为什么与非掩码数组相比，掩码数组似乎更小?

问题描述

我试图了解numpy掩码数组和带有nans的普通数组之间的大小差异是多少.

I am trying to understand what's the size difference between a numpy masked array and a normal array with nans.

import numpy as np
g = np.random.random((5000,5000))
indx = np.random.randint(0,4999,(500,2))
mask =  np.full((5000,5000),False,dtype=bool)
mask[indx] = True
g_mask = np.ma.array(g,mask=mask)

我使用以下 answer 来计算对象的大小:

I used the following answer to compute the size of the object:

import sys
from types import ModuleType, FunctionType
from gc import get_referents
​
# Custom objects know their class.
# Function objects seem to know way too much, including modules.
# Exclude modules as well.
BLACKLIST = type, ModuleType, FunctionType
​
​
def getsize(obj):
    """sum size of object & members."""
    if isinstance(obj, BLACKLIST):
        raise TypeError('getsize() does not take argument of type: '+ str(type(obj)))
    seen_ids = set()
    size = 0
    objects = [obj]
    while objects:
        need_referents = []
        for obj in objects:
            if not isinstance(obj, BLACKLIST) and id(obj) not in seen_ids:
                seen_ids.add(id(obj))
                size += sys.getsizeof(obj)
                need_referents.append(obj)
        objects = get_referents(*need_referents)
    return size

这给了我以下结果:

getsize(g)
>>>200000112
getsize(g_mask)
>>>25000924

为什么未遮罩的数组比遮罩的数组大?如何估算屏蔽数组与未屏蔽数组的实际大小?

Why the unmasked array is bigger compared to the masked array? How can I estimate the real size of the masked array vs the unmasked array?

推荐答案

In [23]: g = np.random.random((5000,5000))
    ...: indx = np.random.randint(0,4999,(500,2))
    ...: mask =  np.full((5000,5000),False,dtype=bool)
    ...: mask[indx] = True
    ...: g_mask = np.ma.array(g,mask=mask)

将g数组与g_mask的_data属性进行比较，我们看到后者只是前者的view:

Comparing the g array with the _data attribute of g_mask, we see that the latter is just a view of the former:

In [24]: g.__array_interface__
Out[24]:
{'data': (139821997776912, False),
 'strides': None,
 'descr': [('', '<f8')],
 'typestr': '<f8',
 'shape': (5000, 5000),
 'version': 3}
In [25]: g_mask._data.__array_interface__
Out[25]:
{'data': (139821997776912, False),
 'strides': None,
 'descr': [('', '<f8')],
 'typestr': '<f8',
 'shape': (5000, 5000),
 'version': 3}

它们具有相同的数据缓冲区，但它们的id不同:

They have the same data buffer, but their id is different:

In [26]: id(g)
Out[26]: 139822758212672
In [27]: id(g_mask._data)
Out[27]: 139822386925440

与面具相同:

In [28]: mask.__array_interface__
Out[28]:
{'data': (139822298669072, False),
 'strides': None,
 'descr': [('', '|b1')],
 'typestr': '|b1',
 'shape': (5000, 5000),
 'version': 3}
In [29]: g_mask._mask.__array_interface__
Out[29]:
{'data': (139822298669072, False),
 'strides': None,
 'descr': [('', '|b1')],
 'typestr': '|b1',
 'shape': (5000, 5000),
 'version': 3}

实际上，这种结构的_mask是相同的数组:

Actually with this construction, the _mask is the same array:

In [30]: id(mask)
Out[30]: 139822385963056
In [31]: id(g_mask._mask)
Out[31]: 139822385963056

掩码数组的

__array_interface__是._data属性的

__array_interface__ of the masked array is that of the ._data attribute:

In [32]: g_mask.__array_interface__
Out[32]:
{'data': (139821997776912, False),

nbytes是数组的数据缓冲区的大小:

nbytes is the size of the data buffer for an array:

In [34]: g_mask.data.nbytes
Out[34]: 200000000
In [35]: g_mask.mask.nbytes
Out[35]: 25000000

一个布尔数组每个元素有1个字节，而float64有8个字节.

A boolean array has 1 byte per element, and a float64, 8 bytes.

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