extra_trees_regressor.rb in rumale-0.14.0

- old
+ new

@@ -50,13 +50,13 @@
       # @param random_seed [Integer] The seed value using to initialize the random generator.
       #   It is used to randomly determine the order of features when deciding spliting point.
       def initialize(n_estimators: 10,
                      criterion: 'mse', max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1,
                      max_features: nil, n_jobs: nil, random_seed: nil)
-        check_params_type_or_nil(Integer, max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
-                                          max_features: max_features, n_jobs: n_jobs, random_seed: random_seed)
-        check_params_integer(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
+        check_params_numeric_or_nil(max_depth: max_depth, max_leaf_nodes: max_leaf_nodes,
+                                    max_features: max_features, n_jobs: n_jobs, random_seed: random_seed)
+        check_params_numeric(n_estimators: n_estimators, min_samples_leaf: min_samples_leaf)
         check_params_string(criterion: criterion)
         check_params_positive(n_estimators: n_estimators, max_depth: max_depth,
                               max_leaf_nodes: max_leaf_nodes, min_samples_leaf: min_samples_leaf,
                               max_features: max_features)
         super
@@ -66,12 +66,12 @@
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The training data to be used for fitting the model.
       # @param y [Numo::DFloat] (shape: [n_samples, n_outputs]) The target values to be used for fitting the model.
       # @return [ExtraTreesRegressor] The learned regressor itself.
       def fit(x, y)
-        check_sample_array(x)
-        check_tvalue_array(y)
+        x = check_convert_sample_array(x)
+        y = check_convert_tvalue_array(y)
         check_sample_tvalue_size(x, y)
         # Initialize some variables.
         n_features = x.shape[1]
         @params[:max_features] = Math.sqrt(n_features).to_i unless @params[:max_features].is_a?(Integer)
         @params[:max_features] = [[1, @params[:max_features]].max, n_features].min
@@ -96,19 +96,19 @@
       # Predict values for samples.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to predict the values.
       # @return [Numo::DFloat] (shape: [n_samples, n_outputs]) Predicted value per sample.
       def predict(x)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         super
       end
 
       # Return the index of the leaf that each sample reached.
       #
       # @param x [Numo::DFloat] (shape: [n_samples, n_features]) The samples to assign each leaf.
       # @return [Numo::Int32] (shape: [n_samples, n_estimators]) Leaf index for sample.
       def apply(x)
-        check_sample_array(x)
+        x = check_convert_sample_array(x)
         super
       end
 
       # Dump marshal data.
       # @return [Hash] The marshal data about ExtraTreesRegressor.