trees

package
v0.0.0-...-05d7f63 Latest Latest
Warning

This package is not in the latest version of its module.

 Go to latest Published: Oct 5, 2020 License: MIT Imports: 11 Imported by: 0

Documentation

Index

Constants

View Source 
const (
	GINI    string = "gini"
	ENTROPY string = "entropy"
)
View Source 
const (
	MAE string = "mae"
	MSE string = "mse"
)

Variables

This section is empty.

Functions

This section is empty.

Types

type CARTDecisionTreeClassifier 

type CARTDecisionTreeClassifier struct {
	RootNode *classifierNode
	// contains filtered or unexported fields
}

CARTDecisionTreeClassifier: Tree struct for Decision Tree Classifier It contains the rootNode, as well as all of the hyperparameters chosen by the user. It also keeps track of all splits done at the tree level.

func NewDecisionTreeClassifier 

func NewDecisionTreeClassifier(criterion string, maxDepth int64, labels []int64) *CARTDecisionTreeClassifier

Function to Create New Decision Tree Classifier. It assigns all of the hyperparameters by user into the tree attributes.

func (*CARTDecisionTreeClassifier) Evaluate 

func (tree *CARTDecisionTreeClassifier) Evaluate(test base.FixedDataGrid) (float64, error)

Given Test data and label, return the accuracy of the classifier. First it retreives predictions from the data, then compares for accuracy. Calls classifierEvaluateFromNode

func (*CARTDecisionTreeClassifier) Fit 

func (tree *CARTDecisionTreeClassifier) Fit(X base.FixedDataGrid) error

Fit - Creates an Empty Root Node Trains the tree by calling recursive function classifierBestSplit

func (*CARTDecisionTreeClassifier) Predict 

func (tree *CARTDecisionTreeClassifier) Predict(X_test base.FixedDataGrid) []int64

Given test data, return predictions for every datapoint. calls classifierPredictFromNode

func (*CARTDecisionTreeClassifier) String 

func (tree *CARTDecisionTreeClassifier) String() string

String : this function prints out entire tree for visualization. Calls a recursive function to print the tree - classifierPrintTreeFromNode

type CARTDecisionTreeRegressor 

type CARTDecisionTreeRegressor struct {
	RootNode *regressorNode
	// contains filtered or unexported fields
}

CARTDecisionTreeRegressor - Tree struct for Decision Tree Regressor It contains the rootNode, as well as the hyperparameters chosen by user. Also keeps track of splits used at tree level.

func NewDecisionTreeRegressor 

func NewDecisionTreeRegressor(criterion string, maxDepth int64) *CARTDecisionTreeRegressor

Interface for creating new Decision Tree Regressor

func (*CARTDecisionTreeRegressor) Fit 

func (tree *CARTDecisionTreeRegressor) Fit(X base.FixedDataGrid) error

Fit - Build the tree using the data Creates empty root node and builds tree by calling regressorBestSplit

func (*CARTDecisionTreeRegressor) Predict 

func (tree *CARTDecisionTreeRegressor) Predict(X_test base.FixedDataGrid) []float64

Predict method for multiple data points. First converts input data into usable format, and then calls regressorPredictFromNode

func (*CARTDecisionTreeRegressor) String 

func (tree *CARTDecisionTreeRegressor) String() string

Print Tree for Visualtion - calls regressorPrintTreeFromNode()

type ClassProba 

type ClassProba struct {
	Probability float64
	ClassValue  string
}

type ClassesProba 

type ClassesProba []ClassProba

func (ClassesProba) Len 

func (o ClassesProba) Len() int

func (ClassesProba) Less 

func (o ClassesProba) Less(i, j int) bool

func (ClassesProba) Swap 

func (o ClassesProba) Swap(i, j int)

type DecisionTreeNode 

type DecisionTreeNode struct {
	Type      NodeType                     `json:"node_type"`
	Children  map[string]*DecisionTreeNode `json:"children"`
	ClassDist map[string]int               `json:"class_dist"`
	Class     string                       `json:"class_string"`
	ClassAttr base.Attribute               `json:"-"`
	SplitRule *DecisionTreeRule            `json:"decision_tree_rule"`
}

DecisionTreeNode represents a given portion of a decision tree.

func InferID3Tree 

func InferID3Tree(from base.FixedDataGrid, with RuleGenerator) *DecisionTreeNode

InferID3Tree builds a decision tree using a RuleGenerator from a set of Instances (implements the ID3 algorithm)

func (*DecisionTreeNode) Load 

func (d *DecisionTreeNode) Load(filePath string) error

Load reads from the classifier from an output file

func (*DecisionTreeNode) LoadWithPrefix 

func (d *DecisionTreeNode) LoadWithPrefix(reader *base.ClassifierDeserializer, prefix string) error

LoadWithPrefix reads from the classifier from part of another model

func (*DecisionTreeNode) Predict 

func (d *DecisionTreeNode) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error)

Predict outputs a base.Instances containing predictions from this tree

func (*DecisionTreeNode) Prune 

func (d *DecisionTreeNode) Prune(using base.FixedDataGrid)

Prune eliminates branches which hurt accuracy

func (*DecisionTreeNode) Save 

func (d *DecisionTreeNode) Save(filePath string) error

Save sends the classification tree to an output file

func (*DecisionTreeNode) SaveWithPrefix 

func (d *DecisionTreeNode) SaveWithPrefix(writer *base.ClassifierSerializer, prefix string) error

func (*DecisionTreeNode) String 

func (d *DecisionTreeNode) String() string

String returns a human-readable representation of a given node and it's children

type DecisionTreeRule 

type DecisionTreeRule struct {
	SplitAttr base.Attribute `json:"split_attribute"`
	SplitVal  float64        `json:"split_val"`
}

DecisionTreeRule represents the "decision" in "decision tree".

func (*DecisionTreeRule) MarshalJSON 

func (d *DecisionTreeRule) MarshalJSON() ([]byte, error)

func (*DecisionTreeRule) String 

func (d *DecisionTreeRule) String() string

String prints a human-readable summary of this thing.

func (*DecisionTreeRule) UnmarshalJSON 

func (d *DecisionTreeRule) UnmarshalJSON(data []byte) error

type GiniCoefficientRuleGenerator 

type GiniCoefficientRuleGenerator struct {
}

GiniCoefficientRuleGenerator generates DecisionTreeRules which minimize the Geni impurity coefficient at each node.

func (*GiniCoefficientRuleGenerator) GenerateSplitRule 

func (g *GiniCoefficientRuleGenerator) GenerateSplitRule(f base.FixedDataGrid) *DecisionTreeRule

GenerateSplitRule returns the non-class Attribute-based DecisionTreeRule which maximises the information gain.

IMPORTANT: passing a base.Instances with no Attributes other than the class variable will panic()

func (*GiniCoefficientRuleGenerator) GetSplitRuleFromSelection 

func (g *GiniCoefficientRuleGenerator) GetSplitRuleFromSelection(consideredAttributes []base.Attribute, f base.FixedDataGrid) *DecisionTreeRule

GetSplitRuleFromSelection returns the DecisionTreeRule which maximises the information gain amongst consideredAttributes

IMPORTANT: passing a zero-length consideredAttributes parameter will panic()

type ID3DecisionTree 

type ID3DecisionTree struct {
	base.BaseClassifier
	Root       *DecisionTreeNode
	PruneSplit float64
	Rule       RuleGenerator
}

ID3DecisionTree represents an ID3-based decision tree using the Information Gain metric to select which attributes to split on at each node.

func NewID3DecisionTree 

func NewID3DecisionTree(prune float64) *ID3DecisionTree

NewID3DecisionTree returns a new ID3DecisionTree with the specified test-prune ratio and InformationGain as the rule generator. If the ratio is less than 0.001, the tree isn't pruned.

func NewID3DecisionTreeFromRule 

func NewID3DecisionTreeFromRule(prune float64, rule RuleGenerator) *ID3DecisionTree

NewID3DecisionTreeFromRule returns a new ID3DecisionTree with the specified test-prun ratio and the given rule gnereator.

func (*ID3DecisionTree) Fit 

func (t *ID3DecisionTree) Fit(on base.FixedDataGrid) error

Fit builds the ID3 decision tree

func (*ID3DecisionTree) GetMetadata 

func (t *ID3DecisionTree) GetMetadata() base.ClassifierMetadataV1

func (*ID3DecisionTree) Load 

func (t *ID3DecisionTree) Load(filePath string) error

func (*ID3DecisionTree) LoadWithPrefix 

func (t *ID3DecisionTree) LoadWithPrefix(reader *base.ClassifierDeserializer, prefix string) error

func (*ID3DecisionTree) Predict 

func (t *ID3DecisionTree) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error)

Predict outputs predictions from the ID3 decision tree

func (*ID3DecisionTree) PredictProba 

func (t *ID3DecisionTree) PredictProba(what base.FixedDataGrid) (ClassesProba, error)

Predict class probabilities of the input samples what, returns a sorted array (by probability) of classes, and another array representing it's probabilities

func (*ID3DecisionTree) Save 

func (t *ID3DecisionTree) Save(filePath string) error

func (*ID3DecisionTree) SaveWithPrefix 

func (t *ID3DecisionTree) SaveWithPrefix(writer *base.ClassifierSerializer, prefix string) error

func (*ID3DecisionTree) String 

func (t *ID3DecisionTree) String() string

String returns a human-readable version of this ID3 tree

type InformationGainRatioRuleGenerator 

type InformationGainRatioRuleGenerator struct {
}

InformationGainRatioRuleGenerator generates DecisionTreeRules which maximise the InformationGain at each node.

func (*InformationGainRatioRuleGenerator) GenerateSplitRule 

func (r *InformationGainRatioRuleGenerator) GenerateSplitRule(f base.FixedDataGrid) *DecisionTreeRule

GenerateSplitRule returns a DecisionTreeRule which maximises information gain ratio considering every available Attribute.

IMPORTANT: passing a base.Instances with no Attributes other than the class variable will panic()

func (*InformationGainRatioRuleGenerator) GetSplitRuleFromSelection 

func (r *InformationGainRatioRuleGenerator) GetSplitRuleFromSelection(consideredAttributes []base.Attribute, f base.FixedDataGrid) *DecisionTreeRule

GetSplitRuleFromSelection returns the DecisionRule which maximizes information gain, considering only a subset of Attributes.

IMPORTANT: passing a zero-length consideredAttributes parameter will panic()

type InformationGainRuleGenerator 

type InformationGainRuleGenerator struct {
}

InformationGainRuleGenerator generates DecisionTreeRules which maximize information gain at each node.

func (*InformationGainRuleGenerator) GenerateSplitRule 

func (r *InformationGainRuleGenerator) GenerateSplitRule(f base.FixedDataGrid) *DecisionTreeRule

GenerateSplitRule returns a DecisionTreeNode based on a non-class Attribute which maximises the information gain.

IMPORTANT: passing a base.Instances with no Attributes other than the class variable will panic()

func (*InformationGainRuleGenerator) GetSplitRuleFromSelection 

func (r *InformationGainRuleGenerator) GetSplitRuleFromSelection(consideredAttributes []base.Attribute, f base.FixedDataGrid) *DecisionTreeRule

GetSplitRuleFromSelection returns a DecisionTreeRule which maximises the information gain amongst the considered Attributes.

IMPORTANT: passing a zero-length consideredAttributes parameter will panic()

type IsolationForest 

type IsolationForest struct {
	// contains filtered or unexported fields
}

func NewIsolationForest 

func NewIsolationForest(nTrees int, maxDepth int, subSpace int) IsolationForest

Function to create a new isolation forest. nTrees is number of trees in Forest. Maxdepth is maximum depth of each tree. Subspace is number of data points to use per tree.

func (*IsolationForest) Fit 

func (iForest *IsolationForest) Fit(X base.FixedDataGrid)

Fit the data based on hyperparameters and data.

func (*IsolationForest) Predict 

func (iForest *IsolationForest) Predict(X base.FixedDataGrid) []float64

Return anamoly score for all datapoints.

type NodeType 

type NodeType int

NodeType determines whether a DecisionTreeNode is a leaf or not. 

const (
	// LeafNode means there are no children
	LeafNode NodeType = 1
	// RuleNode means we should look at the next attribute value
	RuleNode NodeType = 2
)

type RandomTree 

type RandomTree struct {
	base.BaseClassifier
	Root *DecisionTreeNode
	Rule *RandomTreeRuleGenerator
}

RandomTree builds a decision tree by considering a fixed number of randomly-chosen attributes at each node

func NewRandomTree 

func NewRandomTree(attrs int) *RandomTree

NewRandomTree returns a new RandomTree which considers attrs randomly chosen attributes at each node.

func (*RandomTree) Fit 

func (rt *RandomTree) Fit(from base.FixedDataGrid) error

Fit builds a RandomTree suitable for prediction

func (*RandomTree) GetMetadata 

func (rt *RandomTree) GetMetadata() base.ClassifierMetadataV1

GetMetadata returns required serialization metadata

func (*RandomTree) Load 

func (rt *RandomTree) Load(filePath string) error

Load retrieves this model from a file

func (*RandomTree) LoadWithPrefix 

func (rt *RandomTree) LoadWithPrefix(reader *base.ClassifierDeserializer, prefix string) error

LoadWithPrefix retrives this random tree from disk with a given prefix.

func (*RandomTree) Predict 

func (rt *RandomTree) Predict(from base.FixedDataGrid) (base.FixedDataGrid, error)

Predict returns a set of Instances containing predictions

func (*RandomTree) Prune 

func (rt *RandomTree) Prune(with base.FixedDataGrid)

Prune removes nodes from the tree which are detrimental to determining the accuracy of the test set (with)

func (*RandomTree) Save 

func (rt *RandomTree) Save(filePath string) error

Save outputs this model to a file

func (*RandomTree) SaveWithPrefix 

func (rt *RandomTree) SaveWithPrefix(writer *base.ClassifierSerializer, prefix string) error

SaveWithPrefix outputs this model to a file with a prefix.

func (*RandomTree) String 

func (rt *RandomTree) String() string

String returns a human-readable representation of this structure

type RandomTreeRuleGenerator 

type RandomTreeRuleGenerator struct {
	Attributes int
	// contains filtered or unexported fields
}

RandomTreeRuleGenerator is used to generate decision rules for Random Trees

func (*RandomTreeRuleGenerator) GenerateSplitRule 

func (r *RandomTreeRuleGenerator) GenerateSplitRule(f base.FixedDataGrid) *DecisionTreeRule

GenerateSplitRule returns the best attribute out of those randomly chosen which maximises Information Gain

type RuleGenerator 

type RuleGenerator interface {
	GenerateSplitRule(base.FixedDataGrid) *DecisionTreeRule
}

RuleGenerator implementations analyse instances and determine the best value to split on.

type Slice 

type Slice struct {
	sort.Float64Slice
	Idx []int
}

func NewSlice 

func NewSlice(n []float64) *Slice

func (Slice) Swap 

func (s Slice) Swap(i, j int)

Source Files

View all Source files

Jump to

Keyboard shortcuts

? : This menu
/ : Search site
f or F : Jump to
y or Y : Canonical URL
go.dev uses cookies from Google to deliver and enhance the quality of its services and to analyze traffic. Learn more.