sortednp 0.4.1

Merge and intersect sorted numpy arrays.

Sortednp

Numpy and Numpy arrays are a really great tool. However, intersecting and merging multiple sorted numpy arrays is rather less performant. The current numpy implementation concatenates the two arrays and sorts the combination. If you want to merge or intersect multiple numpy arrays, there is a much faster way, by using the property, that the resulting array is sorted.

Sortednp (sorted numpy) operates on sorted numpy arrays to calculate the intersection or the union of two numpy arrays in an efficient way. The resulting array is again a sorted numpy array, which can be merged or intersected with the next array. The intended use case is that sorted numpy arrays are sorted as the basic data structure and merged or intersected at request. Typical applications include information retrieval and search engines in particular.

It is also possible to implement a k-way merging or intersecting algorithm, which operates on an arbitrary number of arrays at the same time. This package is intended to deal with arrays with $10^6$ or $10^{10}$ items. Usually, these arrays are too large to keep more than two of them in memory at the same time. This package implements methods to merge and intersect multiple arrays, which can be loaded on-demand.

Installation

There are two different methods to install sortednp.

Using pip (recommended)

You can install the package directly from PyPI using pip (here pip3). There are pre-compiled wheels for linux 32- and 64bit.

$ pip3 install sortednp

Using setuptools

Alternatively, you can clone the git repository and run the setup script.

$ git clone https://gitlab.sauerburger.com/frank/sortednp.git
$ cd sortednp
$ python3 setup.py install

Numpy Dependency

The installation fails in some cases, because of a build-time dependency on numpy. Usually, the problem can be solved by manually installing a recent numpy version via pip3 install -U numpy.

Usage

The package provides two different kinds of methods. The first class is intended to operate on two arrays. The second class operates on two or more arrays and calls the first class of methods internally.

Two-way methods

Two numpy sorted arrays can be merged with the merge method, which takes two numpy arrays and returns the sorted union of the two arrays.

## merge.py
import numpy as np
import sortednp as snp

a = np.array([0, 3, 4, 6, 7])
b = np.array([1, 2, 3, 5, 7, 9])

m = snp.merge(a, b)
print(m)

If you run this, you should see the union of both arrays as a sorted numpy array.

$ python3 merge.py
[0 1 2 3 3 4 5 6 7 7 9]

Two sorted numpy arrays can be intersected with the intersect method, which takes two numpy arrays and returns the sorted intersection of the two arrays.

## intersect.py
import numpy as np
import sortednp as snp

a = np.array([0, 3, 4, 6, 7])
b = np.array([1, 2, 3, 5, 7, 9])

i = snp.intersect(a, b)
print(i)

If you run this, you should see the intersection of both arrays as a sorted numpy array.

$ python3 intersect.py
[3 7]

Since version 0.4.0, the library provides the issubset(a, b) method which checks if the array a is a subset of b, and the isitem(v, a) method which checks if value is contained in array a.

## set.py
import numpy as np
import sortednp as snp

a = np.array([2, 4, 5, 10])
b = np.array([1, 2, 3, 4, 5, 6, 10, 11])

print(snp.issubset(a, b))  # a is subset of b
print(snp.issubset(b, a))  # b is not a subset of a

print(snp.isitem(4, a))  # 4 is an item of a
print(snp.isitem(3, a))  # 3 is not an item of a

If you execute this example, you get the expected result: a is a subset ob b, 4 is a member of a.

$ python3 set.py
True
False
True
False

Returning array indices

The intersect method takes an optional argument indices which is False by default. If this is set to True, the return value consists of the intersection array and a tuple with the indices of the common values for both arrays. The index arrays have the length of the output. The indices show the position in the input from which the value was copied.

## intersect_indices.py
import numpy as np
import sortednp as snp

a = np.array([2,4,6,8,10])
b = np.array([1,2,3,4])

intersection, indices = snp.intersect(a,b, indices=True)

print(intersection)
print(indices)

The above example gives:

$ python3 intersect_indices.py
[2 4]
(array([0, 1]), array([1, 3]))

The first line shows the intersection of the two arrays. The second line prints a tuple with the indices where the common values appeared in the input arrays. For example, the value 4 is at position 1 in array a and at position 3 in array b.

Since version 0.3.0, the merge has to indices argument too. The returned indices have the length of the inputs. The indices show the position in the output to which an input value was copied.

## merge_indices.py
import numpy as np
import sortednp as snp

a = np.array([2,4])
b = np.array([3,4,5])

merged, indices = snp.merge(a,b, indices=True)

print(merged)
print(indices)

The above example gives:

$ python3 merge_indices.py
[2 3 4 4 5]
(array([0, 2]), array([1, 3, 4]))

The first line shows that the two arrays have been merged. The second line prints a tuple with the indices. For example, the value 3 from array b can be found at position 1 in the output.

Duplicate treatment

Since version 0.3.0, sortednp supported multiple different strategies to deal with duplicated entries.

Duplicates during intersecting

There are three different duplicate treatments for the intersect method:

• sortednp.DROP: Ignore any duplicated entries. The output will contain only unique values.

• sortednp.KEEP_MIN_N: If an entry occurs n times in one input array and m times in the other input array, the output will contain the entry min(n, m) times.

• sortednp.KEEP_MAX_N: If an entry occurs n times in one input array and m times in the other input array, the output will contain the entry max(n, m) times (assuming the entry occurs at least once in both arrays, i.e. n > 0 and m > 0).

The strategy can be selected with the optional duplicates argument of intersect. The default is sortednp.KEEP_MIN_N. Consider the following example.

## intersect_duplicates.py
import numpy as np
import sortednp as snp

a = np.array([2, 4, 4, 5])    # Twice
b = np.array([3, 4, 4, 4, 5]) # Three times

intersect_drop = snp.intersect(a, b, duplicates=snp.DROP)
print(intersect_drop)  # Contains a single 4

intersect_min = snp.intersect(a, b, duplicates=snp.KEEP_MIN_N)
print(intersect_min)  # Contains 4 twice

intersect_max = snp.intersect(a, b, duplicates=snp.KEEP_MAX_N)
print(intersect_max)  # Contains 4 three times

The above example gives:

$ python3 intersect_duplicates.py
[4 5]
[4 4 5]
[4 4 4 5]

Duplicates during merging

The merge method offers three different duplicates treatment strategies:

• sortednp.DROP: Ignore any duplicated entries. The output will contain only unique values.

• sortednp.DROP_IN_INPUT: Ignores duplicated entries in the input arrays separately. This is the same as ensuring that each input array unique values. The output contains every value at most twice.

• sortednp.KEEP: Keep all duplicated entries. If an item occurs n times in one input array and m times in the other input array, the output contains the item n + m times.

The strategy can be selected with the optional duplicates. The default is sortednp.KEEP. Consider the following example.

## merge_duplicates.py
import numpy as np
import sortednp as snp

a = np.array([2, 4, 4, 5])    # Twice
b = np.array([3, 4, 4, 4, 5]) # Three times

merge_drop = snp.merge(a, b, duplicates=snp.DROP)
print(merge_drop)  # Contains a single 4

merge_dii = snp.merge(a, b, duplicates=snp.DROP_IN_INPUT)
print(merge_dii)  # Contains 4 twice

merge_keep = snp.merge(a, b, duplicates=snp.KEEP)
print(merge_keep)  # Contains 4 five times

The above example gives:

$ python3 merge_duplicates.py
[2 3 4 5]
[2 3 4 4 5 5]
[2 3 4 4 4 4 4 5 5]

Duplicates during subset checks

The issubset method offers two different duplicates treatment strategies:

• sortednp.IGNORE: Ignore any duplications. The method returns True if each value in the first array is contained at least once in the second array. Duplicated entries in the first array do not change the return value.

• sortednp.REPEAT: For each duplicated item in the first array, require at least as many items in the second array. If for one value the first array contains more duplicated entries than the second array, the method returns False.

The strategy can be selected with the optional duplicates. The default is sortednp.IGNORE. Consider the following example.

## subset_duplicates.py
import numpy as np
import sortednp as snp

a = np.array([3, 4, 4, 5])    # Twice
b = np.array([3, 4, 4, 4, 5]) # Three times

# Number of occurances ignored
print(snp.issubset(a, b, duplicates=snp.IGNORE))  # is subset
print(snp.issubset(b, a, duplicates=snp.IGNORE))  # is subset

# Number of in subset must be smaller or equal
print(snp.issubset(a, b, duplicates=snp.REPEAT))  # is subset

# three 4s not subset of two 4s
print(snp.issubset(b, a, duplicates=snp.REPEAT))

The above example gives:

$ python3 subset_duplicates.py
True
True
True
False

Index tracking and duplicates

Tracking indices with the indices=True argument is possible while selecting a non-default duplicate treatment strategy. For merging the indices point to the position in the output array. If the input has duplicates that were skipped, the index is simply repeated. For example with snp.DROP, if the input is [9, 9, 9, 9], the index array for this input contains four times the position where 9 is found in the output.

Similarly, with snp.KEEP_MAX_N and intersect, the index of the last item in the array with less occurrences is duplicates.

## duplicates_index.py
import numpy as np
import sortednp as snp

a = np.array([2, 4, 4, 5])    # Twice
b = np.array([3, 4, 4, 4, 5]) # Three times

# Merge
merge_drop, (index_a, index_b) = snp.merge(a, b,
                                           duplicates=snp.DROP,
                                           indices=True)
print(index_b)

# Intersect
intersect_max, (index_a, index_b) = snp.intersect(a, b,
                                                  duplicates=snp.KEEP_MAX_N,
                                                  indices=True)
print(index_a)

The above example gives:

$ python3 duplicates_index.py
[1 2 2 2 3]
[1 2 2 3]

For merging, this means that the three 4s from the input all appear at same position in the output, namely position 2.

For the intersect, this means that the second and third occurrence of 4 in the output, both came from item at position 2 in the input.

k-way methods

Similarly, the k-way intersect and merge methods take two or more arrays and perform the merge or intersect operation on its arguments.

## kway_intersect.py
import numpy as np
import sortednp as snp

a = np.array([0, 3, 4, 6, 7])
b = np.array([0, 3, 5, 7, 9])
c = np.array([1, 2, 3, 5, 7, 9])
d = np.array([2, 3, 6, 7, 8])

i = snp.kway_intersect(a, b, c, d)
print(i)

If you run this, you should see the intersection of all four arrays as a sorted numpy array.

$ python3 kway_intersect.py
[3 7]

The k-way merger sortednp.kway_merge works analogously. However, the native numpy implementation is faster compared to the merge provided by this package. The k-way merger has been added for completeness. The package heapq provides efficient methods to merge multiple arrays simultaneously.

The methods kway_merge and kway_intersect accept the optional keyword argument assume_sorted. By default, it is set to True. If it is set to False, the method calls sort() on the input arrays before performing the operation. The default should be kept if the arrays are already sorted to save the time it takes to sort the arrays.

Since the arrays might be too large to keep all of them in memory at the same time, it is possible to pass a callable instead of an array to the methods. The callable is expected to return the actual array. It is called immediately before the array is required. This reduces the memory consumption.

Algorithms

Intersections are calculated by iterating both arrays. For a given element in one array, the method needs to search the other and check if the element is contained. In order to make this more efficient, we can use the fact that the arrays are sorted. There are three search methods, which can be selected via the optional keyword argument algorithm.

• sortednp.SIMPLE_SEARCH: Search for an element by linearly iterating over the array element-by-element. More Information.
• sortednp.BINARY_SEARCH: Slice the remainder of the array in halves and repeat the procedure on the slice which contains the searched element. More Information.
• sortednp.GALLOPING_SEARCH: First, search for an element linearly, doubling the step size after each step. If a step goes beyond the search element, perform a binary search between the last two positions. More Information.

The default is sortednp.GALLOPING_SEARCH. The performance of all three algorithms is compared in the next section. The methods issubset() and isitem() also support the algorithm keyword.

Performance

The performance of the package can be compared with the default implementation of numpy, the intersect1d` method. The ratio of the execution time between sortednp and numpy is shown for various different benchmark tests.

The merge or intersect time can be estimated under two different assumptions. If the arrays, which are merged or intersected, are already sorted, one should not consider the time it takes to sort the random arrays in the benchmark. On the other hand, if one considers a scenario in which the arrays are not sorted, one should take the sorting time into account. The benchmarks here on this page, assume that the arrays are already sorted. If you would like to benchmark the package and include the sorting time, have a look at the methods defined in ci/benchmark.py.

The random scattering of the points indicates the uncertainty caused by random load fluctuations on the benchmark machine (Spikes of serveral orders of magnitude usualy mean that there was a shortage of memory and large chunks had to be moved to SWAP.)

Intersect

The performance of the intersection operation depends on the sparseness of the two arrays. For example, if the first element of one of the arrays is larger than all elements in the other array, only the other array has to be searched (linearly, binarily, or exponentially). Similarly, if the common elements are far apart in the arrays (sparseness), large chunks of the arrays can be skipped. The arrays in the benchmark contain random (unique) integers. The sparseness is defined as the average difference between two consecutive elements in one array.

The first set of tests studies the performance dependence on the size of the arrays. The second set of tests studies the dependence on the sparseness of the array for a fixed size of array. Every shows a color-coded comparison of the performance of intersecting more than two arrays.

Test	Simple Search	Binary Search	Galloping Search
Intersect
Sparseness

Merge

The following chart shows the performance of merging 2 or more arrays as a function of the array size. It is assumed that the arrays are already sorted.