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add test
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‎.ci/test.sh

Lines changed: 23 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -68,17 +68,35 @@ fi
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# other implementations like pypy
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CONDA_PYTHON_REQUIREMENT="python=${PYTHON_VERSION}[build=*_cp*]"
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if_else_test() {
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cd "$BUILD_DIRECTORY/tests/cpp_tests"
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mv ../../src/boosting/gbdt_prediction.cpp ../../src/boosting/gbdt_prediction.cpp.bak
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../../lightgbm_org config=$1 convert_model_language=cpp convert_model=../../src/boosting/gbdt_prediction.cpp
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../../lightgbm_org config=$2 output_result=origin.pred
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cd "$BUILD_DIRECTORY"
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cmake -B build -S . || exit 1
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cmake --build build --target lightgbm -j4 || exit 1
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cd "$BUILD_DIRECTORY/tests/cpp_tests"
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../../lightgbm config=$2 output_result=ifelse.pred
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python test.py
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mv ../../src/boosting/gbdt_prediction.cpp.bak ../../src/boosting/gbdt_prediction.cpp
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cd "$BUILD_DIRECTORY"
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}
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if [[ $TASK == "if-else" ]]; then
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conda create -q -y -n "${CONDA_ENV}" "${CONDA_PYTHON_REQUIREMENT}" numpy
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# shellcheck disable=SC1091
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source activate "${CONDA_ENV}"
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cmake -B build -S . || exit 1
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cmake --build build --target lightgbm -j4 || exit 1
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cd "$BUILD_DIRECTORY/tests/cpp_tests"
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../../lightgbm config=train.conf convert_model_language=cpp convert_model=../../src/boosting/gbdt_prediction.cpp
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../../lightgbm config=predict.conf output_result=origin.pred
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../../lightgbm config=predict.conf output_result=ifelse.pred
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python test.py
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mv lightgbm lightgbm_org
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if_else_test "train.conf" "predict.conf"
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if_else_test "train_nan_input.conf" "predict_nan_input.conf"
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exit 0
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fi
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‎src/boosting/gbdt_prediction.cpp

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -94,4 +94,4 @@ void GBDT::PredictLeafIndexByMap(const std::unordered_map<int, double>& features
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}
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}
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} // namespace LightGBM
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} // namespace LightGBM

‎tests/cpp_tests/LightGBM_model_for_if_else_test.txt

Lines changed: 199 additions & 0 deletions
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@@ -0,0 +1,199 @@
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tree
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version=v4
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num_class=1
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num_tree_per_iteration=1
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label_index=0
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max_feature_idx=9
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objective=regression
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feature_names=feature1 feature2 feature3 feature4 feature5 feature6 feature7 feature8 feature9 feature10
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feature_infos=[0.0030208713561612477:0.9966832779346797] [3.0718845382415871e-05:0.99971767328613059] [1.1634755366141114e-05:0.99889261203329061] [0.00040154402533987277:0.99946068105967312] [0.0013536257154681541:0.99941372577066656] [0.00022703821825553749:0.99890469976307672] [0.00041028943917487126:0.99413936122116753] [5.2826932296801132e-05:0.99762829740215653] [0.00090583524609022525:0.99742293905355417] [0.0011120941961874076:0.99955770325043858]
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tree_sizes=394 402 402
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Tree=0
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num_leaves=3
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num_cat=0
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split_feature=1 2
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split_gain=151 67.4877
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threshold=0.43908158089896321 0.50555253269984324
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decision_type=10 8
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left_child=-1 -2
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right_child=1 -3
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leaf_value=0.3177485457277795 0.44806984060967808 0.36627160319740354
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leaf_weight=395 190 215
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leaf_count=395 190 215
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internal_value=0.36174 0.404646
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internal_weight=0 405
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internal_count=800 405
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is_linear=0
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shrinkage=1
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Tree=1
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num_leaves=3
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num_cat=0
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split_feature=2 0
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split_gain=80.9689 24.6402
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threshold=0.47134327177395619 1.0000000180025095e-35
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decision_type=8 10
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left_child=1 -1
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right_child=-2 -3
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leaf_value=-0.036787212124237648 -0.026726479674212891 0.047840481589521179
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leaf_weight=39 469 292
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leaf_count=39 469 292
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internal_value=0 0.0378692
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internal_weight=0 331
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internal_count=800 331
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is_linear=0
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shrinkage=0.1
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Tree=2
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num_leaves=3
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num_cat=0
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split_feature=1 0
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split_gain=120.883 23.018
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threshold=0.46947632301562686 1.0000000180025095e-35
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decision_type=10 10
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left_child=-1 -2
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right_child=1 -3
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leaf_value=-0.037347034887016677 -0.036852757930755618 0.048212644289095129
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leaf_weight=416 35 349
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leaf_count=416 35 349
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internal_value=0 0.0404593
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internal_weight=0 384
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internal_count=800 384
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is_linear=0
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shrinkage=0.1
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end of trees
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feature_importances:
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feature1=2
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feature2=2
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feature3=2
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parameters:
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[boosting: gbdt]
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[objective: regression]
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[metric: l2]
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[tree_learner: serial]
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[device_type: cpu]
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[data_sample_strategy: bagging]
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[data: ]
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[valid: ]
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[num_iterations: 3]
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[learning_rate: 0.1]
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[num_leaves: 3]
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[num_threads: 0]
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[seed: 0]
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[deterministic: 0]
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[force_col_wise: 0]
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[force_row_wise: 0]
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[histogram_pool_size: -1]
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[max_depth: -1]
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[min_data_in_leaf: 20]
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[min_sum_hessian_in_leaf: 0.001]
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[bagging_fraction: 1]
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[pos_bagging_fraction: 1]
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[neg_bagging_fraction: 1]
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[bagging_freq: 0]
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[bagging_seed: 3]
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[feature_fraction: 0.9]
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[feature_fraction_bynode: 1]
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[feature_fraction_seed: 2]
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[extra_trees: 0]
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[extra_seed: 6]
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[early_stopping_round: 0]
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[early_stopping_min_delta: 0]
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[first_metric_only: 0]
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[max_delta_step: 0]
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[lambda_l1: 0]
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[lambda_l2: 0]
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[linear_lambda: 0]
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[min_gain_to_split: 0]
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[drop_rate: 0.1]
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[max_drop: 50]
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[skip_drop: 0.5]
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[xgboost_dart_mode: 0]
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[uniform_drop: 0]
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[drop_seed: 4]
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[top_rate: 0.2]
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[other_rate: 0.1]
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[min_data_per_group: 100]
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[max_cat_threshold: 32]
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[cat_l2: 10]
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[cat_smooth: 10]
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[max_cat_to_onehot: 4]
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[top_k: 20]
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[monotone_constraints: ]
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[monotone_constraints_method: basic]
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[monotone_penalty: 0]
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[feature_contri: ]
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[forcedsplits_filename: ]
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[refit_decay_rate: 0.9]
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[cegb_tradeoff: 1]
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[cegb_penalty_split: 0]
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[cegb_penalty_feature_lazy: ]
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[cegb_penalty_feature_coupled: ]
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[path_smooth: 0]
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[interaction_constraints: ]
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[verbosity: -1]
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[saved_feature_importance_type: 0]
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[use_quantized_grad: 0]
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[num_grad_quant_bins: 4]
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[quant_train_renew_leaf: 0]
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[stochastic_rounding: 1]
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[linear_tree: 0]
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[max_bin: 255]
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[max_bin_by_feature: ]
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[min_data_in_bin: 3]
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[bin_construct_sample_cnt: 200000]
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[data_random_seed: 1]
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[is_enable_sparse: 1]
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[enable_bundle: 1]
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[use_missing: 1]
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[zero_as_missing: 0]
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[feature_pre_filter: 1]
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[pre_partition: 0]
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[two_round: 0]
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[header: 0]
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[label_column: ]
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[weight_column: ]
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[group_column: ]
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[ignore_column: ]
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[categorical_feature: ]
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[forcedbins_filename: ]
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[precise_float_parser: 0]
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[parser_config_file: ]
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[objective_seed: 5]
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[num_class: 1]
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[is_unbalance: 0]
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[scale_pos_weight: 1]
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[sigmoid: 1]
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[boost_from_average: 1]
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[reg_sqrt: 0]
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[alpha: 0.9]
177+
[fair_c: 1]
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[poisson_max_delta_step: 0.7]
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[tweedie_variance_power: 1.5]
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[lambdarank_truncation_level: 30]
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[lambdarank_norm: 1]
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[label_gain: ]
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[lambdarank_position_bias_regularization: 0]
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[eval_at: ]
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[multi_error_top_k: 1]
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[auc_mu_weights: ]
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[num_machines: 1]
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[local_listen_port: 12400]
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[time_out: 120]
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[machine_list_filename: ]
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[machines: ]
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[gpu_platform_id: -1]
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[gpu_device_id: -1]
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[gpu_use_dp: 0]
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[num_gpu: 1]
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end of parameters
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pandas_categorical:[]

‎tests/cpp_tests/predict_nan_input.conf

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data=../data/regression_with_nan_input.csv
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input_model=LightGBM_model_for_if_else_test.txt
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task=predict

‎tests/cpp_tests/train_nan_input.conf

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@@ -0,0 +1,3 @@
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task=convert_model
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input_model=LightGBM_model_for_if_else_test.txt

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