## Friday, 4 September 2015

### Bin packing algorithms in Python

The bin packing problem
The bin packing problem is where you have objects of a certain size, and want to put them in bins, but each bin holds a maximum summed size of max_summed_size_per_bin. How many bins do we need for our objects?

An example of a bin packing problem
My example was that I found that certain software bioinformatics package (which I won't name) takes a very long time if the number of DNA sequences in the input fasta file is very large, but is fast if it is small, even if the total length of the sequences is the same. So, for input files with many sequences, I decided to create fake long sequences, by glueing together short sequences. I decided to make the maximum length of a long sequence as 100 Mbase, if I have a list of 10,000 input sequences (of known lengths) How many fake sequences do I need? This is a bin packing problem!

First Fit Decreasing (FFD) algorithm
A simple heuristic solution is the FFD algorithm. It first sorts the items in descending order by their sizes, and then inserts each item in the first bin in the list with sufficient remaining space. This heuristic algorithm is not guaranteed to give the optimal solution, but is fast and easy to code up.

First Fit Decreasing algorithm algorithm in Python
On stackoverflow I found an object-oriented implementation of the FFD algorithm. Just for fun, I decided to see if I could make a single (non-object-oriented) function to carry out the algorithm. Here it is:

# define a function to take a dictionary sizes with sizes for objects,
# and return a list of sets of objects, where the summed size of the objects in
# each set is <= max_summed_size_per_bin. This is a bin packing problem, and uses
# a simple algorithm, the 'First Fit Decreasing' (FFD) algorithm: first sort
# the items in decreasing orders by their sizes, and then insert each item in
# the first bin in the list with sufficient remaining space.

def first_fit_decreasing_algorithm(sizes, max_summed_size_per_bin, return_sizes=None):
"""partition objects into bins, where the summed size of objects in a bin is <= max_summed_size_per_bin
>>> first_fit_decreasing_algorithm({'seq1': 5, 'seq2': 4, 'seq3': 4, 'seq4': 3, 'seq5': 2, 'seq6': 2}, 10.0, return_sizes=True)
[[4, 5], [2, 3, 4], ]
"""
# Returns [{'seq1', 'seq2'}, {'seq3', 'seq4', 'seq5'}, {'seq6'}]
# Note that this algorithm is heuristic and does not give the optimal solution,
# which is ({'seq2', 'seq3', 'seq5'}, {'seq1', 'seq4', 'seq6'}]
# However, optimal bin packing is NP-hard and exact algorithms that do it are complicated.

# define a list of sets, to store the objects to put in each bin:
list_of_bins = []

# sort the objects in decreasing order by their sizes:
objects = list(sizes.keys())
sorted_objects = sorted(objects, key=lambda x: sizes[x], reverse=True) # sort in descending order

# insert each object in the first bin with sufficient remaining space:
for my_object in sorted_objects:
found_a_bin = False
object_size = sizes[my_object]
# check if there is a bin with space for this object
for index, my_bin in enumerate(list_of_bins): # 'my_bin' is a set of objects in a bin
# get the summed size of the objects in my_bin:
summed_sizes_in_bin = sum([sizes[x] for x in my_bin])
# if there is room for this object in the bin, put it in this bin:
if object_size <= (max_summed_size_per_bin - summed_sizes_in_bin):
found_a_bin = True

break # jump out of the 'for index, my_bin' loop
# if we didn't put my_object in any bin, then put it in a new bin:
if found_a_bin == False:
list_of_bins.append({my_object})

# Return a list of sets:
if return_sizes is None:
return list_of_bins
else: # this is just to make testing easy
# make a list of sublists, where each sublist has the sizes of objects in a bin:
list_of_sizes = [sorted([sizes[x] for x in sublist]) for sublist in list_of_bins]
return list_of_sizes

#### 1 comment:

Unknown said...

Thanks for the description! It helped me to understand the algorithm =)