1 | #ifndef _theplu_yat_statistics_roc_ |
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2 | #define _theplu_yat_statistics_roc_ |
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3 | |
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4 | // $Id: ROC.h 1486 2008-09-09 21:17:19Z jari $ |
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5 | |
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6 | /* |
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7 | Copyright (C) 2004 Peter Johansson |
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8 | Copyright (C) 2005, 2006, 2007, 2008 Jari Häkkinen, Peter Johansson |
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9 | |
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10 | This file is part of the yat library, http://dev.thep.lu.se/yat |
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11 | |
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12 | The yat library is free software; you can redistribute it and/or |
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13 | modify it under the terms of the GNU General Public License as |
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14 | published by the Free Software Foundation; either version 3 of the |
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15 | License, or (at your option) any later version. |
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16 | |
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17 | The yat library is distributed in the hope that it will be useful, |
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18 | but WITHOUT ANY WARRANTY; without even the implied warranty of |
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19 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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20 | General Public License for more details. |
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21 | |
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22 | You should have received a copy of the GNU General Public License |
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23 | along with this program; if not, write to the Free Software |
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24 | Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA |
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25 | 02111-1307, USA. |
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26 | */ |
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27 | |
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28 | #include "yat/classifier/Target.h" |
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29 | #include "yat/utility/iterator_traits.h" |
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30 | |
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31 | #include <algorithm> |
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32 | #include <map> |
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33 | #include <utility> |
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34 | |
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35 | namespace theplu { |
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36 | namespace yat { |
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37 | namespace statistics { |
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38 | |
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39 | /// |
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40 | /// @brief Class for Reciever Operating Characteristic. |
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41 | /// |
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42 | /// As the area under an ROC curve is equivalent to Mann-Whitney U |
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43 | /// statistica, this class can be used to perform a Mann-Whitney |
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44 | /// U-test (aka Wilcoxon). |
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45 | /// |
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46 | class ROC |
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47 | { |
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48 | |
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49 | public: |
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50 | /// |
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51 | /// @brief Default constructor |
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52 | /// |
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53 | ROC(void); |
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54 | |
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55 | /// |
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56 | /// @brief The destructor |
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57 | /// |
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58 | virtual ~ROC(void); |
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59 | |
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60 | /** |
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61 | Adding a data value to ROC. |
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62 | |
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63 | \see add(T &o, ForwardIterator first, ForwardIterator last, |
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64 | const classifier::Target &target) |
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65 | */ |
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66 | void add(double value, bool target, double weight=1.0); |
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67 | |
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68 | /** |
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69 | The area is defines as \f$ \frac{\sum w^+w^-} {\sum w^+w^-}\f$, |
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70 | where the sum in the numerator goes over all pairs where value+ |
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71 | is larger than value-. The denominator goes over all pairs. |
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72 | |
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73 | @return Area under curve. |
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74 | */ |
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75 | double area(void); |
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76 | |
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77 | /// |
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78 | /// minimum_size is the threshold for when a normal |
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79 | /// approximation is used for the p-value calculation. |
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80 | /// |
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81 | /// @return reference to minimum_size |
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82 | /// |
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83 | unsigned int& minimum_size(void); |
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84 | |
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85 | /** |
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86 | minimum_size is the threshold for when a normal |
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87 | approximation is used for the p-value calculation. |
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88 | |
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89 | @return const reference to minimum_size |
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90 | */ |
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91 | const unsigned int& minimum_size(void) const; |
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92 | |
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93 | /// |
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94 | /// @return sum of weights |
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95 | /// |
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96 | double n(void) const; |
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97 | |
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98 | /// |
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99 | /// @return sum of weights with negative target |
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100 | /// |
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101 | double n_neg(void) const; |
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102 | |
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103 | /// |
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104 | /// @return sum of weights with positive target |
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105 | /// |
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106 | double n_pos(void) const; |
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107 | |
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108 | /// |
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109 | ///Calculates the p-value, i.e. the probability of observing an |
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110 | ///area equally or larger if the null hypothesis is true. If P is |
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111 | ///near zero, this casts doubt on this hypothesis. The null |
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112 | ///hypothesis is that the values from the 2 classes are generated |
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113 | ///from 2 identical distributions. The alternative is that the |
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114 | ///median of the first distribution is shifted from the median of |
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115 | ///the second distribution by a non-zero amount. If the smallest |
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116 | ///group size is larger than minimum_size (default = 10), then P |
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117 | ///is calculated using a normal approximation. |
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118 | /// |
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119 | /// \note Weights should be either zero or unity, else present |
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120 | /// implementation is nonsense. |
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121 | /// |
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122 | /// @return One-sided p-value. |
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123 | /// |
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124 | double p_value_one_sided(void) const; |
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125 | |
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126 | /** |
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127 | @brief Two-sided p-value. |
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128 | |
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129 | @return min(2*p_value_one_sided, 2-2*p_value_one_sided) |
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130 | */ |
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131 | double p_value(void) const; |
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132 | |
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133 | /** |
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134 | @brief Set everything to zero |
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135 | */ |
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136 | void reset(void); |
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137 | |
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138 | private: |
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139 | |
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140 | /// Implemented as in MatLab 13.1 |
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141 | double get_p_approx(double) const; |
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142 | |
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143 | /// Implemented as in MatLab 13.1 |
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144 | double get_p_exact(const double, const double, const double) const; |
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145 | |
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146 | double area_; |
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147 | unsigned int minimum_size_; |
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148 | double w_neg_; |
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149 | double w_pos_; |
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150 | // <data pair<class, weight> > |
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151 | std::multimap<double, std::pair<bool, double> > multimap_; |
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152 | }; |
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153 | |
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154 | }}} // of namespace statistics, yat, and theplu |
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155 | |
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156 | #endif |
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