NLP（二十七）：DeepCTR框架的使用

一 、算法介绍

左边deep network，右边FM，所以叫deepFM

包含两个部分：

• Part1： FM(Factorization machines)，因子分解机部分

在传统的一阶线性回归之上，加了一个二次项，可以表达两两特征的相互关系。

这里的公式可以简化，减少计算量，下图来至于网络。

• Part2： Deep部分

deep部分是多层dnn网络。

二、算法实现

实现部分，用Keras实现一个DeepFM 和·清尘·《FM、FMM、DeepFM整理（pytorch）》

讲的比较清楚，这里引用keras实现来说明。

整体的网络结构：

特征编码#

特征可以分为3类：

• 连续型field，比如数字类型特征
• 单值离散型特征，比如gender，可选为male、female
• 多值离散型，比如tag，可以有多个

连续型field，可以拼接到一起，dense数据。

单值，多值field进行Onehot后，可见单值离散field对应的独热向量只有一位取1，而多值离散field对应的独热向量有多于一位取1，表示该field可以同时取多个特征值。

label	shop_score	gender=m	gender=f	interest=f	interest=c
0	0.2	1	0	1	1
1	0.8	0	1	0	1

FM 部分#

看公式：

先算 FM一次项：

• 连续型field 可以用Dense(1)层实现
• 单值离散型field 用Embedding(n,1), n是分类中值的个数
• 多值离散型field可以同时取多个特征值，为了batch training，必须对样本进行补零padding。同样可以用Embedding实现，因为有多个Embedding，可以取下平均值。

然后计算FM二次项，这里理解比较费劲一点。

·清尘·《FM、FMM、DeepFM整理（pytorch）》 深入浅出的讲明白了这个过程，大家可以参见。

我们来看具体实现方面，这里的DeepFM模型CTR预估理论与实战
讲解更容易理解。

假设只有前面的C1和C2两个Category的特征，词典大小还是3和2。假设输入还是C1=2，C2=2(下标从1开始)，则Embedding之后为V2=[e21,e22,e23,e24]和V5=[e51,e52,e53,e54]。

因为xi和xj同时不为零才需要计算，所以上面的公式里需要计算的只有i=2和j=5的情况。因此：

扩展到多个，比如C1,C2,C3，需要算内积

怎么用用矩阵乘法一次计算出来呢？我们可以看看这个

对应的代码就是

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       square_of_sum = tf.square(reduce_sum(
			concated_embeds_value, axis=1, keep_dims=True))
		sum_of_square = reduce_sum(
			concated_embeds_value * concated_embeds_value, axis=1, keep_dims=True)
		cross_term = square_of_sum - sum_of_square
		cross_term = 0.5 * reduce_sum(cross_term, axis=2, keep_dims=False)

其中concated_embeds_value是拼接起来的embeds_value。

Deep部分#

DNN比较简单，FM的输入和DNN的输入都是同一个group_embedding_dict。

三、使用movielens 来测试

下载ml-100k 数据集

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wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
unzip ml-100k.zip

安装相关软件包，sklearn，deepctr

导入包：

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import pandas
import pandas as pd
import sklearn
from sklearn.metrics import log_loss, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from tensorflow.python.keras.preprocessing.sequence import pad_sequences
import tensorflow as tf
from tqdm import tqdm

from deepctr.models import DeepFM
from deepctr.feature_column import SparseFeat, VarLenSparseFeat, get_feature_names
import numpy as np

读取评分数据：

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u_data = pd.read_csv("ml-100k/u.data", sep='	', header=None)
u_data.columns = ['user_id', 'movie_id', 'rating', 'timestamp']

有评分的设置为1，随机采用未评分的

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def neg_sample(u_data, neg_rate=1):
    # 全局随机采样
    item_ids = u_data['movie_id'].unique()
    print('start neg sample')
    neg_data = []
    # 负采样
    for user_id, hist in tqdm(u_data.groupby('user_id')):
        # 当前用户movie
        rated_movie_list = hist['movie_id'].tolist()
        candidate_set = list(set(item_ids) - set(rated_movie_list))
        neg_list_id = np.random.choice(candidate_set, size=len(rated_movie_list) * neg_rate, replace=True)
        for id in neg_list_id:
            neg_data.append([user_id, id, -1, 0])
    u_data_neg = pd.DataFrame(neg_data)
    u_data_neg.columns = ['user_id', 'movie_id', 'rating', 'timestamp']
    u_data = pandas.concat([u_data, u_data_neg])
    print('end neg sample')
    return u_data

读取item数据

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u_item = pd.read_csv("ml-100k/u.item", sep='|', header=None, error_bad_lines=False)
    genres_columns = ['Action', 'Adventure',
                      'Animation',
                      'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',
                      'Film_Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi',
                      'Thriller', 'War', 'Western']

    u_item.columns = ['movie_id', 'title', 'release_date', 'video_date', 'url', 'unknown'] + genres_columns

处理genres并删除单独的genres列

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     genres_list = []
    for index, row in u_item.iterrows():
        genres = []
        for item in genres_columns:
            if row[item]:
                genres.append(item)
        genres_list.append('|'.join(genres))

    u_item['genres'] = genres_list
    for item in genres_columns:
        del u_item[item]

读取用户信息：

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  # user id | age | gender | occupation(职业) | zip code（邮编，地区)
    u_user = pd.read_csv("ml-100k/u.user", sep='|', header=None)
    u_user.columns = ['user_id', 'age', 'gender', 'occupation', 'zip']

join到一起：

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 data = pandas.merge(u_data, u_item, on="movie_id", how='left')
 data = pandas.merge(data, u_user, on="user_id", how='left')
 data.to_csv('ml-100k/data.csv', index=False)

处理特征：

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sparse_features = ["movie_id", "user_id",
                   "gender", "age", "occupation", "zip", ]

data[sparse_features] = data[sparse_features].astype(str)
target = ['rating']

# 评分
data['rating'] = [1 if int(x) >= 0 else 0 for x in data['rating']]

先特征编码：

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for feat in sparse_features:
		lbe = LabelEncoder()
		data[feat] = lbe.fit_transform(data[feat])

处理genres特征，一个movie有多个genres，先拆分，然后编码为数字，注意是从1开始；由于每个movie的genres长度不一样，可以计算最大长度，位数不足的后面补零（pad_sequences，在post补0）

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	 def split(x):
			key_ans = x.split('|')
			for key in key_ans:
				if key not in key2index:
					# Notice : input value 0 is a special "padding",so we do not use 0 to encode valid feature for sequence input
					key2index[key] = len(key2index) + 1
			return list(map(lambda x: key2index[x], key_ans))


		key2index = {}
		genres_list = list(map(split, data['genres'].values))
		genres_length = np.array(list(map(len, genres_list)))
		max_len = max(genres_length)
		# Notice : padding=`post`
		genres_list = pad_sequences(genres_list, maxlen=max_len, padding='post', )

构建deepctr的特征列，主要分为两类特征，一是定长的SparseFeat，稀疏的类别特征，二是可变长度的VarLenSparseFeat，像genres这样的包含多个的。

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	   fixlen_feature_columns = [SparseFeat(feat, data[feat].nunique(), embedding_dim=4)
								  for feat in sparse_features]

		use_weighted_sequence = False
		if use_weighted_sequence:
			varlen_feature_columns = [VarLenSparseFeat(SparseFeat('genres', vocabulary_size=len(
				key2index) + 1, embedding_dim=4), maxlen=max_len, combiner='mean',
													   weight_name='genres_weight')]  # Notice : value 0 is for padding for sequence input feature
		else:
			varlen_feature_columns = [VarLenSparseFeat(SparseFeat('genres', vocabulary_size=len(
				key2index) + 1, embedding_dim=4), maxlen=max_len, combiner='mean',
													   weight_name=None)]  # Notice : value 0 is for padding for sequence input feature

		linear_feature_columns = fixlen_feature_columns + varlen_feature_columns
		dnn_feature_columns = fixlen_feature_columns + varlen_feature_columns

		feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)

封装训练数据，先shuffle（乱排）数据，然后生成dict input数据。

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data = sklearn.utils.shuffle(data)
train_model_input = {name: data[name] for name in sparse_features}  #
train_model_input["genres"] = genres_list

构建DeepFM模型，由于目标值是0,1，因此采用binary，损失函数用binary_crossentropy

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model = DeepFM(linear_feature_columns, dnn_feature_columns, task='binary')

model.compile(optimizer=tf.keras.optimizers.Adam(), loss='binary_crossentropy',
              metrics=['AUC', 'Precision', 'Recall'])
model.summary()

训练模型：

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model.fit(train_model_input, data[target].values,
						batch_size=256, epochs=20, verbose=2,
						validation_split=0.2
				)

开始训练：

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Epoch 1/20
625/625 - 3s - loss: 0.5081 - auc: 0.8279 - precision: 0.7419 - recall: 0.7695 - val_loss: 0.4745 - val_auc: 0.8513 - val_precision: 0.7563 - val_recall: 0.7936
Epoch 2/20
625/625 - 2s - loss: 0.4695 - auc: 0.8538 - precision: 0.7494 - recall: 0.8105 - val_loss: 0.4708 - val_auc: 0.8539 - val_precision: 0.7498 - val_recall: 0.8127
Epoch 3/20
625/625 - 2s - loss: 0.4652 - auc: 0.8564 - precision: 0.7513 - recall: 0.8139 - val_loss: 0.4704 - val_auc: 0.8545 - val_precision: 0.7561 - val_recall: 0.8017
Epoch 4/20
625/625 - 2s - loss: 0.4624 - auc: 0.8579 - precision: 0.7516 - recall: 0.8146 - val_loss: 0.4724 - val_auc: 0.8542 - val_precision: 0.7296 - val_recall: 0.8526
Epoch 5/20
625/625 - 2s - loss: 0.4607 - auc: 0.8590 - precision: 0.7521 - recall: 0.8173 - val_loss: 0.4699 - val_auc: 0.8550 - val_precision: 0.7511 - val_recall: 0.8141
Epoch 6/20
625/625 - 2s - loss: 0.4588 - auc: 0.8602 - precision: 0.7545 - recall: 0.8165 - val_loss: 0.4717 - val_auc: 0.8542 - val_precision: 0.7421 - val_recall: 0.8265
Epoch 7/20
625/625 - 2s - loss: 0.4574 - auc: 0.8610 - precision: 0.7535 - recall: 0.8192 - val_loss: 0.4722 - val_auc: 0.8547 - val_precision: 0.7549 - val_recall: 0.8023
Epoch 8/20
625/625 - 2s - loss: 0.4561 - auc: 0.8619 - precision: 0.7543 - recall: 0.8201 - val_loss: 0.4717 - val_auc: 0.8548 - val_precision: 0.7480 - val_recall: 0.8185
Epoch 9/20
625/625 - 2s - loss: 0.4531 - auc: 0.8643 - precision: 0.7573 - recall: 0.8210 - val_loss: 0.4696 - val_auc: 0.8583 - val_precision: 0.7598 - val_recall: 0.8103
Epoch 10/20
625/625 - 2s - loss: 0.4355 - auc: 0.8768 - precision: 0.7787 - recall: 0.8166 - val_loss: 0.4435 - val_auc: 0.8769 - val_precision: 0.7756 - val_recall: 0.8293
Epoch 11/20
625/625 - 2s - loss: 0.4093 - auc: 0.8923 - precision: 0.7915 - recall: 0.8373 - val_loss: 0.4301 - val_auc: 0.8840 - val_precision: 0.7806 - val_recall: 0.8390
Epoch 12/20
625/625 - 2s - loss: 0.3970 - auc: 0.8988 - precision: 0.7953 - recall: 0.8497 - val_loss: 0.4286 - val_auc: 0.8867 - val_precision: 0.7903 - val_recall: 0.8299
Epoch 13/20
625/625 - 2s - loss: 0.3896 - auc: 0.9029 - precision: 0.8001 - recall: 0.8542 - val_loss: 0.4253 - val_auc: 0.8888 - val_precision: 0.7913 - val_recall: 0.8322
Epoch 14/20
625/625 - 2s - loss: 0.3825 - auc: 0.9067 - precision: 0.8038 - recall: 0.8584 - val_loss: 0.4205 - val_auc: 0.8917 - val_precision: 0.7885 - val_recall: 0.8506
Epoch 15/20
625/625 - 2s - loss: 0.3755 - auc: 0.9102 - precision: 0.8074 - recall: 0.8624 - val_loss: 0.4204 - val_auc: 0.8940 - val_precision: 0.7868 - val_recall: 0.8607
Epoch 16/20
625/625 - 2s - loss: 0.3687 - auc: 0.9136 - precision: 0.8117 - recall: 0.8653 - val_loss: 0.4176 - val_auc: 0.8956 - val_precision: 0.8097 - val_recall: 0.8236
Epoch 17/20
625/625 - 2s - loss: 0.3617 - auc: 0.9170 - precision: 0.8155 - recall: 0.8682 - val_loss: 0.4166 - val_auc: 0.8966 - val_precision: 0.8056 - val_recall: 0.8354
Epoch 18/20
625/625 - 2s - loss: 0.3553 - auc: 0.9201 - precision: 0.8188 - recall: 0.8716 - val_loss: 0.4168 - val_auc: 0.8977 - val_precision: 0.7996 - val_recall: 0.8492
Epoch 19/20
625/625 - 2s - loss: 0.3497 - auc: 0.9227 - precision: 0.8214 - recall: 0.8741 - val_loss: 0.4187 - val_auc: 0.8973 - val_precision: 0.8079 - val_recall: 0.8358
Epoch 20/20
625/625 - 2s - loss: 0.3451 - auc: 0.9248 - precision: 0.8244 - recall: 0.8753 - val_loss: 0.4210 - val_auc: 0.8982 - val_precision: 0.7945 - val_recall: 0.8617

最后我们测试下数据：

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 pred_ans = model.predict(train_model_input, batch_size=256)
 count = 0
    for (i, j) in zip(pred_ans, data['rating'].values):
        print(i, j)
        count += 1
        if count > 10:
            break

输出如下：

 

四、参考

• deepFM in pytorch
• 皮果提《Factorization Machines 学习笔记（二）模型方程》
• ·清尘·《FM、FMM、DeepFM整理（pytorch）》
• 用Keras实现一个DeepFM
• https://www.cnblogs.com/xiaoqi/p/deepfm.html

五、实战

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 *
from deepctr.feature_column import SparseFeat, DenseFeat, get_feature_names
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.models import  save_model,load_model
from deepctr.layers import custom_objects
import json
class DeepFmModel(object):
    def __init__(self):
        self.data = pd.read_csv('../data/train.txt', sep = "	")
        # 类别特征(16)
        fixlen_category_columns = ['m_sex', 'm_access_frequencies', 'm_twoA', 'm_twoB', 'm_twoC',
                                   'm_twoD', 'm_twoE', 'm_categoryA', 'm_categoryB', 'm_categoryC',
                                   'm_categoryD', 'm_categoryE', 'm_num_interest_topic',
                                   'num_topic_attention_intersection',
                                   'q_num_topic_words',
                                   'num_topic_interest_intersection'
                                   ]
        # 数值特征(7)
        fixlen_number_columns = ['m_salt_score', 'm_num_atten_topic', 'q_num_title_chars_words',
                                 'q_num_desc_chars_words', 'q_num_desc_words', 'q_num_title_words',
                                 'days_to_invite'
                                 ]
        target = ['label']
        #sparse_类别标签
        self.sparse_features = fixlen_category_columns
        #dense_线性标签
        self.dense_features = fixlen_number_columns

        #对数据预处理，nan填充0、-1
        self.data[self.sparse_features] = self.data[self.sparse_features].fillna('-1', )
        self.data[self.dense_features] = self.data[self.dense_features].fillna(0, )
        self.target = ['label']
        #对类别标签硬编码，由数字表示类别
        for feat in self.sparse_features:
            lbe = LabelEncoder()
            self.data[feat] = lbe.fit_transform(self.data[feat])
        #对线性标签归一化到0-1
        mms = MinMaxScaler(feature_range=(0, 1))
        self.data[self.dense_features] = mms.fit_transform(self.data[self.dense_features])

        #对两种特征词嵌入
        fixlen_feature_columns = 
            [SparseFeat(feat, vocabulary_size=self.data[feat].max() + 1, embedding_dim=4)
             for i, feat in enumerate(self.sparse_features)] 
            + [DenseFeat(feat, 1, ) for feat in self.dense_features]
        self.dnn_feature_columns = fixlen_feature_columns
        self.linear_feature_columns = fixlen_feature_columns

        #得到词嵌入的列名称
        feature_names = get_feature_names(self.linear_feature_columns + self.dnn_feature_columns)

        self.train, self.test = train_test_split(self.data, test_size=0.1, random_state=2020)
        self.train_model_input = {name: self.train[name] for name in feature_names}
        self.test_model_input = {name: self.test[name] for name in feature_names}

    def train_model(self):
        # 4.Define Model,train,predict and evaluate
        model = DeepFM(self.linear_feature_columns, self.dnn_feature_columns, task='binary')
        model.compile("adam", "binary_crossentropy",
                      metrics=['binary_crossentropy', "accuracy"], )
        #按照某个指标提前停止
        es = EarlyStopping(monitor='val_binary_crossentropy', patience=3)
        hi = model.fit(self.train_model_input, self.train[self.target].values,
                            batch_size=256, epochs=100, verbose=2, validation_split=0.2,callbacks=[es] )

        save_model(model, '../checkpoint/DeepFM.cpt')# save_model, same as before
        with open("../logs/deep_fm_train.txt", "w", encoding="utf8") as f:
            for k, v in hi.history.items():
                f.write(k + "	" + json.dumps(v) + "
")

    def predictData(self):
        model = load_model('../checkpoint/DeepFM.cpt',custom_objects)# load_model,just add a parameter
        pred_ans = model.predict(self.test_model_input, batch_size=256)

        with open("../logs/deep_fm_test.txt", "w", encoding="utf8") as f:
            f.write("test LogLoss" + "	" + str(round(log_loss(self.test[self.target].values, pred_ans), 4)) + "
")
            f.write("test AUC" + "	" + str(round(roc_auc_score(self.test[self.target].values, pred_ans), 4)) + "
")
            import numpy as np
            label = np.squeeze(self.test[self.target].values).astype(int)
            pre = np.around(np.squeeze(pred_ans)).astype(int)
            from sklearn.metrics import accuracy_score
            f.write("accuracy_score" + "	" + str(accuracy_score(label, pre)) + "
")
            from sklearn.metrics import recall_score
            f.write("recall_score" + "	" + str(recall_score(label, pre)) + "
")
            from sklearn.metrics import precision_score
            f.write("precision_score" + "	" + str(precision_score(label, pre)) + "
")
            from sklearn.metrics import f1_score
            f.write("f1_score" + "	" + str(f1_score(label, pre)) + "
")
            from sklearn.metrics import confusion_matrix
            confusion = confusion_matrix(label, pre)
            import seaborn as sns
            import matplotlib.pyplot as plt
            sns.heatmap(confusion, annot=True, xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive'], cmap=plt.cm.Blues)
            plt.ylabel("Label")
            plt.xlabel("Predicted")
            plt.savefig("../logs/deep_fm.png")
            plt.show()


if __name__ == '__main__':
    DeepFmModel = DeepFmModel()
    DeepFmModel.train_model()
    DeepFmModel.predictData()