From ab595ec4e3d4b8cef978538738fee9770cfc0827 Mon Sep 17 00:00:00 2001 From: Qinlong Wang Date: Sun, 20 Dec 2020 21:45:49 +0800 Subject: [PATCH 1/3] Add a transform_fn into DenseFeat (#309) * Add transform_fn for DenseFeat --- deepctr/feature_column.py | 16 +++++++++++++--- deepctr/inputs.py | 8 ++++++-- tests/utils.py | 10 +++++++++- 3 files changed, 28 insertions(+), 6 deletions(-) diff --git a/deepctr/feature_column.py b/deepctr/feature_column.py index 64cd4dd3..8e198f38 100644 --- a/deepctr/feature_column.py +++ b/deepctr/feature_column.py @@ -87,11 +87,21 @@ def __hash__(self): return self.name.__hash__() -class DenseFeat(namedtuple('DenseFeat', ['name', 'dimension', 'dtype'])): +class DenseFeat(namedtuple('DenseFeat', ['name', 'dimension', 'dtype', 'transform_fn'])): + """ Dense feature + Args: + name: feature name, + dimension: dimension of the feature, default = 1. + dtype: dtype of the feature, default="float32". + transform_fn: If not None, a function that can be used to transfrom + values of the feature. the function takes the input Tensor as its + argument, and returns the output Tensor. + (e.g. lambda x: (x - 3.0) / 4.2). + """ __slots__ = () - def __new__(cls, name, dimension=1, dtype="float32"): - return super(DenseFeat, cls).__new__(cls, name, dimension, dtype) + def __new__(cls, name, dimension=1, dtype="float32", transform_fn=None): + return super(DenseFeat, cls).__new__(cls, name, dimension, dtype, transform_fn) def __hash__(self): return self.name.__hash__() diff --git a/deepctr/inputs.py b/deepctr/inputs.py index 44e17906..92019a85 100644 --- a/deepctr/inputs.py +++ b/deepctr/inputs.py @@ -9,7 +9,7 @@ from collections import defaultdict from itertools import chain -from tensorflow.python.keras.layers import Embedding +from tensorflow.python.keras.layers import Embedding, Lambda from tensorflow.python.keras.regularizers import l2 from .layers.sequence import SequencePoolingLayer, WeightedSequenceLayer @@ -138,7 +138,11 @@ def get_dense_input(features, feature_columns): filter(lambda x: isinstance(x, fc_lib.DenseFeat), feature_columns)) if feature_columns else [] dense_input_list = [] for fc in dense_feature_columns: - dense_input_list.append(features[fc.name]) + if fc.transform_fn is None: + dense_input_list.append(features[fc.name]) + else: + transform_result = Lambda(fc.transform_fn)(features[fc.name]) + dense_input_list.append(transform_result) return dense_input_list diff --git a/tests/utils.py b/tests/utils.py index c5fa96e2..db570297 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -52,7 +52,15 @@ def get_test_data(sample_size=1000, embedding_size=4, sparse_feature_num=1, dens SparseFeat(prefix + 'sparse_feature_' + str(i), dim, embedding_size, use_hash=hash_flag, dtype=tf.int32,group_name=group_name)) for i in range(dense_feature_num): - feature_columns.append(DenseFeat(prefix + 'dense_feature_' + str(i), 1, dtype=tf.float32)) + transform_fn = lambda x: (x - 0.0)/ 1.0 + feature_columns.append( + DenseFeat( + prefix + 'dense_feature_' + str(i), + 1, + dtype=tf.float32, + transform_fn=transform_fn + ) + ) for i, mode in enumerate(sequence_feature): dim = np.random.randint(1, 10) maxlen = np.random.randint(1, 10) From f937aea4cce154ac0f54f7d65c4ff812152c3118 Mon Sep 17 00:00:00 2001 From: Jason Zan <631763140@qq.com> Date: Sun, 3 Jan 2021 20:06:33 +0800 Subject: [PATCH 2/3] add DCN-M and DCN-Mix (#316) - Add DCN-M and DCN-Mix - update test files for DCN and DCNMix - set `h5py==2.10.0` for pytest - simplify `interaction.py ` - modify BilinearInteraction in FiBiNET --- deepctr/layers/__init__.py | 3 +- deepctr/layers/interaction.py | 210 +++++++++++++++++++++++++++++----- deepctr/models/__init__.py | 3 +- deepctr/models/dcn.py | 14 ++- deepctr/models/dcnmix.py | 83 ++++++++++++++ setup.py | 2 +- tests/models/DCNMix_test.py | 26 +++++ tests/models/DCN_test.py | 10 +- 8 files changed, 314 insertions(+), 37 deletions(-) create mode 100644 deepctr/models/dcnmix.py create mode 100644 tests/models/DCNMix_test.py diff --git a/deepctr/layers/__init__.py b/deepctr/layers/__init__.py index 89cc60fb..9617a8f8 100644 --- a/deepctr/layers/__init__.py +++ b/deepctr/layers/__init__.py @@ -2,7 +2,7 @@ from .activation import Dice from .core import DNN, LocalActivationUnit, PredictionLayer -from .interaction import (CIN, FM, AFMLayer, BiInteractionPooling, CrossNet, +from .interaction import (CIN, FM, AFMLayer, BiInteractionPooling, CrossNet, CrossNetMix, InnerProductLayer, InteractingLayer, OutterProductLayer, FGCNNLayer, SENETLayer, BilinearInteraction, FieldWiseBiInteraction, FwFMLayer) @@ -20,6 +20,7 @@ 'FM': FM, 'AFMLayer': AFMLayer, 'CrossNet': CrossNet, + 'CrossNetMix': CrossNetMix, 'BiInteractionPooling': BiInteractionPooling, 'LocalActivationUnit': LocalActivationUnit, 'Dice': Dice, diff --git a/deepctr/layers/interaction.py b/deepctr/layers/interaction.py index c4a277d1..92107a11 100644 --- a/deepctr/layers/interaction.py +++ b/deepctr/layers/interaction.py @@ -142,7 +142,8 @@ def get_config(self, ): config = {'attention_factor': self.attention_factor, 'l2_reg_w': self.l2_reg_w, 'dropout_rate': self.dropout_rate, 'seed': self.seed} base_config = super(AFMLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class BiInteractionPooling(Layer): @@ -322,7 +323,8 @@ def get_config(self, ): config = {'layer_size': self.layer_size, 'split_half': self.split_half, 'activation': self.activation, 'seed': self.seed} base_config = super(CIN, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class CrossNet(Layer): @@ -340,16 +342,20 @@ class CrossNet(Layer): - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix + - **parameterization**: string, ``"vector"`` or ``"matrix"`` , way to parameterize the cross network. + - **seed**: A Python integer to use as random seed. References - [Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12.](https://arxiv.org/abs/1708.05123) """ - def __init__(self, layer_num=2, l2_reg=0, seed=1024, **kwargs): + def __init__(self, layer_num=2, parameterization='vector', l2_reg=0, seed=1024, **kwargs): self.layer_num = layer_num + self.parameterization = parameterization self.l2_reg = l2_reg self.seed = seed + print('CrossNet parameterization:', self.parameterization) super(CrossNet, self).__init__(**kwargs) def build(self, input_shape): @@ -359,12 +365,22 @@ def build(self, input_shape): "Unexpected inputs dimensions %d, expect to be 2 dimensions" % (len(input_shape),)) dim = int(input_shape[-1]) - self.kernels = [self.add_weight(name='kernel' + str(i), - shape=(dim, 1), - initializer=glorot_normal( - seed=self.seed), - regularizer=l2(self.l2_reg), - trainable=True) for i in range(self.layer_num)] + if self.parameterization == 'vector': + self.kernels = [self.add_weight(name='kernel' + str(i), + shape=(dim, 1), + initializer=glorot_normal( + seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(self.layer_num)] + elif self.parameterization == 'matrix': + self.kernels = [self.add_weight(name='kernel' + str(i), + shape=(dim, dim), + initializer=glorot_normal( + seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(self.layer_num)] + else: # error + raise ValueError("parameterization should be 'vector' or 'matrix'") self.bias = [self.add_weight(name='bias' + str(i), shape=(dim, 1), initializer=Zeros(), @@ -380,18 +396,152 @@ def call(self, inputs, **kwargs): x_0 = tf.expand_dims(inputs, axis=2) x_l = x_0 for i in range(self.layer_num): - xl_w = tf.tensordot(x_l, self.kernels[i], axes=(1, 0)) - dot_ = tf.matmul(x_0, xl_w) - x_l = dot_ + self.bias[i] + x_l + if self.parameterization == 'vector': + xl_w = tf.tensordot(x_l, self.kernels[i], axes=(1, 0)) + dot_ = tf.matmul(x_0, xl_w) + x_l = dot_ + self.bias[i] + elif self.parameterization == 'matrix': + dot_ = tf.einsum('ij,bjk->bik', self.kernels[i], x_l) # W * xi (bs, dim, 1) + dot_ = dot_ + self.bias[i] # W * xi + b + dot_ = x_0 * dot_ # x0 · (W * xi + b) Hadamard-product + else: # error + print("parameterization should be 'vector' or 'matrix'") + x_l = dot_ + x_l x_l = tf.squeeze(x_l, axis=2) return x_l def get_config(self, ): - config = {'layer_num': self.layer_num, + config = {'layer_num': self.layer_num, 'parameterization': self.parameterization, 'l2_reg': self.l2_reg, 'seed': self.seed} base_config = super(CrossNet, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config + + def compute_output_shape(self, input_shape): + return input_shape + + +class CrossNetMix(Layer): + """The Cross Network part of DCN-Mix model, which improves DCN-M by: + 1 add MOE to learn feature interactions in different subspaces + 2 add nonlinear transformations in low-dimensional space + + Input shape + - 2D tensor with shape: ``(batch_size, units)``. + + Output shape + - 2D tensor with shape: ``(batch_size, units)``. + + Arguments + - **low_rank** : Positive integer, dimensionality of low-rank sapce. + + - **num_experts** : Positive integer, number of experts. + + - **layer_num**: Positive integer, the cross layer number + + - **l2_reg**: float between 0 and 1. L2 regularizer strength applied to the kernel weights matrix + + - **seed**: A Python integer to use as random seed. + + References + - [Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020.](https://arxiv.org/abs/2008.13535) + """ + + def __init__(self, low_rank=32, num_experts=4, layer_num=2, l2_reg=0, seed=1024, **kwargs): + self.low_rank = low_rank + self.num_experts = num_experts + self.layer_num = layer_num + self.l2_reg = l2_reg + self.seed = seed + super(CrossNetMix, self).__init__(**kwargs) + + def build(self, input_shape): + + if len(input_shape) != 2: + raise ValueError( + "Unexpected inputs dimensions %d, expect to be 2 dimensions" % (len(input_shape),)) + + dim = int(input_shape[-1]) + + # U: (dim, low_rank) + self.U_list = [self.add_weight(name='U_list' + str(i), + shape=(self.num_experts, dim, self.low_rank), + initializer=glorot_normal( + seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(self.layer_num)] + # V: (dim, low_rank) + self.V_list = [self.add_weight(name='V_list' + str(i), + shape=(self.num_experts, dim, self.low_rank), + initializer=glorot_normal( + seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(self.layer_num)] + # C: (low_rank, low_rank) + self.C_list = [self.add_weight(name='C_list' + str(i), + shape=(self.num_experts, self.low_rank, self.low_rank), + initializer=glorot_normal( + seed=self.seed), + regularizer=l2(self.l2_reg), + trainable=True) for i in range(self.layer_num)] + + self.gating = [tf.keras.layers.Dense(1, use_bias=False) for i in range(self.num_experts)] + + self.bias = [self.add_weight(name='bias' + str(i), + shape=(dim, 1), + initializer=Zeros(), + trainable=True) for i in range(self.layer_num)] + # Be sure to call this somewhere! + super(CrossNetMix, self).build(input_shape) + + def call(self, inputs, **kwargs): + if K.ndim(inputs) != 2: + raise ValueError( + "Unexpected inputs dimensions %d, expect to be 2 dimensions" % (K.ndim(inputs))) + + x_0 = tf.expand_dims(inputs, axis=2) + x_l = x_0 + for i in range(self.layer_num): + output_of_experts = [] + gating_score_of_experts = [] + for expert_id in range(self.num_experts): + # (1) G(x_l) + # compute the gating score by x_l + gating_score_of_experts.append(self.gating[expert_id](tf.squeeze(x_l, axis=2))) + + # (2) E(x_l) + # project the input x_l to $\mathbb{R}^{r}$ + v_x = tf.einsum('ij,bjk->bik', tf.transpose(self.V_list[i][expert_id]), x_l) # (bs, low_rank, 1) + + # nonlinear activation in low rank space + v_x = tf.nn.tanh(v_x) + v_x = tf.einsum('ij,bjk->bik', self.C_list[i][expert_id], v_x) # (bs, low_rank, 1) + v_x = tf.nn.tanh(v_x) + + # project back to $\mathbb{R}^{d}$ + uv_x = tf.einsum('ij,bjk->bik', self.U_list[i][expert_id], v_x) # (bs, dim, 1) + + dot_ = uv_x + self.bias[i] + dot_ = x_0 * dot_ # Hadamard-product + + output_of_experts.append(tf.squeeze(dot_, axis=2)) + + # (3) mixture of low-rank experts + output_of_experts = tf.stack(output_of_experts, 2) # (bs, dim, num_experts) + gating_score_of_experts = tf.stack(gating_score_of_experts, 1) # (bs, num_experts, 1) + moe_out = tf.matmul(output_of_experts, tf.nn.softmax(gating_score_of_experts, 1)) + x_l = moe_out + x_l # (bs, dim, 1) + x_l = tf.squeeze(x_l, axis=2) + return x_l + + def get_config(self, ): + + config = {'low_rank': self.low_rank, 'num_experts': self.num_experts, 'layer_num': self.layer_num, + 'l2_reg': self.l2_reg, 'seed': self.seed} + base_config = super(CrossNetMix, self).get_config() + base_config.update(config) + return base_config def compute_output_shape(self, input_shape): return input_shape @@ -527,7 +677,8 @@ def compute_output_shape(self, input_shape): def get_config(self, ): config = {'reduce_sum': self.reduce_sum, } base_config = super(InnerProductLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class InteractingLayer(Layer): @@ -619,7 +770,8 @@ def get_config(self, ): config = {'att_embedding_size': self.att_embedding_size, 'head_num': self.head_num, 'use_res': self.use_res, 'seed': self.seed} base_config = super(InteractingLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class OutterProductLayer(Layer): @@ -762,7 +914,8 @@ def compute_output_shape(self, input_shape): def get_config(self, ): config = {'kernel_type': self.kernel_type, 'seed': self.seed} base_config = super(OutterProductLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class FGCNNLayer(Layer): @@ -866,7 +1019,8 @@ def get_config(self, ): config = {'kernel_width': self.kernel_width, 'filters': self.filters, 'new_maps': self.new_maps, 'pooling_width': self.pooling_width} base_config = super(FGCNNLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config def _conv_output_shape(self, input_shape, kernel_size): # channels_last @@ -965,20 +1119,21 @@ def compute_mask(self, inputs, mask=None): def get_config(self, ): config = {'reduction_ratio': self.reduction_ratio, 'seed': self.seed} base_config = super(SENETLayer, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class BilinearInteraction(Layer): """BilinearInteraction Layer used in FiBiNET. Input shape - - A list of 3D tensor with shape: ``(batch_size,1,embedding_size)``. + - A list of 3D tensor with shape: ``(batch_size,1,embedding_size)``. Its length is ``filed_size``. Output shape - - 3D tensor with shape: ``(batch_size,1,embedding_size)``. + - 3D tensor with shape: ``(batch_size,filed_size*(filed_size-1)/2,embedding_size)``. Arguments - - **str** : String, types of bilinear functions used in this layer. + - **bilinear_type** : String, types of bilinear functions used in this layer. - **seed** : A Python integer to use as random seed. @@ -1034,18 +1189,20 @@ def call(self, inputs, **kwargs): for v, w in zip(itertools.combinations(inputs, 2), self.W_list)] else: raise NotImplementedError - return concat_func(p) + output = concat_func(p, axis=1) + return output def compute_output_shape(self, input_shape): filed_size = len(input_shape) embedding_size = input_shape[0][-1] - return (None, 1, filed_size * (filed_size - 1) // 2 * embedding_size) + return (None, filed_size * (filed_size - 1) // 2, embedding_size) def get_config(self, ): config = {'bilinear_type': self.bilinear_type, 'seed': self.seed} base_config = super(BilinearInteraction, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class FieldWiseBiInteraction(Layer): @@ -1171,7 +1328,8 @@ def compute_output_shape(self, input_shape): def get_config(self, ): config = {'use_bias': self.use_bias, 'seed': self.seed} base_config = super(FieldWiseBiInteraction, self).get_config() - return dict(list(base_config.items()) + list(config.items())) + base_config.update(config) + return base_config class FwFMLayer(Layer): diff --git a/deepctr/models/__init__.py b/deepctr/models/__init__.py index bc536a5e..a8ec03f4 100644 --- a/deepctr/models/__init__.py +++ b/deepctr/models/__init__.py @@ -2,6 +2,7 @@ from .autoint import AutoInt from .ccpm import CCPM from .dcn import DCN +from .dcnmix import DCNMix from .deepfm import DeepFM from .dien import DIEN from .din import DIN @@ -19,5 +20,5 @@ from .flen import FLEN from .fwfm import FwFM -__all__ = ["AFM", "CCPM","DCN", "MLR", "DeepFM", "MLR", "NFM", "DIN", "DIEN", "FNN", "PNN", +__all__ = ["AFM", "CCPM", "DCN", "DCNMix", "MLR", "DeepFM", "MLR", "NFM", "DIN", "DIEN", "FNN", "PNN", "WDL", "xDeepFM", "AutoInt", "ONN", "FGCNN", "DSIN", "FiBiNET", 'FLEN', "FwFM"] diff --git a/deepctr/models/dcn.py b/deepctr/models/dcn.py index 8762cb1b..7998557f 100644 --- a/deepctr/models/dcn.py +++ b/deepctr/models/dcn.py @@ -3,8 +3,12 @@ Author: Weichen Shen,wcshen1994@163.com + Shuxun Zan, zanshuxun@aliyun.com + Reference: [1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123) + + [2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535) """ import tensorflow as tf @@ -14,8 +18,8 @@ from ..layers.utils import add_func, combined_dnn_input -def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_units=(128, 128,), l2_reg_linear=1e-5, - l2_reg_embedding=1e-5, +def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, cross_parameterization='vector', + dnn_hidden_units=(128, 128,), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, l2_reg_cross=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False, dnn_activation='relu', task='binary'): """Instantiates the Deep&Cross Network architecture. @@ -23,7 +27,9 @@ def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_uni :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 cross_num: positive integet,cross layer number + :param cross_parameterization: str, ``"vector"`` or ``"matrix"``, how to parameterize the cross network. :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_cross: float. L2 regularizer strength applied to cross net :param l2_reg_dnn: float. L2 regularizer strength applied to DNN @@ -51,7 +57,7 @@ def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_uni if len(dnn_hidden_units) > 0 and cross_num > 0: # Deep & Cross deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) - cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input) + cross_out = CrossNet(cross_num, parameterization=cross_parameterization, l2_reg=l2_reg_cross)(dnn_input) stack_out = tf.keras.layers.Concatenate()([cross_out, deep_out]) final_logit = tf.keras.layers.Dense( 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(stack_out) @@ -60,7 +66,7 @@ def DCN(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_uni final_logit = tf.keras.layers.Dense( 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(deep_out) elif cross_num > 0: # Only Cross - cross_out = CrossNet(cross_num, l2_reg=l2_reg_cross)(dnn_input) + cross_out = CrossNet(cross_num, parameterization=cross_parameterization, l2_reg=l2_reg_cross)(dnn_input) final_logit = tf.keras.layers.Dense( 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(cross_out) else: # Error diff --git a/deepctr/models/dcnmix.py b/deepctr/models/dcnmix.py new file mode 100644 index 00000000..41c9b911 --- /dev/null +++ b/deepctr/models/dcnmix.py @@ -0,0 +1,83 @@ +# -*- coding:utf-8 -*- +""" +Author: + Weichen Shen,wcshen1994@163.com + + Shuxun Zan, zanshuxun@aliyun.com + +Reference: + [1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123) + + [2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535) +""" +import tensorflow as tf + +from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns +from ..layers.core import PredictionLayer, DNN +from ..layers.interaction import CrossNetMix +from ..layers.utils import add_func, combined_dnn_input + + +def DCNMix(linear_feature_columns, dnn_feature_columns, cross_num=2, + dnn_hidden_units=(128, 128,), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, low_rank=32, num_experts=4, + l2_reg_cross=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False, + dnn_activation='relu', task='binary'): + """Instantiates the Deep&Cross 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 cross_num: positive integet,cross layer number + :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_cross: float. L2 regularizer strength applied to cross net + :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_use_bn: bool. Whether use BatchNormalization before activation or not DNN + :param dnn_activation: Activation function to use in DNN + :param low_rank: Positive integer, dimensionality of low-rank sapce. + :param num_experts: Positive integer, number of experts. + :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss + :return: A Keras model instance. + + """ + if len(dnn_hidden_units) == 0 and cross_num == 0: + raise ValueError("Either hidden_layer or cross layer must > 0") + + features = build_input_features(dnn_feature_columns) + inputs_list = list(features.values()) + + linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', + l2_reg=l2_reg_linear) + + sparse_embedding_list, dense_value_list = input_from_feature_columns(features, dnn_feature_columns, + l2_reg_embedding, seed) + + dnn_input = combined_dnn_input(sparse_embedding_list, dense_value_list) + + if len(dnn_hidden_units) > 0 and cross_num > 0: # Deep & Cross + deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) + cross_out = CrossNetMix(low_rank=low_rank, num_experts=num_experts, layer_num=cross_num, + l2_reg=l2_reg_cross)(dnn_input) + stack_out = tf.keras.layers.Concatenate()([cross_out, deep_out]) + final_logit = tf.keras.layers.Dense( + 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(stack_out) + elif len(dnn_hidden_units) > 0: # Only Deep + deep_out = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input) + final_logit = tf.keras.layers.Dense( + 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(deep_out) + elif cross_num > 0: # Only Cross + cross_out = CrossNetMix(low_rank=low_rank, num_experts=num_experts, layer_num=cross_num, + l2_reg=l2_reg_cross)(dnn_input) + final_logit = tf.keras.layers.Dense( + 1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(cross_out) + else: # Error + raise NotImplementedError + + final_logit = add_func([final_logit, linear_logit]) + output = PredictionLayer(task)(final_logit) + + model = tf.keras.models.Model(inputs=inputs_list, outputs=output) + + return model diff --git a/setup.py b/setup.py index 5f06066d..16bbd85c 100644 --- a/setup.py +++ b/setup.py @@ -4,7 +4,7 @@ long_description = fh.read() REQUIRED_PACKAGES = [ - 'h5py','requests' + 'h5py==2.10.0','requests' ] setuptools.setup( diff --git a/tests/models/DCNMix_test.py b/tests/models/DCNMix_test.py new file mode 100644 index 00000000..bf465831 --- /dev/null +++ b/tests/models/DCNMix_test.py @@ -0,0 +1,26 @@ +import pytest +import tensorflow as tf + +from deepctr.models import DCNMix +from ..utils import check_model, get_test_data, SAMPLE_SIZE, get_test_data_estimator, check_estimator, \ + Estimator_TEST_TF1 + + +@pytest.mark.parametrize( + 'cross_num,hidden_size,sparse_feature_num', + [(0, (8,), 2), (1, (), 1), (1, (8,), 3) + ] +) +def test_DCNMix(cross_num, hidden_size, sparse_feature_num): + model_name = "DCNMix" + + 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 = DCNMix(feature_columns, feature_columns, cross_num=cross_num, dnn_hidden_units=hidden_size, dnn_dropout=0.5) + check_model(model, model_name, x, y) + + +if __name__ == "__main__": + pass diff --git a/tests/models/DCN_test.py b/tests/models/DCN_test.py index 509e73e9..53fbd095 100644 --- a/tests/models/DCN_test.py +++ b/tests/models/DCN_test.py @@ -8,18 +8,20 @@ @pytest.mark.parametrize( - 'cross_num,hidden_size,sparse_feature_num', - [(0, (8,), 2), (1, (), 1), (1, (8,), 3) + 'cross_num,hidden_size,sparse_feature_num,cross_parameterization', + [(0, (8,), 2, 'vector'), (1, (), 1, 'vector'), (1, (8,), 3, 'vector'), + (0, (8,), 2, 'matrix'), (1, (), 1, 'matrix'), (1, (8,), 3, 'matrix'), ] ) -def test_DCN(cross_num, hidden_size, sparse_feature_num): +def test_DCN(cross_num, hidden_size, sparse_feature_num, cross_parameterization): model_name = "DCN" 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 = DCN(feature_columns, feature_columns, cross_num=cross_num, dnn_hidden_units=hidden_size, dnn_dropout=0.5) + model = DCN(feature_columns, feature_columns, cross_num=cross_num, cross_parameterization=cross_parameterization, + dnn_hidden_units=hidden_size, dnn_dropout=0.5) check_model(model, model_name, x, y) From 8360dc7b4ed4a1f303a2a179b98c088c89de025d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=B5=85=E6=A2=A6?= Date: Wed, 6 Jan 2021 22:09:38 +0800 Subject: [PATCH 3/3] update docs (#317) update docs --- .github/ISSUE_TEMPLATE/bug_report.md | 4 ++-- .github/ISSUE_TEMPLATE/question.md | 4 ++-- .github/workflows/ci.yml | 2 +- CONTRIBUTING.md | 15 +++++++++++-- README.md | 9 ++++---- deepctr/__init__.py | 2 +- deepctr/estimator/models/afm.py | 2 +- deepctr/estimator/models/autoint.py | 2 +- deepctr/estimator/models/ccpm.py | 2 +- deepctr/estimator/models/dcn.py | 2 +- deepctr/estimator/models/deepfm.py | 2 +- deepctr/estimator/models/fibinet.py | 2 +- deepctr/estimator/models/fnn.py | 2 +- deepctr/estimator/models/fwfm.py | 2 +- deepctr/estimator/models/nfm.py | 2 +- deepctr/estimator/models/pnn.py | 2 +- deepctr/estimator/models/wdl.py | 2 +- deepctr/estimator/models/xdeepfm.py | 2 +- deepctr/models/afm.py | 2 +- deepctr/models/autoint.py | 2 +- deepctr/models/ccpm.py | 2 +- deepctr/models/dcn.py | 2 +- deepctr/models/dcnmix.py | 6 ++--- deepctr/models/deepfm.py | 2 +- deepctr/models/dien.py | 2 +- deepctr/models/din.py | 2 +- deepctr/models/dsin.py | 2 +- deepctr/models/fgcnn.py | 2 +- deepctr/models/fibinet.py | 2 +- deepctr/models/flen.py | 2 +- deepctr/models/fnn.py | 2 +- deepctr/models/mlr.py | 2 +- deepctr/models/nfm.py | 2 +- deepctr/models/onn.py | 2 +- deepctr/models/pnn.py | 2 +- deepctr/models/wdl.py | 2 +- deepctr/models/xdeepfm.py | 2 +- docs/pics/DCN-M.png | Bin 0 -> 66893 bytes docs/pics/DCN-Mix.png | Bin 0 -> 57513 bytes docs/source/Features.md | 21 ++++++++++++++++-- docs/source/History.md | 1 + docs/source/Models.rst | 2 ++ docs/source/conf.py | 2 +- docs/source/deepctr.models.dcnmix.rst | 7 ++++++ docs/source/deepctr.models.rst | 1 + docs/source/index.rst | 5 ++--- .../run_estimator_pandas_classification.py | 3 ++- .../run_estimator_tfrecord_classification.py | 6 ++--- setup.py | 2 +- 49 files changed, 96 insertions(+), 56 deletions(-) create mode 100644 docs/pics/DCN-M.png create mode 100644 docs/pics/DCN-Mix.png create mode 100644 docs/source/deepctr.models.dcnmix.rst diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 78b188ff..b5d3b712 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -19,8 +19,8 @@ Steps to reproduce the behavior: **Operating environment(运行环境):** - python version [e.g. 3.5, 3.7] - - tensorflow version [e.g. 1.4.0, 1.15.0, 2.3.0] - - deepctr version [e.g. 0.8.2,] + - tensorflow version [e.g. 1.4.0, 1.15.0, 2.4.0] + - deepctr version [e.g. 0.8.3,] **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 7362b9c8..7f4dec64 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, 2.3.0] - - deepctr version [e.g. 0.8.2,] + - tensorflow version [e.g. 1.4.0, 1.5.0, 2.4.0] + - deepctr version [e.g. 0.8.3,] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b5132f12..88dae87c 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -18,7 +18,7 @@ jobs: strategy: matrix: python-version: [3.6,3.7] - tf-version: [1.4.0,1.15.0,2.1.0,2.3.0] + tf-version: [1.4.0,1.15.0,2.1.0,2.4.0] exclude: - python-version: 3.7 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index cfc7b519..ebc80d58 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,5 +1,5 @@ This project is under development and we need developers to participate in. - +# Join us If you - familiar with and interested in ctr prediction algorithms @@ -7,4 +7,15 @@ If you - have spare time to learn and develop - familiar with git -please send a brief introduction of your background and experience to wcshen1994@163.com, welcome to join us! \ No newline at end of file +please send a brief introduction of your background and experience to wcshen1994@163.com, welcome to join us! + +# Creating a pull request +1. **Become a collaborator**: Send an email with introduction and your github account name to wcshen1994@163.com and waiting for invitation to become a collaborator. +2. **Fork&Dev**: Fork your own branch(`dev_yourname`) in `DeepCTR` from the `master` branch for development.If the `master` is updated during the development process, remember to merge and update to `dev_yourname` regularly. +3. **Testing**: Test logical correctness and effect when finishing the code development of the `dev_yourname` branch. +4. **Pre-release** : After testing contact wcshen1994@163.com for pre-release integration, usually your branch `dev_yourname` will be merged into `release` branch by squash merge. +5. **Release a new version**: After confirming that the change is no longer needed, `release` branch will be merged into `master` and a new python package will be released on pypi. + +# Discussions + +https://github.com/shenweichen/DeepCTR/discussions \ No newline at end of file diff --git a/README.md b/README.md index 4727aa0e..39a24f41 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ [![Documentation Status](https://readthedocs.org/projects/deepctr-doc/badge/?version=latest)](https://deepctr-doc.readthedocs.io/) -[![Build Status](https://travis-ci.org/shenweichen/DeepCTR.svg?branch=master)](https://travis-ci.org/shenweichen/DeepCTR) +![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) [![Codacy Badge](https://api.codacy.com/project/badge/Grade/d4099734dc0e4bab91d332ead8c0bdd0)](https://www.codacy.com/app/wcshen1994/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) @@ -45,8 +45,8 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star | Attentional Factorization Machine | [IJCAI 2017][Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](http://www.ijcai.org/proceedings/2017/435) | | Neural Factorization Machine | [SIGIR 2017][Neural Factorization Machines for Sparse Predictive Analytics](https://arxiv.org/pdf/1708.05027.pdf) | | xDeepFM | [KDD 2018][xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://arxiv.org/pdf/1803.05170.pdf) | -| AutoInt | [arxiv 2018][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) | -| Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) | +| Deep Interest Network | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) +| AutoInt | [CIKM 2019][AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks](https://arxiv.org/abs/1810.11921) || | Deep Interest Evolution Network | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | FwFM | [WWW 2018][Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](https://arxiv.org/pdf/1806.03514.pdf) | | ONN | [arxiv 2019][Operation-aware Neural Networks for User Response Prediction](https://arxiv.org/pdf/1904.12579.pdf) | @@ -54,6 +54,7 @@ Let's [**Get Started!**](https://deepctr-doc.readthedocs.io/en/latest/Quick-Star | Deep Session Interest Network | [IJCAI 2019][Deep Session Interest Network for Click-Through Rate Prediction ](https://arxiv.org/abs/1905.06482) | | 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) | +| 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) | ## Citation @@ -75,7 +76,7 @@ If you find this code useful in your research, please cite it using the followin ## DisscussionGroup 交流群 -Please follow our wechat to join group: +- [Discussions](https://github.com/shenweichen/DeepCTR/discussions) - 公众号：**浅梦的学习笔记** - wechat ID: **deepctrbot** diff --git a/deepctr/__init__.py b/deepctr/__init__.py index c763d56b..b268aaeb 100644 --- a/deepctr/__init__.py +++ b/deepctr/__init__.py @@ -1,4 +1,4 @@ from .utils import check_version -__version__ = '0.8.2' +__version__ = '0.8.3' check_version(__version__) diff --git a/deepctr/estimator/models/afm.py b/deepctr/estimator/models/afm.py index 82066e2b..47fd2204 100644 --- a/deepctr/estimator/models/afm.py +++ b/deepctr/estimator/models/afm.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017. diff --git a/deepctr/estimator/models/autoint.py b/deepctr/estimator/models/autoint.py index f29a0447..40e3b1e1 100644 --- a/deepctr/estimator/models/autoint.py +++ b/deepctr/estimator/models/autoint.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] 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) diff --git a/deepctr/estimator/models/ccpm.py b/deepctr/estimator/models/ccpm.py index 1586b9f7..8d0f6dfd 100644 --- a/deepctr/estimator/models/ccpm.py +++ b/deepctr/estimator/models/ccpm.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746. diff --git a/deepctr/estimator/models/dcn.py b/deepctr/estimator/models/dcn.py index 1f62fa60..ceba2b6b 100644 --- a/deepctr/estimator/models/dcn.py +++ b/deepctr/estimator/models/dcn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123) diff --git a/deepctr/estimator/models/deepfm.py b/deepctr/estimator/models/deepfm.py index 30193c45..0021b506 100644 --- a/deepctr/estimator/models/deepfm.py +++ b/deepctr/estimator/models/deepfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247) diff --git a/deepctr/estimator/models/fibinet.py b/deepctr/estimator/models/fibinet.py index 99f4e746..619f4f8e 100644 --- a/deepctr/estimator/models/fibinet.py +++ b/deepctr/estimator/models/fibinet.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019. diff --git a/deepctr/estimator/models/fnn.py b/deepctr/estimator/models/fnn.py index 3b200ddf..aeb7de1a 100644 --- a/deepctr/estimator/models/fnn.py +++ b/deepctr/estimator/models/fnn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf) diff --git a/deepctr/estimator/models/fwfm.py b/deepctr/estimator/models/fwfm.py index 9948ecf0..f5de40ab 100644 --- a/deepctr/estimator/models/fwfm.py +++ b/deepctr/estimator/models/fwfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Harshit Pande Reference: diff --git a/deepctr/estimator/models/nfm.py b/deepctr/estimator/models/nfm.py index 3bc3378c..ff10b776 100644 --- a/deepctr/estimator/models/nfm.py +++ b/deepctr/estimator/models/nfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027) diff --git a/deepctr/estimator/models/pnn.py b/deepctr/estimator/models/pnn.py index fc0f33fe..add1da8b 100644 --- a/deepctr/estimator/models/pnn.py +++ b/deepctr/estimator/models/pnn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf) diff --git a/deepctr/estimator/models/wdl.py b/deepctr/estimator/models/wdl.py index 29e7df2e..381d5f28 100644 --- a/deepctr/estimator/models/wdl.py +++ b/deepctr/estimator/models/wdl.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf) diff --git a/deepctr/estimator/models/xdeepfm.py b/deepctr/estimator/models/xdeepfm.py index a86b5e97..65a0658a 100644 --- a/deepctr/estimator/models/xdeepfm.py +++ b/deepctr/estimator/models/xdeepfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.(https://arxiv.org/pdf/1803.05170.pdf) diff --git a/deepctr/models/afm.py b/deepctr/models/afm.py index 52743f0a..dd664373 100644 --- a/deepctr/models/afm.py +++ b/deepctr/models/afm.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Xiao J, Ye H, He X, et al. Attentional factorization machines: Learning the weight of feature interactions via attention networks[J]. arXiv preprint arXiv:1708.04617, 2017. diff --git a/deepctr/models/autoint.py b/deepctr/models/autoint.py index 6ea70951..c742e3c2 100644 --- a/deepctr/models/autoint.py +++ b/deepctr/models/autoint.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] 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) diff --git a/deepctr/models/ccpm.py b/deepctr/models/ccpm.py index 7c7731dc..cfe0013f 100644 --- a/deepctr/models/ccpm.py +++ b/deepctr/models/ccpm.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Liu Q, Yu F, Wu S, et al. A convolutional click prediction model[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 1743-1746. diff --git a/deepctr/models/dcn.py b/deepctr/models/dcn.py index 7998557f..5a14f5a7 100644 --- a/deepctr/models/dcn.py +++ b/deepctr/models/dcn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Shuxun Zan, zanshuxun@aliyun.com diff --git a/deepctr/models/dcnmix.py b/deepctr/models/dcnmix.py index 41c9b911..ea62cd24 100644 --- a/deepctr/models/dcnmix.py +++ b/deepctr/models/dcnmix.py @@ -1,14 +1,14 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Shuxun Zan, zanshuxun@aliyun.com Reference: [1] Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12. (https://arxiv.org/abs/1708.05123) - [2] Wang R, Shivanna R, Cheng D Z, et al. DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535) + [2] Wang R, Shivanna R, Cheng D Z, et al. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems[J]. 2020. (https://arxiv.org/abs/2008.13535) """ import tensorflow as tf @@ -22,7 +22,7 @@ def DCNMix(linear_feature_columns, dnn_feature_columns, cross_num=2, dnn_hidden_units=(128, 128,), l2_reg_linear=1e-5, l2_reg_embedding=1e-5, low_rank=32, num_experts=4, l2_reg_cross=1e-5, l2_reg_dnn=0, seed=1024, dnn_dropout=0, dnn_use_bn=False, dnn_activation='relu', task='binary'): - """Instantiates the Deep&Cross Network architecture. + """Instantiates the Deep&Cross Network with mixture of experts 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. diff --git a/deepctr/models/deepfm.py b/deepctr/models/deepfm.py index ebf62946..125744ad 100644 --- a/deepctr/models/deepfm.py +++ b/deepctr/models/deepfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Guo H, Tang R, Ye Y, et al. Deepfm: a factorization-machine based neural network for ctr prediction[J]. arXiv preprint arXiv:1703.04247, 2017.(https://arxiv.org/abs/1703.04247) diff --git a/deepctr/models/dien.py b/deepctr/models/dien.py index aff88585..3b167472 100644 --- a/deepctr/models/dien.py +++ b/deepctr/models/dien.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Zhou G, Mou N, Fan Y, et al. Deep Interest Evolution Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1809.03672, 2018. (https://arxiv.org/pdf/1809.03672.pdf) diff --git a/deepctr/models/din.py b/deepctr/models/din.py index a0235748..1e2c536d 100644 --- a/deepctr/models/din.py +++ b/deepctr/models/din.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068. (https://arxiv.org/pdf/1706.06978.pdf) diff --git a/deepctr/models/dsin.py b/deepctr/models/dsin.py index d80f294c..de52ea0b 100644 --- a/deepctr/models/dsin.py +++ b/deepctr/models/dsin.py @@ -1,7 +1,7 @@ # coding: utf-8 """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Feng Y, Lv F, Shen W, et al. Deep Session Interest Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.06482, 2019.(https://arxiv.org/abs/1905.06482) diff --git a/deepctr/models/fgcnn.py b/deepctr/models/fgcnn.py index 5fb079a6..3ee1eaa4 100644 --- a/deepctr/models/fgcnn.py +++ b/deepctr/models/fgcnn.py @@ -2,7 +2,7 @@ """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Liu B, Tang R, Chen Y, et al. Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1904.04447, 2019. diff --git a/deepctr/models/fibinet.py b/deepctr/models/fibinet.py index d57d1059..c0407c6a 100644 --- a/deepctr/models/fibinet.py +++ b/deepctr/models/fibinet.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Huang T, Zhang Z, Zhang J. FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.09433, 2019. diff --git a/deepctr/models/flen.py b/deepctr/models/flen.py index 3c9d629b..3865dc6c 100644 --- a/deepctr/models/flen.py +++ b/deepctr/models/flen.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Tingyi Tan,5636374@qq.com + Tingyi Tan, 5636374@qq.com Reference: [1] Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction . arXiv preprint arXiv:1911.04690, 2019.(https://arxiv.org/pdf/1911.04690) diff --git a/deepctr/models/fnn.py b/deepctr/models/fnn.py index 69f93e02..b2d729ac 100644 --- a/deepctr/models/fnn.py +++ b/deepctr/models/fnn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Zhang W, Du T, Wang J. Deep learning over multi-field categorical data[C]//European conference on information retrieval. Springer, Cham, 2016: 45-57.(https://arxiv.org/pdf/1601.02376.pdf) diff --git a/deepctr/models/mlr.py b/deepctr/models/mlr.py index ea003ff5..3cff00d4 100644 --- a/deepctr/models/mlr.py +++ b/deepctr/models/mlr.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Gai K, Zhu X, Li H, et al. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction[J]. arXiv preprint arXiv:1704.05194, 2017.(https://arxiv.org/abs/1704.05194) diff --git a/deepctr/models/nfm.py b/deepctr/models/nfm.py index 7e14879c..5c643a94 100644 --- a/deepctr/models/nfm.py +++ b/deepctr/models/nfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] He X, Chua T S. Neural factorization machines for sparse predictive analytics[C]//Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 2017: 355-364. (https://arxiv.org/abs/1708.05027) diff --git a/deepctr/models/onn.py b/deepctr/models/onn.py index bc3a0bed..bc96abaa 100644 --- a/deepctr/models/onn.py +++ b/deepctr/models/onn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Yang Y, Xu B, Shen F, et al. Operation-aware Neural Networks for User Response Prediction[J]. arXiv preprint arXiv:1904.12579, 2019. （https://arxiv.org/pdf/1904.12579） diff --git a/deepctr/models/pnn.py b/deepctr/models/pnn.py index c015759f..c450c6ac 100644 --- a/deepctr/models/pnn.py +++ b/deepctr/models/pnn.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Qu Y, Cai H, Ren K, et al. Product-based neural networks for user response prediction[C]//Data Mining (ICDM), 2016 IEEE 16th International Conference on. IEEE, 2016: 1149-1154.(https://arxiv.org/pdf/1611.00144.pdf) diff --git a/deepctr/models/wdl.py b/deepctr/models/wdl.py index 7820cf0c..1d8a51e4 100644 --- a/deepctr/models/wdl.py +++ b/deepctr/models/wdl.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Cheng H T, Koc L, Harmsen J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016: 7-10.(https://arxiv.org/pdf/1606.07792.pdf) diff --git a/deepctr/models/xdeepfm.py b/deepctr/models/xdeepfm.py index f86652e0..2f518cd5 100644 --- a/deepctr/models/xdeepfm.py +++ b/deepctr/models/xdeepfm.py @@ -1,7 +1,7 @@ # -*- coding:utf-8 -*- """ Author: - Weichen Shen,wcshen1994@163.com + Weichen Shen, wcshen1994@163.com Reference: [1] Lian J, Zhou X, Zhang F, et al. xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems[J]. arXiv preprint arXiv:1803.05170, 2018.(https://arxiv.org/pdf/1803.05170.pdf) diff --git a/docs/pics/DCN-M.png b/docs/pics/DCN-M.png new file mode 100644 index 0000000000000000000000000000000000000000..0504d4e09505582fbb410d3ed110a57a4454465d GIT binary patch literal 66893 zcmeFYcU%OU@vY(~yIRAc{zqoHIxo1{49wA_9UEBnXOtNJes21SIF2 z1VLt0V1NOh#$UMib9bM8ci-LTzb)nWbxzf(u2Xf2^QrSCfa0o_t`-1+0KjAL zA8@`5_-h8aI0AsaJ|GAH00DptVFK_#1hN3i5az$J7K9&w{-)yqK#U83`zOzJ@OhEI z(thXpmy&J>Lan4V}IGz5JZLyg9CjiUYE0y85^mC4%WU=J^dN 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DNN based CTR prediction models usually have following 4 modules: - trainable: default `True`.Whether or not the embedding is trainable. ### DenseFeat -``DenseFeat`` is a namedtuple with signature ``DenseFeat(name, dimension, dtype)`` +``DenseFeat`` is a namedtuple with signature ``DenseFeat(name, dimension, dtype, transform_fn)`` - name : feature name - dimension : dimension of dense feature vector. - dtype : default `float32`.dtype of input tensor. +- transform_fn : If not None, a function that can be used to transfrom + values of the feature. the function takes the input Tensor as its + argument, and returns the output Tensor. + (e.g. `lambda x: (x - 3.0) / 4.2)`. ### VarLenSparseFeat @@ -175,6 +179,18 @@ The output of Cross Net and MLP are concatenated.The concatenated vector are fee [Wang R, Fu B, Fu G, et al. Deep & cross network for ad click predictions[C]//Proceedings of the ADKDD'17. ACM, 2017: 12.](https://arxiv.org/abs/1708.05123) +### DCN-Mix (Improved Deep & Cross Network with mix of experts and matrix kernel) + +DCN-Mix uses a matrix kernel instead of vector kernel in CrossNet compared with DCN,and it uses mixture of experts to learn feature interactions. + +[**DCN-Mix Model API**](./deepctr.models.dcnmix.html) + +![DCN-Mix](../pics/DCN-Mix.png) + +[Wang R, Shivanna R, Cheng D Z, et al. DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems[J]. arXiv preprint arXiv:2008.13535, 2020. +](https://arxiv.org/abs/2008.13535) + + ### DIN (Deep Interest Network) DIN introduce a attention method to learn from sequence(multi-valued) feature. @@ -233,7 +249,8 @@ By stacking multiple interacting layers,AutoInt is able to model different order ![AutoInt](../pics/AutoInt.png) -[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) +[Song W, Shi C, Xiao Z, et al. Autoint: Automatic feature interaction learning via self-attentive neural networks[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019: 1161-1170. +](https://arxiv.org/abs/1810.11921) ### ONN(Operation-aware Neural Networks for User Response Prediction) diff --git a/docs/source/History.md b/docs/source/History.md index 44e7aa43..5653a935 100644 --- a/docs/source/History.md +++ b/docs/source/History.md @@ -1,4 +1,5 @@ # History +- 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`. - 10/11/2020 : [v0.8.2](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.2) released.Refactor `DNN` Layer. - 09/12/2020 : [v0.8.1](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.1) released.Improve the reproducibility & fix some bugs. - 06/27/2020 : [v0.8.0](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.0) released. diff --git a/docs/source/Models.rst b/docs/source/Models.rst index 0479d305..c7c80141 100644 --- a/docs/source/Models.rst +++ b/docs/source/Models.rst @@ -12,6 +12,7 @@ DeepCTR Models API NFM AFM DCN + DCNMix DIN DIEN DSIN @@ -21,4 +22,5 @@ DeepCTR Models API FGCNN FiBiNET FLEN + \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 823e43c0..5a45c11a 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 = '0.8.2' +release = '0.8.3' # -- General configuration --------------------------------------------------- diff --git a/docs/source/deepctr.models.dcnmix.rst b/docs/source/deepctr.models.dcnmix.rst new file mode 100644 index 00000000..98b769e9 --- /dev/null +++ b/docs/source/deepctr.models.dcnmix.rst @@ -0,0 +1,7 @@ +deepctr.models.dcnmix module +========================= + +.. automodule:: deepctr.models.dcnmix + :members: + :no-undoc-members: + :no-show-inheritance: diff --git a/docs/source/deepctr.models.rst b/docs/source/deepctr.models.rst index ee64ff0d..0f1209b2 100644 --- a/docs/source/deepctr.models.rst +++ b/docs/source/deepctr.models.rst @@ -10,6 +10,7 @@ Submodules deepctr.models.autoint deepctr.models.ccpm deepctr.models.dcn + deepctr.models.dcnmix deepctr.models.deepfm deepctr.models.dien deepctr.models.din diff --git a/docs/source/index.rst b/docs/source/index.rst index fa5047e3..6b6130b0 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,17 +42,16 @@ You can read the latest code and related projects News ----- +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 `_ 10/11/2020 : Refactor ``DNN`` Layer. `Changelog `_ 09/12/2020 : Improve the reproducibility & fix some bugs. `Changelog `_ -06/27/2020 : Support ``Tensorflow Estimator`` for large scale data and distributed training.Support different initializers for different embedding weights and loading pretrained embeddings.Add new model ``FwFM``. `Changelog `_ - DisscussionGroup 交流群 ----------------------- -公众号：**浅梦的学习笔记** wechat ID: **deepctrbot** +`Discussions `_ 公众号：**浅梦的学习笔记** wechat ID: **deepctrbot** .. image:: ../pics/code.png diff --git a/examples/run_estimator_pandas_classification.py b/examples/run_estimator_pandas_classification.py index 9d19e2a3..9817d20a 100644 --- a/examples/run_estimator_pandas_classification.py +++ b/examples/run_estimator_pandas_classification.py @@ -47,7 +47,8 @@ test_model_input = input_fn_pandas(test, sparse_features + dense_features, None, shuffle=False) # 4.Define Model,train,predict and evaluate - model = DeepFMEstimator(linear_feature_columns, dnn_feature_columns, task='binary') + model = DeepFMEstimator(linear_feature_columns, dnn_feature_columns, task='binary', + config=tf.estimator.RunConfig(tf_random_seed=2021)) model.train(train_model_input) pred_ans_iter = model.predict(test_model_input) diff --git a/examples/run_estimator_tfrecord_classification.py b/examples/run_estimator_tfrecord_classification.py index 9249387c..dcf43217 100644 --- a/examples/run_estimator_tfrecord_classification.py +++ b/examples/run_estimator_tfrecord_classification.py @@ -1,10 +1,9 @@ import tensorflow as tf -from tensorflow.python.ops.parsing_ops import FixedLenFeature +from tensorflow.python.ops.parsing_ops import FixedLenFeature from deepctr.estimator import DeepFMEstimator from deepctr.estimator.inputs import input_fn_tfrecord - if __name__ == "__main__": # 1.generate feature_column for linear part and dnn part @@ -36,7 +35,8 @@ batch_size=2 ** 14, num_epochs=1, shuffle_factor=0) # 3.Define Model,train,predict and evaluate - model = DeepFMEstimator(linear_feature_columns, dnn_feature_columns, task='binary') + model = DeepFMEstimator(linear_feature_columns, dnn_feature_columns, task='binary', + config=tf.estimator.RunConfig(tf_random_seed=2021)) model.train(train_model_input) eval_result = model.evaluate(test_model_input) diff --git a/setup.py b/setup.py index 16bbd85c..29bbe8fc 100644 --- a/setup.py +++ b/setup.py @@ -9,7 +9,7 @@ setuptools.setup( name="deepctr", - version="0.8.2", + version="0.8.3", author="Weichen Shen", author_email="wcshen1994@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 .",