This clarification makes a lot of sense to me, but if I try and apply the reasoning to the code below (which is based on the employee scheduling example available in optapy) I would have expected set_timeslot_list to be called when set_task is called but it does not look like it is.
The optimisation runs OK and finds a suitable set of tasks to assign to the list of time slots that I have available, but each task.timeslot_list remains empty, and looks like the set_timeslot_list method is never called.
I believe I am missing something..Can you please help me understand what is wrong with how I modified the example in the code below or with I am interpreting how shadow vars work?
I can provide longer snippets, or the #planning_solution class if this is not sufficient.
#planning_entity(pinning_filter=timeslot_pinning_filter)
class Timeslot:
def __init__(self, start: datetime.datetime = None, end: datetime.datetime = None,
location: str = None, required_skill: str = None, task: object = None):
self.start = start
self.end = end
self.location = location
self.required_skill = required_skill
self.task = task
#planning_id
def get_id(self):
return self.id
# The type of the planning variable is Task, but we cannot use it because task refers to Timeslot below.
#planning_variable(object, value_range_provider_refs=['task_range'], nullable=False)
def get_task(self):
return self.task
def set_task(self, task):
self.task = task
#planning_entity
class Task:
def __init__(self, name: str = None, duration: int = None, skill_set: list = None):
self.name = name
self.duration = duration
self.skill_set = skill_set
self.timeslot_list = [] #The shadow property, which is a list, can never be None. If no genuine variable references that shadow entity, then it is an empty list
#inverse_relation_shadow_variable(Timeslot, source_variable_name = "task")
def get_timeslot_list(self):
return self.timeslot_list
def set_timeslot_list(self, ts):
self.timeslot_list = ts
Inverse Relation Shadow variables work differently than other variables: in particular, they directly modify the list returned by get_timeslot_list, so set_timeslot_list is never called. Your code look correct, which leaves me to believe you are checking the original planning entities and not the solution planning entities. In OptaPy (and OptaPlanner), the working solution/planning solution is cloned whenever we find a new best solution. As a result, the original problem (and the original planning entities) are never touched. So if your code look similar to this:
solver = optapy.solver_factory_create(...).buildSolver()
timeslot_list = [...]
task_1 = Task(...)
task_2 = Task(...)
task_list = [task_1, task_2]
problem = EmployeeSchedulingProblem(timeslot_list, task_list, ...)
solution = solver.solve(problem)
# this is incorrect; it prints the timeslot_list of the original problem
print(task_1.timeslot_list)
It should be changed to this instead:
solver = optapy.solver_factory_create(...).buildSolver()
timeslot_list = [...]
task_1 = Task(...)
task_2 = Task(...)
task_list = [task_1, task_2]
problem = EmployeeSchedulingProblem(timeslot_list, task_list, ...)
solution = solver.solve(problem)
# this is correct; it prints the timeslot_list of the solution
print(solution.task_list[0].timeslot_list)
Related
I defined the following Enum in Python:
class Unit(Enum):
GRAM = ("g")
KILOGRAM = ("kg", GRAM, 1000.0)
def __init__(self, symbol, base_unit = None, multiplier = 1.0):
self.symbol = symbol
self.multiplier = multiplier
self.base_unit = self if base_unit is None else base_unit
I would expect that
print(Unit.GRAM.base_unit)
print(Unit.KILOGRAM.base_unit)
will return
Unit.GRAM
Unit.GRAM
However, what I get is quite confusing
Unit.GRAM
g
Why is it so?
The way Python defines a class involves creating a new scope, processing a bunch of statements (variable assignments, function definitions, etc.), and then actually creating a class object based on the local variables which exist after all those statements have run. Nothing gets converted into Enum instances until that last step.
You could understand it somewhat like this:
def make_class_Unit():
GRAM = ("g")
KILOGRAM = ("kg", GRAM, 1000.0)
def __init__(self, symbol, base_unit = None, multiplier = 1.0):
self.symbol = symbol
self.multiplier = multiplier
self.base_unit = self if base_unit is None else base_unit
return make_class(name='Unit', base=Enum, contents=locals())
Unit = make_class_Unit()
Looking at it this way, hopefully you can tell that at the time when KILOGRAM is defined, GRAM is really just a string. It doesn't become a Unit instance until the last stage, where I call the (imaginary) make_class() function.1
1Even though the make_class function I used above doesn't actually exist under that name, it's not too different from what Python really does, which is calling the constructor of type or a metaclass (which in this case is the metaclass for Enums).
DavidZ explained the problem well.
The last bit that you need to solve this problem is this: when the __init__ of each member is being run, the Enum has been created -- so you can call it:
self.base_unit = self if base_unit is None else self.__class__(base_unit)
I am trying to change my code to a more object oriented format. In doing so I am lost on how to 'visualize' what is happening with multiprocessing and how to solve it. On the one hand, the class should track changes to local variables across functions, but on the other I believe multiprocessing creates a copy of the code which the original instance would not have access to. I need to figure out a way to manipulate classes, within a class, using multiprocessing, and have the parent class retain all manipulated values in the nested classes.
A simple version (OLD CODE):
function runMultProc():
...
dictReports = {}
listReports = ['reportName1.txt', 'reportName2.txt']
tasks = []
pool = multiprocessing.Pool()
for report in listReports:
if report not in dictReports:
dictReports[today][report] = {}
tasks.append(pool.apply_async(worker, args=([report, dictReports[today][report]])))
else:
continue
for task in tasks:
report, currentReportDict = task.get()
dictReports[report] = currentFileDict
function worker(report, currentReportDict):
<Manipulate_reports_dict>
return report, currentReportDict
NEW CODE:
class Transfer():
def __init__(self):
self.masterReportDictionary[<todays_date>] = [reportObj1, reportObj2]
def processReports(self):
self.pool = multiprocessing.Pool()
self.pool.map(processWorker, self.masterReportDictionary[<todays_date>])
self.pool.close()
self.pool.join()
def processWorker(self, report):
# **process and manipulate report, currently no return**
report.name = 'foo'
report.path = '/path/to/report'
class Report():
def init(self):
self.name = ''
self.path = ''
self.errors = {}
self.runTime = ''
self.timeProcessed = ''
self.hashes = {}
self.attempts = 0
I don't think this code does what I need it to do, which is to have it process the list of reports in parallel AND, as processWorker manipulates each report class object, store those results. As I am fairly new to this I was hoping someone could help.
The big difference between the two is that the first one build a dictionary and returned it. The second model shouldn't really be returning anything, I just need for the classes to finish being processed and they should have relevant information within them.
Thanks!
I have a program that models kingdoms and other groups (called 'factions' in my code).
class Faction:
def __init__(self, name, allies=[]):
self.name = name
self.allies = allies
def is_ally_of(self, other_faction):
if self in other_faction.allies:
return True
else:
return False
def become_ally(self, other_faction, both_ally=True):
""" If both_ally is false, this does *not* also
add self to other_faction's ally list """
if self.is_ally_of(other_faction):
print("They're already allies!")
else:
self.allies.append(other_faction)
if both_ally == True:
other_faction.become_ally(self, False)
RezlaGovt = Faction("Kingdom of Rezla")
AzosGovt = Faction("Azos Ascendancy")
I want to be able to call a factions become_ally() method to add factions to the ally lists, like this:
RezlaGovt.become_ally(AzosGovt) # Now AzosGovt should be in RezlaGovt.allies,
# and RezlaGovt in AzosGovt.allies
What actually happens is this:
RezlaGovt.become_ally(AzosGovt)
# prints "They're already allies!"
# now AzosGovt is in the allies list of both AzosGovt and RezlaGovt,
# but RezlaGovt isn't in any allies list at all.
Whenever I try to call become_ally(), the code should check to make sure they aren't already allies. This is the part that isn't working. Every time I call become_ally(), it prints "They're already allies!", regardless of if they actually are.
I also tried to use if self in other_faction.allies:, but that had the same problem.
I strongly suspect that the problem is with my use of self, but I don't know what terms to Google for more information.
You can't use mutable arguments as the default argument to a function.
def __init__(self, name, allies=[]):
When the default is used, it's the same list each time, so they have the same allies; mutating one changes the other because they're actually the same thing.
Change to:
def __init__(self, name, allies=None):
if allies is None:
allies = []
Alternatively, copy the allies argument unconditionally (so you're not worried about a reference to it surviving outside the class and getting mutated under the class):
def __init__(self, name, allies=[]):
self.allies = list(allies) # Which also guarantees a tuple argument becomes list
# and non-iterable args are rejected
Change this function.
def is_ally_of(self, other_faction):
if other_faction in self.allies:
return True
else:
return False
Check your own data not that of the passed in object.
Also
def __init__(self, name, allies=[]):
Is a bug waiting to happen. Your allies list will be a static list shared between all instances. Instead use
def __init__(self, name, allies=None):
self.name = name
self.allies = allies or []
I am maintaining a little library of useful functions for interacting with my company's APIs and I have come across (what I think is) a neat question that I can't find the answer to.
I frequently have to request large amounts of data from an API, so I do something like:
class Client(object):
def __init__(self):
self.data = []
def get_data(self, offset = 0):
done = False
while not done:
data = get_more_starting_at(offset)
self.data.extend(data)
offset += 1
if not data:
done = True
This works fine and allows me to restart the retrieval where I left off if something goes horribly wrong. However, since python functions are just regular objects, we can do stuff like:
def yo():
yo.hi = "yo!"
return None
and then we can interrogate yo about its properties later, like:
yo.hi => "yo!"
my question is: Can I rewrite my class-based example to pin the data to the function itself, without referring to the function by name. I know I can do this by:
def get_data(offset=0):
done = False
get_data.data = []
while not done:
data = get_more_starting_from(offset)
get_data.data.extend(data)
offset += 1
if not data:
done = True
return get_data.data
but I would like to do something like:
def get_data(offset=0):
done = False
self.data = [] # <===== this is the bit I can't figure out
while not done:
data = get_more_starting_from(offset)
self.data.extend(data) # <====== also this!
offset += 1
if not data:
done = True
return self.data # <======== want to refer to the "current" object
Is it possible to refer to the "current" object by anything other than its name?
Something like "this", "self", or "memememe!" is what I'm looking for.
I don't understand why you want to do this, but it's what a fixed point combinator allows you to do:
import functools
def Y(f):
#functools.wraps(f)
def Yf(*args):
return inner(*args)
inner = f(Yf)
return Yf
#Y
def get_data(f):
def inner_get_data(*args):
# This is your real get data function
# define it as normal
# but just refer to it as 'f' inside itself
print 'setting get_data.foo to', args
f.foo = args
return inner_get_data
get_data(1, 2, 3)
print get_data.foo
So you call get_data as normal, and it "magically" knows that f means itself.
You could do this, but (a) the data is not per-function-invocation, but per function (b) it's much easier to achieve this sort of thing with a class.
If you had to do it, you might do something like this:
def ybother(a,b,c,yrselflambda = lambda: ybother):
yrself = yrselflambda()
#other stuff
The lambda is necessary, because you need to delay evaluation of the term ybother until something has been bound to it.
Alternatively, and increasingly pointlessly:
from functools import partial
def ybother(a,b,c,yrself=None):
#whatever
yrself.data = [] # this will blow up if the default argument is used
#more stuff
bothered = partial(ybother, yrself=ybother)
Or:
def unbothered(a,b,c):
def inbothered(yrself):
#whatever
yrself.data = []
return inbothered, inbothered(inbothered)
This last version gives you a different function object each time, which you might like.
There are almost certainly introspective tricks to do this, but they are even less worthwhile.
Not sure what doing it like this gains you, but what about using a decorator.
import functools
def add_self(f):
#functools.wraps(f)
def wrapper(*args,**kwargs):
if not getattr(f, 'content', None):
f.content = []
return f(f, *args, **kwargs)
return wrapper
#add_self
def example(self, arg1):
self.content.append(arg1)
print self.content
example(1)
example(2)
example(3)
OUTPUT
[1]
[1, 2]
[1, 2, 3]
I've got a piece of code which contains a for loop to draw things from an XML file;
for evoNode in node.getElementsByTagName('evolution'):
evoName = getText(evoNode.getElementsByTagName( "type")[0].childNodes)
evoId = getText(evoNode.getElementsByTagName( "typeid")[0].childNodes)
evoLevel = getText(evoNode.getElementsByTagName( "level")[0].childNodes)
evoCost = getText(evoNode.getElementsByTagName("costperlevel")[0].childNodes)
evolutions.append("%s x %s" % (evoLevel, evoName))
Currently it outputs into a list called evolutions as it says in the last line of that code, for this and several other for functions with very similar functionality I need it to output into a class instead.
class evolutions:
def __init__(self, evoName, evoId, evoLevel, evoCost)
self.evoName = evoName
self.evoId = evoId
self.evoLevel = evoLevel
self.evoCost = evoCost
How to create a series of instances of this class, each of which is a response from that for function? Or what is a core practical solution? This one doesn't really need the class but one of the others really does.
A list comprehension might be a little cleaner. I'd also move the parsing logic to the constructor to clean up the implemenation:
class Evolution:
def __init__(self, node):
self.node = node
self.type = property("type")
self.typeid = property("typeid")
self.level = property("level")
self.costperlevel = property("costperlevel")
def property(self, prop):
return getText(self.node.getElementsByTagName(prop)[0].childNodes)
evolutionList = [Evolution(evoNode) for evoNode in node.getElementsByTagName('evolution')]
Alternatively, you could use map:
evolutionList = map(Evolution, node.getElementsByTagName('evolution'))
for evoNode in node.getElementsByTagName('evolution'):
evoName = getText(evoNode.getElementsByTagName("type")[0].childNodes)
evoId = getText(evoNode.getElementsByTagName("typeid")[0].childNodes)
evoLevel = getText(evoNode.getElementsByTagName("level")[0].childNodes)
evoCost = getText(evoNode.getElementsByTagName("costperlevel")[0].childNodes)
temporaryEvo = Evolutions(evoName, evoId, evoLevel, evoCost)
evolutionList.append(temporaryEvo)
# Or you can go with the 1 liner
evolutionList.append(Evolutions(evoName, evoId, evoLevel, evoCost))
I renamed your list because it shared the same name as your class and was confusing.