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4559903 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | // Author: qiyiping@gmail.com (Yiping Qi)
#include "math_util.hpp"
#include "tree.hpp"
#include <algorithm>
#include <iostream>
#ifdef USE_OPENMP
#include <parallel/algorithm> // openmp
#endif
namespace {
struct ResidualCompare {
bool operator () (const gbdt::Tuple *t1, const gbdt::Tuple *t2) {
return t1->residual < t2->residual;
}
};
struct LabelCompare {
bool operator () (const gbdt::Tuple *t1, const gbdt::Tuple *t2) {
return t1->label < t2->label;
}
};
}
namespace gbdt {
bool AlmostEqual(ValueType v1, ValueType v2) {
ValueType diff = Abs(v1-v2);
if (diff < 1.0e-5)
return true;
return false;
}
bool Same(const DataVector &data, size_t len) {
assert(len <= data.size());
if (len <= 1)
return true;
ValueType t = data[0]->target;
for (size_t i = 1; i < len; ++i) {
if (!AlmostEqual(t, data[i]->target))
return false;
}
return true;
}
ValueType Average(const DataVector & data, size_t len) {
assert(len <= data.size());
if (len == 0)
return 0;
double s = 0;
double c = 0;
for (size_t i = 0; i < len; ++i) {
s += data[i]->target * data[i]->weight;
c += data[i]->weight;
}
return static_cast<ValueType>(s / c);
}
double RMSE(const DataVector &data, const PredictVector &predict, size_t len) {
assert(data.size() >= len);
assert(predict.size() >= len);
double s = 0;
double c = 0;
for (size_t i = 0; i < data.size(); ++i) {
s += Squared(predict[i] - data[i]->label) * data[i]->weight;
c += data[i]->weight;
}
return std::sqrt(s / c);
}
double MAE(const DataVector &data, const PredictVector &predict, size_t len) {
assert(data.size() >= len);
assert(predict.size() >= len);
double s = 0;
double c = 0;
for (size_t i = 0; i < data.size(); ++i) {
s += Abs(predict[i] - data[i]->label) * data[i]->weight;
c += data[i]->weight;
}
return s / c;
}
ValueType WeightedResidualMedian(DataVector &d, size_t len) {
assert(d.size() >= len);
// simplest implementation using sorting
// sophisticated approch to find the weighted median is selection algorithm(partition algorithm).
std::sort(d.begin(), d.begin() + len, ResidualCompare());
double all_weight = 0.0;
for (size_t i = 0; i < len; ++i) {
all_weight += d[i]->weight;
}
ValueType weighted_median = 0.0;
double weight = 0.0;
for (int i = 0; i < len; ++i) {
weight += d[i]->weight;
if (weight * 2 > all_weight) {
if (i-1 >= 0) {
weighted_median = (d[i]->residual + d[i-1]->residual) / 2.0;
} else {
weighted_median = d[i]->residual;
}
break;
}
}
return weighted_median;
}
ValueType WeightedLabelMedian(DataVector &d, size_t len) {
assert(d.size() >= len);
// simplest implementation using sorting
// sophisticated approch to find the weighted median is selection algorithm(partition algorithm).
std::sort(d.begin(), d.begin() + len, LabelCompare());
double all_weight = 0.0;
for (size_t i = 0; i < len; ++i) {
all_weight += d[i]->weight;
}
ValueType weighted_median = 0.0;
double weight = 0.0;
for (int i = 0; i < len; ++i) {
weight += d[i]->weight;
if (weight * 2 > all_weight) {
if (i-1 >= 0) {
weighted_median = (d[i]->label + d[i-1]->label) / 2.0;
} else {
weighted_median = d[i]->label;
}
break;
}
}
return weighted_median;
}
}
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