回答此问题可获得 20 贡献值,回答如果被采纳可获得 50 分。
<p><strong>定义</strong><br/>
<em>因子分解:将每个唯一的对象映射为唯一的整数。通常,映射到的整数范围是从零到n-1,其中n是唯一对象的数目。两种变化也是典型的。类型1是按照唯一对象的标识顺序进行编号的位置。类型2首先对唯一对象进行排序,然后应用与类型1相同的过程。</em></p>
<p><strong>设置</strong><br/>
考虑元组列表<code>tups</code></p>
<pre><code>tups = [(1, 2), ('a', 'b'), (3, 4), ('c', 5), (6, 'd'), ('a', 'b'), (3, 4)]
</code></pre>
<p>我想把它分解成</p>
^{pr2}$
<p>我知道有很多方法可以做到这一点。但是,我想尽可能有效地完成这项工作。在</p>
<hr/>
<p><strong>我尝试过的</strong></p>
<p><code>pandas.factorize</code>并得到一个错误。。。在</p>
<pre><code>pd.factorize(tups)[0]
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-84-c84947ac948c> in <module>()
----> 1 pd.factorize(tups)[0]
//anaconda/envs/3.6/lib/python3.6/site-packages/pandas/core/algorithms.py in factorize(values, sort, order, na_sentinel, size_hint)
553 uniques = vec_klass()
554 check_nulls = not is_integer_dtype(original)
--> 555 labels = table.get_labels(values, uniques, 0, na_sentinel, check_nulls)
556
557 labels = _ensure_platform_int(labels)
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_labels (pandas/_libs/hashtable.c:21804)()
ValueError: Buffer has wrong number of dimensions (expected 1, got 2)
</code></pre>
<hr/>
<p>或者<code>numpy.unique</code>得到错误的结果。。。在</p>
<pre><code>np.unique(tups, return_inverse=1)[1]
array([0, 1, 6, 7, 2, 3, 8, 4, 5, 9, 6, 7, 2, 3])
</code></pre>
<hr/>
<p>我可以在元组的散列上使用它们中的任何一个</p>
<pre><code>pd.factorize([hash(t) for t in tups])[0]
array([0, 1, 2, 3, 4, 1, 2])
</code></pre>
<hr/>
<p>耶!这就是我想要的。。。有什么问题吗?在</p>
<p><strong>第一个问题</strong><br/>
看看这项技术的性能下降</p>
<pre><code>lst = [10, 7, 4, 33, 1005, 7, 4]
%timeit pd.factorize(lst * 1000)[0]
1000 loops, best of 3: 506 µs per loop
%timeit pd.factorize([hash(i) for i in lst * 1000])[0]
1000 loops, best of 3: 937 µs per loop
</code></pre>
<p><strong>第二个问题</strong><br/>
哈希不能保证是唯一的!在</p>
<hr/>
<p><strong>问题</strong><br/>
什么是对元组列表进行因子分解的超快速方法?在</p>
<hr/>
<p><strong>时间</strong><br/>
<em>两个轴都在log空间中</em></p>
<p><a href="https://i.stack.imgur.com/zIlh0.png" rel="noreferrer"><img src="https://i.stack.imgur.com/zIlh0.png" alt="enter image description here"/></a></p>
<p><em><code>code</code></em></p>
<pre><code>from itertools import count
def champ(tups):
d = {}
c = count()
return np.array(
[d[tup] if tup in d else d.setdefault(tup, next(c)) for tup in tups]
)
def root(tups):
return pd.Series(tups).factorize()[0]
def iobe(tups):
return np.unique(tups, return_inverse=True, axis=0)[1]
def get_row_view(a):
void_dt = np.dtype((np.void, a.dtype.itemsize * np.prod(a.shape[1:])))
a = np.ascontiguousarray(a)
return a.reshape(a.shape[0], -1).view(void_dt).ravel()
def diva(tups):
return np.unique(get_row_view(np.array(tups)), return_inverse=1)[1]
def gdib(tups):
return pd.factorize([str(t) for t in tups])[0]
from string import ascii_letters
def tups_creator_1(size, len_of_str=3, num_ints_to_choose_from=1000, seed=None):
c = len_of_str
n = num_ints_to_choose_from
np.random.seed(seed)
d = pd.DataFrame(np.random.choice(list(ascii_letters), (size, c))).sum(1).tolist()
i = np.random.randint(n, size=size)
return list(zip(d, i))
results = pd.DataFrame(
index=pd.Index([100, 1000, 5000, 10000, 20000, 30000, 40000, 50000], name='Size'),
columns=pd.Index('champ root iobe diva gdib'.split(), name='Method')
)
for i in results.index:
tups = tups_creator_1(i, max(1, int(np.log10(i))), max(10, i // 10))
for j in results.columns:
stmt = '{}(tups)'.format(j)
setup = 'from __main__ import {}, tups'.format(j)
results.set_value(i, j, timeit(stmt, setup, number=100) / 100)
results.plot(title='Avg Seconds', logx=True, logy=True)
</code></pre>