熊猫：与模式匹配的固定补丁滚动相关

Question

新年快乐。

我正在寻找一种方法来计算滚动 window 和固定 window ('patch') 与 pandas 的相关性。最终objective就是做模式匹配

根据我在文档中阅读的内容，希望我遗漏了一些东西，corr() 或 corrwith() 不允许您锁定其中一个系列/数据帧。

目前我能想到的最好的蹩脚解决方案如下所列。当这是在 50K 行上运行时，带有 30 个样本的补丁，处理时间进入 Ctrl+C 范围。

非常感谢您的建议和替代方案。谢谢。

请运行下面的代码，你会很清楚我要做什么：

import numpy as np
import pandas as pd
from pandas import Series
from pandas import DataFrame

# Create test DataFrame df and a patch to be found.
n = 10
rng = pd.date_range('1/1/2000 00:00:00', periods=n, freq='5min')
df = DataFrame(np.random.rand(n, 1), columns=['a'], index=rng)

n = 4
rng = pd.date_range('1/1/2000 00:10:00', periods=n, freq='5min')
patch = DataFrame(np.arange(n), columns=['a'], index=rng)

print
print '    *** Start corr example ***'
# To avoid the automatic alignment between df and patch, 
# I need to reset the index.
patch.reset_index(inplace=True, drop=True)
# Cannot do:
#    df.reset_index(inplace=True, drop=True)

df['corr'] = np.nan

for i in range(df.shape[0]):
    window = df[i : i+patch.shape[0]]
    # If slice has only two rows, I have a line between two points
    # When I corr with to points in patch, I start getting 
    # misleading values like 1 or -1
    if window.shape[0] != patch.shape[0] :
        break
    else:
        # I need to reset_index for the window, 
        # which is less efficient than doing outside the 
        # for loop where the patch has its reset_index done.
        # If I would do the df.reset_index up there, 
        # I would still have automatic realignment but
        # by index.
        window.reset_index(inplace=True, drop=True)

        # On top of the obvious inefficiency
        # of this method, I cannot just corrwith()
        # between specific columns in the dataframe;
        # corrwith() runs for all.
        # Alternatively I could create a new DataFrame
        # only with the needed columns:
        #     df_col = DataFrame(df.a)
        #     patch_col = DataFrame(patch.a)
        # Alternatively I could join the patch to
        # the df and shift it.
        corr = window.corrwith(patch)

        print
        print '==========================='
        print 'window:'
        print window
        print '---------------------------'
        print 'patch:'
        print patch
        print '---------------------------'
        print 'Corr for this window'
        print corr
        print '============================'

        df['corr'][i] = corr.a

print
print '    *** End corr example ***'
print " Please inspect var 'df'"
print

Answer 1

显然，reset_index 的大量使用表明我们正在与 Panda 的索引和自动对齐作斗争。哦，如果我们可以忘记索引，事情会容易得多！事实上，这就是 NumPy 的用途。（一般来说，需要对齐或按索引分组时使用Pandas，对N维数组进行计算时使用NumPy。）

使用 NumPy 将使计算速度更快，因为我们将能够删除 for-loop 并将 for 循环中完成的所有计算处理为 一次计算 在滚动 windows.

的 NumPy 数组上完成

我们可以look inside pandas/core/frame.py's DataFrame.corrwith看看计算是如何完成的。然后将其转换为在 NumPy 数组上完成的相应代码，根据需要进行调整，因为我们希望对整个数组进行计算 windows 而不是一次只有一个 window，同时保持 patch 不变。（注意：Pandas corrwith 方法处理 NaN。为了使代码更简单，我假设输入中没有 NaN。）

import numpy as np
import pandas as pd
from pandas import Series
from pandas import DataFrame
import numpy.lib.stride_tricks as stride
np.random.seed(1)

n = 10
rng = pd.date_range('1/1/2000 00:00:00', periods=n, freq='5min')
df = DataFrame(np.random.rand(n, 1), columns=['a'], index=rng)

m = 4
rng = pd.date_range('1/1/2000 00:10:00', periods=m, freq='5min')
patch = DataFrame(np.arange(m), columns=['a'], index=rng)

def orig(df, patch):
    patch.reset_index(inplace=True, drop=True)

    df['corr'] = np.nan

    for i in range(df.shape[0]):
        window = df[i : i+patch.shape[0]]
        if window.shape[0] != patch.shape[0] :
            break
        else:
            window.reset_index(inplace=True, drop=True)
            corr = window.corrwith(patch)

            df['corr'][i] = corr.a

    return df

def using_numpy(df, patch):
    left = df['a'].values
    itemsize = left.itemsize
    left = stride.as_strided(left, shape=(n-m+1, m), strides = (itemsize, itemsize))

    right = patch['a'].values

    ldem = left - left.mean(axis=1)[:, None]
    rdem = right - right.mean()

    num = (ldem * rdem).sum(axis=1)
    dom = (m - 1) * np.sqrt(left.var(axis=1, ddof=1) * right.var(ddof=1))
    correl = num/dom

    df.ix[:len(correl), 'corr'] = correl
    return df

expected = orig(df.copy(), patch.copy())
result = using_numpy(df.copy(), patch.copy())

print(expected)
print(result)

这证实了 orig 和 using_numpy 生成的值是相同：

assert np.allclose(expected['corr'].dropna(), result['corr'].dropna())

技术说明：

为了以内存友好的方式创建充满滚动 windows 的数组，我 used a striding trick I learned here.

这是一个基准测试，使用 n, m = 1000, 4（很多行和一个小补丁生成很多 windows）：

In [77]: %timeit orig(df.copy(), patch.copy())
1 loops, best of 3: 3.56 s per loop

In [78]: %timeit using_numpy(df.copy(), patch.copy())
1000 loops, best of 3: 1.35 ms per loop

-- 2600 倍加速。

熊猫：与模式匹配的固定补丁滚动相关

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