From 72e50985a669e013c19f70bb707aef38afedcaa4 Mon Sep 17 00:00:00 2001 From: Eric Ge Date: Fri, 15 Nov 2024 08:42:04 +0800 Subject: [PATCH] [feature] Dual Augmented Two-Towers (#503) * dat model implementation * DAT documentation * DAT unit test --- README.md | 2 +- docs/images/models/DAT.png | Bin 0 -> 260656 bytes docs/source/models/dssm_derivatives.md | 55 +++- easy_rec/python/compat/early_stopping.py | 2 +- easy_rec/python/model/dat.py | 138 +++++++++ easy_rec/python/model/match_model.py | 13 + easy_rec/python/model/rank_model.py | 16 +- easy_rec/python/protos/dat.proto | 21 ++ easy_rec/python/protos/easy_rec_model.proto | 2 + easy_rec/python/test/train_eval_test.py | 8 +- requirements/docs.txt | 2 +- samples/model_config/dat_on_taobao.config | 321 ++++++++++++++++++++ setup.cfg | 2 +- 13 files changed, 571 insertions(+), 11 deletions(-) create mode 100644 docs/images/models/DAT.png create mode 100644 easy_rec/python/model/dat.py create mode 100644 easy_rec/python/protos/dat.proto create mode 100644 samples/model_config/dat_on_taobao.config diff --git a/README.md b/README.md index ecab7df50..e011a1d01 100644 --- a/README.md +++ b/README.md @@ -117,7 +117,7 @@ If EasyRec is useful for your research, please cite: ### Join Us -- DingDing Group: 32260796. click [this url](https://page.dingtalk.com/wow/z/dingtalk/simple/ddhomedownload?action=joingroup&code=v1,k1,MwaiOIY1Tb2W+onmBBumO7sQsdDOYjBmv6FXC6wTGns=&_dt_no_comment=1&origin=11#/ ) or scan QrCode to join![dinggroup1.png](docs/images/qrcode/dinggroup1.png) +- DingDing Group: 32260796. click [this url](https://page.dingtalk.com/wow/z/dingtalk/simple/ddhomedownload?action=joingroup&code=v1,k1,MwaiOIY1Tb2W+onmBBumO7sQsdDOYjBmv6FXC6wTGns=&_dt_no_comment=1&origin=11#/) or scan QrCode to join![dinggroup1.png](docs/images/qrcode/dinggroup1.png) - DingDing Group2: 37930014162, click [this 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zzg%Kjx!54H6&*N8Y6gfZDt8B~`^dofCa?@OgYR9f7ftt_q(`D> zZPT}=xvwOLwbGM)VHlh8*r})IL_%)tt!H&v_t!7*CJkq2dWz~6x{DKPelP7bBJnZqupzFz06I4M077143zR?Okr;joMN$sH2iTBg{YVPYD|DfQYjFDXJ> z>6?BD;M-Up(e`|EQ*B){{z+gljq+E$#|e7sGa-%YsXqM@BqE~Ep!;KObQyb5RkOyC zlV<}=_AZjTJ5N=QjBNU@JODmc;WajE!q1GTP9$r;f2`Ha`Qez5YFI%e*nOeYBM4a! zJ@Y{k7eMEl1WiC?W)i3^>}p$Bn$lhN&;6e#K_gBZfM*k#0L8Twt8B#`OXBDxi?79{ zHPr4)u|ZNuo2S~Cw;wD(xUM=C#Rl-bwjc}~!_x9(UNaTU`w`I?g?ybeX$KYbcE;lV zdXEC`x6;h^bZk3m(ui(V#ZAL|`p_Hy2p4$FUKqNyHuiJ?vyq}`Tsr{3Px<<{KnJ3A zoAWIRo2PKRJDjI^A{HI>x$3Dbh0KF}&$qoN@#@OGCr4oRj^>@q_XJ#498Jcm0NXbi zZ@|9Uxk{XNetU@k0UZ=^Ugu9sN9-j#@ISM7z3EK)hxKs}8FZoNJvRiyj{!9=#UbqM zRgir@=FU_p(JSERv(ov+;Y0zzJGwJnlY+h7YcYku5CAC$VGmKT zFT>95@xLtix_u*|i3w8~B< z3dNoGi>!CI)9&z+l?}F8HmjH8ocu_P7$QRhf2=*0z0}=7 zUHt6MDxO=VWj|6)eFP75~_m{l|#2utrACASIN+iyha*II)_&KCUz^ra3OPoJDx%v`Ds3Y(DYd<+B`mfE0ws=_vUGq6koF zx$`bf&I%9^Ac<-KJO6nVym?H**V&N*$Mr6FcU`T|tVlF0f&@3&;fFkmlcc2$nzhEA z@FrK!^o$2G{4Hh)Imkir2zm0t-@*wwNfV(a>6A&TV>{vTbZ|~Xc&Kedi!nu=esPz! zzX&E3O3E3{&mJR6X4U-Xj9mEf)_Z>FNQo|1?8h}W}eN^>4 zjnuGP+%DI@v!gcw;)qu+5h?sLmQ6-(DeiF$*_@1%#F91#IJfy1g|0g@^3Axbi>Oh; zfy;?TGf0ov(olq_eF`FUdMa5aBj1a|DxIg4B1kFvamT#<~i-~R!5O= '2.0': + tf = tf.compat.v1 + + +class DAT(MatchModel): + """Dual Augmented Two-tower Model.""" + + def __init__(self, + model_config, + feature_configs, + features, + labels=None, + is_training=False): + super(DAT, self).__init__(model_config, feature_configs, features, labels, + is_training) + assert self._model_config.WhichOneof('model') == 'dat', \ + 'invalid model config: %s' % self._model_config.WhichOneof('model') + + feature_group_names = [ + fg.group_name for fg in self._model_config.feature_groups + ] + assert 'user' in feature_group_names, 'user feature group not found' + assert 'item' in feature_group_names, 'item feature group not found' + assert 'user_id_augment' in feature_group_names, 'user_id_augment feature group not found' + assert 'item_id_augment' in feature_group_names, 'item_id_augment feature group not found' + + self._model_config = self._model_config.dat + assert isinstance(self._model_config, DATConfig) + + self.user_tower = copy_obj(self._model_config.user_tower) + self.user_deep_feature, _ = self._input_layer(self._feature_dict, 'user') + self.user_augmented_vec, _ = self._input_layer(self._feature_dict, + 'user_id_augment') + + self.item_tower = copy_obj(self._model_config.item_tower) + self.item_deep_feature, _ = self._input_layer(self._feature_dict, 'item') + self.item_augmented_vec, _ = self._input_layer(self._feature_dict, + 'item_id_augment') + + self._user_tower_emb = None + self._item_tower_emb = None + + def build_predict_graph(self): + num_user_dnn_layer = len(self.user_tower.dnn.hidden_units) + last_user_hidden = self.user_tower.dnn.hidden_units.pop() + user_dnn = dnn.DNN(self.user_tower.dnn, self._l2_reg, 'user_dnn', + self._is_training) + + user_tower_feature = tf.concat( + [self.user_deep_feature, self.user_augmented_vec], axis=-1) + user_tower_emb = user_dnn(user_tower_feature) + user_tower_emb = tf.layers.dense( + inputs=user_tower_emb, + units=last_user_hidden, + kernel_regularizer=self._l2_reg, + name='user_dnn/dnn_%d' % (num_user_dnn_layer - 1)) + + num_item_dnn_layer = len(self.item_tower.dnn.hidden_units) + last_item_hidden = self.item_tower.dnn.hidden_units.pop() + item_dnn = dnn.DNN(self.item_tower.dnn, self._l2_reg, 'item_dnn', + self._is_training) + + item_tower_feature = tf.concat( + [self.item_deep_feature, self.item_augmented_vec], axis=-1) + item_tower_emb = item_dnn(item_tower_feature) + item_tower_emb = tf.layers.dense( + inputs=item_tower_emb, + units=last_item_hidden, + kernel_regularizer=self._l2_reg, + name='item_dnn/dnn_%d' % (num_item_dnn_layer - 1)) + + user_tower_emb = self.norm(user_tower_emb) + item_tower_emb = self.norm(item_tower_emb) + temperature = self._model_config.temperature + + y_pred = self.sim(user_tower_emb, item_tower_emb) / temperature + + if self._is_point_wise: + raise ValueError('Currently DAT model only supports list wise mode.') + + if self._loss_type == LossType.CLASSIFICATION: + raise ValueError( + 'Currently DAT model only supports SOFTMAX_CROSS_ENTROPY loss.') + elif self._loss_type == LossType.SOFTMAX_CROSS_ENTROPY: + y_pred = self._mask_in_batch(y_pred) + self._prediction_dict['logits'] = y_pred + self._prediction_dict['probs'] = tf.nn.softmax(y_pred) + else: + self._prediction_dict['y'] = y_pred + + self._prediction_dict['user_tower_emb'] = user_tower_emb + self._prediction_dict['item_tower_emb'] = item_tower_emb + self._prediction_dict['user_emb'] = tf.reduce_join( + tf.as_string(user_tower_emb), axis=-1, separator=',') + self._prediction_dict['item_emb'] = tf.reduce_join( + tf.as_string(item_tower_emb), axis=-1, separator=',') + + augmented_p_u = tf.stop_gradient(user_tower_emb) + augmented_p_i = tf.stop_gradient(item_tower_emb) + + self._prediction_dict['augmented_p_u'] = augmented_p_u + self._prediction_dict['augmented_p_i'] = augmented_p_i + + self._prediction_dict['augmented_a_u'] = self.user_augmented_vec + self._prediction_dict['augmented_a_i'] = self.item_augmented_vec + + return self._prediction_dict + + def get_outputs(self): + if self._loss_type == LossType.CLASSIFICATION: + raise ValueError( + 'Currently DAT model only supports SOFTMAX_CROSS_ENTROPY loss.') + elif self._loss_type == LossType.SOFTMAX_CROSS_ENTROPY: + self._prediction_dict['logits'] = tf.squeeze( + self._prediction_dict['logits'], axis=-1) + self._prediction_dict['probs'] = tf.nn.sigmoid( + self._prediction_dict['logits']) + return [ + 'logits', 'probs', 'user_emb', 'item_emb', 'user_tower_emb', + 'item_tower_emb', 'augmented_p_u', 'augmented_p_i', 'augmented_a_u', + 'augmented_a_i' + ] + else: + raise ValueError('invalid loss type: %s' % str(self._loss_type)) + + def build_output_dict(self): + output_dict = super(DAT, self).build_output_dict() + return output_dict diff --git a/easy_rec/python/model/match_model.py b/easy_rec/python/model/match_model.py index 16d165603..24cd1bcbe 100644 --- a/easy_rec/python/model/match_model.py +++ b/easy_rec/python/model/match_model.py @@ -227,6 +227,19 @@ def _build_list_wise_loss_graph(self): # if pos_simi < 0, produce loss reg_pos_loss = tf.nn.relu(-pos_simi) self._loss_dict['reg_pos_loss'] = tf.reduce_mean(reg_pos_loss) + + # the AMM loss for DAT model + if all([ + k in self._prediction_dict.keys() for k in + ['augmented_p_u', 'augmented_p_i', 'augmented_a_u', 'augmented_a_i'] + ]): + self._loss_dict['amm_loss_u'] = tf.reduce_mean( + tf.square(self._prediction_dict['augmented_a_u'] - + self._prediction_dict['augmented_p_i'][:batch_size])) + self._loss_dict['amm_loss_i'] = tf.reduce_mean( + tf.square(self._prediction_dict['augmented_a_i'][:batch_size] - + self._prediction_dict['augmented_p_u'])) + else: raise ValueError('invalid loss type: %s' % str(self._loss_type)) return self._loss_dict diff --git a/easy_rec/python/model/rank_model.py b/easy_rec/python/model/rank_model.py index 640f52502..dc3771daf 100644 --- a/easy_rec/python/model/rank_model.py +++ b/easy_rec/python/model/rank_model.py @@ -158,7 +158,9 @@ def build_rtp_output_dict(self): 'failed to build RTP rank_predict output: classification model ' + "expect 'probs' prediction, which is not found. Please check if" + ' build_predict_graph() is called.') - elif loss_types & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}: + elif loss_types & { + LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS + }: if 'y' in self._prediction_dict: forwarded = self._prediction_dict['y'] else: @@ -379,7 +381,9 @@ def _build_metric_impl(self, metric.recall_at_topk.topk) elif metric.WhichOneof('metric') == 'mean_absolute_error': label = tf.to_float(self._labels[label_name]) - if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}: + if loss_type & { + LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS + }: metric_dict['mean_absolute_error' + suffix] = metrics_tf.mean_absolute_error( label, self._prediction_dict['y' + suffix]) @@ -391,7 +395,9 @@ def _build_metric_impl(self, assert False, 'mean_absolute_error is not supported for this model' elif metric.WhichOneof('metric') == 'mean_squared_error': label = tf.to_float(self._labels[label_name]) - if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}: + if loss_type & { + LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS + }: metric_dict['mean_squared_error' + suffix] = metrics_tf.mean_squared_error( label, self._prediction_dict['y' + suffix]) @@ -403,7 +409,9 @@ def _build_metric_impl(self, assert False, 'mean_squared_error is not supported for this model' elif metric.WhichOneof('metric') == 'root_mean_squared_error': label = tf.to_float(self._labels[label_name]) - if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}: + if loss_type & { + LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS + }: metric_dict['root_mean_squared_error' + suffix] = metrics_tf.root_mean_squared_error( label, self._prediction_dict['y' + suffix]) diff --git a/easy_rec/python/protos/dat.proto b/easy_rec/python/protos/dat.proto new file mode 100644 index 000000000..3b30735b6 --- /dev/null +++ b/easy_rec/python/protos/dat.proto @@ -0,0 +1,21 @@ +syntax = "proto2"; +package protos; + +import "easy_rec/python/protos/dnn.proto"; +import "easy_rec/python/protos/simi.proto"; + + +message DATTower { + required string id = 1; + required DNN dnn = 2; +}; + + +message DAT { + required DATTower user_tower = 1; + required DATTower item_tower = 2; + required float l2_regularization = 3 [default = 1e-4]; + optional Similarity simi_func = 4 [default=COSINE]; + required bool ignore_in_batch_neg_sam = 5 [default = false]; + optional float temperature = 6 [default = 1.0]; +} diff --git a/easy_rec/python/protos/easy_rec_model.proto b/easy_rec/python/protos/easy_rec_model.proto index 4bd55aa0d..87cd6754d 100644 --- a/easy_rec/python/protos/easy_rec_model.proto +++ b/easy_rec/python/protos/easy_rec_model.proto @@ -29,6 +29,7 @@ import "easy_rec/python/protos/tower.proto"; import "easy_rec/python/protos/pdn.proto"; import "easy_rec/python/protos/dssm_senet.proto"; import "easy_rec/python/protos/simi.proto"; +import "easy_rec/python/protos/dat.proto"; // for input performance test message DummyModel { } @@ -114,6 +115,7 @@ message EasyRecModel { CoMetricLearningI2I metric_learning = 204; PDN pdn = 205; DSSM_SENet dssm_senet = 206; + DAT dat = 207; MMoE mmoe = 301; ESMM esmm = 302; diff --git a/easy_rec/python/test/train_eval_test.py b/easy_rec/python/test/train_eval_test.py index 83656f2a0..a82455cff 100644 --- a/easy_rec/python/test/train_eval_test.py +++ b/easy_rec/python/test/train_eval_test.py @@ -7,11 +7,11 @@ import threading import time import unittest -from distutils.version import LooseVersion import numpy as np import six import tensorflow as tf +from distutils.version import LooseVersion from tensorflow.python.platform import gfile from easy_rec.python.main import predict @@ -1286,6 +1286,12 @@ def test_xdeefm_backbone_on_taobao(self): self._test_dir) self.assertTrue(self._success) + @unittest.skipIf(gl is None, 'graphlearn is not installed') + def test_dat_on_taobao(self): + self._success = test_utils.test_single_train_eval( + 'samples/model_config/dat_on_taobao.config', self._test_dir) + self.assertTrue(self._success) + if __name__ == '__main__': tf.test.main() diff --git a/requirements/docs.txt b/requirements/docs.txt index 596bd527b..9e81da2c6 100644 --- a/requirements/docs.txt +++ b/requirements/docs.txt @@ -5,4 +5,4 @@ recommonmark==0.6.0 sphinx==5.1.1 sphinx_markdown_tables==0.0.17 sphinx_rtd_theme -tensorflow-probability==0.11.0 \ No newline at end of file +tensorflow-probability==0.11.0 diff --git a/samples/model_config/dat_on_taobao.config b/samples/model_config/dat_on_taobao.config new file mode 100644 index 000000000..2a7070557 --- /dev/null +++ b/samples/model_config/dat_on_taobao.config @@ -0,0 +1,321 @@ +train_input_path: "data/test/tb_data/taobao_train_data" +eval_input_path: "data/test/tb_data/taobao_test_data" +model_dir: "experiments/dat_taobao_ckpt" + +train_config { + log_step_count_steps: 200 + optimizer_config: { + adam_optimizer: { + learning_rate: { + exponential_decay_learning_rate { + initial_learning_rate: 0.001 + decay_steps: 1000 + decay_factor: 0.5 + min_learning_rate: 0.00001 + } + } + } + use_moving_average: false + } + save_checkpoints_steps: 4000 + sync_replicas: false + num_steps: 100 +} + +eval_config { + metrics_set: { + recall_at_topk { + topk: 50 + } + } + metrics_set: { + recall_at_topk { + topk: 10 + } + } + metrics_set: { + recall_at_topk { + topk: 5 + } + } + metrics_set: { + recall_at_topk { + topk: 1 + } + } +} + +data_config { + input_fields { + input_name:'clk' + input_type: INT32 + } + input_fields { + input_name:'buy' + input_type: INT32 + } + input_fields { + input_name: 'pid' + input_type: STRING + } + input_fields { + input_name: 'adgroup_id' + input_type: STRING + } + input_fields { + input_name: 'cate_id' + input_type: STRING + } + input_fields { + input_name: 'campaign_id' + input_type: STRING + } + input_fields { + input_name: 'customer' + input_type: STRING + } + input_fields { + input_name: 'brand' + input_type: STRING + } + input_fields { + input_name: 'user_id' + input_type: STRING + } + input_fields { + input_name: 'cms_segid' + input_type: STRING + } + input_fields { + input_name: 'cms_group_id' + input_type: STRING + } + input_fields { + input_name: 'final_gender_code' + input_type: STRING + } + input_fields { + input_name: 'age_level' + input_type: STRING + } + input_fields { + input_name: 'pvalue_level' + input_type: STRING + } + input_fields { + input_name: 'shopping_level' + input_type: STRING + } + input_fields { + input_name: 'occupation' + input_type: STRING + } + input_fields { + input_name: 'new_user_class_level' + input_type: STRING + } + input_fields { + input_name: 'tag_category_list' + input_type: STRING + } + input_fields { + input_name: 'tag_brand_list' + input_type: STRING + } + input_fields { + input_name: 'price' + input_type: INT32 + } + + label_fields: 'clk' + batch_size: 512 + prefetch_size: 32 + input_type: CSVInput + + negative_sampler { + input_path: 'data/test/tb_data/taobao_ad_feature_gl' + num_sample: 2048 + num_eval_sample: 2048 + attr_fields: 'adgroup_id' + attr_fields: 'cate_id' + attr_fields: 'campaign_id' + attr_fields: 'customer' + attr_fields: 'brand' + item_id_field: 'adgroup_id' + } +} + +feature_config: { + features: { + input_names: 'pid' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'adgroup_id' + feature_type: IdFeature + embedding_dim: 32 + hash_bucket_size: 100000 + } + features: { + input_names: 'cate_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10000 + } + features: { + input_names: 'campaign_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'customer' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'brand' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100000 + } + features: { + input_names: 'user_id' + feature_type: IdFeature + embedding_dim: 32 + hash_bucket_size: 100000 + } + features: { + input_names: 'cms_segid' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100 + } + features: { + input_names: 'cms_group_id' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 100 + } + features: { + input_names: 'final_gender_code' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'age_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'pvalue_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'shopping_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'occupation' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'new_user_class_level' + feature_type: IdFeature + embedding_dim: 16 + hash_bucket_size: 10 + } + features: { + input_names: 'tag_category_list' + feature_type: TagFeature + separator: '|' + hash_bucket_size: 100000 + embedding_dim: 16 + } + features: { + input_names: 'tag_brand_list' + feature_type: TagFeature + separator: '|' + hash_bucket_size: 100000 + embedding_dim: 16 + } + features: { + input_names: 'price' + feature_type: IdFeature + embedding_dim: 16 + num_buckets: 50 + } +} +model_config:{ + model_class: "DAT" + feature_groups: { + group_name: 'user' + feature_names: 'user_id' + feature_names: 'cms_segid' + feature_names: 'cms_group_id' + feature_names: 'age_level' + feature_names: 'pvalue_level' + feature_names: 'shopping_level' + feature_names: 'occupation' + feature_names: 'new_user_class_level' + feature_names: 'tag_category_list' + feature_names: 'tag_brand_list' + wide_deep:DEEP + } + feature_groups: { + group_name: "item" + feature_names: 'adgroup_id' + feature_names: 'cate_id' + feature_names: 'campaign_id' + feature_names: 'customer' + feature_names: 'brand' + #feature_names: 'price' + #feature_names: 'pid' + wide_deep:DEEP + } + feature_groups: { + group_name: 'user_id_augment' + feature_names: 'user_id' + wide_deep:DEEP + } + feature_groups: { + group_name: 'item_id_augment' + feature_names: 'adgroup_id' + wide_deep:DEEP + } + + dat { + user_tower { + id: "user_id" + dnn { + hidden_units: [ 128, 32] + # dropout_ratio : [0.1, 0.1, 0.1, 0.1] + } + } + item_tower { + id: "adgroup_id" + dnn { + hidden_units: [ 128, 32] + } + } + simi_func: COSINE + temperature: 0.01 + l2_regularization: 1e-6 + } + embedding_regularization: 5e-5 + loss_type: SOFTMAX_CROSS_ENTROPY +} + +export_config { +} diff --git a/setup.cfg b/setup.cfg index d8ed85f21..f0223c47a 100644 --- a/setup.cfg +++ b/setup.cfg @@ -10,7 +10,7 @@ multi_line_output = 7 force_single_line = true known_standard_library = setuptools known_first_party = easy_rec -known_third_party = absl,common_io,docutils,eas_prediction,faiss,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,scipy,six,sklearn,sparse_operation_kit,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,tensorflow_probability,yaml +known_third_party = absl,common_io,distutils,docutils,eas_prediction,faiss,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,scipy,six,sklearn,sparse_operation_kit,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,tensorflow_probability,yaml no_lines_before = LOCALFOLDER default_section = THIRDPARTY skip = easy_rec/python/protos