nd(b, tf.stack(auxiliary_indices[:-1] + [indices], axis=-1))
However, I wonder if there is a solution which is more readable and doesn't need to create auxiliary_indices above.
Your code have a problem.
b = tf.reshape(tf.range(60), (2, 5, 3, 7))
Because TensorFlow Cannot reshape a tensor with 60 elements to shape [2,5,3,7] (210 elements).
And you can't sort a rank 4 tensor (b) using indices of rank 3 tensors.