diff --git a/aicrowd.json b/aicrowd.json
index 9561d4940d12f39e3be75dfa19ee22781c371e22..f678d4f7d1fad9b9f1a26cb4d421f75eb2f98d4c 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_1_query-product_ranking",
"gpu": true,
"authors": ["aicrowd-username"],
- "description": "task_2_multiclass_product_classification, task_1_query-product_ranking",
+ "description": "task_2_multiclass_product_classification, task_3_product_substitute_identification",
"license": "MIT"
}
\ No newline at end of file
diff --git a/models/lgb-es-jp-task-1.txt b/models/lgb-es-jp-task-1.txt
new file mode 100755
index 0000000000000000000000000000000000000000..d9e9603227310a8cc1be0eb6b084794c3eb4515c
--- /dev/null
+++ b/models/lgb-es-jp-task-1.txt
@@ -0,0 +1,1093 @@
+tree
+version=v3
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+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=12
+objective=lambdarank
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+left_child=1 3 6 13 5 10 -2 -3 15 -10 12 25 -4 14 -1 19 17 18 -17 23 -21 22 -22 -6 -19 -5 -7 -12 -8 -30
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+internal_value=0 -0.00612633 0.0122857 -0.0115651 0.0213071 0.0265352 0.00903919 -0.000662142 0.0141099 0.0613787 0.0239522 -0.0100403 0.0386642 -0.0199177 0.00675855 0.0125933 0.0270216 0.0227453 0.0264871 0.00855841 0.011902 0.0107547 -0.0398296 -0.0162401 -0.0789517 -0.00678034 0.0562141 0.020166 0.00367686 -0.0402135
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+is_linear=0
+shrinkage=0.1
+
+
+end of trees
+
+feature_importances:
+Column_3=363
+Column_0=296
+Column_8=249
+Column_1=174
+Column_2=151
+Column_4=55
+Column_6=53
+Column_12=43
+Column_7=38
+Column_10=35
+Column_5=29
+Column_9=9
+Column_11=5
+
+parameters:
+[boosting: gbdt]
+[objective: lambdarank]
+[metric: ndcg]
+[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: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[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: 0,0.01,0.10000000000000001,1]
+[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-1.txt b/models/lgb-us-task-1.txt
new file mode 100755
index 0000000000000000000000000000000000000000..00b4c1e7ca1c29b8cdfe9c204422303fb31ecde3
--- /dev/null
+++ b/models/lgb-us-task-1.txt
@@ -0,0 +1,1126 @@
+tree
+version=v3
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=11
+objective=lambdarank
+feature_names=pred_0 pred_1 pred_2 pred_3 label_0 label_1 label_2 label_3 query_count is_isbn has_isbn model
+feature_infos=[4.9661470947019742e-05:0.99479547906732435] [9.6694022295222793e-06:0.94371404810966675] [0.00027881828080444476:0.98840956748104036] [0.0032321330737968227:0.99930247880926459] [0:1] [0:1] [0:1] [0:1] [1:188] [0:1] [0:1] -1:0:1:2
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+
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+num_leaves=31
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+internal_value=0 0.00913092 -0.00848421 0.00519658 0.0179028 -0.0043835 -0.0133093 -0.0124743 -0.00656508 0.0221127 0.0206815 -0.0138166 -0.0148902 -0.00845201 -0.0270502 -0.00648514 0.050357 0.081345 -0.0204851 -0.0535335 -0.0881383 0.0204784 0.0598929 -0.00748913 0.0154943 -0.0173176 0.00262839 0.00412806 -0.0118641 0.00935798
+internal_weight=0 33483.1 36035.3 23115.4 10367.7 19480 16555.4 15171.2 11887.1 5667.25 4926.84 12282.5 10234.3 2048.21 195.893 1852.31 32.1491 24.7629 5.74729 7.38619 41.1625 5.54572 3.86567 1820.16 1730.12 5678.82 14293.9 9592.43 4555.44 8821.46
+internal_count=1259313 511018 748295 352561 158457 372325 375970 341606 238396 87303 77377 286482 253398 33084 3888 29196 357 250 81 107 888 69 53 28839 27526 127590 221358 147028 125808 131203
+cat_boundaries=0 1 2
+cat_threshold=2 1
+is_linear=0
+shrinkage=0.1
+
+
+end of trees
+
+feature_importances:
+pred_3=424
+pred_0=240
+query_count=236
+pred_1=112
+pred_2=110
+label_3=100
+label_2=75
+has_isbn=67
+label_0=61
+label_1=36
+model=22
+is_isbn=17
+
+parameters:
+[boosting: gbdt]
+[objective: lambdarank]
+[metric: ndcg]
+[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: 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: 0,0.01,0.10000000000000001,1]
+[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_task1.py b/utils/lgb_predict_task1.py
index 9c3f5560843e5a46c8f4320dd2734a0fca548929..4e8ae31dded1ca1ae79136460a03c61eac7df804 100755
--- a/utils/lgb_predict_task1.py
+++ b/utils/lgb_predict_task1.py
@@ -10,7 +10,7 @@ LGB_CONFIG = {
"model_file": "/models/lgb-us-task-1.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']
+ 'query_count','is_isbn','has_isbn', 'model']
},
"jp": {
"product_feat": "/models/es-jp-product-feat-task-1-only.csv",
@@ -30,7 +30,7 @@ LGB_CONFIG = {
LOCALE_MAP = {'jp':0, 'es':1, 'us':2}
COL_NAME = "product_id"
-WEIGHT_MAP = {0:0.5, 1:0.5, 2:0.5, 3:0.5, 4:0.25, 5:0.25, 6:0.5, 7:0.5} # Need modification
+WEIGHT_MAP = {0:1, 1:1, 2:1, 3:1, 4:1, 5:1}
def lgb_predict_task1(df, locale):
df = df.reset_index(drop=True)
@@ -47,6 +47,10 @@ def lgb_predict_task1(df, locale):
lambda x: t.get(x, 0)
)
+ s = df[['label_0','label_1','label_2','label_3']].values.sum(1)
+ s[s==0] =1
+ df[['label_0','label_1','label_2','label_3']] = df[['label_0','label_1','label_2','label_3']].values/s.reshape(-1,1)
+
temp = (
df.groupby("query_id")["product_id"]
.count()
diff --git a/utils/lgb_predict_task2.py b/utils/lgb_predict_task2.py
index c6f325981ddd688986b8a1b1cc0f26ceb42673a3..81adbf729f6ba0208fd2179b04b9058d4bae2d8d 100755
--- a/utils/lgb_predict_task2.py
+++ b/utils/lgb_predict_task2.py
@@ -46,6 +46,11 @@ def lgb_predict_task2(df, locale):
df[f"label_{i}"] = df.product_id.apply(
lambda x: t.get(x, 0)
)
+
+ s = df[['label_0','label_1','label_2','label_3']].values.sum(1)
+ s[s==0] =1
+ df[['label_0','label_1','label_2','label_3']] = df[['label_0','label_1','label_2','label_3']].values/s.reshape(-1,1)
+
temp = (
df.groupby("query")["example_id"]
.nunique()
diff --git a/utils/lgb_predict_task3.py b/utils/lgb_predict_task3.py
index f972a4b3097bd79002555e3ec65dcc871e3d48b4..adfd2c5cd422a0955ddc0ddd16e1898ecededc2e 100755
--- a/utils/lgb_predict_task3.py
+++ b/utils/lgb_predict_task3.py
@@ -44,6 +44,11 @@ def lgb_predict_task3(df, locale):
df[f"label_{i}"] = df.product_id.apply(
lambda x: t.get(x, 0)
)
+
+ s = df[['label_0','label_1','label_2','label_3']].values.sum(1)
+ s[s==0] =1
+ df[['label_0','label_1','label_2','label_3']] = df[['label_0','label_1','label_2','label_3']].values/s.reshape(-1,1)
+
temp = (
df.groupby("query")["example_id"]
.nunique()
diff --git a/utils/run_task1.py b/utils/run_task1.py
index 09f9bf8725922e8353facf02e11b30254214d54b..4a0dd909976d761983ab82ae6b21a7799c05f7aa 100755
--- a/utils/run_task1.py
+++ b/utils/run_task1.py
@@ -24,8 +24,8 @@ CONFIG = {
"product": "/models/product.h5",
"key": "us",
"type": "deberta",
- "fold_offset": 0, # fold 0, 1
- "description": False,
+ "fold_offset": 0,
+ "description":False,
},
{
"clean": DeBertaCleanV2,
@@ -43,27 +43,8 @@ CONFIG = {
"product": "/models/cocolm.h5",
"key": "us",
"type": "cocolm",
- "fold_offset": 2, # fold 2, 3
- "description": False,
- },
- {
- "clean": DeBertaCleanV2,
- "encode": {
- "query": 48360,
- "product_title": 14691,
- "product_id": 2688,
- "index": 45673,
- "product_description": 41602,
- "product_bullet_point": 47977,
- "product_brand": 42452,
- "product_color_name": 44287,
- },
- "model": ["/models/us-distilbart-two-fold-0-fp16.onnx" ,"/models/us-distilbart-two-fold-1-fp16.onnx"],
- "product": "/models/distilbart.h5",
- "key": "us",
- "type": "distilbart",
- "fold_offset": 4, # fold 4, 5
- "description": False,
+ "fold_offset": 2,
+ "description":False,
},
{
"clean": DeBertaCleanV2,
@@ -77,12 +58,12 @@ CONFIG = {
"product_brand": 4609,
"product_color_name": 3225,
},
- "model": ["/models/us-bigbird-des-0-fp16.onnx", "/models/us-bigbird-des-1-fp16.onnx"],
+ "model": ["/models/us-bigbird-kd-0-fp16.onnx", "/models/us-bigbird-kd-1-fp16.onnx"],
"product": "/models/bigbird.h5",
"key": "us",
"type": "bigbird",
"tokenizer": "/models/bigbird.model",
- "fold_offset": 6, # 6, 7
+ "fold_offset": 4,
"description":True,
},
],
@@ -100,12 +81,12 @@ CONFIG = {
"product_color_name": 2551,
},
"tokenizer": "/models/spm-jp-es.model",
- "model": ['/models/us-es-jp-mdeberta-0-fp16.onnx', '/models/us-es-jp-mdeberta-1-fp16.onnx'],
+ "model":['/models/us-es-jp-mdeberta-des-0-fp16.onnx', '/models/us-es-jp-mdeberta-des-1-fp16.onnx'],
"product": "/models/product.h5",
"key": "jp",
"type": "mdeberta",
"fold_offset": 0,
- "description":False,
+ "description":True,
},
],
"es": [
@@ -122,16 +103,17 @@ CONFIG = {
"product_color_name": 6776,
},
"tokenizer": "/models/spm-jp-es.model",
- "model": ['/models/us-es-jp-mdeberta-0-fp16.onnx', '/models/us-es-jp-mdeberta-1-fp16.onnx'],
+ "model":['/models/us-es-jp-mdeberta-des-0-fp16.onnx', '/models/us-es-jp-mdeberta-des-1-fp16.onnx'],
"product": "/models/product.h5",
"key": "es",
"type": "mdeberta",
"fold_offset": 0,
- "description":False,
+ "description": True,
},
],
}
+
class Task1Predictor(BasePredictor):
def prediction_setup(self):
"""To be implemented by the participants.
@@ -173,6 +155,7 @@ class Task1Predictor(BasePredictor):
pred = onnx_predict_task1(sub_test, config)
pred[list(range(4))] = softmax(pred[list(range(4))].values, 1) # type: ignore
pred.columns = ["query_id", "product_id", "fold"] + ["pred_0",'pred_1','pred_2','pred_3']
+ pred['model'] = i
onnx_pred.append(pred.copy())
progress += 0.1
register_progress(progress)