Mercurial > repos > shellac > guppy_basecaller
annotate env/lib/python3.7/site-packages/boltons/statsutils.py @ 0:26e78fe6e8c4 draft
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author | shellac |
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date | Sat, 02 May 2020 07:14:21 -0400 |
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1 # -*- coding: utf-8 -*- |
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2 """``statsutils`` provides tools aimed primarily at descriptive |
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3 statistics for data analysis, such as :func:`mean` (average), |
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4 :func:`median`, :func:`variance`, and many others, |
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5 |
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6 The :class:`Stats` type provides all the main functionality of the |
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7 ``statsutils`` module. A :class:`Stats` object wraps a given dataset, |
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8 providing all statistical measures as property attributes. These |
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9 attributes cache their results, which allows efficient computation of |
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10 multiple measures, as many measures rely on other measures. For |
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11 example, relative standard deviation (:attr:`Stats.rel_std_dev`) |
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12 relies on both the mean and standard deviation. The Stats object |
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13 caches those results so no rework is done. |
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14 |
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15 The :class:`Stats` type's attributes have module-level counterparts for |
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16 convenience when the computation reuse advantages do not apply. |
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17 |
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18 >>> stats = Stats(range(42)) |
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19 >>> stats.mean |
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20 20.5 |
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21 >>> mean(range(42)) |
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22 20.5 |
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23 |
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24 Statistics is a large field, and ``statsutils`` is focused on a few |
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25 basic techniques that are useful in software. The following is a brief |
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26 introduction to those techniques. For a more in-depth introduction, |
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27 `Statistics for Software |
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28 <https://www.paypal-engineering.com/2016/04/11/statistics-for-software/>`_, |
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29 an article I wrote on the topic. It introduces key terminology vital |
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30 to effective usage of statistics. |
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31 |
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32 Statistical moments |
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33 ------------------- |
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34 |
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35 Python programmers are probably familiar with the concept of the |
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36 *mean* or *average*, which gives a rough quantitiative middle value by |
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37 which a sample can be can be generalized. However, the mean is just |
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38 the first of four `moment`_-based measures by which a sample or |
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39 distribution can be measured. |
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40 |
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41 The four `Standardized moments`_ are: |
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42 |
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43 1. `Mean`_ - :func:`mean` - theoretical middle value |
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44 2. `Variance`_ - :func:`variance` - width of value dispersion |
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45 3. `Skewness`_ - :func:`skewness` - symmetry of distribution |
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46 4. `Kurtosis`_ - :func:`kurtosis` - "peakiness" or "long-tailed"-ness |
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47 |
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48 For more information check out `the Moment article on Wikipedia`_. |
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49 |
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50 .. _moment: https://en.wikipedia.org/wiki/Moment_(mathematics) |
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51 .. _Standardized moments: https://en.wikipedia.org/wiki/Standardized_moment |
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52 .. _Mean: https://en.wikipedia.org/wiki/Mean |
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53 .. _Variance: https://en.wikipedia.org/wiki/Variance |
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54 .. _Skewness: https://en.wikipedia.org/wiki/Skewness |
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55 .. _Kurtosis: https://en.wikipedia.org/wiki/Kurtosis |
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56 .. _the Moment article on Wikipedia: https://en.wikipedia.org/wiki/Moment_(mathematics) |
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57 |
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58 Keep in mind that while these moments can give a bit more insight into |
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59 the shape and distribution of data, they do not guarantee a complete |
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60 picture. Wildly different datasets can have the same values for all |
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61 four moments, so generalize wisely. |
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62 |
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63 Robust statistics |
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64 ----------------- |
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65 |
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66 Moment-based statistics are notorious for being easily skewed by |
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67 outliers. The whole field of robust statistics aims to mitigate this |
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68 dilemma. ``statsutils`` also includes several robust statistical methods: |
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69 |
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70 * `Median`_ - The middle value of a sorted dataset |
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71 * `Trimean`_ - Another robust measure of the data's central tendency |
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72 * `Median Absolute Deviation`_ (MAD) - A robust measure of |
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73 variability, a natural counterpart to :func:`variance`. |
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74 * `Trimming`_ - Reducing a dataset to only the middle majority of |
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75 data is a simple way of making other estimators more robust. |
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76 |
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77 .. _Median: https://en.wikipedia.org/wiki/Median |
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78 .. _Trimean: https://en.wikipedia.org/wiki/Trimean |
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79 .. _Median Absolute Deviation: https://en.wikipedia.org/wiki/Median_absolute_deviation |
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80 .. _Trimming: https://en.wikipedia.org/wiki/Trimmed_estimator |
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81 |
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82 |
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83 Online and Offline Statistics |
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84 ----------------------------- |
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85 |
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86 Unrelated to computer networking, `online`_ statistics involve |
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87 calculating statistics in a `streaming`_ fashion, without all the data |
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88 being available. The :class:`Stats` type is meant for the more |
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89 traditional offline statistics when all the data is available. For |
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90 pure-Python online statistics accumulators, look at the `Lithoxyl`_ |
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91 system instrumentation package. |
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92 |
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93 .. _Online: https://en.wikipedia.org/wiki/Online_algorithm |
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94 .. _streaming: https://en.wikipedia.org/wiki/Streaming_algorithm |
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95 .. _Lithoxyl: https://github.com/mahmoud/lithoxyl |
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96 |
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97 """ |
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98 |
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99 from __future__ import print_function |
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100 |
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101 import bisect |
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102 from math import floor, ceil |
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103 |
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104 |
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105 class _StatsProperty(object): |
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106 def __init__(self, name, func): |
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107 self.name = name |
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108 self.func = func |
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109 self.internal_name = '_' + name |
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110 |
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111 doc = func.__doc__ or '' |
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112 pre_doctest_doc, _, _ = doc.partition('>>>') |
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113 self.__doc__ = pre_doctest_doc |
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114 |
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115 def __get__(self, obj, objtype=None): |
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116 if obj is None: |
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117 return self |
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118 if not obj.data: |
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119 return obj.default |
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120 try: |
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121 return getattr(obj, self.internal_name) |
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122 except AttributeError: |
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123 setattr(obj, self.internal_name, self.func(obj)) |
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124 return getattr(obj, self.internal_name) |
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125 |
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126 |
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127 class Stats(object): |
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128 """The ``Stats`` type is used to represent a group of unordered |
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129 statistical datapoints for calculations such as mean, median, and |
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130 variance. |
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131 |
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132 Args: |
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133 |
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134 data (list): List or other iterable containing numeric values. |
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135 default (float): A value to be returned when a given |
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136 statistical measure is not defined. 0.0 by default, but |
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137 ``float('nan')`` is appropriate for stricter applications. |
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138 use_copy (bool): By default Stats objects copy the initial |
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139 data into a new list to avoid issues with |
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140 modifications. Pass ``False`` to disable this behavior. |
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141 is_sorted (bool): Presorted data can skip an extra sorting |
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142 step for a little speed boost. Defaults to False. |
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143 |
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144 """ |
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145 def __init__(self, data, default=0.0, use_copy=True, is_sorted=False): |
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146 self._use_copy = use_copy |
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147 self._is_sorted = is_sorted |
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148 if use_copy: |
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149 self.data = list(data) |
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150 else: |
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151 self.data = data |
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152 |
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153 self.default = default |
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154 cls = self.__class__ |
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155 self._prop_attr_names = [a for a in dir(self) |
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156 if isinstance(getattr(cls, a, None), |
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157 _StatsProperty)] |
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158 self._pearson_precision = 0 |
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159 |
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160 def __len__(self): |
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161 return len(self.data) |
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162 |
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163 def __iter__(self): |
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164 return iter(self.data) |
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165 |
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166 def _get_sorted_data(self): |
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167 """When using a copy of the data, it's better to have that copy be |
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168 sorted, but we do it lazily using this method, in case no |
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169 sorted measures are used. I.e., if median is never called, |
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170 sorting would be a waste. |
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171 |
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172 When not using a copy, it's presumed that all optimizations |
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173 are on the user. |
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174 """ |
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175 if not self._use_copy: |
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176 return sorted(self.data) |
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177 elif not self._is_sorted: |
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178 self.data.sort() |
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179 return self.data |
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180 |
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181 def clear_cache(self): |
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182 """``Stats`` objects automatically cache intermediary calculations |
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183 that can be reused. For instance, accessing the ``std_dev`` |
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184 attribute after the ``variance`` attribute will be |
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185 significantly faster for medium-to-large datasets. |
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186 |
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187 If you modify the object by adding additional data points, |
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188 call this function to have the cached statistics recomputed. |
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189 |
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190 """ |
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191 for attr_name in self._prop_attr_names: |
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192 attr_name = getattr(self.__class__, attr_name).internal_name |
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193 if not hasattr(self, attr_name): |
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194 continue |
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195 delattr(self, attr_name) |
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196 return |
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197 |
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198 def _calc_count(self): |
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199 """The number of items in this Stats object. Returns the same as |
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200 :func:`len` on a Stats object, but provided for pandas terminology |
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201 parallelism. |
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202 |
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203 >>> Stats(range(20)).count |
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204 20 |
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205 """ |
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206 return len(self.data) |
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207 count = _StatsProperty('count', _calc_count) |
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208 |
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209 def _calc_mean(self): |
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210 """ |
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211 The arithmetic mean, or "average". Sum of the values divided by |
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212 the number of values. |
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213 |
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214 >>> mean(range(20)) |
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215 9.5 |
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216 >>> mean(list(range(19)) + [949]) # 949 is an arbitrary outlier |
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217 56.0 |
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218 """ |
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219 return sum(self.data, 0.0) / len(self.data) |
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220 mean = _StatsProperty('mean', _calc_mean) |
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221 |
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222 def _calc_max(self): |
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223 """ |
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224 The maximum value present in the data. |
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225 |
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226 >>> Stats([2, 1, 3]).max |
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227 3 |
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228 """ |
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229 if self._is_sorted: |
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230 return self.data[-1] |
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231 return max(self.data) |
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232 max = _StatsProperty('max', _calc_max) |
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233 |
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234 def _calc_min(self): |
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235 """ |
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236 The minimum value present in the data. |
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237 |
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238 >>> Stats([2, 1, 3]).min |
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239 1 |
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240 """ |
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241 if self._is_sorted: |
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242 return self.data[0] |
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243 return min(self.data) |
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244 min = _StatsProperty('min', _calc_min) |
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245 |
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246 def _calc_median(self): |
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247 """ |
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248 The median is either the middle value or the average of the two |
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249 middle values of a sample. Compared to the mean, it's generally |
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250 more resilient to the presence of outliers in the sample. |
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251 |
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252 >>> median([2, 1, 3]) |
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253 2 |
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254 >>> median(range(97)) |
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255 48 |
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256 >>> median(list(range(96)) + [1066]) # 1066 is an arbitrary outlier |
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257 48 |
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258 """ |
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259 return self._get_quantile(self._get_sorted_data(), 0.5) |
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260 median = _StatsProperty('median', _calc_median) |
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261 |
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262 def _calc_iqr(self): |
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263 """Inter-quartile range (IQR) is the difference between the 75th |
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264 percentile and 25th percentile. IQR is a robust measure of |
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265 dispersion, like standard deviation, but safer to compare |
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266 between datasets, as it is less influenced by outliers. |
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267 |
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268 >>> iqr([1, 2, 3, 4, 5]) |
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269 2 |
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270 >>> iqr(range(1001)) |
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271 500 |
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272 """ |
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273 return self.get_quantile(0.75) - self.get_quantile(0.25) |
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274 iqr = _StatsProperty('iqr', _calc_iqr) |
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275 |
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276 def _calc_trimean(self): |
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277 """The trimean is a robust measure of central tendency, like the |
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278 median, that takes the weighted average of the median and the |
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279 upper and lower quartiles. |
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280 |
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281 >>> trimean([2, 1, 3]) |
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282 2.0 |
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283 >>> trimean(range(97)) |
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284 48.0 |
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285 >>> trimean(list(range(96)) + [1066]) # 1066 is an arbitrary outlier |
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286 48.0 |
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287 |
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288 """ |
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289 sorted_data = self._get_sorted_data() |
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290 gq = lambda q: self._get_quantile(sorted_data, q) |
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291 return (gq(0.25) + (2 * gq(0.5)) + gq(0.75)) / 4.0 |
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292 trimean = _StatsProperty('trimean', _calc_trimean) |
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293 |
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294 def _calc_variance(self): |
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295 """\ |
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296 Variance is the average of the squares of the difference between |
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297 each value and the mean. |
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298 |
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299 >>> variance(range(97)) |
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300 784.0 |
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301 """ |
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302 global mean # defined elsewhere in this file |
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303 return mean(self._get_pow_diffs(2)) |
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304 variance = _StatsProperty('variance', _calc_variance) |
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305 |
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306 def _calc_std_dev(self): |
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307 """\ |
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308 Standard deviation. Square root of the variance. |
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309 |
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310 >>> std_dev(range(97)) |
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311 28.0 |
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312 """ |
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313 return self.variance ** 0.5 |
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314 std_dev = _StatsProperty('std_dev', _calc_std_dev) |
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315 |
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316 def _calc_median_abs_dev(self): |
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317 """\ |
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318 Median Absolute Deviation is a robust measure of statistical |
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319 dispersion: http://en.wikipedia.org/wiki/Median_absolute_deviation |
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320 |
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321 >>> median_abs_dev(range(97)) |
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322 24.0 |
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323 """ |
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324 global median # defined elsewhere in this file |
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325 sorted_vals = sorted(self.data) |
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326 x = float(median(sorted_vals)) |
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327 return median([abs(x - v) for v in sorted_vals]) |
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328 median_abs_dev = _StatsProperty('median_abs_dev', _calc_median_abs_dev) |
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329 mad = median_abs_dev # convenience |
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330 |
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331 def _calc_rel_std_dev(self): |
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332 """\ |
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333 Standard deviation divided by the absolute value of the average. |
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334 |
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335 http://en.wikipedia.org/wiki/Relative_standard_deviation |
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336 |
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337 >>> print('%1.3f' % rel_std_dev(range(97))) |
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338 0.583 |
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339 """ |
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340 abs_mean = abs(self.mean) |
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341 if abs_mean: |
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342 return self.std_dev / abs_mean |
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343 else: |
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344 return self.default |
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345 rel_std_dev = _StatsProperty('rel_std_dev', _calc_rel_std_dev) |
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346 |
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347 def _calc_skewness(self): |
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348 """\ |
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349 Indicates the asymmetry of a curve. Positive values mean the bulk |
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350 of the values are on the left side of the average and vice versa. |
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351 |
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352 http://en.wikipedia.org/wiki/Skewness |
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353 |
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354 See the module docstring for more about statistical moments. |
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355 |
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356 >>> skewness(range(97)) # symmetrical around 48.0 |
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357 0.0 |
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358 >>> left_skewed = skewness(list(range(97)) + list(range(10))) |
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359 >>> right_skewed = skewness(list(range(97)) + list(range(87, 97))) |
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360 >>> round(left_skewed, 3), round(right_skewed, 3) |
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361 (0.114, -0.114) |
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362 """ |
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363 data, s_dev = self.data, self.std_dev |
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364 if len(data) > 1 and s_dev > 0: |
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365 return (sum(self._get_pow_diffs(3)) / |
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366 float((len(data) - 1) * (s_dev ** 3))) |
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367 else: |
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368 return self.default |
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369 skewness = _StatsProperty('skewness', _calc_skewness) |
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370 |
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371 def _calc_kurtosis(self): |
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372 """\ |
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373 Indicates how much data is in the tails of the distribution. The |
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374 result is always positive, with the normal "bell-curve" |
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375 distribution having a kurtosis of 3. |
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376 |
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377 http://en.wikipedia.org/wiki/Kurtosis |
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378 |
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379 See the module docstring for more about statistical moments. |
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380 |
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381 >>> kurtosis(range(9)) |
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382 1.99125 |
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383 |
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384 With a kurtosis of 1.99125, [0, 1, 2, 3, 4, 5, 6, 7, 8] is more |
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385 centrally distributed than the normal curve. |
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386 """ |
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387 data, s_dev = self.data, self.std_dev |
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388 if len(data) > 1 and s_dev > 0: |
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389 return (sum(self._get_pow_diffs(4)) / |
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390 float((len(data) - 1) * (s_dev ** 4))) |
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391 else: |
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392 return 0.0 |
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393 kurtosis = _StatsProperty('kurtosis', _calc_kurtosis) |
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394 |
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395 def _calc_pearson_type(self): |
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396 precision = self._pearson_precision |
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397 skewness = self.skewness |
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398 kurtosis = self.kurtosis |
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399 beta1 = skewness ** 2.0 |
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400 beta2 = kurtosis * 1.0 |
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401 |
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402 # TODO: range checks? |
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403 |
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404 c0 = (4 * beta2) - (3 * beta1) |
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405 c1 = skewness * (beta2 + 3) |
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406 c2 = (2 * beta2) - (3 * beta1) - 6 |
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407 |
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408 if round(c1, precision) == 0: |
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409 if round(beta2, precision) == 3: |
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410 return 0 # Normal |
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411 else: |
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412 if beta2 < 3: |
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413 return 2 # Symmetric Beta |
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414 elif beta2 > 3: |
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415 return 7 |
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416 elif round(c2, precision) == 0: |
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417 return 3 # Gamma |
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418 else: |
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419 k = c1 ** 2 / (4 * c0 * c2) |
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420 if k < 0: |
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421 return 1 # Beta |
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422 raise RuntimeError('missed a spot') |
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423 pearson_type = _StatsProperty('pearson_type', _calc_pearson_type) |
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424 |
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425 @staticmethod |
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426 def _get_quantile(sorted_data, q): |
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427 data, n = sorted_data, len(sorted_data) |
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428 idx = q / 1.0 * (n - 1) |
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429 idx_f, idx_c = int(floor(idx)), int(ceil(idx)) |
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430 if idx_f == idx_c: |
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431 return data[idx_f] |
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432 return (data[idx_f] * (idx_c - idx)) + (data[idx_c] * (idx - idx_f)) |
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433 |
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434 def get_quantile(self, q): |
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435 """Get a quantile from the dataset. Quantiles are floating point |
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436 values between ``0.0`` and ``1.0``, with ``0.0`` representing |
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437 the minimum value in the dataset and ``1.0`` representing the |
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438 maximum. ``0.5`` represents the median: |
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439 |
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440 >>> Stats(range(100)).get_quantile(0.5) |
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441 49.5 |
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442 """ |
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443 q = float(q) |
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444 if not 0.0 <= q <= 1.0: |
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445 raise ValueError('expected q between 0.0 and 1.0, not %r' % q) |
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446 elif not self.data: |
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447 return self.default |
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448 return self._get_quantile(self._get_sorted_data(), q) |
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449 |
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450 def get_zscore(self, value): |
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451 """Get the z-score for *value* in the group. If the standard deviation |
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452 is 0, 0 inf or -inf will be returned to indicate whether the value is |
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453 equal to, greater than or below the group's mean. |
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454 """ |
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455 mean = self.mean |
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456 if self.std_dev == 0: |
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457 if value == mean: |
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458 return 0 |
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459 if value > mean: |
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460 return float('inf') |
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461 if value < mean: |
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462 return float('-inf') |
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463 return (float(value) - mean) / self.std_dev |
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464 |
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465 def trim_relative(self, amount=0.15): |
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466 """A utility function used to cut a proportion of values off each end |
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467 of a list of values. This has the effect of limiting the |
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468 effect of outliers. |
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469 |
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470 Args: |
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471 amount (float): A value between 0.0 and 0.5 to trim off of |
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472 each side of the data. |
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473 |
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474 .. note: |
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475 |
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476 This operation modifies the data in-place. It does not |
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477 make or return a copy. |
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478 |
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479 """ |
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480 trim = float(amount) |
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481 if not 0.0 <= trim < 0.5: |
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482 raise ValueError('expected amount between 0.0 and 0.5, not %r' |
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483 % trim) |
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484 size = len(self.data) |
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485 size_diff = int(size * trim) |
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486 if size_diff == 0.0: |
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487 return |
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488 self.data = self._get_sorted_data()[size_diff:-size_diff] |
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489 self.clear_cache() |
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490 |
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491 def _get_pow_diffs(self, power): |
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492 """ |
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493 A utility function used for calculating statistical moments. |
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494 """ |
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495 m = self.mean |
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496 return [(v - m) ** power for v in self.data] |
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497 |
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498 def _get_bin_bounds(self, count=None, with_max=False): |
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499 if not self.data: |
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500 return [0.0] # TODO: raise? |
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501 |
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502 data = self.data |
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503 len_data, min_data, max_data = len(data), min(data), max(data) |
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504 |
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505 if len_data < 4: |
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506 if not count: |
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507 count = len_data |
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508 dx = (max_data - min_data) / float(count) |
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509 bins = [min_data + (dx * i) for i in range(count)] |
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510 elif count is None: |
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511 # freedman algorithm for fixed-width bin selection |
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512 q25, q75 = self.get_quantile(0.25), self.get_quantile(0.75) |
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513 dx = 2 * (q75 - q25) / (len_data ** (1 / 3.0)) |
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514 bin_count = max(1, int(ceil((max_data - min_data) / dx))) |
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515 bins = [min_data + (dx * i) for i in range(bin_count + 1)] |
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516 bins = [b for b in bins if b < max_data] |
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517 else: |
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518 dx = (max_data - min_data) / float(count) |
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519 bins = [min_data + (dx * i) for i in range(count)] |
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520 |
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521 if with_max: |
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522 bins.append(float(max_data)) |
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523 |
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524 return bins |
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525 |
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526 def get_histogram_counts(self, bins=None, **kw): |
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527 """Produces a list of ``(bin, count)`` pairs comprising a histogram of |
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528 the Stats object's data, using fixed-width bins. See |
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529 :meth:`Stats.format_histogram` for more details. |
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530 |
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531 Args: |
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532 bins (int): maximum number of bins, or list of |
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533 floating-point bin boundaries. Defaults to the output of |
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534 Freedman's algorithm. |
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535 bin_digits (int): Number of digits used to round down the |
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536 bin boundaries. Defaults to 1. |
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537 |
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538 The output of this method can be stored and/or modified, and |
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539 then passed to :func:`statsutils.format_histogram_counts` to |
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540 achieve the same text formatting as the |
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541 :meth:`~Stats.format_histogram` method. This can be useful for |
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542 snapshotting over time. |
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543 """ |
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544 bin_digits = int(kw.pop('bin_digits', 1)) |
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545 if kw: |
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546 raise TypeError('unexpected keyword arguments: %r' % kw.keys()) |
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547 |
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548 if not bins: |
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549 bins = self._get_bin_bounds() |
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550 else: |
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551 try: |
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552 bin_count = int(bins) |
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553 except TypeError: |
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554 try: |
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555 bins = [float(x) for x in bins] |
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556 except Exception: |
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557 raise ValueError('bins expected integer bin count or list' |
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558 ' of float bin boundaries, not %r' % bins) |
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559 if self.min < bins[0]: |
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560 bins = [self.min] + bins |
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561 else: |
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562 bins = self._get_bin_bounds(bin_count) |
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563 |
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564 # floor and ceil really should have taken ndigits, like round() |
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565 round_factor = 10.0 ** bin_digits |
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566 bins = [floor(b * round_factor) / round_factor for b in bins] |
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567 bins = sorted(set(bins)) |
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568 |
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569 idxs = [bisect.bisect(bins, d) - 1 for d in self.data] |
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570 count_map = {} # would have used Counter, but py26 support |
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571 for idx in idxs: |
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572 try: |
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573 count_map[idx] += 1 |
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574 except KeyError: |
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575 count_map[idx] = 1 |
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576 |
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577 bin_counts = [(b, count_map.get(i, 0)) for i, b in enumerate(bins)] |
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578 |
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579 return bin_counts |
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580 |
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581 def format_histogram(self, bins=None, **kw): |
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582 """Produces a textual histogram of the data, using fixed-width bins, |
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583 allowing for simple visualization, even in console environments. |
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584 |
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585 >>> data = list(range(20)) + list(range(5, 15)) + [10] |
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586 >>> print(Stats(data).format_histogram(width=30)) |
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587 0.0: 5 ######### |
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588 4.4: 8 ############### |
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589 8.9: 11 #################### |
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590 13.3: 5 ######### |
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591 17.8: 2 #### |
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592 |
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593 In this histogram, five values are between 0.0 and 4.4, eight |
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594 are between 4.4 and 8.9, and two values lie between 17.8 and |
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595 the max. |
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596 |
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597 You can specify the number of bins, or provide a list of |
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598 bin boundaries themselves. If no bins are provided, as in the |
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599 example above, `Freedman's algorithm`_ for bin selection is |
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600 used. |
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601 |
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602 Args: |
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603 bins (int): Maximum number of bins for the |
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604 histogram. Also accepts a list of floating-point |
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605 bin boundaries. If the minimum boundary is still |
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606 greater than the minimum value in the data, that |
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607 boundary will be implicitly added. Defaults to the bin |
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608 boundaries returned by `Freedman's algorithm`_. |
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609 bin_digits (int): Number of digits to round each bin |
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610 to. Note that bins are always rounded down to avoid |
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611 clipping any data. Defaults to 1. |
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612 width (int): integer number of columns in the longest line |
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613 in the histogram. Defaults to console width on Python |
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614 3.3+, or 80 if that is not available. |
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615 format_bin (callable): Called on each bin to create a |
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616 label for the final output. Use this function to add |
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617 units, such as "ms" for milliseconds. |
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618 |
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619 Should you want something more programmatically reusable, see |
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620 the :meth:`~Stats.get_histogram_counts` method, the output of |
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621 is used by format_histogram. The :meth:`~Stats.describe` |
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622 method is another useful summarization method, albeit less |
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623 visual. |
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624 |
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625 .. _Freedman's algorithm: https://en.wikipedia.org/wiki/Freedman%E2%80%93Diaconis_rule |
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626 """ |
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627 width = kw.pop('width', None) |
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628 format_bin = kw.pop('format_bin', None) |
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629 bin_counts = self.get_histogram_counts(bins=bins, **kw) |
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630 return format_histogram_counts(bin_counts, |
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631 width=width, |
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632 format_bin=format_bin) |
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633 |
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634 def describe(self, quantiles=None, format=None): |
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635 """Provides standard summary statistics for the data in the Stats |
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636 object, in one of several convenient formats. |
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637 |
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638 Args: |
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639 quantiles (list): A list of numeric values to use as |
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640 quantiles in the resulting summary. All values must be |
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641 0.0-1.0, with 0.5 representing the median. Defaults to |
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642 ``[0.25, 0.5, 0.75]``, representing the standard |
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643 quartiles. |
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644 format (str): Controls the return type of the function, |
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645 with one of three valid values: ``"dict"`` gives back |
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646 a :class:`dict` with the appropriate keys and |
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647 values. ``"list"`` is a list of key-value pairs in an |
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648 order suitable to pass to an OrderedDict or HTML |
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649 table. ``"text"`` converts the values to text suitable |
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650 for printing, as seen below. |
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651 |
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652 Here is the information returned by a default ``describe``, as |
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653 presented in the ``"text"`` format: |
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654 |
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655 >>> stats = Stats(range(1, 8)) |
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656 >>> print(stats.describe(format='text')) |
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657 count: 7 |
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658 mean: 4.0 |
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659 std_dev: 2.0 |
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660 mad: 2.0 |
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661 min: 1 |
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662 0.25: 2.5 |
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663 0.5: 4 |
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664 0.75: 5.5 |
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665 max: 7 |
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666 |
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667 For more advanced descriptive statistics, check out my blog |
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668 post on the topic `Statistics for Software |
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669 <https://www.paypal-engineering.com/2016/04/11/statistics-for-software/>`_. |
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670 |
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671 """ |
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672 if format is None: |
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673 format = 'dict' |
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674 elif format not in ('dict', 'list', 'text'): |
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675 raise ValueError('invalid format for describe,' |
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676 ' expected one of "dict"/"list"/"text", not %r' |
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677 % format) |
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678 quantiles = quantiles or [0.25, 0.5, 0.75] |
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679 q_items = [] |
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680 for q in quantiles: |
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681 q_val = self.get_quantile(q) |
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682 q_items.append((str(q), q_val)) |
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683 |
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684 items = [('count', self.count), |
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685 ('mean', self.mean), |
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686 ('std_dev', self.std_dev), |
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687 ('mad', self.mad), |
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688 ('min', self.min)] |
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689 |
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690 items.extend(q_items) |
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691 items.append(('max', self.max)) |
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692 if format == 'dict': |
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693 ret = dict(items) |
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694 elif format == 'list': |
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695 ret = items |
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696 elif format == 'text': |
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697 ret = '\n'.join(['%s%s' % ((label + ':').ljust(10), val) |
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698 for label, val in items]) |
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699 return ret |
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700 |
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701 |
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702 def describe(data, quantiles=None, format=None): |
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703 """A convenience function to get standard summary statistics useful |
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704 for describing most data. See :meth:`Stats.describe` for more |
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705 details. |
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706 |
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707 >>> print(describe(range(7), format='text')) |
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708 count: 7 |
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709 mean: 3.0 |
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710 std_dev: 2.0 |
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711 mad: 2.0 |
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712 min: 0 |
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713 0.25: 1.5 |
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714 0.5: 3 |
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715 0.75: 4.5 |
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716 max: 6 |
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717 |
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718 See :meth:`Stats.format_histogram` for another very useful |
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719 summarization that uses textual visualization. |
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720 """ |
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721 return Stats(data).describe(quantiles=quantiles, format=format) |
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722 |
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723 |
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724 def _get_conv_func(attr_name): |
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725 def stats_helper(data, default=0.0): |
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726 return getattr(Stats(data, default=default, use_copy=False), |
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727 attr_name) |
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728 return stats_helper |
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|
729 |
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|
730 |
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731 for attr_name, attr in list(Stats.__dict__.items()): |
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732 if isinstance(attr, _StatsProperty): |
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733 if attr_name in ('max', 'min', 'count'): # don't shadow builtins |
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734 continue |
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735 if attr_name in ('mad',): # convenience aliases |
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736 continue |
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737 func = _get_conv_func(attr_name) |
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738 func.__doc__ = attr.func.__doc__ |
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739 globals()[attr_name] = func |
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740 delattr(Stats, '_calc_' + attr_name) |
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741 # cleanup |
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|
742 del attr |
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743 del attr_name |
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744 del func |
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|
745 |
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746 |
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|
747 def format_histogram_counts(bin_counts, width=None, format_bin=None): |
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748 """The formatting logic behind :meth:`Stats.format_histogram`, which |
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749 takes the output of :meth:`Stats.get_histogram_counts`, and passes |
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750 them to this function. |
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|
751 |
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|
752 Args: |
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753 bin_counts (list): A list of bin values to counts. |
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754 width (int): Number of character columns in the text output, |
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755 defaults to 80 or console width in Python 3.3+. |
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756 format_bin (callable): Used to convert bin values into string |
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757 labels. |
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758 """ |
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759 lines = [] |
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760 if not format_bin: |
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761 format_bin = lambda v: v |
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762 if not width: |
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|
763 try: |
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764 import shutil # python 3 convenience |
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765 width = shutil.get_terminal_size()[0] |
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766 except Exception: |
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767 width = 80 |
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|
768 |
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769 bins = [b for b, _ in bin_counts] |
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770 count_max = max([count for _, count in bin_counts]) |
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771 count_cols = len(str(count_max)) |
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|
772 |
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773 labels = ['%s' % format_bin(b) for b in bins] |
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774 label_cols = max([len(l) for l in labels]) |
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775 tmp_line = '%s: %s #' % ('x' * label_cols, count_max) |
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|
776 |
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777 bar_cols = max(width - len(tmp_line), 3) |
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778 line_k = float(bar_cols) / count_max |
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779 tmpl = "{label:>{label_cols}}: {count:>{count_cols}} {bar}" |
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780 for label, (bin_val, count) in zip(labels, bin_counts): |
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781 bar_len = int(round(count * line_k)) |
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782 bar = ('#' * bar_len) or '|' |
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783 line = tmpl.format(label=label, |
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784 label_cols=label_cols, |
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785 count=count, |
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786 count_cols=count_cols, |
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787 bar=bar) |
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788 lines.append(line) |
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789 |
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|
790 return '\n'.join(lines) |