Merge branch 'master' into windows5

CHANGELOG.md

@@ -1,4 +1,15 @@
-## 0.3.3 (unreleased)
+## 0.4.0 (unreleased)
+
+- Added support for hashes and Rover data frames to `predict` method
+- Changed `Dataset` to use column names for feature names with Rover and Daru
+- Changed `predict` method to match feature names with Daru
+- Dropped support for Ruby < 3.1
+
+## 0.3.4 (2024-07-28)
+
+- Updated LightGBM to 4.5.0
+
+## 0.3.3 (2024-06-15)
 
 - Updated LightGBM to 4.4.0
 

Gemfile

@@ -5,7 +5,7 @@ gemspec
 gem "rake"
 gem "minitest", ">= 5"
 gem "daru"
-gem "matrix" # for daru
+gem "matrix"
 gem "numo-narray", platform: [:ruby, :x64_mingw]
 gem "rover-df", platform: [:ruby, :x64_mingw]
 gem "csv"

LICENSE.txt

@@ -1,7 +1,7 @@
 The MIT License (MIT)
 
 Copyright (c) Microsoft Corporation
-Copyright (c) 2019-2023 Andrew Kane
+Copyright (c) 2019-2024 Andrew Kane
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,7 +2,7 @@
 
 [LightGBM](https://github.com/microsoft/LightGBM) - high performance gradient boosting - for Ruby
 
-[![Build Status](https://github.com/ankane/lightgbm-ruby/workflows/build/badge.svg?branch=master)](https://github.com/ankane/lightgbm-ruby/actions)
+[![Build Status](https://github.com/ankane/lightgbm-ruby/actions/workflows/build.yml/badge.svg)](https://github.com/ankane/lightgbm-ruby/actions)
 
 ## Installation
 

Rakefile

@@ -23,7 +23,7 @@ def download_file(file, sha256)
   require "open-uri"
 
   # also update licenses in vendor/
-  version = "4.4.0"
+  version = "4.5.0"
 
   url =
     if file == "lib_lightgbm.arm64.dylib"
@@ -45,24 +45,24 @@ end
 # https://github.com/microsoft/LightGBM/releases
 namespace :vendor do
   task :linux do
-    download_file("lib_lightgbm.so", "fdbb5b5786d4a99f661d453a62cc07c6607b780a1e4e774443df67aded6bb8b3")
+    download_file("lib_lightgbm.so", "4b2b68c4d0fa99bace6cc540224b457ff899ccee0fdc8875e4625a38b00fc5e5")
   end
 
   task :mac do
-    download_file("lib_lightgbm.dylib", "c5824d085fd342c58f92291f40f02554f13ca1504fa26f1b2aef3151e8a70fdc")
-    download_file("lib_lightgbm.arm64.dylib", "58b7d2c1e04c8af20c9558582e07957e3e227ef6bb31a10644b92cc93610a1fc")
+    download_file("lib_lightgbm.dylib", "b02d48071ba4ae1e13e336a902dc5f82a5732de4448d47a20d8e9d94d5d3db2a")
+    download_file("lib_lightgbm.arm64.dylib", "840e16754db0d3e4852bdfdecc1ee08bc367b138e0bf18fabb4ce3d9b39c936a")
   end
 
   task :windows do
-    download_file("lib_lightgbm.dll", "922c627c23e065f85d8e5e975be4ec78c65a424bdf12253f3168110cc2391185")
+    download_file("lib_lightgbm.dll", "1d281ec96684806d83468469fb6052880308f39bf03a34d85ee9aa38195d260c")
   end
 
   task all: [:linux, :mac, :windows]
 
   task :platform do
     if Gem.win_platform?
       Rake::Task["vendor:windows"].invoke
-    elsif RbConfig::CONFIG["host_os"] =~ /darwin/i
+    elsif RbConfig::CONFIG["host_os"].match?(/darwin/i)
       Rake::Task["vendor:mac"].invoke
     else
       Rake::Task["vendor:linux"].invoke

lib/lightgbm.rb

@@ -145,7 +145,7 @@ def cv(params, train_set, num_boost_round: 100, nfold: 5, seed: 0, shuffle: true
         boosters.each(&:update)
 
         scores = {}
-        boosters.map(&:eval_valid).map(&:reverse).flatten(1).each do |r|
+        boosters.map(&:eval_valid).flat_map(&:reverse).each do |r|
           (scores[r[1]] ||= []) << r[2]
         end
 

lib/lightgbm/booster.rb

@@ -41,7 +41,7 @@ def dump_model(num_iteration: nil, start_iteration: 0)
       out_str = ::FFI::MemoryPointer.new(:char, buffer_len)
       feature_importance_type = 0 # TODO add option
       check_result FFI.LGBM_BoosterDumpModel(handle_pointer, start_iteration, num_iteration, feature_importance_type, buffer_len, out_len, out_str)
-      actual_len = read_int64(out_len)
+      actual_len = out_len.read_int64
       if actual_len > buffer_len
         out_str = ::FFI::MemoryPointer.new(:char, actual_len)
         check_result FFI.LGBM_BoosterDumpModel(handle_pointer, start_iteration, num_iteration, feature_importance_type, actual_len, out_len, out_str)
@@ -51,7 +51,7 @@ def dump_model(num_iteration: nil, start_iteration: 0)
     alias_method :to_json, :dump_model
 
     def eval_valid
-      @name_valid_sets.each_with_index.map { |n, i| inner_eval(n, i + 1) }.flatten(1)
+      @name_valid_sets.each_with_index.flat_map { |n, i| inner_eval(n, i + 1) }
     end
 
     def eval_train
@@ -99,6 +99,7 @@ def feature_name
     def model_from_string(model_str)
       out_num_iterations = ::FFI::MemoryPointer.new(:int)
       check_result FFI.LGBM_BoosterLoadModelFromString(model_str, out_num_iterations, @handle)
+      @cached_feature_name = nil
       self
     end
 
@@ -109,7 +110,7 @@ def model_to_string(num_iteration: nil, start_iteration: 0)
       out_str = ::FFI::MemoryPointer.new(:char, buffer_len)
       feature_importance_type = 0 # TODO add option
       check_result FFI.LGBM_BoosterSaveModelToString(handle_pointer, start_iteration, num_iteration, feature_importance_type, buffer_len, out_len, out_str)
-      actual_len = read_int64(out_len)
+      actual_len = out_len.read_int64
       if actual_len > buffer_len
         out_str = ::FFI::MemoryPointer.new(:char, actual_len)
         check_result FFI.LGBM_BoosterSaveModelToString(handle_pointer, start_iteration, num_iteration, feature_importance_type, actual_len, out_len, out_str)
@@ -140,7 +141,14 @@ def num_trees
     def predict(input, start_iteration: nil, num_iteration: nil, **params)
       input =
         if daru?(input)
-          input.map_rows(&:to_a)
+          input[*cached_feature_name].map_rows(&:to_a)
+        elsif input.is_a?(Hash) # sort feature.values to match the order of model.feature_name
+          sorted_feature_values(input)
+        elsif input.is_a?(Array) && input.first.is_a?(Hash) # on multiple elems, if 1st is hash, assume they all are
+          input.map(&method(:sorted_feature_values))
+        elsif rover?(input)
+          # TODO improve performance
+          input[cached_feature_name].to_numo.to_a
         else
           input.to_a
         end
@@ -160,7 +168,7 @@ def predict(input, start_iteration: nil, num_iteration: nil, **params)
       out_len = ::FFI::MemoryPointer.new(:int64)
       out_result = ::FFI::MemoryPointer.new(:double, num_class * input.count)
       check_result FFI.LGBM_BoosterPredictForMat(handle_pointer, data, 1, input.count, input.first.count, 1, 0, start_iteration, num_iteration, params_str(params), out_len, out_result)
-      out = out_result.read_array_of_double(read_int64(out_len))
+      out = out_result.read_array_of_double(out_len.read_int64)
       out = out.each_slice(num_class).to_a if num_class > 1
 
       singular ? out.first : out
@@ -236,9 +244,12 @@ def num_class
       out.read_int
     end
 
-    # read_int64 not available on JRuby
-    def read_int64(ptr)
-      ptr.read_array_of_int64(1).first
+    def sorted_feature_values(input_hash)
+      input_hash.transform_keys(&:to_s).fetch_values(*cached_feature_name)
+    end
+
+    def cached_feature_name
+      @cached_feature_name ||= feature_name
     end
 
     include Utils

lib/lightgbm/dataset.rb

@@ -2,7 +2,7 @@ module LightGBM
   class Dataset
     attr_reader :data, :params
 
-    def initialize(data, label: nil, weight: nil, group: nil, params: nil, reference: nil, used_indices: nil, categorical_feature: "auto", feature_names: nil)
+    def initialize(data, label: nil, weight: nil, group: nil, params: nil, reference: nil, used_indices: nil, categorical_feature: "auto", feature_name: nil, feature_names: nil)
       @data = data
       @label = label
       @weight = weight
@@ -11,7 +11,7 @@ def initialize(data, label: nil, weight: nil, group: nil, params: nil, reference
       @reference = reference
       @used_indices = used_indices
       @categorical_feature = categorical_feature
-      @feature_names = feature_names
+      @feature_name = feature_name || feature_names || "auto"
 
       construct
     end
@@ -24,7 +24,7 @@ def weight
       field("weight")
     end
 
-    def feature_names
+    def feature_name
       # must preallocate space
       num_feature_names = ::FFI::MemoryPointer.new(:int)
       out_buffer_len = ::FFI::MemoryPointer.new(:size_t)
@@ -48,6 +48,7 @@ def feature_names
       # from most recent call (instead of num_features)
       str_ptrs[0, num_feature_names.read_int].map(&:read_string)
     end
+    alias_method :feature_names, :feature_name
 
     def label=(label)
       @label = label
@@ -64,12 +65,15 @@ def group=(group)
       set_field("group", group, type: :int32)
     end
 
-    def feature_names=(feature_names)
+    def feature_name=(feature_names)
       @feature_names = feature_names
       c_feature_names = ::FFI::MemoryPointer.new(:pointer, feature_names.size)
-      c_feature_names.write_array_of_pointer(feature_names.map { |v| ::FFI::MemoryPointer.from_string(v) })
+      # keep reference to string pointers
+      str_ptrs = feature_names.map { |v| ::FFI::MemoryPointer.from_string(v) }
+      c_feature_names.write_array_of_pointer(str_ptrs)
       check_result FFI.LGBM_DatasetSetFeatureNames(handle_pointer, c_feature_names, feature_names.size)
     end
+    alias_method :feature_names=, :feature_name=
 
     # TODO only update reference if not in chain
     def reference=(reference)
@@ -106,12 +110,7 @@ def subset(used_indices, params: nil)
     end
 
     def handle_pointer
-      @handle.read_pointer
-    end
-
-    def self.finalize(addr)
-      # must use proc instead of stabby lambda
-      proc { FFI.LGBM_DatasetFree(::FFI::Pointer.new(:pointer, addr)) }
+      @handle
     end
 
     private
@@ -127,25 +126,33 @@ def construct
       end
       set_verbosity(params)
 
-      @handle = ::FFI::MemoryPointer.new(:pointer)
+      handle = ::FFI::MemoryPointer.new(:pointer)
       parameters = params_str(params)
       reference = @reference.handle_pointer if @reference
       if used_indices
         used_row_indices = ::FFI::MemoryPointer.new(:int32, used_indices.count)
         used_row_indices.write_array_of_int32(used_indices)
-        check_result FFI.LGBM_DatasetGetSubset(reference, used_row_indices, used_indices.count, parameters, @handle)
+        check_result FFI.LGBM_DatasetGetSubset(reference, used_row_indices, used_indices.count, parameters, handle)
       elsif data.is_a?(String)
-        check_result FFI.LGBM_DatasetCreateFromFile(data, parameters, reference, @handle)
+        check_result FFI.LGBM_DatasetCreateFromFile(data, parameters, reference, handle)
       else
         if matrix?(data)
           nrow = data.row_count
           ncol = data.column_count
           flat_data = data.to_a.flatten
         elsif daru?(data)
+          if @feature_name == "auto"
+            @feature_name = data.vectors.to_a
+          end
           nrow, ncol = data.shape
           flat_data = data.map_rows(&:to_a).flatten
-        elsif numo?(data) || rover?(data)
-          data = data.to_numo if rover?(data)
+        elsif numo?(data)
+          nrow, ncol = data.shape
+        elsif rover?(data)
+          if @feature_name == "auto"
+            @feature_name = data.keys
+          end
+          data = data.to_numo
           nrow, ncol = data.shape
         else
           nrow = data.count
@@ -161,14 +168,18 @@ def construct
           c_data.write_array_of_double(flat_data)
         end
 
-        check_result FFI.LGBM_DatasetCreateFromMat(c_data, 1, nrow, ncol, 1, parameters, reference, @handle)
+        check_result FFI.LGBM_DatasetCreateFromMat(c_data, 1, nrow, ncol, 1, parameters, reference, handle)
+      end
+      if used_indices
+        @handle = handle.read_pointer
+      else
+        @handle = ::FFI::AutoPointer.new(handle.read_pointer, FFI.method(:LGBM_DatasetFree))
       end
-      ObjectSpace.define_finalizer(@handle, self.class.finalize(handle_pointer.to_i)) unless used_indices
 
       self.label = @label if @label
       self.weight = @weight if @weight
       self.group = @group if @group
-      self.feature_names = @feature_names if @feature_names
+      self.feature_name = @feature_name if @feature_name && @feature_name != "auto"
     end
 
     def dump_text(filename)

lib/lightgbm/utils.rb

@@ -12,7 +12,7 @@ def params_str(params)
     end
 
     def check_param(v)
-      raise ArgumentError, "Invalid parameter" if /[[:space:]]/.match(v)
+      raise ArgumentError, "Invalid parameter" if /[[:space:]]/.match?(v)
       v
     end
 

lib/lightgbm/version.rb

@@ -1,3 +1,3 @@
 module LightGBM
-  VERSION = "0.3.2"
+  VERSION = "0.3.4"
 end

lightgbm.gemspec

@@ -13,7 +13,7 @@ Gem::Specification.new do |spec|
   spec.files         = Dir["*.{md,txt}", "{lib,vendor}/**/*"]
   spec.require_path  = "lib"
 
-  spec.required_ruby_version = ">= 3"
+  spec.required_ruby_version = ">= 3.1"
 
   spec.add_dependency "ffi"
 end

test/booster_test.rb

@@ -15,6 +15,14 @@ def test_model_str
     assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2)
   end
 
+  def test_model_from_string
+    x_test = [[3.7, 1.2, 7.2, 9.0], [7.5, 0.5, 7.9, 0.0]]
+    booster = LightGBM.train(binary_params, binary_train)
+    booster.model_from_string(File.read("test/support/model.txt"))
+    y_pred = booster.predict(x_test)
+    assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2)
+  end
+
   def test_feature_importance
     assert_equal [280, 285, 335, 148], booster.feature_importance
   end
@@ -30,6 +38,56 @@ def test_feature_importance_bad_importance_type
     assert_includes error.message, "Unknown importance type"
   end
 
+  def test_predict_hash
+    pred = booster.predict({x0: 3.7, x1: 1.2, x2: 7.2, x3: 9.0})
+    assert_in_delta 0.9823112229173586, pred
+
+    pred = booster.predict({"x3" => 9.0, "x2" => 7.2, "x1" => 1.2, "x0" => 3.7})
+    assert_in_delta 0.9823112229173586, pred
+
+    pred =
+      booster.predict([
+        {"x3" => 9.0, "x2" => 7.2, "x1" => 1.2, "x0" => 3.7},
+        {"x3" => 0.0, "x2" => 7.9, "x1" => 0.5, "x0" => 7.5}
+      ])
+    assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], pred.first(2)
+
+    assert_raises(KeyError) do
+      booster.predict({"x0" => 3.7})
+    end
+  end
+
+  def test_predict_daru
+    x_test =
+      Daru::DataFrame.new([
+        {"x3" => 9.0, "x2" => 7.2, "x1" => 1.2, "x0" => 3.7},
+        {"x3" => 0.0, "x2" => 7.9, "x1" => 0.5, "x0" => 7.5}
+      ])
+    pred = booster.predict(x_test)
+    assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], pred.first(2)
+
+    assert_raises(IndexError) do
+      booster.predict(Daru::DataFrame.new([{"x0" => 3.7}]))
+    end
+  end
+
+  def test_predict_rover
+    skip if jruby?
+
+    require "rover"
+    x_test =
+      Rover::DataFrame.new([
+        {"x3" => 9.0, "x2" => 7.2, "x1" => 1.2, "x0" => 3.7},
+        {"x3" => 0.0, "x2" => 7.9, "x1" => 0.5, "x0" => 7.5}
+      ])
+    pred = booster.predict(x_test)
+    assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], pred.first(2)
+
+    assert_raises(KeyError) do
+      booster.predict(Rover::DataFrame.new([{"x0" => 3.7}]))
+    end
+  end
+
   def test_model_to_string
     assert booster.model_to_string
   end