-
-
Notifications
You must be signed in to change notification settings - Fork 182
/
GradientBoost.php
606 lines (507 loc) · 15.7 KB
/
GradientBoost.php
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
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
<?php
namespace Rubix\ML\Regressors;
use Rubix\ML\Learner;
use Rubix\ML\Verbose;
use Rubix\ML\Estimator;
use Rubix\ML\Persistable;
use Rubix\ML\RanksFeatures;
use Rubix\ML\EstimatorType;
use Rubix\ML\Helpers\Stats;
use Rubix\ML\Helpers\Params;
use Rubix\ML\Datasets\Dataset;
use Rubix\ML\Datasets\Labeled;
use Rubix\ML\Traits\LoggerAware;
use Rubix\ML\Traits\AutotrackRevisions;
use Rubix\ML\CrossValidation\Metrics\RMSE;
use Rubix\ML\CrossValidation\Metrics\Metric;
use Rubix\ML\Specifications\DatasetIsLabeled;
use Rubix\ML\Specifications\DatasetIsNotEmpty;
use Rubix\ML\Specifications\SpecificationChain;
use Rubix\ML\Specifications\DatasetHasDimensionality;
use Rubix\ML\Specifications\LabelsAreCompatibleWithLearner;
use Rubix\ML\Specifications\EstimatorIsCompatibleWithMetric;
use Rubix\ML\Specifications\SamplesAreCompatibleWithEstimator;
use Rubix\ML\Exceptions\InvalidArgumentException;
use Rubix\ML\Exceptions\RuntimeException;
use Generator;
use function count;
use function is_nan;
use function get_class;
use function array_map;
use function array_reduce;
use function array_slice;
use function array_fill;
use function in_array;
use function round;
use function max;
use function abs;
use function get_object_vars;
/**
* Gradient Boost
*
* Gradient Boost is a stage-wise additive ensemble that uses a Gradient Descent boosting
* scheme for training boosters (Decision Trees) to correct the error residuals of a
* series of *weak* base learners. Stochastic gradient boosting is achieved by varying
* the ratio of samples to subsample uniformly at random from the training set.
*
* References:
* [1] J. H. Friedman. (2001). Greedy Function Approximation: A Gradient Boosting Machine.
* [2] J. H. Friedman. (1999). Stochastic Gradient Boosting.
* [3] Y. Wei. et al. (2017). Early stopping for kernel boosting algorithms: A general analysis
* with localized complexities.
* [4] G. Ke et al. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree.
*
* @category Machine Learning
* @package Rubix/ML
* @author Andrew DalPino
*/
class GradientBoost implements Estimator, Learner, RanksFeatures, Verbose, Persistable
{
use AutotrackRevisions, LoggerAware;
/**
* The class names of the compatible learners to used as boosters.
*
* @var class-string[]
*/
public const COMPATIBLE_BOOSTERS = [
RegressionTree::class,
ExtraTreeRegressor::class,
];
/**
* The minimum size of each training subset.
*
* @var int
*/
protected const MIN_SUBSAMPLE = 2;
/**
* The regressor that will fix up the error residuals of the *weak* base learner.
*
* @var Learner
*/
protected Learner $booster;
/**
* The learning rate of the ensemble i.e. the *shrinkage* applied to each step.
*
* @var float
*/
protected float $rate;
/**
* The ratio of samples to subsample from the training set for each booster.
*
* @var float
*/
protected float $ratio;
/**
* The maximum number of training epochs. i.e. the number of times to iterate before terminating.
*
* @var int<0,max>
*/
protected int $epochs;
/**
* The minimum change in the training loss necessary to continue training.
*
* @var float
*/
protected float $minChange;
/**
* The number of epochs without improvement in the validation score to wait before considering an
* early stop.
*
* @var positive-int
*/
protected int $window;
/**
* The proportion of training samples to use for validation and progress monitoring.
*
* @var float
*/
protected float $holdOut;
/**
* The metric used to score the generalization performance of the model during training.
*
* @var Metric
*/
protected Metric $metric;
/**
* An ensemble of weak regressors.
*
* @var mixed[]
*/
protected array $ensemble = [
//
];
/**
* The validation scores at each epoch.
*
* @var float[]|null
*/
protected ?array $scores = null;
/**
* The average training loss at each epoch.
*
* @var float[]|null
*/
protected ?array $losses = null;
/**
* The dimensionality of the training set.
*
* @var int<0,max>|null
*/
protected ?int $featureCount = null;
/**
* The mean of the labels of the training set.
*
* @var float|null
*/
protected ?float $mu = null;
/**
* @param Learner|null $booster
* @param float $rate
* @param float $ratio
* @param int $epochs
* @param float $minChange
* @param int $window
* @param float $holdOut
* @param Metric|null $metric
* @throws InvalidArgumentException
*/
public function __construct(
?Learner $booster = null,
float $rate = 0.1,
float $ratio = 0.5,
int $epochs = 1000,
float $minChange = 1e-4,
int $window = 5,
float $holdOut = 0.1,
?Metric $metric = null
) {
if ($booster and !in_array(get_class($booster), self::COMPATIBLE_BOOSTERS)) {
throw new InvalidArgumentException('Booster is not compatible'
. ' with the ensemble.');
}
if ($rate <= 0.0) {
throw new InvalidArgumentException('Learning rate must be'
. " greater than 0, $rate given.");
}
if ($ratio <= 0.0 or $ratio > 1.0) {
throw new InvalidArgumentException('Ratio must be'
. " between 0 and 1, $ratio given.");
}
if ($epochs < 0) {
throw new InvalidArgumentException('Number of epochs'
. " must be greater than 0, $epochs given.");
}
if ($minChange < 0.0) {
throw new InvalidArgumentException('Minimum change must be'
. " greater than 0, $minChange given.");
}
if ($window < 1) {
throw new InvalidArgumentException('Window must be'
. " greater than 0, $window given.");
}
if ($holdOut < 0.0 or $holdOut > 0.5) {
throw new InvalidArgumentException('Hold out ratio must be'
. " between 0 and 0.5, $holdOut given.");
}
if ($metric) {
EstimatorIsCompatibleWithMetric::with($this, $metric)->check();
}
$this->booster = $booster ?? new RegressionTree(3);
$this->rate = $rate;
$this->ratio = $ratio;
$this->epochs = $epochs;
$this->minChange = $minChange;
$this->window = $window;
$this->holdOut = $holdOut;
$this->metric = $metric ?? new RMSE();
}
/**
* Return the estimator type.
*
* @internal
*
* @return EstimatorType
*/
public function type() : EstimatorType
{
return EstimatorType::regressor();
}
/**
* Return the data types that the estimator is compatible with.
*
* @internal
*
* @return list<\Rubix\ML\DataType>
*/
public function compatibility() : array
{
return $this->booster->compatibility();
}
/**
* Return the settings of the hyper-parameters in an associative array.
*
* @internal
*
* @return mixed[]
*/
public function params() : array
{
return [
'booster' => $this->booster,
'rate' => $this->rate,
'ratio' => $this->ratio,
'epochs' => $this->epochs,
'min change' => $this->minChange,
'window' => $this->window,
'hold out' => $this->holdOut,
'metric' => $this->metric,
];
}
/**
* Has the learner been trained?
*
* @return bool
*/
public function trained() : bool
{
return !empty($this->ensemble);
}
/**
* Return an iterable progress table with the steps from the last training session.
*
* @return \Generator<mixed[]>
*/
public function steps() : Generator
{
if (!$this->losses) {
return;
}
foreach ($this->losses as $epoch => $loss) {
yield [
'epoch' => $epoch,
'score' => $this->scores[$epoch] ?? null,
'loss' => $loss,
];
}
}
/**
* Return the validation scores at each epoch from the last training session.
*
* @return float[]|null
*/
public function scores() : ?array
{
return $this->scores;
}
/**
* Return the loss for each epoch from the last training session.
*
* @return float[]|null
*/
public function losses() : ?array
{
return $this->losses;
}
/**
* Train the estimator with a dataset.
*
* @param Labeled $dataset
*/
public function train(Dataset $dataset) : void
{
SpecificationChain::with([
new DatasetIsLabeled($dataset),
new DatasetIsNotEmpty($dataset),
new SamplesAreCompatibleWithEstimator($dataset, $this),
new LabelsAreCompatibleWithLearner($dataset, $this),
])->check();
if ($this->logger) {
$this->logger->info("Training $this");
}
[$testing, $training] = $dataset->randomize()->split($this->holdOut);
[$minScore, $maxScore] = $this->metric->range()->list();
[$m, $n] = $training->shape();
$targets = $training->labels();
$mu = Stats::mean($targets);
$out = array_fill(0, $m, $mu);
if (!$testing->empty()) {
$outTest = array_fill(0, $testing->numSamples(), $mu);
} elseif ($this->logger) {
$this->logger->notice('Insufficient validation data, '
. 'some features are disabled');
}
$p = max(self::MIN_SUBSAMPLE, (int) round($this->ratio * $m));
$weights = array_fill(0, $m, 1.0 / $m);
$this->featureCount = $n;
$this->ensemble = $this->scores = $this->losses = [];
$this->mu = $mu;
$bestScore = $minScore;
$bestEpoch = $numWorseEpochs = 0;
$score = null;
$prevLoss = INF;
for ($epoch = 1; $epoch <= $this->epochs; ++$epoch) {
$gradient = array_map([$this, 'gradient'], $out, $targets);
$loss = array_reduce($gradient, [$this, 'l2Loss'], 0.0);
$loss /= $m;
$lossChange = abs($prevLoss - $loss);
$this->losses[$epoch] = $loss;
if (isset($outTest)) {
$score = $this->metric->score($outTest, $testing->labels());
$this->scores[$epoch] = $score;
}
if ($this->logger) {
$lossDirection = $loss < $prevLoss ? '↓' : '↑';
$message = "Epoch: $epoch, "
. "L2 Loss: $loss, "
. "Loss Change: {$lossDirection}{$lossChange}, "
. "{$this->metric}: " . ($score ?? 'N/A');
$this->logger->info($message);
}
if (is_nan($loss)) {
if ($this->logger) {
$this->logger->warning('Numerical instability detected');
}
break;
}
if (isset($score)) {
if ($score >= $maxScore) {
break;
}
if ($score > $bestScore) {
$bestScore = $score;
$bestEpoch = $epoch;
$numWorseEpochs = 0;
} else {
++$numWorseEpochs;
}
if ($numWorseEpochs >= $this->window) {
break;
}
}
if ($lossChange < $this->minChange) {
break;
}
$training = Labeled::quick($training->samples(), $gradient);
$subset = $training->randomWeightedSubsetWithReplacement($p, $weights);
$booster = clone $this->booster;
$booster->train($subset);
$this->ensemble[] = $booster;
$predictions = $booster->predict($training);
$out = array_map([$this, 'updateOut'], $predictions, $out);
if (isset($outTest)) {
$predictions = $booster->predict($testing);
$outTest = array_map([$this, 'updateOut'], $predictions, $outTest);
}
$weights = array_map('abs', $gradient);
$prevLoss = $loss;
}
if ($this->scores and end($this->scores) <= $bestScore) {
$this->ensemble = array_slice($this->ensemble, 0, $bestEpoch);
if ($this->logger) {
$this->logger->info("Model state restored to epoch $bestEpoch");
}
}
if ($this->logger) {
$this->logger->info('Training complete');
}
}
/**
* Make a prediction from a dataset.
*
* @param Dataset $dataset
* @throws RuntimeException
* @return list<int|float>
*/
public function predict(Dataset $dataset) : array
{
if (!isset($this->ensemble, $this->featureCount, $this->mu)) {
throw new RuntimeException('Estimator has not been trained.');
}
DatasetHasDimensionality::with($dataset, $this->featureCount)->check();
$out = array_fill(0, $dataset->numSamples(), $this->mu);
foreach ($this->ensemble as $estimator) {
$predictions = $estimator->predict($dataset);
$out = array_map([$this, 'updateOut'], $predictions, $out);
}
return $out;
}
/**
* Return the importance scores of each feature column of the training set.
*
* @throws RuntimeException
* @return float[]
*/
public function featureImportances() : array
{
if (!isset($this->ensemble, $this->featureCount)) {
throw new RuntimeException('Estimator has not been trained.');
}
$importances = array_fill(0, $this->featureCount, 0.0);
foreach ($this->ensemble as $tree) {
$scores = $tree->featureImportances();
foreach ($scores as $column => $score) {
$importances[$column] += $score;
}
}
$numEstimators = count($this->ensemble);
foreach ($importances as &$importance) {
$importance /= $numEstimators;
}
return $importances;
}
/**
* Compute the output for an iteration.
*
* @param float $prediction
* @param float $out
* @return float
*/
protected function updateOut(float $prediction, float $out) : float
{
return $this->rate * $prediction + $out;
}
/**
* Compute the gradient for a single sample.
*
* @param float $out
* @param float $target
* @return float
*/
protected function gradient(float $out, float $target) : float
{
return $target - $out;
}
/**
* Compute the cross entropy loss function.
*
* @param float $loss
* @param float $derivative
* @return float
*/
protected function l2Loss(float $loss, float $derivative) : float
{
return $loss + $derivative ** 2;
}
/**
* Return an associative array containing the data used to serialize the object.
*
* @return mixed[]
*/
public function __serialize() : array
{
$properties = get_object_vars($this);
unset($properties['losses'], $properties['scores']);
return $properties;
}
/**
* Return the string representation of the object.
*
* @internal
*
* @return string
*/
public function __toString() : string
{
return 'Gradient Boost (' . Params::stringify($this->params()) . ')';
}
}