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#
# This is part of "python-cluster". A library to group similar items together.
# Copyright (C) 2006 Michel Albert
#
# This library is free software; you can redistribute it and/or modify it
# under the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation; either version 2.1 of the License, or (at your
# option) any later version.
# This library is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License
# for more details.
# You should have received a copy of the GNU Lesser General Public License
# along with this library; if not, write to the Free Software Foundation,
# Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#
import logging
from multiprocessing import Process, Queue, current_process
logger = logging.getLogger(__name__)
def _encapsulate_item_for_combinfunc(item):
"""
This function has been extracted in order to
make Github issue #28 easier to investigate.
It replaces the following two lines of code,
which occur twice in method genmatrix, just
before the invocation of combinfunc.
if not hasattr(item, '__iter__') or isinstance(item, tuple):
item = [item]
Logging was added to the original two lines
and shows that the outcome of this snippet
has changed between Python2.7 and Python3.5.
This logging showed that the difference in
outcome consisted of the handling of the builtin
str class, which was encapsulated into a list in
Python2.7 but returned naked in Python3.5.
Adding a test for this specific class to the
set of conditions appears to give correct behaviour
under both versions.
"""
encapsulated_item = None
if (
not hasattr(item, '__iter__') or
isinstance(item, tuple) or
isinstance(item, str)
):
encapsulated_item = [item]
else:
encapsulated_item = item
logging.debug(
"item class:%s encapsulated as:%s ",
item.__class__.__name__,
encapsulated_item.__class__.__name__
)
return encapsulated_item
class Matrix(object):
"""
Object representation of the item-item matrix.
"""
def __init__(self, data, combinfunc, symmetric=False, diagonal=None):
"""
Takes a list of data and generates a 2D-matrix using the supplied
combination function to calculate the values.
:param data: the list of items.
:param combinfunc: the function that is used to calculate teh value in a
cell. It has to cope with two arguments.
:param symmetric: Whether it will be a symmetric matrix along the
diagonal. For example, if the list contains integers, and the
combination function is ``abs(x-y)``, then the matrix will be
symmetric.
:param diagonal: The value to be put into the diagonal. For some
functions, the diagonal will stay constant. An example could be the
function ``x-y``. Then each diagonal cell will be ``0``. If this
value is set to None, then the diagonal will be calculated.
"""
self.data = data
self.combinfunc = combinfunc
self.symmetric = symmetric
self.diagonal = diagonal
def worker(self):
"""
Multiprocessing task function run by worker processes
"""
tasks_completed = 0
for task in iter(self.task_queue.get, 'STOP'):
col_index, item, item2 = task
if not hasattr(item, '__iter__') or isinstance(item, tuple):
item = [item]
if not hasattr(item2, '__iter__') or isinstance(item2, tuple):
item2 = [item2]
result = (col_index, self.combinfunc(item, item2))
self.done_queue.put(result)
tasks_completed += 1
logger.info("Worker %s performed %s tasks",
current_process().name,
tasks_completed)
def genmatrix(self, num_processes=1):
"""
Actually generate the matrix
:param num_processes: If you want to use multiprocessing to split up the
work and run ``combinfunc()`` in parallel, specify
``num_processes > 1`` and this number of workers will be spun up,
the work is split up amongst them evenly.
"""
use_multiprocessing = num_processes > 1
if use_multiprocessing:
self.task_queue = Queue()
self.done_queue = Queue()
self.matrix = []
logger.info("Generating matrix for %s items - O(n^2)", len(self.data))
if use_multiprocessing:
logger.info("Using multiprocessing on %s processes!", num_processes)
if use_multiprocessing:
logger.info("Spinning up %s workers", num_processes)
processes = [Process(target=self.worker) for i in range(num_processes)]
[process.start() for process in processes]
for row_index, item in enumerate(self.data):
logger.debug("Generating row %s/%s (%0.2f%%)",
row_index,
len(self.data),
100.0 * row_index / len(self.data))
row = {}
if use_multiprocessing:
num_tasks_queued = num_tasks_completed = 0
for col_index, item2 in enumerate(self.data):
if self.diagonal is not None and col_index == row_index:
# This is a cell on the diagonal
row[col_index] = self.diagonal
elif self.symmetric and col_index < row_index:
# The matrix is symmetric and we are "in the lower left
# triangle" - fill this in after (in case of multiprocessing)
pass
# Otherwise, this cell is not on the diagonal and we do indeed
# need to call combinfunc()
elif use_multiprocessing:
# Add that thing to the task queue!
self.task_queue.put((col_index, item, item2))
num_tasks_queued += 1
# Start grabbing the results as we go, so as not to stuff all of
# the worker args into memory at once (as Queue.get() is a
# blocking operation)
if num_tasks_queued > num_processes:
col_index, result = self.done_queue.get()
row[col_index] = result
num_tasks_completed += 1
else:
# Otherwise do it here, in line
"""
if not hasattr(item, '__iter__') or isinstance(item, tuple):
item = [item]
if not hasattr(item2, '__iter__') or isinstance(item2, tuple):
item2 = [item2]
"""
# See the comment in function _encapsulate_item_for_combinfunc
# for details of why the lines above have been replaced
# by function invocations
item = _encapsulate_item_for_combinfunc(item)
item2 = _encapsulate_item_for_combinfunc(item2)
row[col_index] = self.combinfunc(item, item2)
if self.symmetric:
# One more iteration to get symmetric lower left triangle
for col_index, item2 in enumerate(self.data):
if col_index >= row_index:
break
# post-process symmetric "lower left triangle"
row[col_index] = self.matrix[col_index][row_index]
if use_multiprocessing:
# Grab the remaining worker task results
while num_tasks_completed < num_tasks_queued:
col_index, result = self.done_queue.get()
row[col_index] = result
num_tasks_completed += 1
row_indexed = [row[index] for index in range(len(self.data))]
self.matrix.append(row_indexed)
if use_multiprocessing:
logger.info("Stopping/joining %s workers", num_processes)
[self.task_queue.put('STOP') for i in range(num_processes)]
[process.join() for process in processes]
logger.info("Matrix generated")
def __str__(self):
"""
Returns a 2-dimensional list of data as text-string which can be
displayed to the user.
"""
# determine maximum length
maxlen = 0
colcount = len(self.data[0])
for col in self.data:
for cell in col:
maxlen = max(len(str(cell)), maxlen)
format = " %%%is |" % maxlen
format = "|" + format * colcount
rows = [format % tuple(row) for row in self.data]
return "\n".join(rows)
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