proto

type DepthDependentParam struct {
	// Types that are valid to be assigned to ParamType:
	//	*DepthDependentParam_ConstantValue
	//	*DepthDependentParam_Linear
	//	*DepthDependentParam_Exponential
	//	*DepthDependentParam_Threshold
	ParamType isDepthDependentParam_ParamType `protobuf_oneof:"ParamType"`
}

type DepthDependentParam_ConstantValue struct {
	ConstantValue float32 `protobuf:"fixed32,1,opt,name=constant_value,json=constantValue,oneof"`
}

type DepthDependentParam_Exponential struct {
	Exponential *ExponentialParam `protobuf:"bytes,3,opt,name=exponential,oneof"`
}

type DepthDependentParam_Linear struct {
	Linear *LinearParam `protobuf:"bytes,2,opt,name=linear,oneof"`
}

type DepthDependentParam_Threshold struct {
	Threshold *ThresholdParam `protobuf:"bytes,4,opt,name=threshold,oneof"`
}

type ExponentialParam struct {
	Bias            float32 `protobuf:"fixed32,1,opt,name=bias" json:"bias,omitempty"`
	Base            float32 `protobuf:"fixed32,2,opt,name=base" json:"base,omitempty"`
	Multiplier      float32 `protobuf:"fixed32,3,opt,name=multiplier" json:"multiplier,omitempty"`
	DepthMultiplier float32 `protobuf:"fixed32,4,opt,name=depth_multiplier,json=depthMultiplier" json:"depth_multiplier,omitempty"`
}

type FertileSlot struct {
	// The statistics for *all* the examples seen at this leaf.
	LeafStats  *LeafStat         `protobuf:"bytes,4,opt,name=leaf_stats,json=leafStats" json:"leaf_stats,omitempty"`
	Candidates []*SplitCandidate `protobuf:"bytes,1,rep,name=candidates" json:"candidates,omitempty"`
	// The statistics for the examples seen at this leaf after all the
	// splits have been initialized.  If post_init_leaf_stats.weight_sum
	// is > 0, then all candidates have been initialized.  We need to track
	// both leaf_stats and post_init_leaf_stats because the first is used
	// to create the decision_tree::Leaf and the second is used to infer
	// the statistics for the right side of a split (given the leaf side
	// stats).
	PostInitLeafStats *LeafStat `protobuf:"bytes,6,opt,name=post_init_leaf_stats,json=postInitLeafStats" json:"post_init_leaf_stats,omitempty"`
	NodeId            int32     `protobuf:"varint,5,opt,name=node_id,json=nodeId" json:"node_id,omitempty"`
	Depth             int32     `protobuf:"varint,7,opt,name=depth" json:"depth,omitempty"`
}

type FertileStats struct {
	// Tracks stats for each node.  node_to_slot[i] is the FertileSlot for node i.
	// This may be sized to max_nodes initially, or grow dynamically as needed.
	NodeToSlot []*FertileSlot `protobuf:"bytes,1,rep,name=node_to_slot,json=nodeToSlot" json:"node_to_slot,omitempty"`
}

type GiniStats struct {
	// This allows us to quickly track and calculate impurity (classification)
	//  by storing the sum of input weights and the sum of the squares of the
	// input weights.  Weighted gini is then: 1 - (square / sum * sum).
	// Updates to these numbers are:
	//   old_i = leaf->value(label)
	//   new_i = old_i + incoming_weight
	//   sum -> sum + incoming_weight
	//   square -> square - (old_i ^ 2) + (new_i ^ 2)
	//   total_left_sum -> total_left_sum - old_left_i * old_total_i +
	//                                      new_left_i * new_total_i
	Square float32 `protobuf:"fixed32,2,opt,name=square" json:"square,omitempty"`
}

type LeafModelType int32

type LeafStat struct {
	// The sum of the weights of the training examples that we have seen.
	// This is here, outside of the leaf_stat oneof, because almost all
	// types will want it.
	WeightSum float32 `protobuf:"fixed32,3,opt,name=weight_sum,json=weightSum" json:"weight_sum,omitempty"`
	// Types that are valid to be assigned to LeafStat:
	//	*LeafStat_Classification
	//	*LeafStat_Regression
	LeafStat isLeafStat_LeafStat `protobuf_oneof:"leaf_stat"`
}

type LeafStat_Classification struct {
	Classification *LeafStat_GiniImpurityClassificationStats `protobuf:"bytes,1,opt,name=classification,oneof"`
}

type LeafStat_GiniImpurityClassificationStats struct {
	// Types that are valid to be assigned to Counts:
	//	*LeafStat_GiniImpurityClassificationStats_DenseCounts
	//	*LeafStat_GiniImpurityClassificationStats_SparseCounts
	Counts isLeafStat_GiniImpurityClassificationStats_Counts `protobuf_oneof:"counts"`
	Gini   *GiniStats                                        `protobuf:"bytes,3,opt,name=gini" json:"gini,omitempty"`
}

type LeafStat_GiniImpurityClassificationStats_DenseCounts struct {
	DenseCounts *tensorflow_decision_trees.Vector `protobuf:"bytes,1,opt,name=dense_counts,json=denseCounts,oneof"`
}

type LeafStat_GiniImpurityClassificationStats_SparseCounts struct {
	SparseCounts *tensorflow_decision_trees.SparseVector `protobuf:"bytes,2,opt,name=sparse_counts,json=sparseCounts,oneof"`
}

type LeafStat_LeastSquaresRegressionStats struct {
	MeanOutput        *tensorflow_decision_trees.Vector `protobuf:"bytes,1,opt,name=mean_output,json=meanOutput" json:"mean_output,omitempty"`
	MeanOutputSquares *tensorflow_decision_trees.Vector `protobuf:"bytes,2,opt,name=mean_output_squares,json=meanOutputSquares" json:"mean_output_squares,omitempty"`
}

type LeafStat_Regression struct {
	Regression *LeafStat_LeastSquaresRegressionStats `protobuf:"bytes,2,opt,name=regression,oneof"`
}

type LinearParam struct {
	Slope      float32 `protobuf:"fixed32,1,opt,name=slope" json:"slope,omitempty"`
	YIntercept float32 `protobuf:"fixed32,2,opt,name=y_intercept,json=yIntercept" json:"y_intercept,omitempty"`
	MinVal     float32 `protobuf:"fixed32,3,opt,name=min_val,json=minVal" json:"min_val,omitempty"`
	MaxVal     float32 `protobuf:"fixed32,4,opt,name=max_val,json=maxVal" json:"max_val,omitempty"`
}

type SplitCandidate struct {
	// proto representing the potential node.
	Split *tensorflow_decision_trees.BinaryNode `protobuf:"bytes,1,opt,name=split" json:"split,omitempty"`
	// Right counts are inferred from FertileSlot.leaf_stats and left.
	LeftStats *LeafStat `protobuf:"bytes,4,opt,name=left_stats,json=leftStats" json:"left_stats,omitempty"`
	// Right stats (not full counts) are kept here.
	RightStats *LeafStat `protobuf:"bytes,5,opt,name=right_stats,json=rightStats" json:"right_stats,omitempty"`
	// Fields used when training with a graph runner.
	UniqueId string `protobuf:"bytes,6,opt,name=unique_id,json=uniqueId" json:"unique_id,omitempty"`
}

type SplitCollectionType int32

type SplitFinishConfig struct {
	// Configure how often we check for finish, because some finish methods
	// are expensive to perform.
	CheckEverySteps *DepthDependentParam    `protobuf:"bytes,1,opt,name=check_every_steps,json=checkEverySteps" json:"check_every_steps,omitempty"`
	Type            SplitFinishStrategyType `protobuf:"varint,2,opt,name=type,enum=tensorflow.tensorforest.SplitFinishStrategyType" json:"type,omitempty"`
}

type SplitFinishStrategyType int32

type SplitPruningConfig struct {
	PruneEverySamples *DepthDependentParam     `protobuf:"bytes,1,opt,name=prune_every_samples,json=pruneEverySamples" json:"prune_every_samples,omitempty"`
	Type              SplitPruningStrategyType `protobuf:"varint,2,opt,name=type,enum=tensorflow.tensorforest.SplitPruningStrategyType" json:"type,omitempty"`
}

type SplitPruningStrategyType int32

type StatsModelType int32

type TensorForestParams struct {
	// ------------ Types that control training subsystems ------ //
	LeafType       LeafModelType       `protobuf:"varint,1,opt,name=leaf_type,json=leafType,enum=tensorflow.tensorforest.LeafModelType" json:"leaf_type,omitempty"`
	StatsType      StatsModelType      `` /* 126-byte string literal not displayed */
	CollectionType SplitCollectionType `` /* 146-byte string literal not displayed */
	PruningType    *SplitPruningConfig `protobuf:"bytes,4,opt,name=pruning_type,json=pruningType" json:"pruning_type,omitempty"`
	FinishType     *SplitFinishConfig  `protobuf:"bytes,5,opt,name=finish_type,json=finishType" json:"finish_type,omitempty"`
	// --------- Parameters that can't change by definition --------------- //
	NumTrees           int32                                         `protobuf:"varint,6,opt,name=num_trees,json=numTrees" json:"num_trees,omitempty"`
	MaxNodes           int32                                         `protobuf:"varint,7,opt,name=max_nodes,json=maxNodes" json:"max_nodes,omitempty"`
	NumFeatures        int32                                         `protobuf:"varint,21,opt,name=num_features,json=numFeatures" json:"num_features,omitempty"`
	InequalityTestType tensorflow_decision_trees.InequalityTest_Type `` /* 163-byte string literal not displayed */
	// Some booleans controlling execution
	IsRegression          bool `protobuf:"varint,8,opt,name=is_regression,json=isRegression" json:"is_regression,omitempty"`
	DropFinalClass        bool `protobuf:"varint,9,opt,name=drop_final_class,json=dropFinalClass" json:"drop_final_class,omitempty"`
	CollateExamples       bool `protobuf:"varint,10,opt,name=collate_examples,json=collateExamples" json:"collate_examples,omitempty"`
	CheckpointStats       bool `protobuf:"varint,11,opt,name=checkpoint_stats,json=checkpointStats" json:"checkpoint_stats,omitempty"`
	UseRunningStatsMethod bool `protobuf:"varint,20,opt,name=use_running_stats_method,json=useRunningStatsMethod" json:"use_running_stats_method,omitempty"`
	// Number of classes (classification) or targets (regression)
	NumOutputs int32 `protobuf:"varint,12,opt,name=num_outputs,json=numOutputs" json:"num_outputs,omitempty"`
	// --------- Parameters that could be depth-dependent --------------- //
	NumSplitsToConsider *DepthDependentParam `protobuf:"bytes,13,opt,name=num_splits_to_consider,json=numSplitsToConsider" json:"num_splits_to_consider,omitempty"`
	SplitAfterSamples   *DepthDependentParam `protobuf:"bytes,14,opt,name=split_after_samples,json=splitAfterSamples" json:"split_after_samples,omitempty"`
	DominateFraction    *DepthDependentParam `protobuf:"bytes,15,opt,name=dominate_fraction,json=dominateFraction" json:"dominate_fraction,omitempty"`
	MinSplitSamples     *DepthDependentParam `protobuf:"bytes,18,opt,name=min_split_samples,json=minSplitSamples" json:"min_split_samples,omitempty"`
	// --------- Parameters for experimental features ---------------------- //
	GraphDir          string `protobuf:"bytes,16,opt,name=graph_dir,json=graphDir" json:"graph_dir,omitempty"`
	NumSelectFeatures int32  `protobuf:"varint,17,opt,name=num_select_features,json=numSelectFeatures" json:"num_select_features,omitempty"`
}

type ThresholdParam struct {
	OnValue   float32 `protobuf:"fixed32,1,opt,name=on_value,json=onValue" json:"on_value,omitempty"`
	OffValue  float32 `protobuf:"fixed32,2,opt,name=off_value,json=offValue" json:"off_value,omitempty"`
	Threshold float32 `protobuf:"fixed32,3,opt,name=threshold" json:"threshold,omitempty"`
}