diff --git a/.gitignore b/.gitignore index 2e44321..dca694f 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,4 @@ /tmp/ *.lock /vendor/lib_lightgbm.* +/.venv/ diff --git a/Gemfile b/Gemfile index 17877a1..436ad30 100644 --- a/Gemfile +++ b/Gemfile @@ -9,3 +9,4 @@ gem "matrix" gem "numo-narray", platform: [:ruby, :x64_mingw] gem "rover-df", platform: [:ruby, :x64_mingw] gem "csv" +gem "debug" diff --git a/lib/lightgbm/booster.rb b/lib/lightgbm/booster.rb index 29a0ef2..416e2f4 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 @@ -5,11 +7,13 @@ class Booster def initialize(params: nil, train_set: nil, model_file: nil, model_str: nil) 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) create_handle do |handle| check_result FFI.LGBM_BoosterCreateFromModelfile(model_file, out_num_iterations, handle) end + @categorical_feature_encoder = CategoricalFeatureEncoder.new(File.foreach(model_file)) else params ||= {} set_verbosity(params) @@ -164,7 +168,12 @@ def predict(input, start_iteration: nil, num_iteration: nil, **params) num_iteration ||= best_iteration num_class = self.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..4ee582e --- /dev/null +++ b/lib/lightgbm/categorical_feature_encoder.rb @@ -0,0 +1,73 @@ +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| + pandas_categorical_entry = @pandas_categorical[pandas_categorical_index] + value = feature_values[feature_index] + transformed_features[feature_index] = pandas_categorical_entry.fetch(value, Float::NAN).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], ["", "a"], [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 + end +end diff --git a/test/booster_test.rb b/test/booster_test.rb index d1d93b4..e409868 100644 --- a/test/booster_test.rb +++ b/test/booster_test.rb @@ -8,6 +8,25 @@ def test_model_file assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2) end + def test_model_file_with_categorical_features + booster = LightGBM::Booster.new(model_file: "test/support/model_categorical.txt") + + x_test = [[3.7, 1.2, 7.2, "9"], [7.5, 0.5, 7.9, "0"]] + y_pred = booster.predict(x_test) + assert_elements_in_delta [1.014580415457883, 0.9327349972866771], y_pred.first(2) + + x_test = [ + {"x0" => 3.7, "x1" => 1.2, "x2" => 7.2, "x3" => "9"}, + {"x0" => 7.5, "x1" => 0.5, "x2" => 7.9, "x3" => "0"} + ] + y_pred = booster.predict(x_test) + assert_elements_in_delta [1.014580415457883, 0.9327349972866771], y_pred.first(2) + + x_test = {"x0" => 3.7, "x1" => 1.2, "x2" => 7.2, "x3" => "9"} + y_pred = booster.predict(x_test) + assert_in_delta 1.014580415457883, y_pred + 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")) @@ -23,6 +42,25 @@ def test_model_from_string assert_elements_in_delta [0.9823112229173586, 0.9583143724610858], y_pred.first(2) end + def test_model_str_with_categorical_features + booster = LightGBM::Booster.new(model_str: File.read("test/support/model_categorical.txt")) + + x_test = [[3.7, 1.2, 7.2, "9"], [7.5, 0.5, 7.9, "0"]] + y_pred = booster.predict(x_test) + assert_elements_in_delta [1.014580415457883, 0.9327349972866771], y_pred.first(2) + + x_test = [ + {"x0" => 3.7, "x1" => 1.2, "x2" => 7.2, "x3" => "9"}, + {"x0" => 7.5, "x1" => 0.5, "x2" => 7.9, "x3" => "0"} + ] + y_pred = booster.predict(x_test) + assert_elements_in_delta [1.014580415457883, 0.9327349972866771], y_pred.first(2) + + x_test = {"x0" => 3.7, "x1" => 1.2, "x2" => 7.2, "x3" => "9"} + y_pred = booster.predict(x_test) + assert_in_delta 1.014580415457883, y_pred + 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 = [42.0, 1.0, 1.0, 1.0] + + assert_equal(expected, @encoder.apply(input)) + end + + def test_apply_with_non_categorical_features + input = [42.0, "non_categorical", 39.0, false] + expected = [42.0, Float::NAN, Float::NAN, 0] + + assert_equal(expected, @encoder.apply(input)) + end + + def test_apply_with_missing_values + input = [42.0, nil, "red", nil] + expected = [42.0, Float::NAN, 0.0, Float::NAN] + result = @encoder.apply(input) + + assert_equal(expected, result) + end + + def test_apply_with_boolean_values + input = [42.0, -1.0, "green", false] + expected = [42.0, 0.0, 1.0, 0.0] + + assert_equal(expected, @encoder.apply(input)) + end +end diff --git a/test/support/booster.py b/test/support/booster.py index 0f2f8f1..0ef7f0f 100644 --- a/test/support/booster.py +++ b/test/support/booster.py @@ -1,22 +1,57 @@ +# Run this script to regenerate the test/support/model.txt and test/support/model_categorical.txt files + import lightgbm as lgb import pandas as pd -df = pd.read_csv('test/support/data.csv') +params = {'verbosity': -1} + +def booster(): + df = pd.read_csv('test/support/data.csv') + + X = df.drop(columns=['y']) + y = df['y'] + + X_train = X[:300] + y_train = y[:300] + X_test = X[300:] + y_test = y[300:] + + train_data = lgb.Dataset(X_train, label=y_train) + bst = lgb.train(params, train_data) + bst.save_model('test/support/model.txt') + + bst = lgb.Booster(model_file='test/support/model.txt') + print('x', X_train[:2].to_numpy().tolist()) + print('predict', bst.predict(X_train)[:2].tolist()) + print('feature_importance', bst.feature_importance().tolist()) + print('feature_name', bst.feature_name()) + +def booster_categorical(): + df = pd.read_csv('test/support/data.csv', dtype={'x3': 'category'}) + + X = df.drop(columns=['y']) + y = df['y'] + + X_train = X[:300] + y_train = y[:300] + X_test = X[300:] + y_test = y[300:] + + train_data = lgb.Dataset(X_train, label=y_train, categorical_feature='auto') + bst = lgb.train(params, train_data) + bst.save_model('test/support/model_categorical.txt') + + bst = lgb.Booster(model_file='test/support/model_categorical.txt') + print('x', X_train[:2].to_numpy().tolist()) + print('predict', bst.predict(X_train)[:2].tolist()) + print('feature_importance', bst.feature_importance().tolist()) + print('feature_name', bst.feature_name()) -X = df.drop(columns=['y']) -y = df['y'] -X_train = X[:300] -y_train = y[:300] -X_test = X[300:] -y_test = y[300:] +print('booster -> model.txt') +booster() -train_data = lgb.Dataset(X_train, label=y_train) -bst = lgb.train({}, train_data) -bst.save_model('test/support/model.txt') +print('') -bst = lgb.Booster(model_file='test/support/model.txt') -print('x', X_train[:2].to_numpy().tolist()) -print('predict', bst.predict(X_train)[:2].tolist()) -print('feature_importance', bst.feature_importance().tolist()) -print('feature_name', bst.feature_name()) +print('categorical') +booster_categorical() diff --git a/test/support/classifier.py b/test/support/classifier.py index 1abb4f4..b92ee98 100644 --- a/test/support/classifier.py +++ b/test/support/classifier.py @@ -25,7 +25,7 @@ print() print('test_multiclass') -model = lgb.LGBMClassifier() +model = lgb.LGBMClassifier(verbosity=-1) model.fit(X_train, ym_train) print(model.predict(X_test)[0:100].tolist()) print(model.predict_proba(X_test)[0].tolist()) @@ -33,7 +33,8 @@ print() print('test_early_stopping') -model.fit(X_train, ym_train, eval_set=[(X_test, ym_test)], early_stopping_rounds=5, verbose=True) +model = lgb.LGBMClassifier(early_stopping_round=5, verbosity=1) +model.fit(X_train, ym_train, eval_set=[(X_test, ym_test)]) print() print('test_missing_numeric') diff --git a/test/support/cv.py b/test/support/cv.py index b2b3916..c4425f5 100644 --- a/test/support/cv.py +++ b/test/support/cv.py @@ -16,10 +16,10 @@ regression_params = {'objective': 'regression', 'verbosity': -1} regression_train = lgb.Dataset(X_train, label=y_train) eval_hist = lgb.cv(regression_params, regression_train, shuffle=False, stratified=False) -print(eval_hist['l2-mean'][0]) -print(eval_hist['l2-mean'][-1]) -print(eval_hist['l2-stdv'][0]) -print(eval_hist['l2-stdv'][-1]) +print(eval_hist['valid l2-mean'][0]) +print(eval_hist['valid l2-mean'][-1]) +print(eval_hist['valid l2-stdv'][0]) +print(eval_hist['valid l2-stdv'][-1]) print() print('test_binary') @@ -27,10 +27,10 @@ binary_params = {'objective': 'binary', 'verbosity': -1} binary_train = lgb.Dataset(X_train, label=y_train.replace(2, 1)) eval_hist = lgb.cv(binary_params, binary_train, shuffle=False, stratified=False) -print(eval_hist['binary_logloss-mean'][0]) -print(eval_hist['binary_logloss-mean'][-1]) -print(eval_hist['binary_logloss-stdv'][0]) -print(eval_hist['binary_logloss-stdv'][-1]) +print(eval_hist['valid binary_logloss-mean'][0]) +print(eval_hist['valid binary_logloss-mean'][-1]) +print(eval_hist['valid binary_logloss-stdv'][0]) +print(eval_hist['valid binary_logloss-stdv'][-1]) print() print('test_multiclass') @@ -38,20 +38,23 @@ multiclass_params = {'objective': 'multiclass', 'num_class': 3, 'verbosity': -1} multiclass_train = lgb.Dataset(X_train, label=y_train) eval_hist = lgb.cv(multiclass_params, multiclass_train, shuffle=False, stratified=False) -print(eval_hist['multi_logloss-mean'][0]) -print(eval_hist['multi_logloss-mean'][-1]) -print(eval_hist['multi_logloss-stdv'][0]) -print(eval_hist['multi_logloss-stdv'][-1]) +print(eval_hist['valid multi_logloss-mean'][0]) +print(eval_hist['valid multi_logloss-mean'][-1]) +print(eval_hist['valid multi_logloss-stdv'][0]) +print(eval_hist['valid multi_logloss-stdv'][-1]) print('') print('test_early_stopping_early') -eval_hist = lgb.cv(regression_params, regression_train, shuffle=False, stratified=False, verbose_eval=True, early_stopping_rounds=5) -print(len(eval_hist['l2-mean'])) +regression_params = {'objective': 'regression', 'verbosity': 1, 'early_stopping_round': 5} +eval_hist = lgb.cv(regression_params, regression_train, shuffle=False, stratified=False) +print(len(eval_hist['valid l2-mean'])) print('') print('test_early_stopping_not_early') -eval_hist = lgb.cv(regression_params, regression_train, shuffle=False, stratified=False, verbose_eval=True, early_stopping_rounds=500) -print(len(eval_hist['l2-mean'])) +regression_params = {'objective': 'regression', 'verbosity': 1, 'early_stopping_round': 500} +eval_hist = lgb.cv(regression_params, regression_train, shuffle=False, stratified=False) +print(len(eval_hist['valid l2-mean'])) + diff --git a/test/support/model.txt b/test/support/model.txt index 8ced3c0..edc50eb 100644 --- a/test/support/model.txt +++ b/test/support/model.txt @@ -1,5 +1,5 @@ tree -version=v3 +version=v4 num_class=1 num_tree_per_iteration=1 label_index=0 @@ -7,7 +7,7 @@ max_feature_idx=3 objective=regression feature_names=x0 x1 x2 x3 feature_infos=[0:9.9000000000000004] [0:9.8000000000000007] [0:9.9000000000000004] [0:9] -tree_sizes=925 951 954 1195 1035 1036 1035 1203 953 1035 1040 1041 1212 1042 1205 1042 1039 961 970 1223 1053 1141 1056 1135 1148 1047 1230 1145 1137 1052 1137 1150 1060 976 1146 1148 1145 1066 1155 1070 1060 1149 1072 1065 1231 1072 1153 1066 1067 1152 975 1065 1064 1158 1067 1152 1154 1078 1064 1233 1069 1147 1069 1168 1059 1149 1145 1159 1160 990 1149 1062 1155 988 1150 1151 976 1066 1073 1152 1151 1160 1068 1142 1070 1160 1063 1068 1240 1157 1246 1156 982 1156 1159 1157 1155 1066 1158 1153 +tree_sizes=931 971 974 1218 1056 1056 1056 1226 973 1056 1061 1062 1235 1063 1228 1063 1060 981 990 1246 1073 1163 1077 1156 1170 1068 1253 1167 1159 1073 1159 1172 1081 995 1168 1170 1167 1087 1176 1090 1081 1170 1092 1085 1254 1092 1174 1086 1087 1174 995 1086 1085 1179 1087 1174 1175 1099 1085 1256 1090 1169 1090 1188 1080 1170 1167 1179 1182 1010 1170 1083 1176 1008 1172 1173 996 1086 1093 1174 1173 1182 1087 1164 1091 1182 1084 1088 1262 1178 1269 1177 1002 1176 1180 1178 1176 1087 1178 1174 Tree=0 num_leaves=10 @@ -21,9 +21,10 @@ right_child=1 -3 6 -5 5 -7 -8 -9 -10 leaf_value=0.91066666613953806 0.9889999999835466 1.0683589738903518 0.96899999985316143 0.96323076904631955 0.99400000001614286 1.0332857140457434 0.99400000001614286 1.0019999999639888 0.99400000001614286 leaf_weight=36 20 39 20 26 21 28 34 25 51 leaf_count=36 20 39 20 26 21 28 34 25 51 -internal_value=0 0.229493 -0.364023 -0.606237 0.0894253 0.231156 -0.0859259 0.0170833 -0.00741784 +internal_value=0.993333 1.01628 0.956931 0.93271 1.00228 1.01645 0.984741 0.995042 0.992592 internal_weight=0 184 116 62 145 49 54 96 71 internal_count=300 184 116 62 145 49 54 96 71 +is_linear=0 shrinkage=1 @@ -39,9 +40,10 @@ right_child=1 -3 6 -5 5 -7 -8 -9 -10 leaf_value=-0.076532351072220242 -0.003688265393222017 0.067523078200144651 -0.017899999655783178 -0.028270968151909694 0.004530612144264437 0.041676189969959004 0.0011645161216297458 0.0080324322463491485 7.1804507292414963e-05 leaf_weight=34 28 39 20 31 21 21 31 37 38 leaf_count=34 28 39 20 31 21 21 31 37 38 -internal_value=0 0.206543 -0.327621 -0.535154 0.0804828 0.231034 -0.0631176 0.0190929 -0.0152338 +internal_value=0 0.0206543 -0.0327621 -0.0535154 0.00804828 0.0231034 -0.00631176 0.00190929 -0.00152338 internal_weight=0 184 116 65 145 42 51 103 66 internal_count=300 184 116 65 145 42 51 103 66 +is_linear=0 shrinkage=0.1 @@ -57,9 +59,10 @@ right_child=1 -3 5 -5 -6 -7 7 -9 -10 leaf_value=-0.071365883899852642 -0.005536310314200819 0.060770767163007694 -0.022856777518987657 -0.025495980858802798 0.023983837119714524 0.0021225635071887689 0.00012588065132618912 0.011611045537621625 -0.00062996406702260524 leaf_weight=32 20 39 25 25 34 34 31 31 29 leaf_count=32 20 39 25 25 34 34 31 31 29 -internal_value=0 0.185889 -0.294859 -0.512475 0.0724345 -0.084619 0.0211576 0.0586846 -0.0263255 +internal_value=0 0.0185889 -0.0294859 -0.0512475 0.00724345 -0.0084619 0.00211576 0.00586846 -0.00263255 internal_weight=0 184 116 57 145 59 111 62 49 internal_count=300 184 116 57 145 59 111 62 49 +is_linear=0 shrinkage=0.1 @@ -75,9 +78,10 @@ right_child=1 3 7 -5 -6 6 -8 -9 9 10 11 -13 leaf_value=-0.076276844739913946 -0.012271453170105815 0.021397085603149166 -0.025375127499657021 0.080334717035293587 -0.039962041974067691 0.0005535114492321837 0.032933746451245888 0.0025276197327507872 -0.0069276730596487014 1.5363872696000797e-05 0.00060767032206058506 0.010239836359202552 leaf_weight=22 20 21 21 21 20 29 28 27 21 26 20 24 leaf_count=22 20 21 21 21 20 29 28 27 21 26 20 24 -internal_value=0 0.140093 -0.326885 0.508659 -0.589841 0.0479521 0.164596 -0.0967983 -0.0119461 0.0123987 0.0369013 0.0586158 +internal_value=0 0.0140093 -0.0326885 0.0508659 -0.0589841 0.00479521 0.0164596 -0.00967983 -0.00119461 0.00123987 0.00369013 0.00586158 internal_weight=0 210 90 42 42 168 57 48 111 91 70 44 internal_count=300 210 90 42 42 168 57 48 111 91 70 44 +is_linear=0 shrinkage=0.1 @@ -93,9 +97,10 @@ right_child=1 2 -4 -5 6 9 -8 -9 -10 -11 leaf_value=-0.060740401331455474 -0.027841924689593723 0.0038034221149947766 0.064915694499557669 -0.015461565321020316 0.0055197326948109547 0.0012004062226465481 0.023730750903487207 0.0086549895911486128 -0.0019078226105586904 -0.00035020611481741072 leaf_weight=31 25 35 33 30 20 22 20 27 37 20 leaf_count=31 25 35 33 30 20 22 20 27 37 20 -internal_value=0 0.0981924 0.334608 -0.384721 0.00417904 -0.100992 0.0719332 0.0325583 0.00698337 0.00462019 +internal_value=0 0.00981924 0.0334608 -0.0384721 0.000417904 -0.0100992 0.00719332 0.00325583 0.000698337 0.000462019 internal_weight=0 239 68 61 171 67 104 84 57 42 internal_count=300 239 68 61 171 67 104 84 57 42 +is_linear=0 shrinkage=0.1 @@ -111,9 +116,10 @@ right_child=1 2 4 -5 -6 6 8 -9 -10 -11 leaf_value=-0.052308947401387355 -0.019957683151587843 -0.0033818439787864918 0.032222057819705122 -0.010819472078807079 0.062894394359102962 -0.017265938874916172 -0.00065500374093971922 0.00042078272711175185 0.013058711127710662 0.0092908982272175231 leaf_weight=35 20 32 22 26 27 20 38 30 28 22 leaf_count=35 20 32 22 26 27 20 38 30 28 22 -internal_value=0 0.0883732 0.243031 -0.346249 0.491231 -0.028757 0.000694696 -0.0665391 0.0516294 0.0178113 +internal_value=0 0.00883732 0.0243031 -0.0346249 0.0491231 -0.0028757 6.94696e-05 -0.00665391 0.00516294 0.00178113 internal_weight=0 239 103 61 49 136 116 50 66 54 internal_count=300 239 103 61 49 136 116 50 66 54 +is_linear=0 shrinkage=0.1 @@ -129,9 +135,10 @@ right_child=1 -3 3 7 -6 -7 -8 -9 -10 -11 leaf_value=-0.045916911922395234 -0.0029965755106373271 0.053119936978651416 -0.028322171978652479 0.0037277831251436334 -0.0018819083792290519 0.016755555116611979 -0.030615492314100268 0.011008037760367861 -0.003715086660629727 0.0050828907150952595 leaf_weight=20 33 36 34 31 21 23 20 23 38 21 leaf_count=20 33 36 34 31 21 23 20 23 38 21 -internal_value=0 0.204031 -0.123291 -0.0527144 -0.182268 0.051069 -0.382662 0.0247362 -0.00371189 0.00145439 +internal_value=0 0.0204031 -0.0123291 -0.00527144 -0.0182268 0.0051069 -0.0382662 0.00247362 -0.000371189 0.000145439 internal_weight=0 113 187 147 55 77 40 92 69 54 internal_count=300 113 187 147 55 77 40 92 69 54 +is_linear=0 shrinkage=0.1 @@ -147,9 +154,10 @@ right_child=1 3 7 -5 -6 6 -8 -9 10 -11 11 -13 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42 internal_count=300 63 237 216 193 173 152 104 48 47 57 42 +is_linear=0 shrinkage=0.1 @@ -1659,9 +1750,10 @@ right_child=1 4 -4 -5 5 6 8 -9 9 -11 -12 leaf_value=0.0016161434951105288 -0.00067841278389096265 -0.003616081734242685 0.0064258282407536173 -0.0036369849112816155 -0.0034674347580774972 0.0051310983752565729 0.00096497986353291524 0.0012210435155187347 0.00079955156504487007 -0.0055183076543644785 0.0026631023778463715 leaf_weight=28 20 34 20 26 23 21 29 24 30 21 24 leaf_count=28 20 34 20 26 23 21 29 24 30 21 24 -internal_value=0 -0.00166368 0.0093668 -0.00631649 -0.00711831 -0.000446377 0.00585157 0.0304574 -0.00798919 -0.0180192 0.0114423 +internal_value=0 -0.000166368 0.00093668 -0.000631649 -0.000711831 -4.46377e-05 0.000585157 0.00304574 -0.000798919 -0.00180192 0.00114423 internal_weight=0 272 90 70 182 148 125 45 80 51 44 internal_count=300 272 90 70 182 148 125 45 80 51 44 +is_linear=0 shrinkage=0.1 @@ -1677,9 +1769,10 @@ right_child=-2 3 5 4 -6 8 -8 -9 -10 leaf_value=0.0020674155869831643 0.0016796287599830859 0.0030385281483923353 -0.0034256816160474976 -0.0038103000446706296 0.0020048237068944481 -0.0049456925752262276 0.0016466037396134601 0.0010187280796734352 -0.0026513104337606242 leaf_weight=39 31 39 37 26 28 21 36 21 22 leaf_count=39 31 39 37 26 28 21 36 21 22 -internal_value=0 -0.00193563 -0.00725218 0.00812579 -0.00795051 -0.015202 -0.00490204 -0.018165 -0.0377182 +internal_value=0 -0.000193563 -0.000725218 0.000812579 -0.000795051 -0.0015202 -0.000490204 -0.0018165 -0.00377182 internal_weight=0 269 176 93 54 137 94 58 43 internal_count=300 269 176 93 54 137 94 58 43 +is_linear=0 shrinkage=0.1 @@ -1695,9 +1788,10 @@ right_child=-2 2 -4 4 5 9 -8 8 -10 -11 -12 leaf_value=-0.0026457320351619277 0.0015116659563875969 0.0029655175405028074 0.0032686300209024926 -0.0042603290174156429 0.0026698930817656221 -0.004192044162191451 -0.0031327552402702474 0.0022769254066848329 -0.0021769941357585292 0.00038572572404518726 0.004090926064552902 leaf_weight=20 31 23 20 28 20 20 30 35 30 20 23 leaf_count=20 31 23 20 28 20 20 30 35 30 20 23 -internal_value=0 -0.00174207 0.000243092 -0.00259038 -0.00619061 -0.00046277 0.0049195 0.0149881 0.0022127 -0.0190316 0.0342998 +internal_value=0 -0.000174207 2.43092e-05 -0.000259038 -0.000619061 -4.6277e-05 0.00049195 0.00149881 0.00022127 -0.00190316 0.00342998 internal_weight=0 269 249 229 206 178 138 108 65 40 43 internal_count=300 269 249 229 206 178 138 108 65 40 43 +is_linear=0 shrinkage=0.1 @@ -1713,9 +1807,10 @@ right_child=1 2 9 4 -6 -7 -8 -9 10 -11 -12 leaf_value=0.001778524967708758 -0.0024684532288266649 0.00247076662656452 -0.0006910800230054354 0.00078536151365066567 -0.0053234087387099872 0.0042955857542851432 0.0024737199281204356 -0.0043513523605228826 -0.0010682166821921286 0.003156213427026724 -0.0023305400979006666 leaf_weight=28 31 28 22 24 25 22 27 22 22 29 20 leaf_count=28 31 28 22 24 25 22 27 22 22 29 20 -internal_value=0 -0.00183083 0.00110885 -0.00261069 -0.0233136 0.00458393 -0.00251004 -0.0105065 -0.000132872 0.014966 -0.0166932 +internal_value=0 -0.000183083 0.000110885 -0.000261069 -0.00233136 0.000458393 -0.000251004 -0.00105065 -1.32872e-05 0.0014966 -0.00166932 internal_weight=0 272 241 190 49 141 119 92 70 51 42 internal_count=300 272 241 190 49 141 119 92 70 51 42 +is_linear=0 shrinkage=0.1 @@ -1731,9 +1826,10 @@ right_child=1 3 -4 4 9 -7 -8 -9 -10 -11 -12 leaf_value=0.0016006724543071221 -0.00071761153638362886 -0.0043501243660492564 0.0059819861466530715 0.00092846733529031991 0.0035359874914359791 -0.0048786768000572923 0.002870628598009654 -0.0031220477132592353 -0.0034440486665604816 0.00025127895059995353 0.002459886201055876 leaf_weight=28 20 21 20 21 23 25 29 26 31 32 24 leaf_count=28 20 21 20 21 23 25 29 26 31 32 24 -internal_value=0 -0.00164775 0.00923906 -0.00703134 -0.00227439 -0.0118855 -0.00049623 -0.0052126 -0.0167822 0.0162488 0.0101557 +internal_value=0 -0.000164775 0.000923906 -0.000703134 -0.000227439 -0.00118855 -4.9623e-05 -0.00052126 -0.00167822 0.00162488 0.00101557 internal_weight=0 272 90 182 161 106 81 70 52 55 44 internal_count=300 272 90 182 161 106 81 70 52 55 44 +is_linear=0 shrinkage=0.1 @@ -1749,9 +1845,10 @@ right_child=2 -3 -4 -5 5 -7 -8 9 -10 -11 -12 leaf_value=0.0018064170330762863 -0.0042357496521435679 -0.0019625395261259242 0.0029105038559507756 -0.0044934900021990354 -0.002392007363960147 -0.0017196056167853788 -0.0026786260231165216 0.0052283432068569325 0.0018472793236679328 0.002221535610345503 0.0032065639427552618 leaf_weight=20 20 22 21 23 33 36 20 28 26 30 21 leaf_count=20 20 22 21 23 33 36 20 28 26 30 21 -internal_value=0 0.00956989 -0.00254389 -0.00562087 -0.000935783 0.00385285 0.00938395 0.0155669 -0.00523847 0.036731 0.0252357 +internal_value=0 0.000956989 -0.000254389 -0.000562087 -9.35783e-05 0.000385285 0.000938395 0.00155669 -0.000523847 0.0036731 0.00252357 internal_weight=0 63 237 216 193 173 137 117 59 58 41 internal_count=300 63 237 216 193 173 137 117 59 58 41 +is_linear=0 shrinkage=0.1 @@ -1767,9 +1864,10 @@ right_child=-2 3 5 4 8 9 -8 -9 -10 -11 leaf_value=0.0016896104290842627 0.0013245301561490182 -0.0024298215802991763 -0.0030643851036910672 0.0051607523651910018 -0.0025007720082066958 -0.0046947998482556571 0.0015713068786734606 0.00059898725670316951 0.001317702299205675 -0.0025630589336453177 leaf_weight=39 31 20 37 32 20 21 34 23 21 22 leaf_count=39 31 20 37 32 20 21 34 23 21 22 -internal_value=0 -0.00152641 -0.00768548 0.0101295 0.0195617 -0.0146832 -0.00491288 -0.0166009 -0.00544968 -0.0360414 +internal_value=0 -0.000152641 -0.000768548 0.00101295 0.00195617 -0.00146832 -0.000491288 -0.00166009 -0.000544968 -0.00360414 internal_weight=0 269 176 93 73 137 94 60 41 43 internal_count=300 269 176 93 73 137 94 60 41 43 +is_linear=0 shrinkage=0.1 @@ -1785,9 +1883,10 @@ right_child=-2 -3 -4 -5 5 6 7 -9 -10 10 -12 leaf_value=0.00065368117232407843 -0.0012416051444000542 0.0031843378607715879 0.0019796374921078005 -0.0043852782752808366 0.0045340736685118279 -0.0032008635218847888 -0.0010065357327799906 0.004044778995035637 -0.0040767670537416754 0.002621323576507469 -0.0035658507394449169 leaf_weight=21 39 21 26 21 23 22 22 31 26 24 24 leaf_count=21 39 21 26 21 23 22 22 31 26 24 24 -internal_value=0 0.00185527 -0.000768688 -0.00326725 0.0011488 0.00783838 0.000825749 0.0194801 -0.0196316 -0.0132982 -0.00472264 +internal_value=0 0.000185527 -7.68688e-05 -0.000326725 0.00011488 0.000783838 8.25749e-05 0.00194801 -0.00196316 -0.00132982 -0.000472264 internal_weight=0 261 240 214 193 146 123 53 47 70 48 internal_count=300 261 240 214 193 146 123 53 47 70 48 +is_linear=0 shrinkage=0.1 @@ -1803,15 +1902,16 @@ right_child=1 2 3 -5 -6 6 -8 -9 -10 10 -12 leaf_value=-0.0008189125651759761 -0.0037030477697650591 0.0048937522318391576 -0.0029559939925093205 -0.0036433350099250674 -0.0028209372605804521 0.0011041226075781932 0.0063159570452712838 -0.00017049106769263746 0.0033783739974276856 0.0015670212048634485 -0.0025142128806605584 leaf_weight=28 30 21 20 25 23 31 21 20 23 37 21 leaf_count=28 30 21 20 25 23 31 21 20 23 37 21 -internal_value=0 -0.00219972 0.00257162 -0.00241801 0.00314005 0.00868649 0.032089 0.0242339 0.0107398 -0.0069152 0.00089333 +internal_value=0 -0.000219972 0.000257162 -0.000241801 0.000314005 0.000868649 0.0032089 0.00242339 0.00107398 -0.00069152 8.9333e-05 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 +is_linear=0 shrinkage=0.1 end of trees -feature importances: +feature_importances: x2=335 x1=285 x0=280 @@ -1820,15 +1920,21 @@ x3=148 parameters: [boosting: gbdt] [objective: regression] -[metric: ] +[metric: l2] [tree_learner: serial] [device_type: cpu] +[data_sample_strategy: bagging] [data: ] [valid: ] [num_iterations: 100] [learning_rate: 0.1] [num_leaves: 31] [num_threads: 0] +[seed: 0] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] [max_depth: -1] [min_data_in_leaf: 20] [min_sum_hessian_in_leaf: 0.001] @@ -1840,11 +1946,15 @@ parameters: [feature_fraction: 1] [feature_fraction_bynode: 1] [feature_fraction_seed: 2] +[extra_trees: 0] +[extra_seed: 6] [early_stopping_round: 0] +[early_stopping_min_delta: 0] [first_metric_only: 0] [max_delta_step: 0] [lambda_l1: 0] [lambda_l2: 0] +[linear_lambda: 0] [min_gain_to_split: 0] [drop_rate: 0.1] [max_drop: 50] @@ -1861,51 +1971,46 @@ parameters: [max_cat_to_onehot: 4] [top_k: 20] [monotone_constraints: ] +[monotone_constraints_method: basic] +[monotone_penalty: 0] [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] +[path_smooth: 0] +[interaction_constraints: ] +[verbosity: -1] +[saved_feature_importance_type: 0] +[use_quantized_grad: 0] +[num_grad_quant_bins: 4] +[quant_train_renew_leaf: 0] +[stochastic_rounding: 1] +[linear_tree: 0] [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] +[enable_bundle: 1] [use_missing: 1] [zero_as_missing: 0] +[feature_pre_filter: 1] +[pre_partition: 0] [two_round: 0] -[save_binary: 0] [header: 0] [label_column: ] [weight_column: ] [group_column: ] [ignore_column: ] [categorical_feature: ] -[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] +[forcedbins_filename: ] +[precise_float_parser: 0] +[parser_config_file: ] +[objective_seed: 5] [num_class: 1] [is_unbalance: 0] [scale_pos_weight: 1] @@ -1916,13 +2021,13 @@ parameters: [fair_c: 1] [poisson_max_delta_step: 0.7] [tweedie_variance_power: 1.5] -[max_position: 20] -[lambdamart_norm: 1] +[lambdarank_truncation_level: 30] +[lambdarank_norm: 1] [label_gain: ] -[metric_freq: 1] -[is_provide_training_metric: 0] +[lambdarank_position_bias_regularization: 0] [eval_at: ] [multi_error_top_k: 1] +[auc_mu_weights: ] [num_machines: 1] [local_listen_port: 12400] [time_out: 120] @@ -1931,6 +2036,7 @@ parameters: [gpu_platform_id: -1] [gpu_device_id: -1] [gpu_use_dp: 0] +[num_gpu: 1] end of parameters diff --git a/test/support/model_categorical.txt b/test/support/model_categorical.txt new file mode 100644 index 0000000..e41c1ee --- /dev/null +++ b/test/support/model_categorical.txt @@ -0,0 +1,2167 @@ +tree +version=v4 +num_class=1 +num_tree_per_iteration=1 +label_index=0 +max_feature_idx=3 +objective=regression +feature_names=x0 x1 x2 x3 +feature_infos=[0:9.9000000000000004] [0:9.8000000000000007] [0:9.9000000000000004] -1:9:0:2:4:5:6:7:3:8:1 +tree_sizes=931 1058 976 1135 1140 1141 1221 1057 1064 1061 1064 1146 1146 1170 1258 1064 1083 1083 1174 1073 1176 1169 1088 1179 1091 1182 1180 1245 1096 1179 1092 1096 1182 1179 1271 1179 1099 1186 1191 1185 1189 1082 1189 1191 1185 1102 1099 1080 1190 1103 1095 1104 1101 1080 1088 1104 1189 1191 1174 1192 1193 1172 1016 1106 997 1017 1109 1193 1195 1194 1178 998 1175 1191 1192 1084 1280 1111 1176 1194 1198 1173 1279 1110 1109 1196 1107 1108 1101 1260 1089 1091 1179 1174 1198 1195 1173 1182 998 1086 + +Tree=0 +num_leaves=10 +num_cat=0 +split_feature=2 1 1 0 0 2 0 1 0 +split_gain=25.0622 13.4213 7.81363 4.17122 1.4867 1.85204 0.787037 0.163668 0.0359155 +threshold=4.0500000000000007 7.7500000000000009 4.5500000000000007 5.8000000000000016 6.7500000000000009 6.3500000000000005 3.4500000000000006 5.8500000000000005 1.8500000000000003 +decision_type=2 2 2 2 2 2 2 2 2 +left_child=2 4 3 -1 7 -6 -4 8 -2 +right_child=1 -3 6 -5 5 -7 -8 -9 -10 +leaf_value=0.91066666613953806 0.9889999999835466 1.0683589738903518 0.96899999985316143 0.96323076904631955 0.99400000001614286 1.0332857140457434 0.99400000001614286 1.0019999999639888 0.99400000001614286 +leaf_weight=36 20 39 20 26 21 28 34 25 51 +leaf_count=36 20 39 20 26 21 28 34 25 51 +internal_value=0.993333 1.01628 0.956931 0.93271 1.00228 1.01645 0.984741 0.995042 0.992592 +internal_weight=0 184 116 62 145 49 54 96 71 +internal_count=300 184 116 62 145 49 54 96 71 +is_linear=0 +shrinkage=1 + + +Tree=1 +num_leaves=11 +num_cat=0 +split_feature=2 1 1 0 1 2 2 2 2 0 +split_gain=20.3004 10.8713 6.36761 3.77682 1.34014 1.44878 0.441848 0.19812 0.026579 0.00350037 +threshold=4.0500000000000007 7.7500000000000009 5.0500000000000007 3.8500000000000001 5.7500000000000009 6.4500000000000011 2.2500000000000004 7.8500000000000005 5.1500000000000012 5.6500000000000012 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3.6500000000000008 6.4500000000000011 1.6500000000000001 +decision_type=2 2 2 2 2 2 2 2 2 +left_child=2 4 3 -1 6 -4 8 -8 -2 +right_child=1 -3 5 -5 -6 -7 7 -9 -10 +leaf_value=-0.071365883899852642 -0.0042178705557370964 0.060770767163007694 -0.022856777518987657 -0.025495980858802798 0.024048169260598065 0.0021225635071887689 8.1711309515715891e-05 0.0096219042442472931 -0.00031222260219912078 +leaf_weight=32 23 39 25 25 34 34 24 35 29 +leaf_count=32 23 39 25 25 34 34 24 35 29 +internal_value=0 0.0185889 -0.0294859 -0.0512475 0.00724345 -0.0084619 0.00209606 0.00574115 -0.00203972 +internal_weight=0 184 116 57 145 59 111 59 52 +internal_count=300 184 116 57 145 59 111 59 52 +is_linear=0 +shrinkage=0.1 + + +Tree=3 +num_leaves=12 +num_cat=0 +split_feature=2 1 1 2 0 0 2 0 0 1 2 +split_gain=13.7383 7.13163 5.44524 3.64733 1.38156 1.17214 1.44484 0.919678 0.290498 0.163577 0.0302091 +threshold=3.2500000000000004 7.7500000000000009 4.1500000000000012 8.1500000000000004 4.7500000000000009 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4.1500000000000012 3.9500000000000006 4.8500000000000005 6.0500000000000007 +decision_type=2 2 2 2 2 2 2 2 2 2 2 +left_child=1 2 10 4 5 -2 -7 -8 -9 -4 -1 +right_child=3 -3 9 -5 -6 6 7 8 -10 -11 -12 +leaf_value=0.0019500432256609201 0.0033043113814312077 -0.0037481023227363564 0.00029261789244154228 0.0034853277405580648 -0.0038068809062242505 -0.0028405306023509746 0.0031633974052965642 -0.0026475100200817302 0.001708078088833847 -0.003149875619315675 0.0035819096279076559 +leaf_weight=20 21 35 23 26 25 26 24 21 36 21 22 +leaf_count=20 21 35 23 26 25 26 24 21 36 21 22 +internal_value=0 -0.000601635 0.000678903 0.000406692 -0.000116475 0.000604308 7.44004e-05 0.00101006 0.000103388 -0.00135039 0.00280483 +internal_weight=0 121 86 179 153 128 107 81 57 44 42 +internal_count=300 121 86 179 153 128 107 81 57 44 42 +is_linear=0 +shrinkage=0.1 + + +Tree=97 +num_leaves=12 +num_cat=0 +split_feature=1 2 0 2 0 2 1 1 0 1 0 +split_gain=0.00744269 0.0218862 0.025834 0.0210576 0.0362997 0.0265915 0.0545702 0.0343739 0.0294591 0.0190603 0.00365669 +threshold=0.55000000000000016 1.3500000000000003 1.4500000000000002 9.2500000000000018 8.8500000000000032 6.3500000000000005 4.3500000000000005 4.8500000000000005 6.3500000000000005 2.6500000000000008 3.8500000000000001 +decision_type=2 2 2 2 2 2 2 2 2 2 2 +left_child=-1 -2 -3 4 5 6 9 -7 10 -4 -8 +right_child=1 2 3 -5 -6 7 8 -9 -10 -11 -12 +leaf_value=-0.0016169365531263444 0.0029136836242217284 -0.0028872481733560563 0.00017836161960776037 0.003252898548020139 -0.0039066937286406757 -0.00070289578870870179 -0.0017591157392598689 0.0042709159570001771 -0.0051159157456519702 0.0040863851453953734 7.1722435920188823e-05 +leaf_weight=26 26 30 26 21 22 24 20 33 24 24 24 +leaf_count=26 26 30 26 21 22 24 20 33 24 24 24 +internal_value=0 0.000153432 -0.000135949 0.00024267 -7.82175e-05 0.000403077 -0.000453664 0.00217668 -0.00229769 0.00205421 -0.000760477 +internal_weight=0 274 248 218 197 175 118 57 68 50 44 +internal_count=300 274 248 218 197 175 118 57 68 50 44 +is_linear=0 +shrinkage=0.1 + + +Tree=98 +num_leaves=10 +num_cat=0 +split_feature=0 2 2 1 2 2 1 2 0 +split_gain=0.00793416 0.0239716 0.0318906 0.0312953 0.0188833 0.0379955 0.0256008 0.023045 0.0461231 +threshold=9.3500000000000032 8.9000000000000021 7.8500000000000005 8.3500000000000032 4.3500000000000005 6.6500000000000012 4.8500000000000005 1.5500000000000003 3.8500000000000001 +decision_type=2 2 2 2 2 2 2 2 2 +left_child=1 2 3 4 7 6 -6 -1 -9 +right_child=-2 -3 -4 -5 5 -7 -8 8 -10 +leaf_value=0.0020909305323253981 0.0018281043422493069 -0.0028145483883175379 0.0031785299776182063 -0.0031919162359929851 0.001045622943681391 -0.0014395027209750631 0.0053113742339438857 -0.0042933006510686355 0.0007457515571075373 +leaf_weight=22 22 30 31 31 34 33 24 34 39 +leaf_count=22 22 30 31 31 34 33 24 34 39 +internal_value=0 -0.00014467 0.000178299 -0.000250305 0.000239963 0.00126946 0.00281076 -0.000746184 -0.0016012 +internal_weight=0 278 248 217 186 91 58 95 73 +internal_count=300 278 248 217 186 91 58 95 73 +is_linear=0 +shrinkage=0.1 + + +Tree=99 +num_leaves=11 +num_cat=0 +split_feature=1 0 1 1 2 2 2 0 1 2 +split_gain=0.00753002 0.0217126 0.0573794 0.039553 0.0874298 0.0411609 0.0724317 0.0194802 0.0161805 0.0110373 +threshold=8.8500000000000032 6.6500000000000012 1.2500000000000002 7.0500000000000007 4.5500000000000007 7.9500000000000011 4.3500000000000005 8.1500000000000004 4.0500000000000007 2.6500000000000008 +decision_type=2 2 2 2 2 2 2 2 2 2 +left_child=1 3 -3 5 -5 6 9 -4 -8 -1 +right_child=-2 2 7 4 -6 -7 8 -9 -10 -11 +leaf_value=-0.00054637853416704364 0.0014758177356974734 -0.0035334566386327859 0.0042463373669306753 0.0011884665931575001 -0.0080499376425342186 -0.0034892549013925927 0.0021216863274042099 0.00093611062970012427 0.0056872471835231406 -0.0034750896760008553 +leaf_weight=31 31 21 32 20 21 27 35 40 20 22 +leaf_count=31 31 21 32 20 21 27 35 40 20 22 +internal_value=0 -0.000170076 0.00106586 -0.000823153 -0.0035434 2.99537e-06 0.000876058 0.00240732 0.00341825 -0.00176207 +internal_weight=0 269 93 176 41 135 108 72 55 53 +internal_count=300 269 93 176 41 135 108 72 55 53 +is_linear=0 +shrinkage=0.1 + + +end of trees + +feature_importances: +x2=399 +x1=310 +x0=284 +x3=62 + +parameters: +[boosting: gbdt] +[objective: regression] +[metric: l2] +[tree_learner: serial] +[device_type: cpu] +[data_sample_strategy: bagging] +[data: ] +[valid: ] +[num_iterations: 100] +[learning_rate: 0.1] +[num_leaves: 31] +[num_threads: 0] +[seed: 0] +[deterministic: 0] +[force_col_wise: 0] +[force_row_wise: 0] +[histogram_pool_size: -1] +[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] +[extra_trees: 0] +[extra_seed: 6] +[early_stopping_round: 0] +[early_stopping_min_delta: 0] +[first_metric_only: 0] +[max_delta_step: 0] +[lambda_l1: 0] +[lambda_l2: 0] +[linear_lambda: 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: ] +[monotone_constraints_method: basic] +[monotone_penalty: 0] +[feature_contri: ] +[forcedsplits_filename: ] +[refit_decay_rate: 0.9] +[cegb_tradeoff: 1] +[cegb_penalty_split: 0] +[cegb_penalty_feature_lazy: ] +[cegb_penalty_feature_coupled: ] +[path_smooth: 0] +[interaction_constraints: ] +[verbosity: -1] +[saved_feature_importance_type: 0] +[use_quantized_grad: 0] +[num_grad_quant_bins: 4] +[quant_train_renew_leaf: 0] +[stochastic_rounding: 1] +[linear_tree: 0] +[max_bin: 255] +[max_bin_by_feature: ] +[min_data_in_bin: 3] +[bin_construct_sample_cnt: 200000] +[data_random_seed: 1] +[is_enable_sparse: 1] +[enable_bundle: 1] +[use_missing: 1] +[zero_as_missing: 0] +[feature_pre_filter: 1] +[pre_partition: 0] +[two_round: 0] +[header: 0] +[label_column: ] +[weight_column: ] +[group_column: ] +[ignore_column: ] +[categorical_feature: 3] +[forcedbins_filename: ] +[precise_float_parser: 0] +[parser_config_file: ] +[objective_seed: 5] +[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] +[lambdarank_truncation_level: 30] +[lambdarank_norm: 1] +[label_gain: ] +[lambdarank_position_bias_regularization: 0] +[eval_at: ] +[multi_error_top_k: 1] +[auc_mu_weights: ] +[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] +[num_gpu: 1] + +end of parameters + +pandas_categorical:[["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]] diff --git a/test/support/regressor.py b/test/support/regressor.py index 6f732a9..ef878b4 100644 --- a/test/support/regressor.py +++ b/test/support/regressor.py @@ -11,7 +11,7 @@ X_test = X[300:] y_test = y[300:] -model = lgb.LGBMRegressor() +model = lgb.LGBMRegressor(verbosity=-1) model.fit(X_train, y_train) print('predict', model.predict(X_test)[0:6].tolist()) @@ -19,4 +19,5 @@ print('feature_importances', model.feature_importances_.tolist()) print('early_stopping') -model.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=True) +model = lgb.LGBMRegressor(early_stopping_round=5, verbosity=1) +regressor = model.fit(X_train, y_train, eval_set=[(X_test, y_test)]) diff --git a/test/support/train.py b/test/support/train.py index 7c764c5..bf14f82 100644 --- a/test/support/train.py +++ b/test/support/train.py @@ -17,7 +17,7 @@ regression_params = {'objective': 'regression', 'verbosity': -1} regression_train = lgb.Dataset(X_train, label=y_train) regression_test = lgb.Dataset(X_test, label=y_test) -bst = lgb.train(regression_params, regression_train, valid_sets=[regression_train, regression_test], verbose_eval=False) +bst = lgb.train(regression_params, regression_train, valid_sets=[regression_train, regression_test]) y_pred = bst.predict(X_test) print(np.sqrt(np.mean((y_pred - y_test)**2))) @@ -27,7 +27,7 @@ binary_params = {'objective': 'binary', 'verbosity': -1} binary_train = lgb.Dataset(X_train, label=y_train.replace(2, 1)) binary_test = lgb.Dataset(X_test, label=y_test.replace(2, 1)) -bst = lgb.train(binary_params, binary_train, valid_sets=[binary_train, binary_test], verbose_eval=False) +bst = lgb.train(binary_params, binary_train, valid_sets=[binary_train, binary_test]) y_pred = bst.predict(X_test) print(y_pred[0]) @@ -37,33 +37,37 @@ multiclass_params = {'objective': 'multiclass', 'num_class': 3, 'verbosity': -1} multiclass_train = lgb.Dataset(X_train, label=y_train) multiclass_test = lgb.Dataset(X_test, label=y_test) -bst = lgb.train(multiclass_params, multiclass_train, valid_sets=[multiclass_train, multiclass_test], verbose_eval=False) +bst = lgb.train(multiclass_params, multiclass_train, valid_sets=[multiclass_train, multiclass_test]) y_pred = bst.predict(X_test) print(y_pred[0].tolist()) print('') print('test_early_stopping_early') -bst = lgb.train(regression_params, regression_train, valid_sets=[regression_train, regression_test], early_stopping_rounds=5) +regression_params['early_stopping_round'] = 5 +bst = lgb.train(regression_params, regression_train, valid_sets=[regression_train, regression_test]) print(bst.best_iteration) print('') print('test_early_stopping_not_early') -bst = lgb.train(regression_params, regression_train, valid_sets=[regression_train, regression_test], early_stopping_rounds=500) +regression_params['early_stopping_round'] = 500 +bst = lgb.train(regression_params, regression_train, valid_sets=[regression_train, regression_test]) # appears to be using training set for best iteration instead of validation set print(bst.best_iteration) print('') print('test_early_stopping_early_higher_better') -params = {'objective': 'binary', 'metric': 'auc', 'verbosity': -1} -bst = lgb.train(params, binary_train, valid_sets=[binary_train, binary_test], early_stopping_rounds=5, verbose_eval=False) +binary_params['early_stopping_round'] = 5 +binary_params['metric'] = 'auc' +bst = lgb.train(binary_params, binary_train, valid_sets=[binary_train, binary_test]) print(bst.best_iteration) print('') print('test_categorical_feature') +regression_params = {'objective': 'regression', 'verbosity': -1} train_set = lgb.Dataset(X_train, label=y_train, categorical_feature=[3]) bst = lgb.train(regression_params, train_set) print(bst.predict(X_test)[0])