The whole point of the cache is to speed things up.
Aaron Meurer
On Wed, Aug 26, 2015 at 2:33 PM, Denis Akhiyarov <denis.akhiya...@gmail.com>
wrote:
> 1. regarding count_ops, are we now jumping to computation cost? :)
>
> 2. if size of sympy objects is proportional to computation cost involving
> them, then cachey does not make sense for sympy at all.
>
> 3. not sure if computation cost should just be tracked using time()
> function?
>
> 4. i think it is possible to override __sizeof__ just like the __hash__
> function in sympy objects.
>
>
> On Wednesday, August 26, 2015 at 1:42:40 PM UTC-5, Aaron Meurer wrote:
>>
>> Probably count_ops() would be a close approximation of both how expensive
>> an object is to create and how big it is (SymPy objects really shouldn't be
>> doing much computation at creation time).
>>
>> Aaron Meurer
>>
>> On Wed, Aug 26, 2015 at 12:51 PM, Denis Akhiyarov <denis.a...@gmail.com>
>> wrote:
>>
>>> what is the heuristic? number of **Basic** sympy objects?
>>>
>>> On Tuesday, August 25, 2015 at 7:50:43 PM UTC-5, Aaron Meurer wrote:
>>>>
>>>> Hashing in SymPy is done recursively (due to the nature of SymPy
>>>> objects), but amounts to hashes of tuples of integers and strings, which is
>>>> done in C. But it's also highly optimized: the hash is memoized and stored
>>>> in __slots__.
>>>>
>>>> If we really cared about sizes of objects, we could probably do a
>>>> similar thing. And it is probably sufficient to use heuristics rather than
>>>> a true sizeof.
>>>>
>>>> Aaron Meurer
>>>>
>>>> On Tue, Aug 25, 2015 at 6:12 PM, Denis Akhiyarov <denis.a...@gmail.com>
>>>> wrote:
>>>>
>>>>> pympler is very slow, hash is probably pure C, like fastcache.
>>>>>
>>>>> But it is understandable why it can get slow for collecting all this
>>>>> information in Python:
>>>>>
>>>>> asizeof(y1,stats=8)
>>>>>
>>>>> asizeof(((c/(3*a) - b**2/(9*a**2))/(sqrt((c/(3....) +
>>>>> b**3/(27*a**3))**(1/3) - b/(3*a),), stats=8) ...
>>>>> 52136 bytes or 50.9 KiB
>>>>> 8 byte aligned
>>>>> 8 byte sizeof(void*)
>>>>> 1 object given
>>>>> 222 objects sized
>>>>> 1840 objects seen
>>>>> 24 recursion depth
>>>>>
>>>>> 15 profiles: total (% of grand total), average, and largest flat
>>>>> size: largest object
>>>>> 42 class sympy.core.assumptions.StdFactKB objects: 42048 or 41.1 KiB
>>>>> (81%), 1001, 1632 or 1.6 KiB: {'prime': False, 'infinite': False,
>>>>> 'r....maginary': False, 'irrational': False} leng 32!
>>>>> 44 class str objects: 2632 or 2.6 KiB (5%), 59, 64: 'infinite' leng
>>>>> 9!
>>>>> 28 class tuple objects: 1888 or 1.8 KiB (4%), 67, 80:
>>>>> (sqrt((c/(3*a) - b**2/(9*a**2))**3 + (..../(2*a), b**3/(27*a**3),
>>>>> -b*c/(6*a**2)) leng 4
>>>>> 49 class int objects: 1848 or 1.8 KiB (4%), 37, 40:
>>>>> 5976377932654160047 leng 2!
>>>>> 12 class sympy.core.mul.Mul objects: 864 (2%), 72, 72:
>>>>> -(sqrt((c/(3*a) - b**2/(9*a**2))**3 + .... b*c/(6*a**2) +
>>>>> b**3/(27*a**3))**(1/3)
>>>>> 10 class sympy.core.power.Pow objects: 720 (1%), 72, 72:
>>>>> (sqrt((c/(3*a) - b**2/(9*a**2))**3 + (.... b*c/(6*a**2) +
>>>>> b**3/(27*a**3))**(1/3)
>>>>> 7 class sympy.core.numbers.Rational objects: 560 (1%), 80, 80: -1/9
>>>>> 5 class sympy.core.add.Add objects: 360 (1%), 72, 72: (c/(3*a) -
>>>>> b**2/(9*a**2))/(sqrt((c/(3*....*2) + b**3/(27*a**3))**(1/3) - b/(3*a)
>>>>> 4 class sympy.core.numbers.Integer objects: 352 (1%), 88, 88: -2
>>>>> 4 class pympler.asizeof._Slots objects: 336 (1%), 84, 88: ('p',
>>>>> 'q', '_assumptions', '_args', '_mhash') leng 4
>>>>> 4 class sympy.core.symbol.Symbol objects: 288 (1%), 72, 72: a
>>>>> 1 class sympy.core.numbers.NegativeOne object: 88 (0%), 88, 88: -1
>>>>> 1 class sympy.core.numbers.Half object: 80 (0%), 80, 80: 1/2
>>>>> 2 class bool objects: 56 (0%), 28, 32: True
>>>>> 1 class NoneType object: 16 (0%), 16, 16: None
>>>>>
>>>>> 42 static types: basicsize, itemsize, _len_(), _refs()
>>>>> class Exception: 88, 0, n/a, _exc_refs
>>>>> class NoneType: 16, 0, n/a, n/a
>>>>> class NotImplementedType: 16, 0, n/a, n/a
>>>>> class Struct: 56, 1, _len_struct, n/a
>>>>> class array.array: 64, 1, _len_array, n/a
>>>>> class bool: 32, 4, n/a, n/a
>>>>> class bytearray: 56, 1, _len_bytearray, n/a
>>>>> class bytearray_iterator: 56, 0, _len_iter, _iter_refs
>>>>> class callable_iterator: 56, 0, _len_iter, _iter_refs
>>>>> class complex: 32, 0, n/a, n/a
>>>>> class dict: 64, 24, _len_dict, _dict_refs
>>>>> class dict_itemiterator: 80, 0, _len_iter, _iter_refs
>>>>> class dict_keyiterator: 80, 0, _len_iter, _iter_refs
>>>>> class dict_valueiterator: 80, 0, _len_iter, _iter_refs
>>>>> class ellipsis: 16, 0, n/a, n/a
>>>>> class enumerate: 72, 0, n/a, _enum_refs
>>>>> class float: 24, 0, n/a, n/a
>>>>> class frozenset: 224, 16, _len_set, _seq_refs
>>>>> class getset_descriptor: 72, 0, n/a, n/a
>>>>> class int: 24, 4, _len_int, n/a
>>>>> class list: 64, 8, _len_list, _seq_refs
>>>>> class list_iterator: 56, 0, _len_iter, _iter_refs
>>>>> class list_reverseiterator: 56, 0, _len_iter, _iter_refs
>>>>> class mappingproxy: 48, 24, _len_dict, _dict_refs
>>>>> class member_descriptor: 72, 0, n/a, n/a
>>>>> class module: 88, 48, _len_module, _module_refs
>>>>> class os.stat_result: 48, 8, n/a, _stat_refs
>>>>> class property: 80, 0, n/a, _prop_refs
>>>>> class pympler.asizeof._Slots: 56, 8, _len_slots, n/a
>>>>> class range: 48, 0, n/a, n/a
>>>>> class reversed: 56, 0, n/a, _enum_refs
>>>>> class set: 224, 16, _len_set, _seq_refs
>>>>> class set_iterator: 72, 0, _len_iter, _iter_refs
>>>>> class slice: 40, 8, _len_slice, n/a
>>>>> class str: 80, 2, _len_unicode, n/a
>>>>> class str_iterator: 56, 0, _len_iter, _iter_refs
>>>>> class traceback: 64, 0, n/a, _tb_refs
>>>>> class tuple: 48, 8, _len, _seq_refs
>>>>> class tuple_iterator: 56, 0, _len_iter, _iter_refs
>>>>> class weakproxy: 80, 0, n/a, n/a
>>>>> class weakref: 80, 0, n/a, _weak_refs
>>>>> class weakref.KeyedRef: 88, 0, n/a, _weak_refs
>>>>>
>>>>> 8 dynamic types: basicsize, itemsize, _len_(), _refs()
>>>>> class sympy.core.add.Add: 72, 0, n/a, _inst_refs
>>>>> class sympy.core.mul.Mul: 72, 0, n/a, _inst_refs
>>>>> class sympy.core.numbers.Half: 80, 0, n/a, _inst_refs
>>>>> class sympy.core.numbers.Integer: 88, 0, n/a, _inst_refs
>>>>> class sympy.core.numbers.NegativeOne: 88, 0, n/a, _inst_refs
>>>>> class sympy.core.numbers.Rational: 80, 0, n/a, _inst_refs
>>>>> class sympy.core.power.Pow: 72, 0, n/a, _inst_refs
>>>>> class sympy.core.symbol.Symbol: 72, 0, n/a, _inst_refs
>>>>>
>>>>> 1 derived type: basicsize, itemsize, _len_(), _refs()
>>>>> class sympy.core.assumptions.StdFactKB: 64, 24, _len_dict,
>>>>> _dict_refs
>>>>>
>>>>> 4 dict/-like classes:
>>>>> UserDict: (IterableUserDict, UserDict)
>>>>> weakref: (WeakKeyDictionary, WeakValueDictionary)
>>>>>
>>>>>
>>>>>
>>>>> On Tuesday, August 25, 2015 at 11:38:45 AM UTC-5, Peter Brady wrote:
>>>>>
>>>>>> Thanks for trying that out. I had never heard of pympler before.
>>>>>> The current caching mechanism is based on hashing. By my tests,
>>>>>> 'pympler.asizeof' is 500-1000x slower than hashing. That's a strong
>>>>>> deficit for cachey to overcome (as far as sympy objects are concerned).
>>>>>>
>>>>>> In [1]: import sympy
>>>>>>
>>>>>> In [2]: from sympy.abc import a, b, c, d, e, x, y
>>>>>>
>>>>>> In [3]: from pympler.asizeof import asizeof
>>>>>>
>>>>>> In [4]: y=a*x**3+b*x**2+c*x+d
>>>>>>
>>>>>> In [5]: y1, y2, y3 = sympy.solve(y, x, check=False)
>>>>>>
>>>>>> In [6]: %time asizeof(y1)
>>>>>> CPU times: user 9.63 ms, sys: 0 ns, total: 9.63 ms
>>>>>> Wall time: 9.56 ms
>>>>>> Out[6]: 52608
>>>>>>
>>>>>> In [7]: %time hash(y1)
>>>>>> CPU times: user 14 µs, sys: 1 µs, total: 15 µs
>>>>>> Wall time: 19.8 µs
>>>>>> Out[7]: 5743556980832125790
>>>>>>
>>>>>> In [8]: y=a*x**4+b*x**3+c*x**2+d*x+e
>>>>>>
>>>>>> In [9]: y1,y2,y3,y4=sympy.solve(y,x,check=False)
>>>>>>
>>>>>> In [10]: %time asizeof(y4)
>>>>>> CPU times: user 16.7 ms, sys: 2.05 ms, total: 18.8 ms
>>>>>> Wall time: 18.6 ms
>>>>>> Out[10]: 85208
>>>>>>
>>>>>> In [11]: %time hash(y4)
>>>>>> CPU times: user 14 µs, sys: 1 µs, total: 15 µs
>>>>>> Wall time: 19.8 µs
>>>>>> Out[11]: 4388441583750016728
>>>>>>
>>>>>>
>>>>>> On Mon, Aug 24, 2015 at 10:49 PM, Denis Akhiyarov <
>>>>>> denis.a...@gmail.com> wrote:
>>>>>>
>>>>>>> It looks like pympler works pretty well on sympy symbols, here is my
>>>>>>> notebook:
>>>>>>>
>>>>>>> https://gist.github.com/denfromufa/4d0e6a94f70fac155b66
>>>>>>>
>>>>>>>
>>>>>>> On Monday, August 24, 2015 at 10:03:30 PM UTC-5, Denis Akhiyarov
>>>>>>> wrote:
>>>>>>>>
>>>>>>>> Nbytes is very hard in Python, and getsizeof() does not work very
>>>>>>>> well. People has addressed this using github.com/pympler.
>>>>>>>> Not sure if anyone tried it on sympy objects and how costly is that
>>>>>>>> calculation. Cachey has very simple nbytes calculation, mainly
>>>>>>>> intended for
>>>>>>>> numpy and pandas objects.
>>>>>>>>
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