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
> <javascript:>> 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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