-
Notifications
You must be signed in to change notification settings - Fork 3.8k
/
predictor.hpp
261 lines (244 loc) · 9.93 KB
/
predictor.hpp
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
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
/*!
* Copyright (c) 2016 Microsoft Corporation. All rights reserved.
* Licensed under the MIT License. See LICENSE file in the project root for license information.
*/
#ifndef LIGHTGBM_PREDICTOR_HPP_
#define LIGHTGBM_PREDICTOR_HPP_
#include <LightGBM/boosting.h>
#include <LightGBM/dataset.h>
#include <LightGBM/meta.h>
#include <LightGBM/utils/openmp_wrapper.h>
#include <LightGBM/utils/text_reader.h>
#include <string>
#include <cstdio>
#include <cstring>
#include <functional>
#include <map>
#include <memory>
#include <unordered_map>
#include <utility>
#include <vector>
namespace LightGBM {
/*!
* \brief Used to predict data with input model
*/
class Predictor {
public:
/*!
* \brief Constructor
* \param boosting Input boosting model
* \param num_iteration Number of boosting round
* \param is_raw_score True if need to predict result with raw score
* \param predict_leaf_index True to output leaf index instead of prediction score
* \param predict_contrib True to output feature contributions instead of prediction score
*/
Predictor(Boosting* boosting, int num_iteration,
bool is_raw_score, bool predict_leaf_index, bool predict_contrib,
bool early_stop, int early_stop_freq, double early_stop_margin) {
early_stop_ = CreatePredictionEarlyStopInstance("none", LightGBM::PredictionEarlyStopConfig());
if (early_stop && !boosting->NeedAccuratePrediction()) {
PredictionEarlyStopConfig pred_early_stop_config;
CHECK(early_stop_freq > 0);
CHECK(early_stop_margin >= 0);
pred_early_stop_config.margin_threshold = early_stop_margin;
pred_early_stop_config.round_period = early_stop_freq;
if (boosting->NumberOfClasses() == 1) {
early_stop_ = CreatePredictionEarlyStopInstance("binary", pred_early_stop_config);
} else {
early_stop_ = CreatePredictionEarlyStopInstance("multiclass", pred_early_stop_config);
}
}
#pragma omp parallel
#pragma omp master
{
num_threads_ = omp_get_num_threads();
}
boosting->InitPredict(num_iteration, predict_contrib);
boosting_ = boosting;
num_pred_one_row_ = boosting_->NumPredictOneRow(num_iteration, predict_leaf_index, predict_contrib);
num_feature_ = boosting_->MaxFeatureIdx() + 1;
predict_buf_ = std::vector<std::vector<double>>(num_threads_, std::vector<double>(num_feature_, 0.0f));
const int kFeatureThreshold = 100000;
const size_t KSparseThreshold = static_cast<size_t>(0.01 * num_feature_);
if (predict_leaf_index) {
predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
int tid = omp_get_thread_num();
if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
auto buf = CopyToPredictMap(features);
boosting_->PredictLeafIndexByMap(buf, output);
} else {
CopyToPredictBuffer(predict_buf_[tid].data(), features);
// get result for leaf index
boosting_->PredictLeafIndex(predict_buf_[tid].data(), output);
ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
}
};
} else if (predict_contrib) {
predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
int tid = omp_get_thread_num();
CopyToPredictBuffer(predict_buf_[tid].data(), features);
// get result for leaf index
boosting_->PredictContrib(predict_buf_[tid].data(), output, &early_stop_);
ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
};
} else {
if (is_raw_score) {
predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
int tid = omp_get_thread_num();
if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
auto buf = CopyToPredictMap(features);
boosting_->PredictRawByMap(buf, output, &early_stop_);
} else {
CopyToPredictBuffer(predict_buf_[tid].data(), features);
boosting_->PredictRaw(predict_buf_[tid].data(), output, &early_stop_);
ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
}
};
} else {
predict_fun_ = [=](const std::vector<std::pair<int, double>>& features, double* output) {
int tid = omp_get_thread_num();
if (num_feature_ > kFeatureThreshold && features.size() < KSparseThreshold) {
auto buf = CopyToPredictMap(features);
boosting_->PredictByMap(buf, output, &early_stop_);
} else {
CopyToPredictBuffer(predict_buf_[tid].data(), features);
boosting_->Predict(predict_buf_[tid].data(), output, &early_stop_);
ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(), features);
}
};
}
}
}
/*!
* \brief Destructor
*/
~Predictor() {
}
inline const PredictFunction& GetPredictFunction() const {
return predict_fun_;
}
/*!
* \brief predicting on data, then saving result to disk
* \param data_filename Filename of data
* \param result_filename Filename of output result
*/
void Predict(const char* data_filename, const char* result_filename, bool header) {
auto writer = VirtualFileWriter::Make(result_filename);
if (!writer->Init()) {
Log::Fatal("Prediction results file %s cannot be found", result_filename);
}
auto parser = std::unique_ptr<Parser>(Parser::CreateParser(data_filename, header, boosting_->MaxFeatureIdx() + 1, boosting_->LabelIdx()));
if (parser == nullptr) {
Log::Fatal("Could not recognize the data format of data file %s", data_filename);
}
TextReader<data_size_t> predict_data_reader(data_filename, header);
std::unordered_map<int, int> feature_names_map_;
bool need_adjust = false;
if (header) {
std::string first_line = predict_data_reader.first_line();
std::vector<std::string> header_words = Common::Split(first_line.c_str(), "\t,");
header_words.erase(header_words.begin() + boosting_->LabelIdx());
for (int i = 0; i < static_cast<int>(header_words.size()); ++i) {
for (int j = 0; j < static_cast<int>(boosting_->FeatureNames().size()); ++j) {
if (header_words[i] == boosting_->FeatureNames()[j]) {
feature_names_map_[i] = j;
break;
}
}
}
for (auto s : feature_names_map_) {
if (s.first != s.second) {
need_adjust = true;
break;
}
}
}
// function for parse data
std::function<void(const char*, std::vector<std::pair<int, double>>*)> parser_fun;
double tmp_label;
parser_fun = [&]
(const char* buffer, std::vector<std::pair<int, double>>* feature) {
parser->ParseOneLine(buffer, feature, &tmp_label);
if (need_adjust) {
int i = 0, j = static_cast<int>(feature->size());
while (i < j) {
if (feature_names_map_.find((*feature)[i].first) != feature_names_map_.end()) {
(*feature)[i].first = feature_names_map_[(*feature)[i].first];
++i;
} else {
// move the non-used features to the end of the feature vector
std::swap((*feature)[i], (*feature)[--j]);
}
}
feature->resize(i);
}
};
std::function<void(data_size_t, const std::vector<std::string>&)> process_fun = [&]
(data_size_t, const std::vector<std::string>& lines) {
std::vector<std::pair<int, double>> oneline_features;
std::vector<std::string> result_to_write(lines.size());
OMP_INIT_EX();
#pragma omp parallel for schedule(static) firstprivate(oneline_features)
for (data_size_t i = 0; i < static_cast<data_size_t>(lines.size()); ++i) {
OMP_LOOP_EX_BEGIN();
oneline_features.clear();
// parser
parser_fun(lines[i].c_str(), &oneline_features);
// predict
std::vector<double> result(num_pred_one_row_);
predict_fun_(oneline_features, result.data());
auto str_result = Common::Join<double>(result, "\t");
result_to_write[i] = str_result;
OMP_LOOP_EX_END();
}
OMP_THROW_EX();
for (data_size_t i = 0; i < static_cast<data_size_t>(result_to_write.size()); ++i) {
writer->Write(result_to_write[i].c_str(), result_to_write[i].size());
writer->Write("\n", 1);
}
};
predict_data_reader.ReadAllAndProcessParallel(process_fun);
}
private:
void CopyToPredictBuffer(double* pred_buf, const std::vector<std::pair<int, double>>& features) {
int loop_size = static_cast<int>(features.size());
for (int i = 0; i < loop_size; ++i) {
if (features[i].first < num_feature_) {
pred_buf[features[i].first] = features[i].second;
}
}
}
void ClearPredictBuffer(double* pred_buf, size_t buf_size, const std::vector<std::pair<int, double>>& features) {
if (features.size() > static_cast<size_t>(buf_size / 2)) {
std::memset(pred_buf, 0, sizeof(double)*(buf_size));
} else {
int loop_size = static_cast<int>(features.size());
for (int i = 0; i < loop_size; ++i) {
if (features[i].first < num_feature_) {
pred_buf[features[i].first] = 0.0f;
}
}
}
}
std::unordered_map<int, double> CopyToPredictMap(const std::vector<std::pair<int, double>>& features) {
std::unordered_map<int, double> buf;
int loop_size = static_cast<int>(features.size());
for (int i = 0; i < loop_size; ++i) {
if (features[i].first < num_feature_) {
buf[features[i].first] = features[i].second;
}
}
return buf;
}
/*! \brief Boosting model */
const Boosting* boosting_;
/*! \brief function for prediction */
PredictFunction predict_fun_;
PredictionEarlyStopInstance early_stop_;
int num_feature_;
int num_pred_one_row_;
int num_threads_;
std::vector<std::vector<double>> predict_buf_;
};
} // namespace LightGBM
#endif // LightGBM_PREDICTOR_HPP_