diff --git a/aicrowd.json b/aicrowd.json
index dbeb3abf0f02be3cb0854aa45847bfe67fda2dee..e92c6a67eababe64e6e128f66ab8bff543cafb48 100755
--- a/aicrowd.json
+++ b/aicrowd.json
@@ -1,8 +1,8 @@
{
"challenge_id": "esci-challenge-for-improving-product-search",
- "task": "task_3_product_substitute_identification",
+ "task": "task_2_multiclass_product_classification",
"gpu": true,
"authors": ["aicrowd-username"],
- "description": "task_2_multiclass_product_classification",
+ "description": "task_3_product_substitute_identification",
"license": "MIT"
}
\ No newline at end of file
diff --git a/models/lgb-us-task-2-only.txt b/models/lgb-us-task-2-only.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cb17716cf86eaaf60fead133a7f3f64426bfe36c
--- /dev/null
+++ b/models/lgb-us-task-2-only.txt
@@ -0,0 +1,4264 @@
+tree
+version=v3
+num_class=4
+num_tree_per_iteration=4
+label_index=0
+max_feature_idx=11
+objective=multiclass num_class:4
+feature_names=pred_0 pred_1 pred_2 pred_3 label_0 label_1 label_2 label_3 query_count is_isbn has_isbn fold
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+
+Tree=197
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+
+Tree=198
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+
+Tree=199
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+cat_boundaries=0 1
+cat_threshold=3
+is_linear=0
+shrinkage=0.1
+
+
+end of trees
+
+feature_importances:
+pred_3=997
+query_count=965
+pred_0=912
+pred_1=852
+pred_2=828
+fold=320
+label_3=305
+label_2=233
+has_isbn=210
+label_0=177
+label_1=118
+is_isbn=83
+
+parameters:
+[boosting: gbdt]
+[objective: multiclass]
+[metric: multi_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data: ]
+[valid: ]
+[num_iterations: 50]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 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]
+[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]
+[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: 11]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[objective_seed: 5]
+[num_class: 4]
+[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: ]
+[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:null
diff --git a/models/lgb-us-task-3-only.txt b/models/lgb-us-task-3-only.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ee0867ff2172dc8966594d29e03421996963a73c
--- /dev/null
+++ b/models/lgb-us-task-3-only.txt
@@ -0,0 +1,2230 @@
+tree
+version=v3
+num_class=2
+num_tree_per_iteration=2
+label_index=0
+max_feature_idx=11
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+split_feature=3 2 8 11 3 1 4 0 8 8 0 3 7 6 11 0 1 8 1 9 0 0 1 1 8 5 8 1 2 1
+split_gain=65.7874 23.79 14.1608 13.733 13.5611 14.1927 20.8936 16.9802 26.5013 25.5903 19.3049 13.8849 13.8184 15.833 13.6429 16.0565 17.1896 12.6695 13.2945 15.0486 14.7387 12.5156 20.7228 14.0202 12.4738 12.3974 11.8518 11.5242 11.5053 10.9548
+threshold=0.99654131124764023 0.013273847061427197 33.000000000000007 0 0.11221230490672088 0.0016694127336133787 0.054093567251461992 0.028277448747792856 41.000000000000007 77.000000000000014 0.0093160600921628773 0.049794110432056067 0.06186224489795919 0.74342105263157909 1 0.025878305372050017 0.00089907351431666946 79.000000000000014 0.050751290031137299 1.0000000180025095e-35 0.019445409832785164 0.0047745270537820428 0.0012811781976472443 0.00038711159616507926 79.000000000000014 0.033060109289617494 17.000000000000004 0.10557016395897574 0.00087464118449526607 0.61712286939879502
+decision_type=2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 -1 -2 5 7 12 8 11 -10 -11 -3 17 -14 21 29 -17 18 19 -7 -20 22 -6 -23 -12 27 -22 -8 -5 -16
+right_child=3 4 -4 28 14 6 25 -9 9 10 24 -13 13 -15 15 16 -18 -19 20 -21 26 23 -24 -25 -26 -27 -28 -29 -30 -31
+leaf_value=0.0017111285089027211 0.018340686100435211 -0.0055130259818883809 0.0075292205899115273 0.029800660644521134 0.0011768980495682592 0.0023772429386807606 0.002754691754399444 -0.0085571509652987102 -0.021768882217918561 -0.0089359307086235591 0.042886046195270935 0.0011348837773776455 -0.003534649866547562 -0.026272948341037911 -0.00087301753804386838 0.0053974619610482159 0.00029797863017457931 -0.0044007171836270573 -0.023320433896699414 -0.0034812666526411276 -0.1051269392288867 0.0056556638701515819 0.0069618061796214471 1.6643955737945908e-06 0.0034078687041305907 0.0126302535578806 -0.0026238126455993666 -0.011871187245135381 0.0051156026317477812 0.015113883298665835
+leaf_weight=18204.568539940752 1970.9670090256259 3997.4975843206048 5431.5118203917518 205.60181898158044 25169.727567266673 22143.261499967426 11672.083703227341 4813.5515459254384 1117.4155157655478 2956.3665570914745 86.704825058579445 14676.222120083869 13931.68793380633 313.11154944449663 206805.50067865849 6969.1162363290787 128345.04823071137 6502.5839084349573 395.96791373193264 5467.0219206735492 11.316859737038611 4641.0054873861372 8212.9072102233768 79734.258258577436 1040.5629563778639 1225.2314228266478 3462.9947956465185 564.79239856824279 2312.0222339034081 429.51326084136963
+leaf_count=371141 134853 13079 140507 22725 104469 53753 30578 11949 3523 8886 257 40406 37005 809 722708 23405 347880 18212 1033 11816 37 15933 31807 221804 3033 4131 12143 2543 151487 3340
+internal_value=0 -5.89375e-05 0.00304811 0.0120944 -0.000191334 -0.0012833 -0.000472905 -0.00313894 -0.00204654 -0.00835951 -0.00469027 -0.000288239 -0.00137835 -0.00403445 3.25564e-05 -0.000286647 0.000560618 -0.000382232 0.000447821 0.00121722 -0.00504101 0.000961039 0.00260012 0.000312659 0.00644437 0.00303988 -0.0029577 0.00207964 0.00710329 -0.000839883
+internal_weight=0 578322 23636.1 4488.59 554685 94378.4 65690.1 28688.3 23874.8 5201.05 4083.63 18673.7 52227.9 14244.8 460307 342549 135314 37983.1 31480.6 27610.3 3870.28 117758 33382.6 84375.3 1127.27 13462.1 3474.31 12236.9 2517.62 207235
+internal_count=2545252 2236187 511648 309065 1724539 253193 172060 81133 69184 15699 12176 53485 134808 37814 1471346 1097333 371285 96994 78782 65569 13213 374013 136276 237737 3290 37252 12180 33121 174212 726048
+cat_boundaries=0 1 2
+cat_threshold=3 4
+is_linear=0
+shrinkage=0.1
+
+
+Tree=99
+num_leaves=31
+num_cat=2
+split_feature=3 2 8 11 3 1 4 0 8 8 0 3 7 6 11 0 1 8 1 9 0 0 1 1 8 5 8 1 2 1
+split_gain=65.7874 23.79 14.1608 13.733 13.5611 14.1927 20.8936 16.9802 26.5013 25.5903 19.3049 13.8849 13.8184 15.833 13.6429 16.0565 17.1896 12.6695 13.2945 15.0486 14.7387 12.5156 20.7228 14.0202 12.4738 12.3974 11.8518 11.5242 11.5053 10.9548
+threshold=0.99654131124764023 0.013273847061427197 33.000000000000007 0 0.11221230490672088 0.0016694127336133787 0.054093567251461992 0.028277448747792856 41.000000000000007 77.000000000000014 0.0093160600921628773 0.049794110432056067 0.06186224489795919 0.74342105263157909 1 0.025878305372050017 0.00089907351431666946 79.000000000000014 0.050751290031137299 1.0000000180025095e-35 0.019445409832785164 0.0047745270537820428 0.0012811781976472443 0.00038711159616507926 79.000000000000014 0.033060109289617494 17.000000000000004 0.10557016395897574 0.00087464118449526607 0.61712286939879502
+decision_type=2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 -1 28 5 7 12 8 11 -10 -11 -3 17 -14 21 29 -17 18 19 -7 -20 22 -6 -23 -12 27 -22 -8 -2 -16
+right_child=3 4 -4 -5 14 6 25 -9 9 10 24 -13 13 -15 15 16 -18 -19 20 -21 26 23 -24 -25 -26 -27 -28 -29 -30 -31
+leaf_value=-0.0017111285089027211 -0.029800660644521134 0.0055130259818883809 -0.0075292205899115273 -0.018340686100435211 -0.0011768980495682592 -0.0023772429386807606 -0.002754691754399444 0.0085571509652987102 0.021768882217918561 0.0089359307086235591 -0.042886046195270935 -0.0011348837773776455 0.003534649866547562 0.026272948341037911 0.00087301753804386838 -0.0053974619610482159 -0.00029797863017457931 0.0044007171836270573 0.023320433896699414 0.0034812666526411276 0.1051269392288867 -0.0056556638701515819 -0.0069618061796214471 -1.6643955737945908e-06 -0.0034078687041305907 -0.0126302535578806 0.0026238126455993666 0.011871187245135381 -0.0051156026317477812 -0.015113883298665835
+leaf_weight=18204.568539940752 205.60181898158044 3997.4975843206048 5431.5118203917518 1970.9670090256259 25169.727567266673 22143.261499967426 11672.083703227341 4813.5515459254384 1117.4155157655478 2956.3665570914745 86.704825058579445 14676.222120083869 13931.68793380633 313.11154944449663 206805.50067865849 6969.1162363290787 128345.04823071137 6502.5839084349573 395.96791373193264 5467.0219206735492 11.316859737038611 4641.0054873861372 8212.9072102233768 79734.258258577436 1040.5629563778639 1225.2314228266478 3462.9947956465185 564.79239856824279 2312.0222339034081 429.51326084136963
+leaf_count=371141 22725 13079 140507 134853 104469 53753 30578 11949 3523 8886 257 40406 37005 809 722708 23405 347880 18212 1033 11816 37 15933 31807 221804 3033 4131 12143 2543 151487 3340
+internal_value=0 5.89375e-05 -0.00304811 -0.0120944 0.000191334 0.0012833 0.000472905 0.00313894 0.00204654 0.00835951 0.00469027 0.000288239 0.00137835 0.00403445 -3.25564e-05 0.000286647 -0.000560618 0.000382232 -0.000447821 -0.00121722 0.00504101 -0.000961039 -0.00260012 -0.000312659 -0.00644437 -0.00303988 0.0029577 -0.00207964 -0.00710329 0.000839883
+internal_weight=0 578322 23636.1 4488.59 554685 94378.4 65690.1 28688.3 23874.8 5201.05 4083.63 18673.7 52227.9 14244.8 460307 342549 135314 37983.1 31480.6 27610.3 3870.28 117758 33382.6 84375.3 1127.27 13462.1 3474.31 12236.9 2517.62 207235
+internal_count=2545252 2236187 511648 309065 1724539 253193 172060 81133 69184 15699 12176 53485 134808 37814 1471346 1097333 371285 96994 78782 65569 13213 374013 136276 237737 3290 37252 12180 33121 174212 726048
+cat_boundaries=0 1 2
+cat_threshold=12 4
+is_linear=0
+shrinkage=0.1
+
+
+end of trees
+
+feature_importances:
+pred_2=798
+query_count=538
+pred_1=342
+pred_0=304
+fold=246
+pred_3=212
+label_2=160
+label_3=154
+has_isbn=92
+label_0=74
+is_isbn=60
+label_1=20
+
+parameters:
+[boosting: gbdt]
+[objective: multiclass]
+[metric: multi_logloss]
+[tree_learner: serial]
+[device_type: cpu]
+[data: ]
+[valid: ]
+[num_iterations: 50]
+[learning_rate: 0.1]
+[num_leaves: 31]
+[num_threads: 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]
+[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]
+[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: 11]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[objective_seed: 5]
+[num_class: 2]
+[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: ]
+[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:null
diff --git a/utils/lgb_predict_task2.py b/utils/lgb_predict_task2.py
index c899e3926074ce048b95de232f8bb75cdff3d80f..b133c90e0f138d21f198c95fc3f2dbc7a1ff9f74 100755
--- a/utils/lgb_predict_task2.py
+++ b/utils/lgb_predict_task2.py
@@ -7,7 +7,7 @@ __MAP__ = ["irrelevant", "complement", "substitute", "exact"]
LGB_CONFIG = {
"us": {
"product_feat": "/models/us-product-feat.csv",
- "model_file": "/models/lgb-us-task-2.txt",
+ "model_file": "/models/lgb-us-task-2-only.txt",
"features": ['pred_0','pred_1','pred_2','pred_3',
'label_0','label_1','label_2','label_3',
'query_count','is_isbn','has_isbn','fold']
diff --git a/utils/lgb_predict_task3.py b/utils/lgb_predict_task3.py
index 7d6daf60f30472ba17a033898e40e00ab27469d3..16d877b885e8866545c75e44b421f2a83423060c 100755
--- a/utils/lgb_predict_task3.py
+++ b/utils/lgb_predict_task3.py
@@ -4,7 +4,7 @@ import pandas as pd
LGB_CONFIG = {
"us": {
"product_feat": "/models/us-product-feat.csv",
- "model_file": "/models/lgb-us-task-3.txt",
+ "model_file": "/models/lgb-us-task-3-only.txt",
"features": ['pred_0','pred_1','pred_2','pred_3',
'label_0','label_1','label_2','label_3',
'query_count','is_isbn','has_isbn','fold']