I have a very large 2D array which looks something like this:
a= [[a1, b1, c1], [a2, b2, c2], ..., [an, bn, cn]]
Using numpy, is there an easy way to get a new 2D array with, e.g., 2 random rows from the initial array
a (without replacement)?
b= [[a4, b4, c4], [a99, b99, c99]]
5, size=(10,3)) A array([[1, 3, 0], [3, 2, 0], [0, 2, 1], [1, 1, 4], [3, 2, 2], [0, 1, 0], [1, 3, 1], [0, 4, 1], [2, 4, 2], [3, 3, 1]]) idx = np.random.randint(10, size=2) idx array([7, 6]) A[idx,:] array([[0, 4, 1], [1, 3, 1]])A = np.random.randint(
Putting it together for a general case:
A[np.random.randint(A.shape, size=2), :]
For non replacement (numpy 1.7.0+):
A[np.random.choice(A.shape, 2, replace=False), :]
I do not believe there is a good way to generate random list without replacement before 1.7. Perhaps you can setup a small definition that ensures the two values are not the same.
This is an old post, but this is what works best for me:
A[np.random.choice(A.shape, num_rows_2_sample, replace=False)]
change the replace=False to True to get the same thing, but with replacement.
Another option is to create a random mask if you just want to down-sample your data by a certain factor. Say I want to down-sample to 25% of my original data set, which is currently held in the array
# generate random boolean mask the length of data # use p 0.75 for False and 0.25 for True mask = numpy.random.choice([False, True], len(data_arr), p=[0.75, 0.25])
Now you can call
data_arr[mask] and return ~25% of the rows, randomly sampled.
This is a similar answer to the one Hezi Rasheff provided, but simplified so newer python users understand what’s going on (I noticed many new datascience students fetch random samples in the weirdest ways because they don’t know what they are doing in python).
You can get a number of random indices from your array by using:
indices = np.random.choice(A.shape, amount_of_samples, replace=False)
You can then use fancy indexing with your numpy array to get the samples at those indices:
This will get you the specified number of random samples from your data.
I see permutation has been suggested. In fact it can be made into one line:
5, size=(10,3)) np.random.permutation(A)[:2] array([[0, 3, 0], [3, 1, 2]])A = np.random.randint(
If you need the same rows but just a random sample then,
import random new_array = random.sample(old_array,x)
Here x, has to be an ‘int’ defining the number of rows you want to randomly pick.
If you want to generate multiple random subsets of rows, for example if your doing RANSAC.
num_pop = 10 num_samples = 2 pop_in_sample = 3 rows_to_sample = np.random.random([num_pop, 5]) random_numbers = np.random.random([num_samples, num_pop]) samples = np.argsort(random_numbers, axis=1)[:, :pop_in_sample] # will be shape [num_samples, pop_in_sample, 5] row_subsets = rows_to_sample[samples, :]
An alternative way of doing it is by using the
choice method of the
Generator class, https://github.com/numpy/numpy/issues/10835
import numpy as np # generate the random array A = np.random.randint(5, size=(10,3)) # use the choice method of the Generator class rng = np.random.default_rng() A_sampled = rng.choice(A, 2)
leading to a sampled data,
array([[1, 3, 2], [1, 2, 1]])
The running time is also profiled compared as follows,
%timeit rng.choice(A, 2) 15.1 µs ± 115 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) %timeit np.random.permutation(A)[:2] 4.22 µs ± 83.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) %timeit A[np.random.randint(A.shape, size=2), :] 10.6 µs ± 418 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
But when the array goes big,
A = np.random.randint(10, size=(1000,300)). working on the index is the best way.
%timeit A[np.random.randint(A.shape, size=50), :] 17.6 µs ± 657 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each) %timeit rng.choice(A, 50) 22.3 µs ± 134 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each) %timeit np.random.permutation(A)[:50] 143 µs ± 1.33 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
permutation method seems to be the most efficient one when your array is small while working on the index is the optimal solution when your array goes big.