Class: SVMKit::Tree::DecisionTreeClassifier

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Classifier
Defined in:
lib/svmkit/tree/decision_tree_classifier.rb

Overview

DecisionTreeClassifier is a class that implements decision tree for classification.

Examples:

estimator =
  SVMKit::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)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Methods included from Base::Classifier

#score

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 evalue 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.



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
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 119

def initialize(criterion: 'gini', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil,
               random_seed: nil)
  SVMKit::Validation.check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                                       max_features: max_features, random_seed: random_seed)
  SVMKit::Validation.check_params_integer(min_samples_leaf: min_samples_leaf)
  SVMKit::Validation.check_params_string(criterion: criterion)
  SVMKit::Validation.check_params_positive(max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
                                           min_samples_leaf: min_samples_leaf, max_features: max_features)
  @params = {}
  @params[:criterion] = criterion
  @params[:max_depth] = max_depth
  @params[:max_leaf_nodes] = max_leaf_nodes
  @params[:min_samples_leaf] = min_samples_leaf
  @params[:max_features] = max_features
  @params[:random_seed] = random_seed
  @params[:random_seed] ||= srand
  @criterion = :gini
  @criterion = :entropy if @params[:criterion] == 'entropy'
  @tree = nil
  @classes = nil
  @feature_importances = nil
  @n_leaves = nil
  @leaf_labels = nil
  @rng = Random.new(@params[:random_seed])
end

Instance Attribute Details

#classes ⇒ Numo::Int32 (readonly)

Return the class labels.

Returns:

  • (Numo::Int32) —

    (size: n_classes)



89
90
91
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 89

def classes
  @classes
end

#feature_importances ⇒ Numo::DFloat (readonly)

Return the importance for each feature.

Returns:

  • (Numo::DFloat) —

    (size: n_features)



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

def feature_importances
  @feature_importances
end

#leaf_labels ⇒ Numo::Int32 (readonly)

Return the labels assigned each leaf.

Returns:

  • (Numo::Int32) —

    (size: n_leafs)



105
106
107
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 105

def leaf_labels
  @leaf_labels
end

#rng ⇒ Random (readonly)

Return the random generator for performing random sampling in the Pegasos algorithm.

Returns:

  • (Random) 


101
102
103
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 101

def rng
  @rng
end

#tree ⇒ Node (readonly)

Return the learned tree.

Returns:



97
98
99
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 97

def tree
  @tree
end

Instance Method Details

#apply(x) ⇒ Numo::Int32

Return the index of the leaf that each sample reached.

Parameters:

  • x (Numo::DFloat) —

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

Returns:

  • (Numo::Int32) —

    (shape: [n_samples]) Leaf index for sample.



186
187
188
189
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 186

def apply(x)
  SVMKit::Validation.check_sample_array(x)
  Numo::Int32[*(Array.new(x.shape[0]) { |n| apply_at_node(@tree, x[n, true]) })]
end

#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:



150
151
152
153
154
155
156
157
158
159
160
161
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 150

def fit(x, y)
  SVMKit::Validation.check_sample_array(x)
  SVMKit::Validation.check_label_array(y)
  SVMKit::Validation.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
  @classes = Numo::Int32.asarray(y.to_a.uniq.sort)
  build_tree(x, y)
  eval_importance(n_samples, n_features)
  self
end

#marshal_dump ⇒ Hash

Dump marshal data.

Returns:

  • (Hash) —

    The marshal data about DecisionTreeClassifier



193
194
195
196
197
198
199
200
201
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 193

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

#marshal_load(obj) ⇒ nil

Load marshal data.

Returns:

  • (nil) 


205
206
207
208
209
210
211
212
213
214
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 205

def marshal_load(obj)
  @params = obj[:params]
  @classes = obj[:classes]
  @criterion = obj[:criterion]
  @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.



167
168
169
170
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 167

def predict(x)
  SVMKit::Validation.check_sample_array(x)
  @leaf_labels[apply(x)]
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.



176
177
178
179
180
 
# File 'lib/svmkit/tree/decision_tree_classifier.rb', line 176

def predict_proba(x)
  SVMKit::Validation.check_sample_array(x)
  probs = Numo::DFloat[*(Array.new(x.shape[0]) { |n| predict_at_node(@tree, x[n, true]) })]
  probs[true, @classes]
end