Class: Rumale::Tree::DecisionTreeClassifier

Inherits:
BaseDecisionTree show all
Includes:
Base::Classifier
Defined in:
lib/rumale/tree/decision_tree_classifier.rb

Overview

DecisionTreeClassifier is a class that implements decision tree for classification.

Examples:

estimator =
  Rumale::Tree::DecisionTreeClassifier.new(
    criterion: 'gini', max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)

Direct Known Subclasses

ExtraTreeClassifier

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Classifier

#score

Methods inherited from BaseDecisionTree

#apply

Constructor Details

#initialize(criterion: 'gini', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) ⇒ DecisionTreeClassifier

Create a new classifier with decision tree algorithm.

Parameters:

  • criterion (String) (defaults to: 'gini') —

    The function to evaluate spliting point. Supported criteria are ‘gini’ and ‘entropy’.

  • max_depth (Integer) (defaults to: nil) —

    The maximum depth of the tree. If nil is given, decision tree grows without concern for depth.

  • max_leaf_nodes (Integer) (defaults to: nil) —

    The maximum number of leaves on decision tree. If nil is given, number of leaves is not limited.

  • min_samples_leaf (Integer) (defaults to: 1) —

    The minimum number of samples at a leaf node.

  • max_features (Integer) (defaults to: nil) —

    The number of features to consider when searching optimal split point. If nil is given, split process considers all features.

  • random_seed (Integer) (defaults to: nil) —

    The seed value using to initialize the random generator. It is used to randomly determine the order of features when deciding spliting point.



54
55
56
57
58
59
60
61
62
63
64
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 54

def initialize(criterion: 'gini', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil,
               random_seed: nil)
  check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                    max_features: max_features, random_seed: random_seed)
  check_params_integer(min_samples_leaf: min_samples_leaf)
  check_params_string(criterion: criterion)
  check_params_positive(max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                        min_samples_leaf: min_samples_leaf, max_features: max_features)
  super
  @leaf_labels = nil
end

Instance Attribute Details

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (size: n_classes)



24
25
26
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 24

def classes
  @classes
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



28
29
30
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 28

def feature_importances
  @feature_importances
end

#leaf_labels ⇒ Numo::Int32 (readonly)

Return the labels assigned each leaf.

Returns:

  • (Numo::Int32) —

    (size: n_leafs)



40
41
42
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 40

def leaf_labels
  @leaf_labels
end

#rng ⇒ Random (readonly)

Return the random generator for random selection of feature index.

Returns:

  • (Random) 


36
37
38
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 36

def rng
  @rng
end

#tree ⇒ Node (readonly)

Return the learned tree.

Returns:



32
33
34
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 32

def tree
  @tree
end

Instance Method Details

#fit(x, y) ⇒ DecisionTreeClassifier

Fit the model with given training data.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The training data to be used for fitting the model.

  • y (Numo::Int32) —

    (shape: [n_samples]) The labels to be used for fitting the model.

Returns:



71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 71

def fit(x, y)
  check_sample_array(x)
  check_label_array(y)
  check_sample_label_size(x, y)
  n_samples, n_features = x.shape
  @params[:max_features] = n_features if @params[:max_features].nil?
  @params[:max_features] = [@params[:max_features], n_features].min
  uniq_y = y.to_a.uniq.sort
  @classes = Numo::Int32.asarray(uniq_y)
  @n_leaves = 0
  @leaf_labels = []
  @sub_rng = @rng.dup
  build_tree(x, y.map { |v| uniq_y.index(v) })
  eval_importance(n_samples, n_features)
  @leaf_labels = Numo::Int32[*@leaf_labels]
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about DecisionTreeClassifier



109
110
111
112
113
114
115
116
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 109

def marshal_dump
  { params: @params,
    classes: @classes,
    tree: @tree,
    feature_importances: @feature_importances,
    leaf_labels: @leaf_labels,
    rng: @rng }
end

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


120
121
122
123
124
125
126
127
128
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 120

def marshal_load(obj)
  @params = obj[:params]
  @classes = obj[:classes]
  @tree = obj[:tree]
  @feature_importances = obj[:feature_importances]
  @leaf_labels = obj[:leaf_labels]
  @rng = obj[:rng]
  nil
end

#predict(x) ⇒ Numo::Int32

Predict class labels for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the labels.

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Predicted class label per sample.



93
94
95
96
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 93

def predict(x)
  check_sample_array(x)
  @leaf_labels[apply(x)].dup
end

#predict_proba(x) ⇒ Numo::DFloat

Predict probability for samples.

Parameters:

  • x (Numo::DFloat) —

    (shape: [n_samples, n_features]) The samples to predict the probailities.

Returns:

  • (Numo::DFloat) —

    (shape: [n_samples, n_classes]) Predicted probability of each class per sample.



102
103
104
105
 
# File 'lib/rumale/tree/decision_tree_classifier.rb', line 102

def predict_proba(x)
  check_sample_array(x)
  Numo::DFloat[*(Array.new(x.shape[0]) { |n| predict_proba_at_node(@tree, x[n, true]) })]
end