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binpacking-1.5.2
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مشاهده بیشترتوضیحات
Heuristic distribution of weighted items to bins (either a fixed number of bins or a fixed number of volume per bin). Data may be in form of list, dictionary, list of tuples or csv-file.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | binpacking-1.5.2 |
| نام | binpacking |
| نسخه کتابخانه | 1.5.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Benjamin F. Maier |
| ایمیل نویسنده | bfmaier@physik.hu-berlin.de |
| آدرس صفحه اصلی | https://www.github.com/benmaier/binpacking |
| آدرس اینترنتی | https://pypi.org/project/binpacking/ |
| مجوز | MIT |
binpacking
==========
This package contains greedy algorithms to solve two typical bin packing
problems, (i) sorting items into a constant number of bins, (ii) sorting
items into a low number of bins of constant size. Here's a usage example
.. code:: python
>>> import binpacking
>>>
>>> b = { 'a': 10, 'b': 10, 'c':11, 'd':1, 'e': 2,'f':7 }
>>> bins = binpacking.to_constant_bin_number(b,4) # 4 being the number of bins
>>> print("===== dict\n",b,"\n",bins)
===== dict
{'a': 10, 'b': 10, 'c': 11, 'd': 1, 'e': 2, 'f': 7}
[{'c': 11}, {'b': 10}, {'a': 10}, {'f': 7, 'e': 2, 'd': 1}]
>>>
>>> b = list(b.values())
>>> bins = binpacking.to_constant_volume(b,11) # 11 being the bin volume
>>> print("===== list\n",b,"\n",bins)
===== list
[10, 10, 11, 1, 2, 7]
[[11], [10], [10], [7, 2, 1]]
Consider you have a list of items, each carrying a weight *w\_i*.
Typical questions are
#. How can we distribute the items to a minimum number of bins *N* of
equal volume *V*?
#. How can we distribute the items to exactly *N* bins where each
carries items that sum up to approximately equal weight?
Problems like this can easily occur in modern computing. Assume you have
to run computations where a lot of files of different sizes have to be
loaded into the memory. However, you only have a machine with 8GB of
RAM. How should you bind the files such that you have to run your
program a minimum amount of times? This is equivalent to solving problem
1.
What about problem 2? Say you have to run a large number of
computations. For each of the jobs you know the time it will probably
take to finish. However, you only have a CPU with 4 cores. How should
you distribute the jobs to the 4 cores such that they will all finish at
approximately the same time?
The package provides the command line tool "binpacking" using which one
can easily bin pack csv-files containing a column that can be identified
with a weight. To see the usage enter
.. code:: bash
$ binpacking -h
Usage: binpacking [options]
Options:
-h, --help show this help message and exit
-f FILEPATH, --filepath=FILEPATH
path to the csv-file to be bin-packed
-V V_MAX, --volume=V_MAX
maximum volume per bin (constant volume algorithm will
be used)
-N N_BIN, --n-bin=N_BIN
number of bins (constant bin number algorithm will be
used)
-c WEIGHT_COLUMN, --weight-column=WEIGHT_COLUMN
integer (or string) giving the column number (or
column name in header) where the weight is stored
-H, --has-header parse this option if there is a header in the csv-file
-d DELIM, --delimiter=DELIM
delimiter in the csv-file (use "tab" for tabs)
-q QUOTECHAR, --quotechar=QUOTECHAR
quotecharacter in the csv-file
-l LOWER_BOUND, --lower-bound=LOWER_BOUND
weights below this bound will not be considered
-u UPPER_BOUND, --upper-bound=UPPER_BOUND
weights exceeding this bound will not be considered
Install
-------
.. code:: bash
$ pip install binpacking
Examples
--------
In the repository's directory
.. code:: bash
cd examples/
binpacking -f hamlet_word_count.csv -V 2000 -H -c count -l 10 -u 1000
binpacking -f hamlet_word_count.csv -N 4 -H -c count
in Python
.. code:: python
import binpacking
b = { 'a': 10, 'b': 10, 'c':11, 'd':1, 'e': 2,'f':7 }
bins = binpacking.to_constant_bin_number(b,4)
print("===== dict\n",b,"\n",bins)
b = list(b.values())
bins = binpacking.to_constant_volume(b,11)
print("===== list\n",b,"\n",bins)
نحوه نصب
نصب پکیج whl binpacking-1.5.2:
pip install binpacking-1.5.2.whl
نصب پکیج tar.gz binpacking-1.5.2:
pip install binpacking-1.5.2.tar.gz