matplotlib:绘图时忽略异常值 [英] matplotlib: disregard outliers when plotting

问题描述

我正在绘制来自各种测试的一些数据.有时在测试中，我碰巧有一个异常值(例如0.1)，而所有其他值都小三个数量级.

I'm plotting some data from various tests. Sometimes in a test I happen to have one outlier (say 0.1), while all other values are three orders of magnitude smaller.

使用matplotlib，我针对范围[0, max_data_value]

With matplotlib, I plot against the range [0, max_data_value]

如何仅放大数据而不显示异常值，否则异常值会弄乱我的绘图中的x轴?

How can I just zoom into my data and not display outliers, which would mess up the x-axis in my plot?

我是否应该简单地将95个百分位数设为x轴上的范围[0, 95_percentile]?

Should I simply take the 95 percentile and have the range [0, 95_percentile] on the x-axis?

推荐答案

离群值没有单一的最佳"测试.理想情况下，您应该合并先验信息(例如，因为等等，此参数不应超过x ...").

There's no single "best" test for an outlier. Ideally, you should incorporate a-priori information (e.g. "This parameter shouldn't be over x because of blah...").

大多数离群值测试使用的是绝对绝对值中位数，而不是第95个百分位数或其他一些基于差异的度量.否则，计算出的方差/stddev将被异常值严重偏斜.

Most tests for outliers use the median absolute deviation, rather than the 95th percentile or some other variance-based measurement. Otherwise, the variance/stddev that is calculated will be heavily skewed by the outliers.

这是一个实现更常见异常值测试的函数.

Here's a function that implements one of the more common outlier tests.

def is_outlier(points, thresh=3.5):
    """
    Returns a boolean array with True if points are outliers and False 
    otherwise.

    Parameters:
    -----------
        points : An numobservations by numdimensions array of observations
        thresh : The modified z-score to use as a threshold. Observations with
            a modified z-score (based on the median absolute deviation) greater
            than this value will be classified as outliers.

    Returns:
    --------
        mask : A numobservations-length boolean array.

    References:
    ----------
        Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
        Handle Outliers", The ASQC Basic References in Quality Control:
        Statistical Techniques, Edward F. Mykytka, Ph.D., Editor. 
    """
    if len(points.shape) == 1:
        points = points[:,None]
    median = np.median(points, axis=0)
    diff = np.sum((points - median)**2, axis=-1)
    diff = np.sqrt(diff)
    med_abs_deviation = np.median(diff)

    modified_z_score = 0.6745 * diff / med_abs_deviation

    return modified_z_score > thresh

作为使用它的示例，您将执行以下操作:

As an example of using it, you'd do something like the following:

import numpy as np
import matplotlib.pyplot as plt

# The function above... In my case it's in a local utilities module
from sci_utilities import is_outlier

# Generate some data
x = np.random.random(100)

# Append a few "bad" points
x = np.r_[x, -3, -10, 100]

# Keep only the "good" points
# "~" operates as a logical not operator on boolean numpy arrays
filtered = x[~is_outlier(x)]

# Plot the results
fig, (ax1, ax2) = plt.subplots(nrows=2)

ax1.hist(x)
ax1.set_title('Original')

ax2.hist(filtered)
ax2.set_title('Without Outliers')

plt.show()

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