proto

package
v0.0.0-...-7d47eef Latest Latest
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 Go to latest Published: Apr 12, 2023 License: Apache-2.0, MIT Imports: 4 Imported by: 0

Documentation

Overview

Package proto includes all proto definitions used in the golang package in one large package.

It uses go generate tools to generate it from the source code, but we include the generated files in github, so one doesn't need to install anything.

Index

Constants

View Source 
const (
	Default_Header_NumTreesPerIter = int32(1)
	Default_Header_NodeFormat      = string("TFE_RECORDIO")
	Default_Header_OutputLogits    = bool(false)
)

Default values for Header fields.

View Source 
const (
	Default_TrainingLogs_Entry_SubsampleFactor = float32(1)
)

Default values for TrainingLogs_Entry fields.

Variables

View Source 
var (
	Loss_name = map[int32]string{
		0: "DEFAULT",
		1: "BINOMIAL_LOG_LIKELIHOOD",
		2: "SQUARED_ERROR",
		3: "MULTINOMIAL_LOG_LIKELIHOOD",
		4: "LAMBDA_MART_NDCG5",
		5: "XE_NDCG_MART",
		6: "BINARY_FOCAL_LOSS",
		7: "POISSON",
	}
	Loss_value = map[string]int32{
		"DEFAULT":                    0,
		"BINOMIAL_LOG_LIKELIHOOD":    1,
		"SQUARED_ERROR":              2,
		"MULTINOMIAL_LOG_LIKELIHOOD": 3,
		"LAMBDA_MART_NDCG5":          4,
		"XE_NDCG_MART":               5,
		"BINARY_FOCAL_LOSS":          6,
		"POISSON":                    7,
	}
)

Enum value maps for Loss.

View Source 
var File_yggdrasil_decision_forests_model_gradient_boosted_trees_gradient_boosted_trees_proto protoreflect.FileDescriptor

Functions

This section is empty.

Types 

type Header struct {

	// Number of shards used to store the nodes.
	NumNodeShards *int32 `protobuf:"varint,1,opt,name=num_node_shards,json=numNodeShards" json:"num_node_shards,omitempty"`
	// Number of trees.
	NumTrees *int64 `protobuf:"varint,2,opt,name=num_trees,json=numTrees" json:"num_trees,omitempty"`
	// Loss used to train the model.
	Loss *Loss `` /* 127-byte string literal not displayed */
	// Initial predictions of the model (before any tree is applied). The semantic
	// of the prediction depend on the "loss".
	InitialPredictions []float32 `protobuf:"fixed32,4,rep,name=initial_predictions,json=initialPredictions" json:"initial_predictions,omitempty"`
	// Number of trees extracted at each gradient boosting operation.
	NumTreesPerIter *int32 `protobuf:"varint,5,opt,name=num_trees_per_iter,json=numTreesPerIter,def=1" json:"num_trees_per_iter,omitempty"`
	// Loss evaluated on the validation dataset. Only available is a validation
	// dataset was provided during training.
	ValidationLoss *float32 `protobuf:"fixed32,6,opt,name=validation_loss,json=validationLoss" json:"validation_loss,omitempty"`
	// Container used to store the trees' nodes.
	NodeFormat *string `protobuf:"bytes,7,opt,name=node_format,json=nodeFormat,def=TFE_RECORDIO" json:"node_format,omitempty"`
	// Evaluation metrics and other meta-data computed during training.
	TrainingLogs *TrainingLogs `protobuf:"bytes,8,opt,name=training_logs,json=trainingLogs" json:"training_logs,omitempty"`
	// If true, call to predict methods return logits (e.g. instead of probability
	// in the case of classification).
	OutputLogits *bool `protobuf:"varint,9,opt,name=output_logits,json=outputLogits,def=0" json:"output_logits,omitempty"`
	// contains filtered or unexported fields
}

Header for the gradient boosted trees model.

func (*Header) Descriptor deprecated 

func (*Header) Descriptor() ([]byte, []int)

Deprecated: Use Header.ProtoReflect.Descriptor instead.

func (*Header) GetInitialPredictions 

func (x *Header) GetInitialPredictions() []float32

func (*Header) GetLoss 

func (x *Header) GetLoss() Loss

func (*Header) GetNodeFormat 

func (x *Header) GetNodeFormat() string

func (*Header) GetNumNodeShards 

func (x *Header) GetNumNodeShards() int32

func (*Header) GetNumTrees 

func (x *Header) GetNumTrees() int64

func (*Header) GetNumTreesPerIter 

func (x *Header) GetNumTreesPerIter() int32

func (*Header) GetOutputLogits 

func (x *Header) GetOutputLogits() bool

func (*Header) GetTrainingLogs 

func (x *Header) GetTrainingLogs() *TrainingLogs

func (*Header) GetValidationLoss 

func (x *Header) GetValidationLoss() float32

func (*Header) ProtoMessage 

func (*Header) ProtoMessage()

func (*Header) ProtoReflect 

func (x *Header) ProtoReflect() protoreflect.Message

func (*Header) Reset 

func (x *Header) Reset()

func (*Header) String 

func (x *Header) String() string

type Loss 

type Loss int32
const (
	// Selects the most adapted loss according to the nature of the task and the
	// statistics of the label.
	// - Binary classification -> BINOMIAL_LOG_LIKELIHOOD.
	Loss_DEFAULT Loss = 0
	// Binomial log likelihood. Only valid for binary classification.
	Loss_BINOMIAL_LOG_LIKELIHOOD Loss = 1
	// Least square loss. Only valid for regression.
	Loss_SQUARED_ERROR Loss = 2
	// Multinomial log likelihood i.e. cross-entropy.
	Loss_MULTINOMIAL_LOG_LIKELIHOOD Loss = 3
	// LambdaMART with NDCG5
	Loss_LAMBDA_MART_NDCG5 Loss = 4
	// XE_NDCG_MART [arxiv.org/abs/1911.09798]
	Loss_XE_NDCG_MART Loss = 5
	// EXPERIMENTAl. Focal loss. Only valid for binary classification.
	// [https://arxiv.org/pdf/1708.02002.pdf]
	Loss_BINARY_FOCAL_LOSS Loss = 6
	// Poisson loss. Only valid for regression.
	Loss_POISSON Loss = 7
)

func (Loss) Descriptor 

func (Loss) Descriptor() protoreflect.EnumDescriptor

func (Loss) Enum 

func (x Loss) Enum() *Loss

func (Loss) EnumDescriptor deprecated 

func (Loss) EnumDescriptor() ([]byte, []int)

Deprecated: Use Loss.Descriptor instead.

func (Loss) Number 

func (x Loss) Number() protoreflect.EnumNumber

func (Loss) String 

func (x Loss) String() string

func (Loss) Type 

func (Loss) Type() protoreflect.EnumType

func (*Loss) UnmarshalJSON deprecated 

func (x *Loss) UnmarshalJSON(b []byte) error

Deprecated: Do not use.

type TrainingLogs 

type TrainingLogs struct {

	// Measurements the model size and performances during the training.
	Entries []*TrainingLogs_Entry `protobuf:"bytes,1,rep,name=entries" json:"entries,omitempty"`
	// Names of the metrics stored in "secondary_metrics" field. The secondary
	// metrics depends on the task (e.g. classification) and is accessible with
	// "SecondaryMetricNames()". The i-th metric name of "secondary_metric_names"
	// correspond to the i-th metric value in "training_secondary_metrics" and
	// "validation_secondary_metrics".
	SecondaryMetricNames []string `protobuf:"bytes,2,rep,name=secondary_metric_names,json=secondaryMetricNames" json:"secondary_metric_names,omitempty"`
	// Number of trees in the final model. Without early stopping,
	// "number_of_trees_in_final_model" is equal to the "number_of_trees" of the
	// last "entries".
	NumberOfTreesInFinalModel *int32 `` /* 138-byte string literal not displayed */
	// contains filtered or unexported fields
}

Log of the training. This proto is generated during the training of the model and optionally exported (as a plot) in the training logs directory.

func (*TrainingLogs) Descriptor deprecated 

func (*TrainingLogs) Descriptor() ([]byte, []int)

Deprecated: Use TrainingLogs.ProtoReflect.Descriptor instead.

func (*TrainingLogs) GetEntries 

func (x *TrainingLogs) GetEntries() []*TrainingLogs_Entry

func (*TrainingLogs) GetNumberOfTreesInFinalModel 

func (x *TrainingLogs) GetNumberOfTreesInFinalModel() int32

func (*TrainingLogs) GetSecondaryMetricNames 

func (x *TrainingLogs) GetSecondaryMetricNames() []string

func (*TrainingLogs) ProtoMessage 

func (*TrainingLogs) ProtoMessage()

func (*TrainingLogs) ProtoReflect 

func (x *TrainingLogs) ProtoReflect() protoreflect.Message

func (*TrainingLogs) Reset 

func (x *TrainingLogs) Reset()

func (*TrainingLogs) String 

func (x *TrainingLogs) String() string

type TrainingLogs_Entry 

type TrainingLogs_Entry struct {

	// Number of trees. In the case of multi-dimensional gradients,
	// "number_of_trees" is the number of training step.
	NumberOfTrees *int32 `protobuf:"varint,1,opt,name=number_of_trees,json=numberOfTrees" json:"number_of_trees,omitempty"`
	// Performance of the model on the training dataset.
	TrainingLoss             *float32  `protobuf:"fixed32,2,opt,name=training_loss,json=trainingLoss" json:"training_loss,omitempty"`
	TrainingSecondaryMetrics []float32 `` /* 130-byte string literal not displayed */
	// Performance of the model on the validation dataset.
	ValidationLoss             *float32  `protobuf:"fixed32,4,opt,name=validation_loss,json=validationLoss" json:"validation_loss,omitempty"`
	ValidationSecondaryMetrics []float32 `` /* 136-byte string literal not displayed */
	// Average of the absolute value of the new tree predictions estimated on
	// the training dataset. See Dart paper
	// (http://proceedings.mlr.press/v38/korlakaivinayak15.pdf) for details on
	// how to interpret it.
	MeanAbsPrediction *float64 `protobuf:"fixed64,6,opt,name=mean_abs_prediction,json=meanAbsPrediction" json:"mean_abs_prediction,omitempty"`
	// Sub-sampling factor applied during training on top of the "sampling"
	// hyper-parameter. Currently, the "subsample_factor" is only controlled by
	// the "adapt_subsample_for_maximum_training_duration" field i.e. the
	// "subsample_factor" factor (default to 1) is reduced so the training
	// finishes in "maximum_training_duration".
	SubsampleFactor *float32 `protobuf:"fixed32,7,opt,name=subsample_factor,json=subsampleFactor,def=1" json:"subsample_factor,omitempty"`
	// contains filtered or unexported fields
}

func (*TrainingLogs_Entry) Descriptor deprecated 

func (*TrainingLogs_Entry) Descriptor() ([]byte, []int)

Deprecated: Use TrainingLogs_Entry.ProtoReflect.Descriptor instead.

func (*TrainingLogs_Entry) GetMeanAbsPrediction 

func (x *TrainingLogs_Entry) GetMeanAbsPrediction() float64

func (*TrainingLogs_Entry) GetNumberOfTrees 

func (x *TrainingLogs_Entry) GetNumberOfTrees() int32

func (*TrainingLogs_Entry) GetSubsampleFactor 

func (x *TrainingLogs_Entry) GetSubsampleFactor() float32

func (*TrainingLogs_Entry) GetTrainingLoss 

func (x *TrainingLogs_Entry) GetTrainingLoss() float32

func (*TrainingLogs_Entry) GetTrainingSecondaryMetrics 

func (x *TrainingLogs_Entry) GetTrainingSecondaryMetrics() []float32

func (*TrainingLogs_Entry) GetValidationLoss 

func (x *TrainingLogs_Entry) GetValidationLoss() float32

func (*TrainingLogs_Entry) GetValidationSecondaryMetrics 

func (x *TrainingLogs_Entry) GetValidationSecondaryMetrics() []float32

func (*TrainingLogs_Entry) ProtoMessage 

func (*TrainingLogs_Entry) ProtoMessage()

func (*TrainingLogs_Entry) ProtoReflect 

func (x *TrainingLogs_Entry) ProtoReflect() protoreflect.Message

func (*TrainingLogs_Entry) Reset 

func (x *TrainingLogs_Entry) Reset()

func (*TrainingLogs_Entry) String 

func (x *TrainingLogs_Entry) String() string

Source Files

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