From 353aa0679dc1171e27a4aa62b426a2f1da4396db Mon Sep 17 00:00:00 2001 From: Jason Zan Date: Wed, 5 May 2021 18:29:27 +0800 Subject: [PATCH 1/4] Add IFM and DIFM model (#353) Add IFM and DIFM model --- README.md | 2 + deepctr/feature_column.py | 11 ++++- deepctr/layers/__init__.py | 19 ++++---- deepctr/layers/interaction.py | 5 ++- deepctr/models/__init__.py | 4 +- deepctr/models/difm.py | 83 +++++++++++++++++++++++++++++++++++ deepctr/models/ifm.py | 74 +++++++++++++++++++++++++++++++ tests/models/DIFM_test.py | 22 ++++++++++ tests/models/IFM_test.py | 24 ++++++++++ 9 files changed, 231 insertions(+), 13 deletions(-) create mode 100644 deepctr/models/difm.py create mode 100644 deepctr/models/ifm.py create mode 100644 tests/models/DIFM_test.py create mode 100644 tests/models/IFM_test.py diff --git a/README.md b/README.md index 7404a224..a9e8a1f5 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,9 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star | FiBiNET | [RecSys 2019][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction](https://arxiv.org/pdf/1905.09433.pdf) | | FLEN | [arxiv 2019][FLEN: Leveraging Field for Scalable CTR Prediction](https://arxiv.org/pdf/1911.04690.pdf) | | BST | [DLP-KDD 2019][Behavior sequence transformer for e-commerce recommendation in Alibaba](https://arxiv.org/pdf/1905.06874.pdf) | +| IFM | [IJCAI 2019][An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/Proceedings/2019/0203.pdf) | | DCN V2 | [arxiv 2020][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535) | +| DIFM | [IJCAI 2020][A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/Proceedings/2020/0434.pdf) | ## Citation diff --git a/deepctr/feature_column.py b/deepctr/feature_column.py index 69f55b09..cb54713b 100644 --- a/deepctr/feature_column.py +++ b/deepctr/feature_column.py @@ -1,9 +1,10 @@ +import tensorflow as tf from collections import namedtuple, OrderedDict from copy import copy from itertools import chain from tensorflow.python.keras.initializers import RandomNormal, Zeros -from tensorflow.python.keras.layers import Input +from tensorflow.python.keras.layers import Input, Lambda from .inputs import create_embedding_matrix, embedding_lookup, get_dense_input, varlen_embedding_lookup, \ get_varlen_pooling_list, mergeDict @@ -145,7 +146,7 @@ def build_input_features(feature_columns, prefix=''): def get_linear_logit(features, feature_columns, units=1, use_bias=False, seed=1024, prefix='linear', - l2_reg=0): + l2_reg=0, sparse_feat_refine_weight=None): linear_feature_columns = copy(feature_columns) for i in range(len(linear_feature_columns)): if isinstance(linear_feature_columns[i], SparseFeat): @@ -166,9 +167,15 @@ def get_linear_logit(features, feature_columns, units=1, use_bias=False, seed=10 if len(linear_emb_list[i]) > 0 and len(dense_input_list) > 0: sparse_input = concat_func(linear_emb_list[i]) dense_input = concat_func(dense_input_list) + if sparse_feat_refine_weight is not None: + sparse_input = Lambda(lambda x: x[0] * tf.expand_dims(x[1], axis=1))( + [sparse_input, sparse_feat_refine_weight]) linear_logit = Linear(l2_reg, mode=2, use_bias=use_bias, seed=seed)([sparse_input, dense_input]) elif len(linear_emb_list[i]) > 0: sparse_input = concat_func(linear_emb_list[i]) + if sparse_feat_refine_weight is not None: + sparse_input = Lambda(lambda x: x[0] * tf.expand_dims(x[1], axis=1))( + [sparse_input, sparse_feat_refine_weight]) linear_logit = Linear(l2_reg, mode=0, use_bias=use_bias, seed=seed)(sparse_input) elif len(dense_input_list) > 0: dense_input = concat_func(dense_input_list) diff --git a/deepctr/layers/__init__.py b/deepctr/layers/__init__.py index 9617a8f8..8899aae8 100644 --- a/deepctr/layers/__init__.py +++ b/deepctr/layers/__init__.py @@ -8,9 +8,9 @@ FieldWiseBiInteraction, FwFMLayer) from .normalization import LayerNormalization from .sequence import (AttentionSequencePoolingLayer, BiasEncoding, BiLSTM, - KMaxPooling, SequencePoolingLayer,WeightedSequenceLayer, + KMaxPooling, SequencePoolingLayer, WeightedSequenceLayer, Transformer, DynamicGRU) -from .utils import NoMask, Hash,Linear,Add,combined_dnn_input +from .utils import NoMask, Hash, Linear, Add, combined_dnn_input, softmax custom_objects = {'tf': tf, 'InnerProductLayer': InnerProductLayer, @@ -36,12 +36,13 @@ 'KMaxPooling': KMaxPooling, 'FGCNNLayer': FGCNNLayer, 'Hash': Hash, - 'Linear':Linear, + 'Linear': Linear, 'DynamicGRU': DynamicGRU, - 'SENETLayer':SENETLayer, - 'BilinearInteraction':BilinearInteraction, - 'WeightedSequenceLayer':WeightedSequenceLayer, - 'Add':Add, - 'FieldWiseBiInteraction':FieldWiseBiInteraction, - 'FwFMLayer': FwFMLayer + 'SENETLayer': SENETLayer, + 'BilinearInteraction': BilinearInteraction, + 'WeightedSequenceLayer': WeightedSequenceLayer, + 'Add': Add, + 'FieldWiseBiInteraction': FieldWiseBiInteraction, + 'FwFMLayer': FwFMLayer, + 'softmax': softmax, } diff --git a/deepctr/layers/interaction.py b/deepctr/layers/interaction.py index fa32f047..9e9311b6 100644 --- a/deepctr/layers/interaction.py +++ b/deepctr/layers/interaction.py @@ -700,13 +700,14 @@ class InteractingLayer(Layer): - [Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.](https://arxiv.org/abs/1810.11921) """ - def __init__(self, att_embedding_size=8, head_num=2, use_res=True, seed=1024, **kwargs): + def __init__(self, att_embedding_size=8, head_num=2, use_res=True, scaling=False, seed=1024, **kwargs): if head_num <= 0: raise ValueError('head_num must be a int > 0') self.att_embedding_size = att_embedding_size self.head_num = head_num self.use_res = use_res self.seed = seed + self.scaling = scaling super(InteractingLayer, self).__init__(**kwargs) def build(self, input_shape): @@ -748,6 +749,8 @@ def call(self, inputs, **kwargs): inner_product = tf.matmul( querys, keys, transpose_b=True) # head_num None F F + if self.scaling: + inner_product /= self.att_embedding_size ** 0.5 self.normalized_att_scores = softmax(inner_product) result = tf.matmul(self.normalized_att_scores, diff --git a/deepctr/models/__init__.py b/deepctr/models/__init__.py index 217b357b..dd3fdf19 100644 --- a/deepctr/models/__init__.py +++ b/deepctr/models/__init__.py @@ -4,6 +4,8 @@ from .dcn import DCN from .dcnmix import DCNMix from .deepfm import DeepFM +from .ifm import IFM +from .difm import DIFM from .dien import DIEN from .din import DIN from .fnn import FNN @@ -20,5 +22,5 @@ from .fwfm import FwFM from .bst import BST -__all__ = ["AFM", "CCPM", "DCN", "DCNMix", "MLR", "DeepFM", "MLR", "NFM", "DIN", "DIEN", "FNN", "PNN", +__all__ = ["AFM", "CCPM", "DCN", "IFM", "DIFM", "DCNMix", "MLR", "DeepFM", "MLR", "NFM", "DIN", "DIEN", "FNN", "PNN", "WDL", "xDeepFM", "AutoInt", "ONN", "FGCNN", "DSIN", "FiBiNET", 'FLEN', "FwFM", "BST"] diff --git a/deepctr/models/difm.py b/deepctr/models/difm.py new file mode 100644 index 00000000..8b04977a --- /dev/null +++ b/deepctr/models/difm.py @@ -0,0 +1,83 @@ +# -*- coding:utf-8 -*- +""" +Author: + zanshuxun, zanshuxun@aliyun.com +Reference: + [1] Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C] + //IJCAI. 2020: 3139-3145.(https://www.ijcai.org/Proceedings/2020/0434.pdf) +""" + +import tensorflow as tf + +from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns, SparseFeat, \ + VarLenSparseFeat +from ..layers.core import PredictionLayer, DNN +from ..layers.interaction import FM, InteractingLayer +from ..layers.utils import concat_func, add_func, combined_dnn_input + + +def DIFM(linear_feature_columns, dnn_feature_columns, + att_embedding_size=8, att_head_num=8, att_res=True, dnn_hidden_units=(128, 128), + l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0, + dnn_activation='relu', dnn_use_bn=False, task='binary'): + """Instantiates the DIFM Network architecture. + + :param linear_feature_columns: An iterable containing all the features used by linear part of the model. + :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. + :param att_embedding_size: integer, the embedding size in multi-head self-attention network. + :param att_head_num: int. The head number in multi-head self-attention network. + :param att_res: bool. Whether or not use standard residual connections before output. + :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN + :param l2_reg_linear: float. L2 regularizer strength applied to linear part + :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector + :param l2_reg_dnn: float. L2 regularizer strength applied to DNN + :param seed: integer ,to use as random seed. + :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. + :param dnn_activation: Activation function to use in DNN + :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN + :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss + :return: A Keras model instance. + """ + + if not len(dnn_hidden_units) > 0: + raise ValueError("dnn_hidden_units is null!") + + features = build_input_features( + linear_feature_columns + dnn_feature_columns) + + sparse_feat_num = len(list(filter(lambda x: isinstance(x, SparseFeat) or isinstance(x, VarLenSparseFeat), + dnn_feature_columns))) + inputs_list = list(features.values()) + + sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, + l2_reg_embedding, seed) + + if not len(sparse_embedding_list) > 0: + raise ValueError("there are no sparse features") + + att_input = concat_func(sparse_embedding_list, axis=1) + att_out = InteractingLayer(att_embedding_size, att_head_num, att_res, scaling=True)(att_input) + att_out = tf.keras.layers.Flatten()(att_out) + m_vec = tf.keras.layers.Dense( + sparse_feat_num, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed=seed))(att_out) + + dnn_input = combined_dnn_input(sparse_embedding_list, []) + dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) + m_bit = tf.keras.layers.Dense( + sparse_feat_num, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed=seed))(dnn_output) + + input_aware_factor = add_func([m_vec, m_bit]) # the complete input-aware factor m_x + + linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', + l2_reg=l2_reg_linear, sparse_feat_refine_weight=input_aware_factor) + + fm_input = concat_func(sparse_embedding_list, axis=1) + refined_fm_input = tf.keras.layers.Lambda(lambda x: x[0] * tf.expand_dims(x[1], axis=-1))( + [fm_input, input_aware_factor]) + fm_logit = FM()(refined_fm_input) + + final_logit = add_func([linear_logit, fm_logit]) + + output = PredictionLayer(task)(final_logit) + model = tf.keras.models.Model(inputs=inputs_list, outputs=output) + return model diff --git a/deepctr/models/ifm.py b/deepctr/models/ifm.py new file mode 100644 index 00000000..68db0bd0 --- /dev/null +++ b/deepctr/models/ifm.py @@ -0,0 +1,74 @@ +# -*- coding:utf-8 -*- +""" +Author: + zanshuxun, zanshuxun@aliyun.com +Reference: + [1] Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction[C]//IJCAI. 2019: 1466-1472. + (https://www.ijcai.org/Proceedings/2019/0203.pdf) +""" + +import tensorflow as tf +from tensorflow.python.keras.layers import Lambda + +from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns, SparseFeat, \ + VarLenSparseFeat +from ..layers.core import PredictionLayer, DNN +from ..layers.interaction import FM +from ..layers.utils import concat_func, add_func, combined_dnn_input, softmax + + +def IFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), + l2_reg_linear=0.00001, l2_reg_embedding=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0, + dnn_activation='relu', dnn_use_bn=False, task='binary'): + """Instantiates the IFM Network architecture. + + :param linear_feature_columns: An iterable containing all the features used by linear part of the model. + :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. + :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN + :param l2_reg_linear: float. L2 regularizer strength applied to linear part + :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector + :param l2_reg_dnn: float. L2 regularizer strength applied to DNN + :param seed: integer ,to use as random seed. + :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. + :param dnn_activation: Activation function to use in DNN + :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN + :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss + :return: A Keras model instance. + """ + + if not len(dnn_hidden_units) > 0: + raise ValueError("dnn_hidden_units is null!") + + features = build_input_features( + linear_feature_columns + dnn_feature_columns) + + sparse_feat_num = len(list(filter(lambda x: isinstance(x, SparseFeat) or isinstance(x, VarLenSparseFeat), + dnn_feature_columns))) + inputs_list = list(features.values()) + + sparse_embedding_list, _ = input_from_feature_columns(features, dnn_feature_columns, + l2_reg_embedding, seed) + if not len(sparse_embedding_list) > 0: + raise ValueError("there are no sparse features") + + dnn_input = combined_dnn_input(sparse_embedding_list, []) + dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) + # here, dnn_output is the m'_{x} + dnn_output = tf.keras.layers.Dense( + sparse_feat_num, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed=seed))(dnn_output) + # input_aware_factor m_{x,i} + input_aware_factor = Lambda(lambda x: tf.cast(tf.shape(x)[-1], tf.float32) * softmax(x, dim=1))(dnn_output) + + linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', + l2_reg=l2_reg_linear, sparse_feat_refine_weight=input_aware_factor) + + fm_input = concat_func(sparse_embedding_list, axis=1) + refined_fm_input = Lambda(lambda x: x[0] * tf.expand_dims(x[1], axis=-1))( + [fm_input, input_aware_factor]) + fm_logit = FM()(refined_fm_input) + + final_logit = add_func([linear_logit, fm_logit]) + + output = PredictionLayer(task)(final_logit) + model = tf.keras.models.Model(inputs=inputs_list, outputs=output) + return model diff --git a/tests/models/DIFM_test.py b/tests/models/DIFM_test.py new file mode 100644 index 00000000..4a46a036 --- /dev/null +++ b/tests/models/DIFM_test.py @@ -0,0 +1,22 @@ +import pytest + +from deepctr.models import DIFM +from ..utils import check_model, get_test_data, SAMPLE_SIZE + + +@pytest.mark.parametrize( + 'att_head_num,dnn_hidden_units,sparse_feature_num', + [(1, (4,), 2), (2, (4, 4,), 2), (1, (4,), 1)] +) +def test_DIFM(att_head_num, dnn_hidden_units, sparse_feature_num): + model_name = "DIFM" + sample_size = SAMPLE_SIZE + x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num, + dense_feature_num=sparse_feature_num) + + model = DIFM(feature_columns, feature_columns, dnn_hidden_units=dnn_hidden_units, dnn_dropout=0.5) + check_model(model, model_name, x, y) + + +if __name__ == "__main__": + pass diff --git a/tests/models/IFM_test.py b/tests/models/IFM_test.py new file mode 100644 index 00000000..63ebd21d --- /dev/null +++ b/tests/models/IFM_test.py @@ -0,0 +1,24 @@ +import pytest + +from deepctr.models import IFM +from ..utils import check_model, get_test_data, SAMPLE_SIZE + + +@pytest.mark.parametrize( + 'hidden_size,sparse_feature_num', + [((2,), 1), + ((3,), 2) + ] +) +def test_IFM(hidden_size, sparse_feature_num): + model_name = "IFM" + sample_size = SAMPLE_SIZE + x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num, + dense_feature_num=sparse_feature_num) + + model = IFM(feature_columns, feature_columns, dnn_hidden_units=hidden_size, dnn_dropout=0.5) + check_model(model, model_name, x, y) + + +if __name__ == "__main__": + pass From c98a78383771235eaa97efe94f9f0065fc21ee19 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=B5=85=E6=A2=A6?= Date: Wed, 5 May 2021 20:49:24 +0800 Subject: [PATCH 2/4] fix error when linear_feature_columns is empty in tf1.12 --- deepctr/feature_column.py | 5 ++--- examples/run_classification_criteo.py | 10 +++++----- tests/models/DCN_test.py | 12 ++++++++++++ 3 files changed, 19 insertions(+), 8 deletions(-) diff --git a/deepctr/feature_column.py b/deepctr/feature_column.py index cb54713b..cebf099f 100644 --- a/deepctr/feature_column.py +++ b/deepctr/feature_column.py @@ -180,9 +180,8 @@ def get_linear_logit(features, feature_columns, units=1, use_bias=False, seed=10 elif len(dense_input_list) > 0: dense_input = concat_func(dense_input_list) linear_logit = Linear(l2_reg, mode=1, use_bias=use_bias, seed=seed)(dense_input) - else: - # raise NotImplementedError - return add_func([]) + else: #empty feature_columns + return Lambda(lambda x: tf.constant([[0.0]]))(features.values()[0]) linear_logit_list.append(linear_logit) return concat_func(linear_logit_list) diff --git a/examples/run_classification_criteo.py b/examples/run_classification_criteo.py index d2e7c6a0..366b9e11 100644 --- a/examples/run_classification_criteo.py +++ b/examples/run_classification_criteo.py @@ -25,9 +25,9 @@ # 2.count #unique features for each sparse field,and record dense feature field name - fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].max() + 1,embedding_dim=4 ) - for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,) - for feat in dense_features] + fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].max() + 1, embedding_dim=4) + for i, feat in enumerate(sparse_features)] + [DenseFeat(feat, 1, ) + for feat in dense_features] dnn_feature_columns = fixlen_feature_columns linear_feature_columns = fixlen_feature_columns @@ -37,8 +37,8 @@ # 3.generate input data for model train, test = train_test_split(data, test_size=0.2, random_state=2020) - train_model_input = {name:train[name] for name in feature_names} - test_model_input = {name:test[name] for name in feature_names} + train_model_input = {name: train[name] for name in feature_names} + test_model_input = {name: test[name] for name in feature_names} # 4.Define Model,train,predict and evaluate model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary') diff --git a/tests/models/DCN_test.py b/tests/models/DCN_test.py index 772fccc9..20d68ba3 100644 --- a/tests/models/DCN_test.py +++ b/tests/models/DCN_test.py @@ -25,6 +25,18 @@ def test_DCN(cross_num, hidden_size, sparse_feature_num, cross_parameterization) check_model(model, model_name, x, y) +def test_DCN_2(): + model_name = "DCN" + + sample_size = SAMPLE_SIZE + x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=3, + dense_feature_num=2) + + model = DCN([], feature_columns, cross_num=1, cross_parameterization=cross_parameterization, + dnn_hidden_units=8, dnn_dropout=0.5) + check_model(model, model_name, x, y) + + @pytest.mark.parametrize( 'cross_num,hidden_size,sparse_feature_num', [(1, (8,), 3) From ef3eff6855a210f52cbd8bea193bcd64eaa005b3 Mon Sep 17 00:00:00 2001 From: Harshit Pande Date: Sat, 12 Jun 2021 09:36:10 -0400 Subject: [PATCH 3/4] FEFM/DeepFEFM (#364) add FEFM and DeepFEFM Co-authored-by: Harshit Pande --- README.md | 1 + deepctr/estimator/models/__init__.py | 1 + deepctr/estimator/models/deepfefm.py | 92 ++++++++++++++++++++++++ deepctr/feature_column.py | 2 +- deepctr/layers/__init__.py | 7 +- deepctr/layers/interaction.py | 85 ++++++++++++++++++++++ deepctr/models/__init__.py | 3 +- deepctr/models/deepfefm.py | 102 +++++++++++++++++++++++++++ examples/run_deepfefm.py | 54 ++++++++++++++ tests/layers/interaction_test.py | 7 ++ tests/models/DCN_test.py | 3 +- tests/models/DeepFEFM_test.py | 54 ++++++++++++++ 12 files changed, 405 insertions(+), 6 deletions(-) create mode 100644 deepctr/estimator/models/deepfefm.py create mode 100644 deepctr/models/deepfefm.py create mode 100644 examples/run_deepfefm.py create mode 100644 tests/models/DeepFEFM_test.py diff --git a/README.md b/README.md index a9e8a1f5..655fb592 100644 --- a/README.md +++ b/README.md @@ -58,6 +58,7 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star | IFM | [IJCAI 2019][An Input-aware Factorization Machine for Sparse Prediction](https://www.ijcai.org/Proceedings/2019/0203.pdf) | | DCN V2 | [arxiv 2020][DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems](https://arxiv.org/abs/2008.13535) | | DIFM | [IJCAI 2020][A Dual Input-aware Factorization Machine for CTR Prediction](https://www.ijcai.org/Proceedings/2020/0434.pdf) | +| FEFM and DeepFEFM | [arxiv 2020][Field-Embedded Factorization Machines for Click-through rate prediction](https://arxiv.org/abs/2009.09931) | ## Citation diff --git a/deepctr/estimator/models/__init__.py b/deepctr/estimator/models/__init__.py index 1b6d2541..9bc1e120 100644 --- a/deepctr/estimator/models/__init__.py +++ b/deepctr/estimator/models/__init__.py @@ -10,3 +10,4 @@ from .pnn import PNNEstimator from .wdl import WDLEstimator from .xdeepfm import xDeepFMEstimator +from .deepfefm import DeepFEFMEstimator diff --git a/deepctr/estimator/models/deepfefm.py b/deepctr/estimator/models/deepfefm.py new file mode 100644 index 00000000..08df778b --- /dev/null +++ b/deepctr/estimator/models/deepfefm.py @@ -0,0 +1,92 @@ +# -*- coding:utf-8 -*- +""" +Author: + Harshit Pande + +Reference: + [1] Field-Embedded Factorization Machines for Click-through Rate Prediction] + (https://arxiv.org/abs/2009.09931) + +""" + +import tensorflow as tf + +from ..feature_column import get_linear_logit, input_from_feature_columns +from ..utils import DNN_SCOPE_NAME, deepctr_model_fn, variable_scope +from ...layers.core import DNN +from ...layers.interaction import FEFMLayer +from ...layers.utils import concat_func, add_func, combined_dnn_input, reduce_sum + + +def DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, embedding_size=48, + dnn_hidden_units=(1024, 1024, 1024), l2_reg_linear=0.000001, l2_reg_embedding_feat=0.00001, + l2_reg_embedding_field=0.0000001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.2, + dnn_activation='relu', dnn_use_bn=False, task='binary', model_dir=None, + config=None, linear_optimizer='Ftrl', dnn_optimizer='Adagrad', training_chief_hooks=None): + """Instantiates the DeepFEFM Network architecture or the shallow FEFM architecture (Ablation support not provided + as estimator is meant for production, Ablation support provided in DeepFEFM implementation in models + + :param linear_feature_columns: An iterable containing all the features used by linear part of the model. + :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. + :param embedding_size: positive integer,sparse feature embedding_size + :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN + :param l2_reg_linear: float. L2 regularizer strength applied to linear part + :param l2_reg_embedding_feat: float. L2 regularizer strength applied to embedding vector of features + :param l2_reg_embedding_field: float, L2 regularizer to field embeddings + :param l2_reg_dnn: float. L2 regularizer strength applied to DNN + :param seed: integer ,to use as random seed. + :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. + :param dnn_activation: Activation function to use in DNN + :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN + :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss + :param model_dir: Directory to save model parameters, graph and etc. This can + also be used to load checkpoints from the directory into a estimator + to continue training a previously saved model. + :param config: tf.RunConfig object to configure the runtime settings. + :param linear_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the linear part of the model. Defaults to FTRL optimizer. + :param dnn_optimizer: An instance of `tf.Optimizer` used to apply gradients to + the deep part of the model. Defaults to Adagrad optimizer. + :param training_chief_hooks: Iterable of `tf.train.SessionRunHook` objects to + run on the chief worker during training. + :return: A Tensorflow Estimator instance. + """ + + def _model_fn(features, labels, mode, config): + train_flag = (mode == tf.estimator.ModeKeys.TRAIN) + + linear_logits = get_linear_logit(features, linear_feature_columns, l2_reg_linear=l2_reg_linear) + final_logit_components = [linear_logits] + + with variable_scope(DNN_SCOPE_NAME): + sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, + l2_reg_embedding=l2_reg_embedding_feat) + + fefm_interaction_embedding = FEFMLayer(num_fields=len(sparse_embedding_list), embedding_size=embedding_size, + regularizer=l2_reg_embedding_field)(concat_func(sparse_embedding_list, axis=1)) + + fefm_logit = tf.keras.layers.Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=True))(fefm_interaction_embedding) + + final_logit_components.append(fefm_logit) + + if dnn_hidden_units: + dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list) + dnn_input = concat_func([dnn_input, fefm_interaction_embedding], axis=1) + + dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)( + dnn_input, training=train_flag) + + dnn_logit = tf.keras.layers.Dense( + 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_output) + + final_logit_components.append(dnn_logit) + + logits = add_func(final_logit_components) + + return deepctr_model_fn(features, mode, logits, labels, task, linear_optimizer, dnn_optimizer, + training_chief_hooks=training_chief_hooks) + + return tf.estimator.Estimator(_model_fn, model_dir=model_dir, config=config) + + + diff --git a/deepctr/feature_column.py b/deepctr/feature_column.py index cebf099f..ba08151b 100644 --- a/deepctr/feature_column.py +++ b/deepctr/feature_column.py @@ -181,7 +181,7 @@ def get_linear_logit(features, feature_columns, units=1, use_bias=False, seed=10 dense_input = concat_func(dense_input_list) linear_logit = Linear(l2_reg, mode=1, use_bias=use_bias, seed=seed)(dense_input) else: #empty feature_columns - return Lambda(lambda x: tf.constant([[0.0]]))(features.values()[0]) + return Lambda(lambda x: tf.constant([[0.0]]))(list(features.values())[0]) linear_logit_list.append(linear_logit) return concat_func(linear_logit_list) diff --git a/deepctr/layers/__init__.py b/deepctr/layers/__init__.py index 8899aae8..5199ce4e 100644 --- a/deepctr/layers/__init__.py +++ b/deepctr/layers/__init__.py @@ -5,12 +5,13 @@ from .interaction import (CIN, FM, AFMLayer, BiInteractionPooling, CrossNet, CrossNetMix, InnerProductLayer, InteractingLayer, OutterProductLayer, FGCNNLayer, SENETLayer, BilinearInteraction, - FieldWiseBiInteraction, FwFMLayer) + FieldWiseBiInteraction, FwFMLayer, FEFMLayer) from .normalization import LayerNormalization from .sequence import (AttentionSequencePoolingLayer, BiasEncoding, BiLSTM, KMaxPooling, SequencePoolingLayer, WeightedSequenceLayer, Transformer, DynamicGRU) -from .utils import NoMask, Hash, Linear, Add, combined_dnn_input, softmax + +from .utils import NoMask, Hash, Linear, Add, combined_dnn_input, softmax, reduce_sum custom_objects = {'tf': tf, 'InnerProductLayer': InnerProductLayer, @@ -45,4 +46,6 @@ 'FieldWiseBiInteraction': FieldWiseBiInteraction, 'FwFMLayer': FwFMLayer, 'softmax': softmax, + 'FEFMLayer': FEFMLayer, + 'reduce_sum': reduce_sum } diff --git a/deepctr/layers/interaction.py b/deepctr/layers/interaction.py index 9e9311b6..44dd7c55 100644 --- a/deepctr/layers/interaction.py +++ b/deepctr/layers/interaction.py @@ -1409,3 +1409,88 @@ def get_config(self): 'regularizer': self.regularizer }) return config + + +class FEFMLayer(Layer): + """Field-Embedded Factorization Machines + + Input shape + - 3D tensor with shape: ``(batch_size,field_size,embedding_size)``. + + Output shape + - 2D tensor with shape: + ``(batch_size, (num_fields * (num_fields-1))/2)`` # concatenated FEFM interaction embeddings + + Arguments + - **num_fields** : integer for number of fields + - **embedding_size** : integer for embedding dimension + - **regularizer** : L2 regularizer weight for the field pair matrix embeddings parameters of FEFM + + References + - [Field-Embedded Factorization Machines for Click-through Rate Prediction] + https://arxiv.org/pdf/2009.09931.pdf + """ + + def __init__(self, num_fields, embedding_size, regularizer, **kwargs): + self.num_fields = num_fields + self.embedding_size = embedding_size + self.regularizer = regularizer + super(FEFMLayer, self).__init__(**kwargs) + + def build(self, input_shape): + if len(input_shape) != 3: + raise ValueError("Unexpected inputs dimensions % d,\ + expect to be 3 dimensions" % (len(input_shape))) + + if input_shape[1] != self.num_fields: + raise ValueError("Mismatch in number of fields {} and \ + concatenated embeddings dims {}".format(self.num_fields, input_shape[2])) + + self.field_embeddings = {} + + for fi, fj in itertools.combinations(range(self.num_fields), 2): + field_pair_id = str(fi) + "-" + str(fj) + self.field_embeddings[field_pair_id] = self.add_weight(name='field_embeddings' + field_pair_id, + shape=(self.embedding_size, self.embedding_size), + initializer=TruncatedNormal(), + regularizer=l2(self.regularizer), + trainable=True) + + super(FEFMLayer, self).build(input_shape) # Be sure to call this somewhere! + + def call(self, inputs, **kwargs): + if K.ndim(inputs) != 3: + raise ValueError( + "Unexpected inputs dimensions %d, expect to be 3 dimensions" + % (K.ndim(inputs))) + + if inputs.shape[1] != self.num_fields: + raise ValueError("Mismatch in number of fields {} and \ + concatenated embeddings dims {}".format(self.num_fields, inputs.shape[1])) + + pairwise_inner_prods = [] + for fi, fj in itertools.combinations(range(self.num_fields), 2): + field_pair_id = str(fi) + "-" + str(fj) + feat_embed_i = tf.squeeze(inputs[0:, fi:fi + 1, 0:], axis=1) + feat_embed_j = tf.squeeze(inputs[0:, fj:fj + 1, 0:], axis=1) + field_pair_embed_ij = self.field_embeddings[field_pair_id] + + feat_embed_i_tr = tf.matmul(feat_embed_i, field_pair_embed_ij + tf.transpose(field_pair_embed_ij)) + + f = batch_dot(feat_embed_i_tr, feat_embed_j, axes=1) + pairwise_inner_prods.append(f) + + concat_vec = tf.concat(pairwise_inner_prods, axis=1) + return concat_vec + + def compute_output_shape(self, input_shape): + return (None, (self.num_fields * (self.num_fields-1))/2) + + def get_config(self): + config = super(FEFMLayer, self).get_config().copy() + config.update({ + 'num_fields': self.num_fields, + 'regularizer': self.regularizer, + 'embedding_size': self.embedding_size + }) + return config diff --git a/deepctr/models/__init__.py b/deepctr/models/__init__.py index dd3fdf19..139e587b 100644 --- a/deepctr/models/__init__.py +++ b/deepctr/models/__init__.py @@ -21,6 +21,7 @@ from .flen import FLEN from .fwfm import FwFM from .bst import BST +from .deepfefm import DeepFEFM __all__ = ["AFM", "CCPM", "DCN", "IFM", "DIFM", "DCNMix", "MLR", "DeepFM", "MLR", "NFM", "DIN", "DIEN", "FNN", "PNN", - "WDL", "xDeepFM", "AutoInt", "ONN", "FGCNN", "DSIN", "FiBiNET", 'FLEN', "FwFM", "BST"] + "WDL", "xDeepFM", "AutoInt", "ONN", "FGCNN", "DSIN", "FiBiNET", 'FLEN', "FwFM", "BST", "DeepFEFM"] diff --git a/deepctr/models/deepfefm.py b/deepctr/models/deepfefm.py new file mode 100644 index 00000000..4ee7d20f --- /dev/null +++ b/deepctr/models/deepfefm.py @@ -0,0 +1,102 @@ +# -*- coding:utf-8 -*- +""" +Author: + Harshit Pande + +Reference: + [1] Field-Embedded Factorization Machines for Click-through Rate Prediction] + (https://arxiv.org/pdf/2009.09931.pdf) + + this file also supports all the possible Ablation studies for reproducibility + +""" + +from itertools import chain + +import tensorflow as tf + +from ..feature_column import input_from_feature_columns, get_linear_logit, build_input_features, DEFAULT_GROUP_NAME +from ..layers.core import PredictionLayer, DNN +from ..layers.interaction import FEFMLayer +from ..layers.utils import concat_func, combined_dnn_input, reduce_sum + + +def DeepFEFM(linear_feature_columns, dnn_feature_columns, embedding_size=48, use_fefm=True, + dnn_hidden_units=(1024, 1024, 1024), l2_reg_linear=0.000001, l2_reg_embedding_feat=0.00001, + l2_reg_embedding_field=0.0000001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.2, exclude_feature_embed_in_dnn=False, + use_linear=True, use_fefm_embed_in_dnn=True, dnn_activation='relu', dnn_use_bn=False, task='binary'): + """Instantiates the DeepFEFM Network architecture or the shallow FEFM architecture (Ablation studies supported) + + :param linear_feature_columns: An iterable containing all the features used by linear part of the model. + :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. + :param fm_group: list, group_name of features that will be used to do feature interactions. + :param embedding_size: positive integer,sparse feature embedding_size + :param use_fefm: bool,use FEFM logit or not (doesn't effect FEFM embeddings in DNN, controls only the use of final FEFM logit) + :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN + :param l2_reg_linear: float. L2 regularizer strength applied to linear part + :param l2_reg_embedding_feat: float. L2 regularizer strength applied to embedding vector of features + :param l2_reg_embedding_field: float, L2 regularizer to field embeddings + :param l2_reg_dnn: float. L2 regularizer strength applied to DNN + :param seed: integer ,to use as random seed. + :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. + :param exclude_feature_embed_in_dnn: bool, used in ablation studies for removing feature embeddings in DNN + :param use_linear: bool, used in ablation studies + :param use_fefm_embed_in_dnn: bool, True if FEFM interaction embeddings are to be used in FEFM (set False for Ablation) + :param dnn_activation: Activation function to use in DNN + :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN + :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss + :return: A Keras model instance. + """ + + features = build_input_features(linear_feature_columns + dnn_feature_columns) + + inputs_list = list(features.values()) + + linear_logit = get_linear_logit(features, linear_feature_columns, l2_reg=l2_reg_linear, seed=seed, prefix='linear') + + group_embedding_dict, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, + l2_reg_embedding_feat, + seed, support_group=True) + + fefm_interaction_embedding = concat_func([FEFMLayer(num_fields=len(v), embedding_size=embedding_size, + regularizer=l2_reg_embedding_field)(concat_func(v, axis=1)) + for k, v in group_embedding_dict.items() if k in [DEFAULT_GROUP_NAME]], axis=1) + + dnn_input = combined_dnn_input(list(chain.from_iterable(group_embedding_dict.values())), dense_value_list) + + # if use_fefm_embed_in_dnn is set to False it is Ablation4 (Use false only for Ablation) + if use_fefm_embed_in_dnn: + if exclude_feature_embed_in_dnn: + # Ablation3: remove feature vector embeddings from the DNN input + dnn_input = fefm_interaction_embedding + else: + # No ablation + dnn_input = concat_func([dnn_input, fefm_interaction_embedding], axis=1) + + dnn_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) + + dnn_logit = tf.keras.layers.Dense( + 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(dnn_out) + + fefm_logit = tf.keras.layers.Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=True))(fefm_interaction_embedding) + + if len(dnn_hidden_units) == 0 and use_fefm is False and use_linear is True: # only linear + final_logit = linear_logit + elif len(dnn_hidden_units) == 0 and use_fefm is True and use_linear is True: # linear + FEFM + final_logit = tf.keras.layers.add([linear_logit, fefm_logit]) + elif len(dnn_hidden_units) > 0 and use_fefm is False and use_linear is True: # linear +　Deep # Ablation1 + final_logit = tf.keras.layers.add([linear_logit, dnn_logit]) + elif len(dnn_hidden_units) > 0 and use_fefm is True and use_linear is True: # linear + FEFM + Deep + final_logit = tf.keras.layers.add([linear_logit, fefm_logit, dnn_logit]) + elif len(dnn_hidden_units) == 0 and use_fefm is True and use_linear is False: # only FEFM (shallow) + final_logit = fefm_logit + elif len(dnn_hidden_units) > 0 and use_fefm is False and use_linear is False: # only Deep + final_logit = dnn_logit + elif len(dnn_hidden_units) > 0 and use_fefm is True and use_linear is False: # FEFM + Deep # Ablation2 + final_logit = tf.keras.layers.add([fefm_logit, dnn_logit]) + else: + raise NotImplementedError + + output = PredictionLayer(task)(final_logit) + model = tf.keras.models.Model(inputs=inputs_list, outputs=output) + return model diff --git a/examples/run_deepfefm.py b/examples/run_deepfefm.py new file mode 100644 index 00000000..f1c9122f --- /dev/null +++ b/examples/run_deepfefm.py @@ -0,0 +1,54 @@ +import pandas as pd +from sklearn.metrics import log_loss, roc_auc_score +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder, MinMaxScaler + +from deepctr.models import DeepFEFM +from deepctr.feature_column import SparseFeat, DenseFeat, get_feature_names + +if __name__ == "__main__": + data = pd.read_csv('./criteo_sample.txt') + + sparse_features = ['C' + str(i) for i in range(1, 27)] + dense_features = ['I' + str(i) for i in range(1, 14)] + + data[sparse_features] = data[sparse_features].fillna('-1', ) + data[dense_features] = data[dense_features].fillna(0, ) + target = ['label'] + + # 1.Label Encoding for sparse features,and do simple Transformation for dense features + for feat in sparse_features: + lbe = LabelEncoder() + data[feat] = lbe.fit_transform(data[feat]) + mms = MinMaxScaler(feature_range=(0, 1)) + data[dense_features] = mms.fit_transform(data[dense_features]) + + # 2.count #unique features for each sparse field,and record dense feature field name + + fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4 ) + for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,) + for feat in dense_features] + + dnn_feature_columns = fixlen_feature_columns + linear_feature_columns = fixlen_feature_columns + + feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns) + + # 3.generate input data for model + + train, test = train_test_split(data, test_size=0.2, random_state=2020) + train_model_input = {name: train[name] for name in feature_names} + test_model_input = {name: test[name] for name in feature_names} + + # 4.Define Model,train,predict and evaluate + model = DeepFEFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), use_fefm=True, + use_fefm_embed_in_dnn=True, embedding_size=4, task='binary') + + model.compile("adam", "binary_crossentropy", + metrics=['binary_crossentropy'], ) + + history = model.fit(train_model_input, train[target].values, + batch_size=256, epochs=10, verbose=2, validation_split=0.2, ) + pred_ans = model.predict(test_model_input, batch_size=256) + print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4)) + print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4)) diff --git a/tests/layers/interaction_test.py b/tests/layers/interaction_test.py index 8f7657d8..cf57a623 100644 --- a/tests/layers/interaction_test.py +++ b/tests/layers/interaction_test.py @@ -17,6 +17,13 @@ EMBEDDING_SIZE = 3 SEQ_LENGTH = 10 + +def test_FEFMLayer(): + with CustomObjectScope({'FEFMLayer': layers.FEFMLayer}): + layer_test(layers.FEFMLayer, kwargs={'num_fields': FIELD_SIZE, 'embedding_size': EMBEDDING_SIZE, + 'regularizer': 0.000001}, + input_shape=(BATCH_SIZE, FIELD_SIZE, EMBEDDING_SIZE)) + @pytest.mark.parametrize( 'reg_strength', [0.000001] diff --git a/tests/models/DCN_test.py b/tests/models/DCN_test.py index 20d68ba3..45f713f7 100644 --- a/tests/models/DCN_test.py +++ b/tests/models/DCN_test.py @@ -32,8 +32,7 @@ def test_DCN_2(): x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=3, dense_feature_num=2) - model = DCN([], feature_columns, cross_num=1, cross_parameterization=cross_parameterization, - dnn_hidden_units=8, dnn_dropout=0.5) + model = DCN([], feature_columns, cross_num=1, dnn_hidden_units=(8,), dnn_dropout=0.5) check_model(model, model_name, x, y) diff --git a/tests/models/DeepFEFM_test.py b/tests/models/DeepFEFM_test.py new file mode 100644 index 00000000..e952948e --- /dev/null +++ b/tests/models/DeepFEFM_test.py @@ -0,0 +1,54 @@ +import pytest +import tensorflow as tf + +from deepctr.estimator import DeepFEFMEstimator +from deepctr.models import DeepFEFM +from ..utils import check_model, get_test_data, SAMPLE_SIZE, get_test_data_estimator, check_estimator, \ + Estimator_TEST_TF1 + +@pytest.mark.parametrize( + 'hidden_size,sparse_feature_num,use_fefm,use_linear,use_fefm_embed_in_dnn', + [((2,), 1, True, True, True), + ((2,), 1, True, True, False), + ((2,), 1, True, False, True), + ((2,), 1, False, True, True), + ((2,), 1, True, False, False), + ((2,), 1, False, True, False), + ((2,), 1, False, False, True), + ((2,), 1, False, False, False), + ((), 1, True, True, True) + ] +) +def test_DeepFEFM(hidden_size, sparse_feature_num, use_fefm, use_linear, use_fefm_embed_in_dnn): + model_name = "DeepFEFM" + sample_size = SAMPLE_SIZE + x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num, + dense_feature_num=sparse_feature_num) + model = DeepFEFM(feature_columns, feature_columns, embedding_size=4, dnn_hidden_units=hidden_size, dnn_dropout=0.5, + use_linear=use_linear, use_fefm=use_fefm, use_fefm_embed_in_dnn=use_fefm_embed_in_dnn) + + check_model(model, model_name, x, y) + + +@pytest.mark.parametrize( + 'hidden_size,sparse_feature_num', + [((2,), 2), + ((), 2), + ] +) +def test_DeepFEFMEstimator(hidden_size, sparse_feature_num): + if not Estimator_TEST_TF1 and tf.__version__ < "2.2.0": + return + sample_size = SAMPLE_SIZE + linear_feature_columns, dnn_feature_columns, input_fn = get_test_data_estimator(sample_size, + sparse_feature_num=sparse_feature_num, + dense_feature_num=sparse_feature_num) + + model = DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, embedding_size=4, + dnn_hidden_units=hidden_size, dnn_dropout=0.5) + + check_estimator(model, input_fn) + + +if __name__ == "__main__": + pass From d5f664784bfc8be7fbb0e91fbec883403c7da2af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=B5=85=E6=A2=A6?= Date: Mon, 14 Jun 2021 17:12:10 +0800 Subject: [PATCH 4/4] Dev swc (#377) - enhance the compatibility for tf 2.5 - add training param to activation layer - add PositionEncoding Layer --- .github/ISSUE_TEMPLATE/bug_report.md | 8 +- .github/ISSUE_TEMPLATE/question.md | 4 +- .github/workflows/ci.yml | 8 +- README.md | 6 +- deepctr/__init__.py | 2 +- deepctr/estimator/models/deepfefm.py | 17 +- deepctr/estimator/utils.py | 21 ++- deepctr/feature_column.py | 2 +- deepctr/layers/__init__.py | 5 +- deepctr/layers/core.py | 7 +- deepctr/layers/interaction.py | 23 +-- deepctr/layers/sequence.py | 147 ++++++++++++------ deepctr/models/bst.py | 2 +- deepctr/models/deepfefm.py | 15 +- deepctr/models/dien.py | 1 + docs/pics/DIFM.jpg | Bin 0 -> 123144 bytes docs/pics/DeepFEFM.jpg | Bin 0 -> 220530 bytes docs/pics/IFM.jpg | Bin 0 -> 100224 bytes docs/source/Features.md | 29 ++++ docs/source/History.md | 1 + docs/source/Models.rst | 3 + docs/source/conf.py | 2 +- .../deepctr.estimator.models.deepfefm.rst | 7 + docs/source/deepctr.models.deepfefm.rst | 7 + docs/source/deepctr.models.difm.rst | 7 + docs/source/deepctr.models.ifm.rst | 7 + docs/source/deepctr.models.rst | 3 + docs/source/index.rst | 6 +- examples/run_deepfefm.py | 54 ------- examples/run_din.py | 2 +- setup.py | 2 +- tests/layers/interaction_test.py | 9 +- tests/layers/sequence_test.py | 44 ++++-- tests/models/AFM_test.py | 1 - tests/models/DeepFEFM_test.py | 9 +- tests/models/PNN_test.py | 2 +- 36 files changed, 276 insertions(+), 187 deletions(-) create mode 100644 docs/pics/DIFM.jpg create mode 100644 docs/pics/DeepFEFM.jpg create mode 100644 docs/pics/IFM.jpg create mode 100644 docs/source/deepctr.estimator.models.deepfefm.rst create mode 100644 docs/source/deepctr.models.deepfefm.rst create mode 100644 docs/source/deepctr.models.difm.rst create mode 100644 docs/source/deepctr.models.ifm.rst delete mode 100644 examples/run_deepfefm.py diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index b5d3b712..3a09ec50 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -8,7 +8,7 @@ assignees: '' --- **Describe the bug(问题描述)** -A clear and concise description of what the bug is. +A clear and concise description of what the bug is.Better with standalone code to reproduce the issue. **To Reproduce(复现步骤)** Steps to reproduce the behavior: @@ -18,9 +18,9 @@ Steps to reproduce the behavior: 4. See error **Operating environment(运行环境):** - - python version [e.g. 3.5, 3.7] - - tensorflow version [e.g. 1.4.0, 1.15.0, 2.4.0] - - deepctr version [e.g. 0.8.3,] + - python version [e.g. 3.6, 3.7] + - tensorflow version [e.g. 1.4.0, 1.15.0, 2.5.0] + - deepctr version [e.g. 0.8.6,] **Additional context** Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md index 7f4dec64..c762300a 100644 --- a/.github/ISSUE_TEMPLATE/question.md +++ b/.github/ISSUE_TEMPLATE/question.md @@ -16,5 +16,5 @@ Add any other context about the problem here. **Operating environment(运行环境):** - python version [e.g. 3.6] - - tensorflow version [e.g. 1.4.0, 1.5.0, 2.4.0] - - deepctr version [e.g. 0.8.3,] + - tensorflow version [e.g. 1.4.0, 1.15.0, 2.5.0] + - deepctr version [e.g. 0.8.6,] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 64ef108c..8849c4e6 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -14,15 +14,17 @@ jobs: build: runs-on: ubuntu-latest - timeout-minutes: 120 + timeout-minutes: 180 strategy: matrix: python-version: [3.6,3.7] - tf-version: [1.4.0,1.15.0,2.1.0,2.4.0] + tf-version: [1.4.0,1.15.0,2.1.0,2.5.0] exclude: - python-version: 3.7 tf-version: 1.4.0 + - python-version: 3.7 + tf-version: 1.15.0 steps: @@ -40,7 +42,7 @@ jobs: pip install -q requests pip install -e . - name: Test with pytest - timeout-minutes: 120 + timeout-minutes: 180 run: | pip install -q pytest pip install -q pytest-cov diff --git a/README.md b/README.md index 655fb592..31820f38 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/) ![CI status](https://github.com/shenweichen/deepctr/workflows/CI/badge.svg) -[![Coverage Status](https://coveralls.io/repos/github/shenweichen/DeepCTR/badge.svg?branch=master)](https://coveralls.io/github/shenweichen/DeepCTR?branch=master) +[![codecov](https://codecov.io/gh/shenweichen/DeepCTR/branch/master/graph/badge.svg)](https://codecov.io/gh/shenweichen/DeepCTR) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/gh/shenweichen/DeepCTR?utm_source=github.com&utm_medium=referral&utm_content=shenweichen/DeepCTR&utm_campaign=Badge_Grade) [![Disscussion](https://img.shields.io/badge/chat-wechat-brightgreen?style=flat)](./README.md#DisscussionGroup) [![License](https://img.shields.io/github/license/shenweichen/deepctr.svg)](https://github.com/shenweichen/deepctr/blob/master/LICENSE) @@ -81,7 +81,7 @@ If you find this code useful in your research, please cite it using the followin ## DisscussionGroup - [Discussions](https://github.com/shenweichen/DeepCTR/discussions) -- 公众号：**浅梦的学习笔记** +- 公众号：**浅梦学习笔记** - wechat ID: **deepctrbot** ![wechat](./docs/pics/code.png) @@ -115,7 +115,7 @@ If you find this code useful in your research, please cite it using the followin ​ pic
- LeoCai + Tan Tingyi

Chongqing University
of Posts and
Telecommunications

​ diff --git a/deepctr/__init__.py b/deepctr/__init__.py index b3b93e89..7c97d7aa 100644 --- a/deepctr/__init__.py +++ b/deepctr/__init__.py @@ -1,4 +1,4 @@ from .utils import check_version -__version__ = '0.8.5' +__version__ = '0.8.6' check_version(__version__) diff --git a/deepctr/estimator/models/deepfefm.py b/deepctr/estimator/models/deepfefm.py index 08df778b..10711316 100644 --- a/deepctr/estimator/models/deepfefm.py +++ b/deepctr/estimator/models/deepfefm.py @@ -18,9 +18,9 @@ from ...layers.utils import concat_func, add_func, combined_dnn_input, reduce_sum -def DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, embedding_size=48, - dnn_hidden_units=(1024, 1024, 1024), l2_reg_linear=0.000001, l2_reg_embedding_feat=0.00001, - l2_reg_embedding_field=0.0000001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.2, +def DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, + dnn_hidden_units=(128, 128), l2_reg_linear=0.00001, l2_reg_embedding_feat=0.00001, + l2_reg_embedding_field=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.0, dnn_activation='relu', dnn_use_bn=False, task='binary', model_dir=None, config=None, linear_optimizer='Ftrl', dnn_optimizer='Adagrad', training_chief_hooks=None): """Instantiates the DeepFEFM Network architecture or the shallow FEFM architecture (Ablation support not provided @@ -28,7 +28,6 @@ def DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, embedding_siz :param linear_feature_columns: An iterable containing all the features used by linear part of the model. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. - :param embedding_size: positive integer,sparse feature embedding_size :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN :param l2_reg_linear: float. L2 regularizer strength applied to linear part :param l2_reg_embedding_feat: float. L2 regularizer strength applied to embedding vector of features @@ -62,10 +61,11 @@ def _model_fn(features, labels, mode, config): sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, l2_reg_embedding=l2_reg_embedding_feat) - fefm_interaction_embedding = FEFMLayer(num_fields=len(sparse_embedding_list), embedding_size=embedding_size, - regularizer=l2_reg_embedding_field)(concat_func(sparse_embedding_list, axis=1)) + fefm_interaction_embedding = FEFMLayer( + regularizer=l2_reg_embedding_field)(concat_func(sparse_embedding_list, axis=1)) - fefm_logit = tf.keras.layers.Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=True))(fefm_interaction_embedding) + fefm_logit = tf.keras.layers.Lambda(lambda x: reduce_sum(x, axis=1, keep_dims=True))( + fefm_interaction_embedding) final_logit_components.append(fefm_logit) @@ -87,6 +87,3 @@ def _model_fn(features, labels, mode, config): training_chief_hooks=training_chief_hooks) return tf.estimator.Estimator(_model_fn, model_dir=model_dir, config=config) - - - diff --git a/deepctr/estimator/utils.py b/deepctr/estimator/utils.py index fc71eb98..5d722515 100644 --- a/deepctr/estimator/utils.py +++ b/deepctr/estimator/utils.py @@ -44,27 +44,29 @@ def _eval_metric_ops(self, _summary_key(self._name, "prediction/mean"): metrics.mean(predictions, weights=weights), _summary_key(self._name, "label/mean"): metrics.mean(labels, weights=weights), } - tf.summary.scalar("prediction/mean", metric_ops[_summary_key(self._name, "prediction/mean")][1]) - tf.summary.scalar("label/mean", metric_ops[_summary_key(self._name, "label/mean")][1]) + + summary_scalar("prediction/mean", metric_ops[_summary_key(self._name, "prediction/mean")][1]) + summary_scalar("label/mean", metric_ops[_summary_key(self._name, "label/mean")][1]) + mean_loss = losses.compute_weighted_loss( unweighted_loss, weights=1.0, reduction=losses.Reduction.MEAN) if self._task == "binary": metric_ops[_summary_key(self._name, "LogLoss")] = metrics.mean(mean_loss, weights=weights, ) - tf.summary.scalar("LogLoss", mean_loss) + summary_scalar("LogLoss", mean_loss) metric_ops[_summary_key(self._name, "AUC")] = metrics.auc(labels, predictions, weights=weights) - tf.summary.scalar("AUC", metric_ops[_summary_key(self._name, "AUC")][1]) + summary_scalar("AUC", metric_ops[_summary_key(self._name, "AUC")][1]) else: metric_ops[_summary_key(self._name, "MSE")] = metrics.mean_squared_error(labels, predictions, weights=weights) - tf.summary.scalar("MSE", mean_loss) + summary_scalar("MSE", mean_loss) metric_ops[_summary_key(self._name, "MAE")] = metrics.mean_absolute_error(labels, predictions, weights=weights) - tf.summary.scalar("MAE", metric_ops[_summary_key(self._name, "MAE")][1]) + summary_scalar("MAE", metric_ops[_summary_key(self._name, "MAE")][1]) return metric_ops @@ -206,3 +208,10 @@ def to_float(x, name="ToFloat"): return tf.to_float(x, name) except AttributeError: return tf.compat.v1.to_float(x, name) + + +def summary_scalar(name, data): + try: + tf.summary.scalar(name, data) + except AttributeError: # tf version 2.5.0+:AttributeError: module 'tensorflow._api.v2.summary' has no attribute 'scalar' + tf.compat.v1.summary.scalar(name, data) \ No newline at end of file diff --git a/deepctr/feature_column.py b/deepctr/feature_column.py index ba08151b..cb04ce1d 100644 --- a/deepctr/feature_column.py +++ b/deepctr/feature_column.py @@ -9,7 +9,7 @@ from .inputs import create_embedding_matrix, embedding_lookup, get_dense_input, varlen_embedding_lookup, \ get_varlen_pooling_list, mergeDict from .layers import Linear -from .layers.utils import concat_func, add_func +from .layers.utils import concat_func DEFAULT_GROUP_NAME = "default_group" diff --git a/deepctr/layers/__init__.py b/deepctr/layers/__init__.py index 5199ce4e..324f4040 100644 --- a/deepctr/layers/__init__.py +++ b/deepctr/layers/__init__.py @@ -9,7 +9,7 @@ from .normalization import LayerNormalization from .sequence import (AttentionSequencePoolingLayer, BiasEncoding, BiLSTM, KMaxPooling, SequencePoolingLayer, WeightedSequenceLayer, - Transformer, DynamicGRU) + Transformer, DynamicGRU,PositionEncoding) from .utils import NoMask, Hash, Linear, Add, combined_dnn_input, softmax, reduce_sum @@ -47,5 +47,6 @@ 'FwFMLayer': FwFMLayer, 'softmax': softmax, 'FEFMLayer': FEFMLayer, - 'reduce_sum': reduce_sum + 'reduce_sum': reduce_sum, + 'PositionEncoding':PositionEncoding } diff --git a/deepctr/layers/core.py b/deepctr/layers/core.py index 6ee4b77b..9ee5e248 100644 --- a/deepctr/layers/core.py +++ b/deepctr/layers/core.py @@ -189,8 +189,11 @@ def call(self, inputs, training=None, **kwargs): if self.use_bn: fc = self.bn_layers[i](fc, training=training) - - fc = self.activation_layers[i](fc) + try: + fc = self.activation_layers[i](fc, training=training) + except TypeError as e: # TypeError: call() got an unexpected keyword argument 'training' + print("make sure the activation function use training flag properly", e) + fc = self.activation_layers[i](fc) fc = self.dropout_layers[i](fc, training=training) deep_input = fc diff --git a/deepctr/layers/interaction.py b/deepctr/layers/interaction.py index 44dd7c55..3be2acb4 100644 --- a/deepctr/layers/interaction.py +++ b/deepctr/layers/interaction.py @@ -1422,8 +1422,6 @@ class FEFMLayer(Layer): ``(batch_size, (num_fields * (num_fields-1))/2)`` # concatenated FEFM interaction embeddings Arguments - - **num_fields** : integer for number of fields - - **embedding_size** : integer for embedding dimension - **regularizer** : L2 regularizer weight for the field pair matrix embeddings parameters of FEFM References @@ -1431,9 +1429,7 @@ class FEFMLayer(Layer): https://arxiv.org/pdf/2009.09931.pdf """ - def __init__(self, num_fields, embedding_size, regularizer, **kwargs): - self.num_fields = num_fields - self.embedding_size = embedding_size + def __init__(self, regularizer, **kwargs): self.regularizer = regularizer super(FEFMLayer, self).__init__(**kwargs) @@ -1442,16 +1438,14 @@ def build(self, input_shape): raise ValueError("Unexpected inputs dimensions % d,\ expect to be 3 dimensions" % (len(input_shape))) - if input_shape[1] != self.num_fields: - raise ValueError("Mismatch in number of fields {} and \ - concatenated embeddings dims {}".format(self.num_fields, input_shape[2])) + self.num_fields = int(input_shape[1]) + embedding_size = int(input_shape[2]) self.field_embeddings = {} - for fi, fj in itertools.combinations(range(self.num_fields), 2): field_pair_id = str(fi) + "-" + str(fj) self.field_embeddings[field_pair_id] = self.add_weight(name='field_embeddings' + field_pair_id, - shape=(self.embedding_size, self.embedding_size), + shape=(embedding_size, embedding_size), initializer=TruncatedNormal(), regularizer=l2(self.regularizer), trainable=True) @@ -1464,10 +1458,6 @@ def call(self, inputs, **kwargs): "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (K.ndim(inputs))) - if inputs.shape[1] != self.num_fields: - raise ValueError("Mismatch in number of fields {} and \ - concatenated embeddings dims {}".format(self.num_fields, inputs.shape[1])) - pairwise_inner_prods = [] for fi, fj in itertools.combinations(range(self.num_fields), 2): field_pair_id = str(fi) + "-" + str(fj) @@ -1484,13 +1474,12 @@ def call(self, inputs, **kwargs): return concat_vec def compute_output_shape(self, input_shape): - return (None, (self.num_fields * (self.num_fields-1))/2) + num_fields = int(input_shape[1]) + return (None, (num_fields * (num_fields - 1)) / 2) def get_config(self): config = super(FEFMLayer, self).get_config().copy() config.update({ - 'num_fields': self.num_fields, 'regularizer': self.regularizer, - 'embedding_size': self.embedding_size }) return config diff --git a/deepctr/layers/sequence.py b/deepctr/layers/sequence.py index 4160fb11..5c4b5b50 100644 --- a/deepctr/layers/sequence.py +++ b/deepctr/layers/sequence.py @@ -493,13 +493,12 @@ def build(self, input_shape): self.fw2 = self.add_weight('fw2', shape=[4 * self.num_units, self.num_units], dtype=tf.float32, initializer=tf.keras.initializers.glorot_uniform(seed=self.seed)) - # if self.use_positional_encoding: - # - # self.kpe = Position_Embedding(input_shape[0][-1].value) - # self.qpe = Position_Embedding(input_shape[1][-1].value) self.dropout = tf.keras.layers.Dropout( self.dropout_rate, seed=self.seed) self.ln = LayerNormalization() + if self.use_positional_encoding: + self.query_pe = PositionEncoding() + self.key_pe = PositionEncoding() # Be sure to call this somewhere! super(Transformer, self).build(input_shape) @@ -521,8 +520,8 @@ def call(self, inputs, mask=None, training=None, **kwargs): key_masks = tf.squeeze(key_masks, axis=1) if self.use_positional_encoding: - queries = positional_encoding(queries) - keys = positional_encoding(queries) + queries = self.query_pe(queries) + keys = self.key_pe(queries) querys = tf.tensordot(queries, self.W_Query, axes=(-1, 0)) # None T_q D*head_num @@ -545,7 +544,7 @@ def call(self, inputs, mask=None, training=None, **kwargs): outputs = tf.tanh(tf.nn.bias_add(querys_reshaped + keys_reshaped, self.b)) outputs = tf.squeeze(tf.tensordot(outputs, tf.expand_dims(self.v, axis=-1), axes=[-1, 0]), axis=-1) else: - NotImplementedError + raise ValueError("attention_type must be scaled_dot_product or additive") key_masks = tf.tile(key_masks, [self.head_num, 1]) @@ -620,54 +619,62 @@ def get_config(self, ): return dict(list(base_config.items()) + list(config.items())) -def positional_encoding(inputs, - pos_embedding_trainable=True, - zero_pad=False, - scale=True, - ): - '''Sinusoidal Positional_Encoding. +class PositionEncoding(Layer): + def __init__(self, pos_embedding_trainable=True, + zero_pad=False, + scale=True, **kwargs): + self.pos_embedding_trainable = pos_embedding_trainable + self.zero_pad = zero_pad + self.scale = scale + super(PositionEncoding, self).__init__(**kwargs) - Args: + def build(self, input_shape): + # Create a trainable weight variable for this layer. + _, T, num_units = input_shape.as_list() # inputs.get_shape().as_list() + # First part of the PE function: sin and cos argument + position_enc = np.array([ + [pos / np.power(10000, 2. * i / num_units) + for i in range(num_units)] + for pos in range(T)]) - - inputs: A 2d Tensor with shape of (N, T). - - num_units: Output dimensionality - - zero_pad: Boolean. If True, all the values of the first row (id = 0) should be constant zero - - scale: Boolean. If True, the output will be multiplied by sqrt num_units(check details from paper) - - scope: Optional scope for `variable_scope`. - - reuse: Boolean, whether to reuse the weights of a previous layer by the same name. + # Second part, apply the cosine to even columns and sin to odds. + position_enc[:, 0::2] = np.sin(position_enc[:, 0::2]) # dim 2i + position_enc[:, 1::2] = np.cos(position_enc[:, 1::2]) # dim 2i+1 - Returns: + self.lookup_table = self.add_weight("lookup_table", (T, num_units), + initializer=tf.initializers.identity(position_enc), + trainable=self.pos_embedding_trainable) - - A 'Tensor' with one more rank than inputs's, with the dimensionality should be 'num_units' - ''' + # Be sure to call this somewhere! + super(PositionEncoding, self).build(input_shape) - _, T, num_units = inputs.get_shape().as_list() - # with tf.variable_scope(scope, reuse=reuse): - position_ind = tf.expand_dims(tf.range(T), 0) - # First part of the PE function: sin and cos argument - position_enc = np.array([ - [pos / np.power(10000, 2. * i / num_units) - for i in range(num_units)] - for pos in range(T)]) + def call(self, inputs, mask=None): + _, T, num_units = inputs.get_shape().as_list() + position_ind = tf.expand_dims(tf.range(T), 0) - # Second part, apply the cosine to even columns and sin to odds. - position_enc[:, 0::2] = np.sin(position_enc[:, 0::2]) # dim 2i - position_enc[:, 1::2] = np.cos(position_enc[:, 1::2]) # dim 2i+1 + if self.zero_pad: + self.lookup_table = tf.concat((tf.zeros(shape=[1, num_units]), + self.lookup_table[1:, :]), 0) - # Convert to a tensor + outputs = tf.nn.embedding_lookup(self.lookup_table, position_ind) - if pos_embedding_trainable: - lookup_table = K.variable(position_enc, dtype=tf.float32) + if self.scale: + outputs = outputs * num_units ** 0.5 + return outputs + inputs - if zero_pad: - lookup_table = tf.concat((tf.zeros(shape=[1, num_units]), - lookup_table[1:, :]), 0) + def compute_output_shape(self, input_shape): - outputs = tf.nn.embedding_lookup(lookup_table, position_ind) + return input_shape - if scale: - outputs = outputs * num_units ** 0.5 - return outputs + inputs + def compute_mask(self, inputs, mask=None): + return mask + + def get_config(self, ): + + config = {'pos_embedding_trainable': self.pos_embedding_trainable, 'zero_pad': self.zero_pad, + 'scale': self.scale} + base_config = super(PositionEncoding, self).get_config() + return dict(list(base_config.items()) + list(config.items())) class BiasEncoding(Layer): @@ -743,7 +750,7 @@ def build(self, input_shape): self.gru_cell = VecAttGRUCell(self.num_units) else: try: - self.gru_cell = tf.nn.rnn_cell.GRUCell(self.num_units) + self.gru_cell = tf.nn.rnn_cell.GRUCell(self.num_units) # tf.keras.layers.GRUCell except: self.gru_cell = tf.compat.v1.nn.rnn_cell.GRUCell(self.num_units) @@ -839,3 +846,53 @@ def get_config(self, ): config = {'k': self.k, 'axis': self.axis} base_config = super(KMaxPooling, self).get_config() return dict(list(base_config.items()) + list(config.items())) + + +# def positional_encoding(inputs, +# pos_embedding_trainable=True, +# zero_pad=False, +# scale=True, +# ): +# '''Sinusoidal Positional_Encoding. +# +# Args: +# +# - inputs: A 2d Tensor with shape of (N, T). +# - num_units: Output dimensionality +# - zero_pad: Boolean. If True, all the values of the first row (id = 0) should be constant zero +# - scale: Boolean. If True, the output will be multiplied by sqrt num_units(check details from paper) +# - scope: Optional scope for `variable_scope`. +# - reuse: Boolean, whether to reuse the weights of a previous layer by the same name. +# +# Returns: +# +# - A 'Tensor' with one more rank than inputs's, with the dimensionality should be 'num_units' +# ''' +# +# _, T, num_units = inputs.get_shape().as_list() +# # with tf.variable_scope(scope, reuse=reuse): +# position_ind = tf.expand_dims(tf.range(T), 0) +# # First part of the PE function: sin and cos argument +# position_enc = np.array([ +# [pos / np.power(10000, 2. * i / num_units) +# for i in range(num_units)] +# for pos in range(T)]) +# +# # Second part, apply the cosine to even columns and sin to odds. +# position_enc[:, 0::2] = np.sin(position_enc[:, 0::2]) # dim 2i +# position_enc[:, 1::2] = np.cos(position_enc[:, 1::2]) # dim 2i+1 +# +# # Convert to a tensor +# +# if pos_embedding_trainable: +# lookup_table = K.variable(position_enc, dtype=tf.float32) +# +# if zero_pad: +# lookup_table = tf.concat((tf.zeros(shape=[1, num_units]), +# lookup_table[1:, :]), 0) +# +# outputs = tf.nn.embedding_lookup(lookup_table, position_ind) +# +# if scale: +# outputs = outputs * num_units ** 0.5 +# return outputs + inputs diff --git a/deepctr/models/bst.py b/deepctr/models/bst.py index ba5a8bb8..d4fe8a02 100644 --- a/deepctr/models/bst.py +++ b/deepctr/models/bst.py @@ -82,7 +82,7 @@ def BST(dnn_feature_columns, history_feature_list, transformer_num=1, att_head_n hist_emb = concat_func(hist_emb_list) transformer_output = hist_emb - for i in range(transformer_num): + for _ in range(transformer_num): att_embedding_size = transformer_output.get_shape().as_list()[-1] // att_head_num transformer_layer = Transformer(att_embedding_size=att_embedding_size, head_num=att_head_num, dropout_rate=dnn_dropout, use_positional_encoding=True, use_res=True, diff --git a/deepctr/models/deepfefm.py b/deepctr/models/deepfefm.py index 4ee7d20f..ac03e973 100644 --- a/deepctr/models/deepfefm.py +++ b/deepctr/models/deepfefm.py @@ -21,16 +21,16 @@ from ..layers.utils import concat_func, combined_dnn_input, reduce_sum -def DeepFEFM(linear_feature_columns, dnn_feature_columns, embedding_size=48, use_fefm=True, - dnn_hidden_units=(1024, 1024, 1024), l2_reg_linear=0.000001, l2_reg_embedding_feat=0.00001, - l2_reg_embedding_field=0.0000001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.2, exclude_feature_embed_in_dnn=False, +def DeepFEFM(linear_feature_columns, dnn_feature_columns, use_fefm=True, + dnn_hidden_units=(128, 128), l2_reg_linear=0.00001, l2_reg_embedding_feat=0.00001, + l2_reg_embedding_field=0.00001, l2_reg_dnn=0, seed=1024, dnn_dropout=0.0, + exclude_feature_embed_in_dnn=False, use_linear=True, use_fefm_embed_in_dnn=True, dnn_activation='relu', dnn_use_bn=False, task='binary'): """Instantiates the DeepFEFM Network architecture or the shallow FEFM architecture (Ablation studies supported) :param linear_feature_columns: An iterable containing all the features used by linear part of the model. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param fm_group: list, group_name of features that will be used to do feature interactions. - :param embedding_size: positive integer,sparse feature embedding_size :param use_fefm: bool,use FEFM logit or not (doesn't effect FEFM embeddings in DNN, controls only the use of final FEFM logit) :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN :param l2_reg_linear: float. L2 regularizer strength applied to linear part @@ -58,9 +58,10 @@ def DeepFEFM(linear_feature_columns, dnn_feature_columns, embedding_size=48, use l2_reg_embedding_feat, seed, support_group=True) - fefm_interaction_embedding = concat_func([FEFMLayer(num_fields=len(v), embedding_size=embedding_size, - regularizer=l2_reg_embedding_field)(concat_func(v, axis=1)) - for k, v in group_embedding_dict.items() if k in [DEFAULT_GROUP_NAME]], axis=1) + fefm_interaction_embedding = concat_func([FEFMLayer( + regularizer=l2_reg_embedding_field)(concat_func(v, axis=1)) + for k, v in group_embedding_dict.items() if k in [DEFAULT_GROUP_NAME]], + axis=1) dnn_input = combined_dnn_input(list(chain.from_iterable(group_embedding_dict.values())), dense_value_list) diff --git a/deepctr/models/dien.py b/deepctr/models/dien.py index 98ce9f1d..a06250a1 100644 --- a/deepctr/models/dien.py +++ b/deepctr/models/dien.py @@ -213,4 +213,5 @@ def DIEN(dnn_feature_columns, history_feature_list, 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FLEN: Leveraging Field for Scalable CTR Prediction[J]. arXiv preprint arXiv:1911.04690, 2019.](https://arxiv.org/pdf/1911.04690.pdf) +### IFM(Input-aware Factorization Machine) + +IFM improves FMs by explicitly considering the impact of each individual input upon the representation of features, which learns a unique input-aware factor for the same feature in different instances via a neural network. + +[**IFM Model API**](./deepctr.models.ifm.html) + +![IFM](../pics/IFM.jpg) + +[Yu Y, Wang Z, Yuan B. An Input-aware Factorization Machine for Sparse Prediction[C]//IJCAI. 2019: 1466-1472.](https://www.ijcai.org/Proceedings/2019/0203.pdf) + +### DIFM(Dual Input-aware Factorization Machine) + +Dual Input-aware Factorization Machines (DIFMs) can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. +[**DIFM Model API**](./deepctr.models.difm.html) + +![DIFM](../pics/DIFM.jpg) + +[Lu W, Yu Y, Chang Y, et al. A Dual Input-aware Factorization Machine for CTR Prediction[C]//IJCAI. 2020: 3139-3145.](https://www.ijcai.org/Proceedings/2020/0434.pdf) + +### DeepFEFM(Deep Field-Embedded Factorization Machine) + +FEFM learns symmetric matrix embeddings for each field pair along with the usual single vector embeddings for each feature. FEFM has significantly lower model complexity than FFM and roughly the same complexity as FwFM. +[**DeepFEFM Model API**](./deepctr.models.deepfefm.html) + +![DeepFEFM](../pics/DeepFEFM.jpg) + +[Pande H. Field-Embedded Factorization Machines for Click-through rate prediction[J]. arXiv preprint arXiv:2009.09931, 2020.](https://arxiv.org/pdf/2009.09931) + + ## Layers The models of deepctr are modular, diff --git a/docs/source/History.md b/docs/source/History.md index 2559dccc..b0304655 100644 --- a/docs/source/History.md +++ b/docs/source/History.md @@ -1,4 +1,5 @@ # History +- 06/14/2021 : [v0.8.6](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.6) released.Add [IFM](./Features.html#ifm-input-aware-factorization-machine) [DIFM](./Features.html#difm-dual-input-aware-factorization-machine), [FEFM and DeepFEFM](./Features.html#deepfefm-deep-field-embedded-factorization-machine) model. - 03/13/2021 : [v0.8.5](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.5) released.Add [BST](./Features.html#bst-behavior-sequence-transformer) model. - 02/12/2021 : [v0.8.4](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.4) released.Fix bug in DCN-Mix. - 01/06/2021 : [v0.8.3](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.3) released.Add [DCN-Mix](./Features.html#dcn-mix-improved-deep-cross-network-with-mix-of-experts-and-matrix-kernel) model.Support `transform_fn` in `DenseFeat`. diff --git a/docs/source/Models.rst b/docs/source/Models.rst index f123dea6..164a7ba0 100644 --- a/docs/source/Models.rst +++ b/docs/source/Models.rst @@ -23,5 +23,8 @@ DeepCTR Models API FGCNN FiBiNET FLEN + IFM + DIFM + DeepFEFM \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index f36db6d8..536ff116 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -26,7 +26,7 @@ # The short X.Y version version = '' # The full version, including alpha/beta/rc tags -release = '0.8.5' +release = '0.8.6' # -- General configuration --------------------------------------------------- diff --git a/docs/source/deepctr.estimator.models.deepfefm.rst b/docs/source/deepctr.estimator.models.deepfefm.rst new file mode 100644 index 00000000..8395999d --- /dev/null +++ b/docs/source/deepctr.estimator.models.deepfefm.rst @@ -0,0 +1,7 @@ +deepctr.estimator.models.deepfefm module +====================================== + +.. automodule:: deepctr.estimator.models.deepfefm + :members: + :no-undoc-members: + :no-show-inheritance: diff --git a/docs/source/deepctr.models.deepfefm.rst b/docs/source/deepctr.models.deepfefm.rst new file mode 100644 index 00000000..614d6fe0 --- /dev/null +++ b/docs/source/deepctr.models.deepfefm.rst @@ -0,0 +1,7 @@ +deepctr.models.deepfefm module +============================== + +.. automodule:: deepctr.models.deepfefm + :members: + :no-undoc-members: + :no-show-inheritance: diff --git a/docs/source/deepctr.models.difm.rst b/docs/source/deepctr.models.difm.rst new file mode 100644 index 00000000..fc24d0b2 --- /dev/null +++ b/docs/source/deepctr.models.difm.rst @@ -0,0 +1,7 @@ +deepctr.models.difm module +============================= + +.. automodule:: deepctr.models.difm + :members: + :no-undoc-members: + :no-show-inheritance: diff --git a/docs/source/deepctr.models.ifm.rst b/docs/source/deepctr.models.ifm.rst new file mode 100644 index 00000000..ad835759 --- /dev/null +++ b/docs/source/deepctr.models.ifm.rst @@ -0,0 +1,7 @@ +deepctr.models.ifm module +============================= + +.. automodule:: deepctr.models.ifm + :members: + :no-undoc-members: + :no-show-inheritance: diff --git a/docs/source/deepctr.models.rst b/docs/source/deepctr.models.rst index 0f1209b2..3ee25047 100644 --- a/docs/source/deepctr.models.rst +++ b/docs/source/deepctr.models.rst @@ -25,6 +25,9 @@ Submodules deepctr.models.wdl deepctr.models.xdeepfm deepctr.models.flen + deepctr.models.ifm + deepctr.models.difm + deepctr.models.deepfefm Module contents --------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index f5acd97f..a8904b99 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,16 +42,16 @@ You can read the latest code and related projects News ----- +06/14/2021 : Add `IFM <./Features.html#ifm-input-aware-factorization-machine>`_ , `DIFM <./Features.html#difm-dual-input-aware-factorization-machine>`_ and `DeepFEFM <./Features.html#deepfefm-deep-field-embedded-factorization-machine>`_ . `Changelog `_ + 03/13/2021 : Add `BST <./Features.html#bst-behavior-sequence-transformer>`_ . `Changelog `_ 02/12/2021 : Fix bug in DCN-Mix. `Changelog `_ -01/06/2021 : Add `DCN-Mix <./Features.html#dcn-mix-improved-deep-cross-network-with-mix-of-experts-and-matrix-kernel>`_ (`中文介绍 `_) and support ``transform_fn`` in ``DenseFeat``. `Changelog `_ - DisscussionGroup ----------------------- -`Discussions `_ 公众号：**浅梦的学习笔记** wechat ID: **deepctrbot** +`Discussions `_ 公众号：**浅梦学习笔记** wechat ID: **deepctrbot** .. image:: ../pics/code.png diff --git a/examples/run_deepfefm.py b/examples/run_deepfefm.py deleted file mode 100644 index f1c9122f..00000000 --- a/examples/run_deepfefm.py +++ /dev/null @@ -1,54 +0,0 @@ -import pandas as pd -from sklearn.metrics import log_loss, roc_auc_score -from sklearn.model_selection import train_test_split -from sklearn.preprocessing import LabelEncoder, MinMaxScaler - -from deepctr.models import DeepFEFM -from deepctr.feature_column import SparseFeat, DenseFeat, get_feature_names - -if __name__ == "__main__": - data = pd.read_csv('./criteo_sample.txt') - - sparse_features = ['C' + str(i) for i in range(1, 27)] - dense_features = ['I' + str(i) for i in range(1, 14)] - - data[sparse_features] = data[sparse_features].fillna('-1', ) - data[dense_features] = data[dense_features].fillna(0, ) - target = ['label'] - - # 1.Label Encoding for sparse features,and do simple Transformation for dense features - for feat in sparse_features: - lbe = LabelEncoder() - data[feat] = lbe.fit_transform(data[feat]) - mms = MinMaxScaler(feature_range=(0, 1)) - data[dense_features] = mms.fit_transform(data[dense_features]) - - # 2.count #unique features for each sparse field,and record dense feature field name - - fixlen_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4 ) - for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,) - for feat in dense_features] - - dnn_feature_columns = fixlen_feature_columns - linear_feature_columns = fixlen_feature_columns - - feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns) - - # 3.generate input data for model - - train, test = train_test_split(data, test_size=0.2, random_state=2020) - train_model_input = {name: train[name] for name in feature_names} - test_model_input = {name: test[name] for name in feature_names} - - # 4.Define Model,train,predict and evaluate - model = DeepFEFM(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128), use_fefm=True, - use_fefm_embed_in_dnn=True, embedding_size=4, task='binary') - - model.compile("adam", "binary_crossentropy", - metrics=['binary_crossentropy'], ) - - history = model.fit(train_model_input, train[target].values, - batch_size=256, epochs=10, verbose=2, validation_split=0.2, ) - pred_ans = model.predict(test_model_input, batch_size=256) - print("test LogLoss", round(log_loss(test[target].values, pred_ans), 4)) - print("test AUC", round(roc_auc_score(test[target].values, pred_ans), 4)) diff --git a/examples/run_din.py b/examples/run_din.py index 44f162ee..725409c2 100644 --- a/examples/run_din.py +++ b/examples/run_din.py @@ -1,6 +1,6 @@ import numpy as np -from deepctr.models import DIN, BST +from deepctr.models import DIN from deepctr.feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, get_feature_names diff --git a/setup.py b/setup.py index 746c1136..a5b5219d 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ setuptools.setup( name="deepctr", - version="0.8.5", + version="0.8.6", author="Weichen Shen", author_email="weichenswc@163.com", description="Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x .", diff --git a/tests/layers/interaction_test.py b/tests/layers/interaction_test.py index cf57a623..9030955b 100644 --- a/tests/layers/interaction_test.py +++ b/tests/layers/interaction_test.py @@ -2,12 +2,8 @@ try: from tensorflow.python.keras.utils import CustomObjectScope - from tensorflow.python.keras.regularizers import l2 - from tensorflow.python.keras.initializers import TruncatedNormal except: from tensorflow.keras.utils import CustomObjectScope - from tensorflow.keras.regularizers import l2 - from tensorflow.keras.initializers import TruncatedNormal from deepctr import layers from tests.utils import layer_test @@ -20,10 +16,10 @@ def test_FEFMLayer(): with CustomObjectScope({'FEFMLayer': layers.FEFMLayer}): - layer_test(layers.FEFMLayer, kwargs={'num_fields': FIELD_SIZE, 'embedding_size': EMBEDDING_SIZE, - 'regularizer': 0.000001}, + layer_test(layers.FEFMLayer, kwargs={'regularizer': 0.000001}, input_shape=(BATCH_SIZE, FIELD_SIZE, EMBEDDING_SIZE)) + @pytest.mark.parametrize( 'reg_strength', [0.000001] @@ -33,6 +29,7 @@ def test_FwFM(reg_strength): layer_test(layers.FwFMLayer, kwargs={'num_fields': FIELD_SIZE, 'regularizer': reg_strength}, input_shape=(BATCH_SIZE, FIELD_SIZE, EMBEDDING_SIZE)) + @pytest.mark.parametrize( 'layer_num', diff --git a/tests/layers/sequence_test.py b/tests/layers/sequence_test.py index 75d1d5d2..dd030d74 100644 --- a/tests/layers/sequence_test.py +++ b/tests/layers/sequence_test.py @@ -1,5 +1,6 @@ import pytest from packaging import version + try: from tensorflow.python.keras.utils import CustomObjectScope except: @@ -26,20 +27,22 @@ def test_AttentionSequencePoolingLayer(weight_normalization): with CustomObjectScope({'AttentionSequencePoolingLayer': sequence.AttentionSequencePoolingLayer}): layer_test(sequence.AttentionSequencePoolingLayer, kwargs={'weight_normalization': weight_normalization}, - input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE), (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, 1)]) + input_shape=[(BATCH_SIZE, 1, EMBEDDING_SIZE), (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), + (BATCH_SIZE, 1)]) @pytest.mark.parametrize( 'mode,supports_masking,input_shape', - [('sum', False, [(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, 1)]), ('mean', True, (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)), ('max', True, (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)) + [('sum', False, [(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, 1)]), + ('mean', True, (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)), ('max', True, (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)) ] ) def test_SequencePoolingLayer(mode, supports_masking, input_shape): - if version.parse(tf.__version__) >= version.parse('1.14.0') and mode!='sum': #todo check further version - return + if version.parse(tf.__version__) >= version.parse('1.14.0') and mode != 'sum': # todo check further version + return with CustomObjectScope({'SequencePoolingLayer': sequence.SequencePoolingLayer}): layer_test(sequence.SequencePoolingLayer, kwargs={'mode': mode, 'supports_masking': supports_masking}, input_shape=input_shape, supports_masking=supports_masking) @@ -65,24 +68,41 @@ def test_SequencePoolingLayer(mode, supports_masking, input_shape): @pytest.mark.parametrize( 'merge_mode', - ['concat', 'ave', 'fw', - ] + ['concat', 'ave', 'fw', 'bw', 'sum', 'mul'] ) def test_BiLSTM(merge_mode): with CustomObjectScope({'BiLSTM': sequence.BiLSTM}): - layer_test(sequence.BiLSTM, kwargs={'merge_mode': merge_mode, 'units': EMBEDDING_SIZE,'dropout_rate':0.0}, #todo 0.5 + layer_test(sequence.BiLSTM, kwargs={'merge_mode': merge_mode, 'units': EMBEDDING_SIZE, 'dropout_rate': 0.0}, + # todo 0.5 input_shape=(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)) def test_Transformer(): if tf.__version__ >= '2.0.0': - tf.compat.v1.disable_eager_execution() #todo + tf.compat.v1.disable_eager_execution() # todo with CustomObjectScope({'Transformer': sequence.Transformer}): - layer_test(sequence.Transformer, kwargs={'att_embedding_size': 1, 'head_num': 8, 'use_layer_norm': True, 'supports_masking': False,'dropout_rate':0.5}, - input_shape=[(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, 1), (BATCH_SIZE, 1)]) + layer_test(sequence.Transformer, + kwargs={'att_embedding_size': 1, 'head_num': 8, 'use_layer_norm': True, 'supports_masking': False, + 'attention_type': 'additive', 'dropout_rate': 0.5, 'output_type': 'sum'}, + input_shape=[(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), + (BATCH_SIZE, 1), (BATCH_SIZE, 1)]) def test_KMaxPooling(): - with CustomObjectScope({'KMaxPooling':sequence.KMaxPooling}): - layer_test(sequence.KMaxPooling,kwargs={'k':3,'axis':1},input_shape=(BATCH_SIZE,SEQ_LENGTH,EMBEDDING_SIZE,2)) + with CustomObjectScope({'KMaxPooling': sequence.KMaxPooling}): + layer_test(sequence.KMaxPooling, kwargs={'k': 3, 'axis': 1}, + input_shape=(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE, 2)) + + +@pytest.mark.parametrize( + + 'pos_embedding_trainable,zero_pad', + [(True, False), (False, True) + ] +) +def test_PositionEncoding(pos_embedding_trainable, zero_pad): + with CustomObjectScope({'PositionEncoding': sequence.PositionEncoding}): + layer_test(sequence.PositionEncoding, + kwargs={'pos_embedding_trainable': pos_embedding_trainable, 'zero_pad': zero_pad}, + input_shape=(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)) diff --git a/tests/models/AFM_test.py b/tests/models/AFM_test.py index 8dd267c2..64a1bd4f 100644 --- a/tests/models/AFM_test.py +++ b/tests/models/AFM_test.py @@ -33,7 +33,6 @@ def test_AFMEstimator(use_attention, sparse_feature_num, dense_feature_num): if not Estimator_TEST_TF1 and version.parse(tf.__version__) < version.parse('2.2.0'): return - model_name = "AFM" sample_size = SAMPLE_SIZE linear_feature_columns, dnn_feature_columns, input_fn = get_test_data_estimator(sample_size, diff --git a/tests/models/DeepFEFM_test.py b/tests/models/DeepFEFM_test.py index e952948e..c2cfbefa 100644 --- a/tests/models/DeepFEFM_test.py +++ b/tests/models/DeepFEFM_test.py @@ -6,6 +6,7 @@ from ..utils import check_model, get_test_data, SAMPLE_SIZE, get_test_data_estimator, check_estimator, \ Estimator_TEST_TF1 + @pytest.mark.parametrize( 'hidden_size,sparse_feature_num,use_fefm,use_linear,use_fefm_embed_in_dnn', [((2,), 1, True, True, True), @@ -20,12 +21,14 @@ ] ) def test_DeepFEFM(hidden_size, sparse_feature_num, use_fefm, use_linear, use_fefm_embed_in_dnn): + if tf.__version__ == "1.15.0": # slow in tf 1.15 + return model_name = "DeepFEFM" sample_size = SAMPLE_SIZE x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num, dense_feature_num=sparse_feature_num) - model = DeepFEFM(feature_columns, feature_columns, embedding_size=4, dnn_hidden_units=hidden_size, dnn_dropout=0.5, - use_linear=use_linear, use_fefm=use_fefm, use_fefm_embed_in_dnn=use_fefm_embed_in_dnn) + model = DeepFEFM(feature_columns, feature_columns, dnn_hidden_units=hidden_size, dnn_dropout=0.5, + use_linear=use_linear, use_fefm=use_fefm, use_fefm_embed_in_dnn=use_fefm_embed_in_dnn) check_model(model, model_name, x, y) @@ -44,7 +47,7 @@ def test_DeepFEFMEstimator(hidden_size, sparse_feature_num): sparse_feature_num=sparse_feature_num, dense_feature_num=sparse_feature_num) - model = DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, embedding_size=4, + model = DeepFEFMEstimator(linear_feature_columns, dnn_feature_columns, dnn_hidden_units=hidden_size, dnn_dropout=0.5) check_estimator(model, input_fn) diff --git a/tests/models/PNN_test.py b/tests/models/PNN_test.py index 2d5571f6..46e60c17 100644 --- a/tests/models/PNN_test.py +++ b/tests/models/PNN_test.py @@ -30,7 +30,7 @@ def test_PNNEstimator(use_inner, use_outter, sparse_feature_num): if not Estimator_TEST_TF1 and tf.__version__ < "2.2.0": return sample_size = SAMPLE_SIZE - linear_feature_columns, dnn_feature_columns, input_fn = get_test_data_estimator(sample_size, + _, dnn_feature_columns, input_fn = get_test_data_estimator(sample_size, sparse_feature_num=sparse_feature_num, dense_feature_num=sparse_feature_num)