diff --git a/lib/lightgbm/booster.rb b/lib/lightgbm/booster.rb index 4cd09f8..1750114 100644 --- a/lib/lightgbm/booster.rb +++ b/lib/lightgbm/booster.rb @@ -1,3 +1,5 @@ +require_relative "categorical_feature_encoder" + module LightGBM class Booster attr_accessor :best_iteration, :train_data_name @@ -6,9 +8,11 @@ def initialize(params: nil, train_set: nil, model_file: nil, model_str: nil) @handle = ::FFI::MemoryPointer.new(:pointer) if model_str model_from_string(model_str) + @categorical_feature_encoder = CategoricalFeatureEncoder.new(model_str.each_line) elsif model_file out_num_iterations = ::FFI::MemoryPointer.new(:int) check_result FFI.LGBM_BoosterCreateFromModelfile(model_file, out_num_iterations, @handle) + @categorical_feature_encoder = CategoricalFeatureEncoder.new(File.foreach(model_file)) else params ||= {} set_verbosity(params) @@ -152,7 +156,12 @@ def predict(input, start_iteration: nil, num_iteration: nil, **params) num_iteration ||= best_iteration num_class ||= num_class() - flat_input = input.flatten + flat_input = if @categorical_feature_encoder + input.flat_map { |row| @categorical_feature_encoder.apply(row) } + else + input.flatten + end + handle_missing(flat_input) data = ::FFI::MemoryPointer.new(:double, input.count * input.first.count) data.write_array_of_double(flat_input) diff --git a/lib/lightgbm/categorical_feature_encoder.rb b/lib/lightgbm/categorical_feature_encoder.rb new file mode 100644 index 0000000..8addc22 --- /dev/null +++ b/lib/lightgbm/categorical_feature_encoder.rb @@ -0,0 +1,82 @@ +require "json" + +module LightGBM + # Converts LightGBM categorical featulres to Float, using label encoding. + # The categorical and mappings are extracted from the LightGBM model file. + class CategoricalFeatureEncoder + # Initializes a new CategoricalFeatureEncoder instance. + # + # @param model_enumerable [Enumerable] Enumerable with each line of LightGBM model file. + def initialize(model_enumerable) + @categorical_feature = [] + @pandas_categorical = [] + + load_categorical_features(model_enumerable) + end + + # Returns a new array with categorical features converted to Float, using label encoding. + def apply(feature_values) + return feature_values if @categorical_feature.empty? + + transformed_features = feature_values.dup + + @categorical_feature.each_with_index do |feature_index, pandas_categorical_index| + value = feature_values[feature_index] + + pandas_categorical_entry = @pandas_categorical[pandas_categorical_index] + transformed_value = pandas_categorical_entry[value] + transformed_features[feature_index] = transformed_value.nil? ? Float::NAN : transformed_value.to_f + end + + transformed_features + end + + private + + def load_categorical_features(model_enumerable) + categorical_found = false + pandas_found = false + + model_enumerable.each_entry do |line| + # Format: "[categorical_feature: 0,1,2,3,4,5]" + if line.start_with?("[categorical_feature:") + parts = line.split("categorical_feature:") + last_part = parts.last + next if last_part.nil? + + values = last_part.strip[0...-1] + next if values.nil? + + @categorical_feature = values.split(",").map(&:to_i) + categorical_found = true + end + + # Format: "pandas_categorical:[[-1.0, 0.0, 1.0], ["", "shop_pay"], [false, true]]" + if line.start_with?("pandas_categorical:") + parts = line.split("pandas_categorical:") + values = parts[1] + next if values.nil? + + @pandas_categorical = JSON.parse(values).map do |array| + array.each_with_index.to_h + end + pandas_found = true + end + + # Break the loop if both lines are found + break if categorical_found && pandas_found + end + + if @categorical_feature.size != @pandas_categorical.size + raise "categorical_feature and pandas_categorical mismatch" + end + end + + def transform_categorical_value(value, index) + pandas_categorical_entry = @pandas_categorical[index] + transformed_value = pandas_categorical_entry.find_index(value) + + transformed_value.nil? ? Float::NAN : transformed_value.to_f + end + end +end diff --git a/test/booster_test.rb b/test/booster_test.rb index b317587..20d23db 100644 --- a/test/booster_test.rb +++ b/test/booster_test.rb @@ -8,6 +8,13 @@ def test_model_file assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2) end + def test_model_file_with_categorical_features + x_test = [[false, "green", 7.2, 9.0], [true, "blue", 7.9, 0.0]] + booster = LightGBM::Booster.new(model_file: "test/support/model_with_categorical_features.txt") + y_pred = booster.predict(x_test) + assert_elements_in_delta [0.9948804305465, 0.792909968121466], y_pred.first(2) + end + def test_model_str x_test = [[3.7, 1.2, 7.2, 9.0], [7.5, 0.5, 7.9, 0.0]] booster = LightGBM::Booster.new(model_str: File.read("test/support/model.txt")) @@ -15,6 +22,13 @@ def test_model_str assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2) end + def test_model_str_with_categorical_features + x_test = [[false, "green", 7.2, 9.0], [true, "blue", 7.9, 0.0]] + booster = LightGBM::Booster.new(model_str: File.read("test/support/model_with_categorical_features.txt")) + y_pred = booster.predict(x_test) + assert_elements_in_delta [0.9948804305465, 0.792909968121466], y_pred.first(2) + end + def test_feature_importance assert_equal [280, 285, 335, 148], booster.feature_importance end diff --git a/test/categorical_feature_encoder_test.rb b/test/categorical_feature_encoder_test.rb new file mode 100644 index 0000000..b12b0e8 --- /dev/null +++ b/test/categorical_feature_encoder_test.rb @@ -0,0 +1,41 @@ +require_relative "test_helper" + +class CategoricalFeatureEncoder < Minitest::Test + def setup + model = <<~MODEL + [categorical_feature: 1,2,3] + pandas_categorical:[[-1.0, 0.0, 1.0], ["red", "green", "blue"], [false, true]] + MODEL + + @encoder = LightGBM::CategoricalFeatureEncoder.new(model.each_line) + end + + def test_apply_with_categorical_features + input = [42.0, 0.0, "green", true] + expected = 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0.00314005 0.00868649 0.032089 0.0242339 0.0107398 -0.0069152 0.00089333 +internal_weight=0 249 219 178 153 130 52 41 51 78 58 +internal_count=300 249 219 178 153 130 52 41 51 78 58 +shrinkage=0.1 + + +end of trees + +feature importances: +x2=335 +x1=285 +x0=280 +x3=148 + +parameters: +[boosting: gbdt] +[objective: regression] +[metric: ] +[tree_learner: serial] +[device_type: cpu] +[data: ] +[valid: ] +[num_iterations: 100] +[learning_rate: 0.1] +[num_leaves: 31] +[num_threads: 0] +[max_depth: -1] +[min_data_in_leaf: 20] +[min_sum_hessian_in_leaf: 0.001] +[bagging_fraction: 1] +[pos_bagging_fraction: 1] +[neg_bagging_fraction: 1] +[bagging_freq: 0] +[bagging_seed: 3] +[feature_fraction: 1] +[feature_fraction_bynode: 1] +[feature_fraction_seed: 2] +[early_stopping_round: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[min_gain_to_split: 0] +[drop_rate: 0.1] +[max_drop: 50] +[skip_drop: 0.5] +[xgboost_dart_mode: 0] +[uniform_drop: 0] +[drop_seed: 4] +[top_rate: 0.2] +[other_rate: 0.1] +[min_data_per_group: 100] +[max_cat_threshold: 32] +[cat_l2: 10] +[cat_smooth: 10] +[max_cat_to_onehot: 4] +[top_k: 20] +[monotone_constraints: ] +[feature_contri: ] +[forcedsplits_filename: ] +[forcedbins_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[verbosity: 1] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[histogram_pool_size: -1] +[data_random_seed: 1] +[output_model: LightGBM_model.txt] +[snapshot_freq: -1] +[input_model: ] +[output_result: LightGBM_predict_result.txt] +[initscore_filename: ] +[valid_data_initscores: ] +[pre_partition: 0] +[enable_bundle: 1] +[max_conflict_rate: 0] +[is_enable_sparse: 1] +[sparse_threshold: 0.8] +[use_missing: 1] +[zero_as_missing: 0] +[two_round: 0] +[save_binary: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: 0,1] +[predict_raw_score: 0] +[predict_leaf_index: 0] +[predict_contrib: 0] +[num_iteration_predict: -1] +[pred_early_stop: 0] +[pred_early_stop_freq: 10] +[pred_early_stop_margin: 10] +[convert_model_language: ] +[convert_model: gbdt_prediction.cpp] +[num_class: 1] +[is_unbalance: 0] +[scale_pos_weight: 1] +[sigmoid: 1] +[boost_from_average: 1] +[reg_sqrt: 0] +[alpha: 0.9] +[fair_c: 1] +[poisson_max_delta_step: 0.7] +[tweedie_variance_power: 1.5] +[max_position: 20] +[lambdamart_norm: 1] +[label_gain: ] +[metric_freq: 1] +[is_provide_training_metric: 0] +[eval_at: ] +[multi_error_top_k: 1] +[num_machines: 1] +[local_listen_port: 12400] +[time_out: 120] +[machine_list_filename: ] +[machines: ] +[gpu_platform_id: -1] +[gpu_device_id: -1] +[gpu_use_dp: 0] + +end of parameters + +pandas_categorical:[[false, true], ["red", "green", "blue"]]