From 0768269c38b23e2b871446818cadfdad95a68cba Mon Sep 17 00:00:00 2001
From: abel <1085670763@qq.com>
Date: Thu, 24 Jun 2021 21:01:51 +0800
Subject: [PATCH] first commit

---
 Data/criteo/origin_data/.gitkeep       |    0
 Data/criteo/origin_data/test.csv       |  401 ++++++
 Data/criteo/origin_data/train.csv      | 1600 ++++++++++++++++++++++++
 Data/criteo/processed_da/.gitkeep      |    0
 Data/criteo/processed_da/fea_col.npy   |  Bin 0 -> 1183 bytes
 Data/criteo/processed_da/test_set.csv  |  401 ++++++
 Data/criteo/processed_da/train_set.csv | 1280 +++++++++++++++++++
 Data/criteo/processed_da/val_set.csv   |  321 +++++
 TrainedModels/.gitkeep                 |    0
 TrainedModels/WideDeep.model           |  Bin 0 -> 824115 bytes
 WideDeep/criteo_loader.py              |   79 ++
 WideDeep/network.py                    |   76 ++
 WideDeep/trainer.py                    |   69 +
 tool/train_test.py                     |   61 +
 14 files changed, 4288 insertions(+)
 create mode 100644 Data/criteo/origin_data/.gitkeep
 create mode 100644 Data/criteo/origin_data/test.csv
 create mode 100644 Data/criteo/origin_data/train.csv
 create mode 100644 Data/criteo/processed_da/.gitkeep
 create mode 100644 Data/criteo/processed_da/fea_col.npy
 create mode 100644 Data/criteo/processed_da/test_set.csv
 create mode 100644 Data/criteo/processed_da/train_set.csv
 create mode 100644 Data/criteo/processed_da/val_set.csv
 create mode 100644 TrainedModels/.gitkeep
 create mode 100644 TrainedModels/WideDeep.model
 create mode 100644 WideDeep/criteo_loader.py
 create mode 100644 WideDeep/network.py
 create mode 100644 WideDeep/trainer.py
 create mode 100644 tool/train_test.py

diff --git a/Data/criteo/origin_data/.gitkeep b/Data/criteo/origin_data/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/Data/criteo/origin_data/test.csv b/Data/criteo/origin_data/test.csv
new file mode 100644
index 0000000..e856630
--- /dev/null
+++ b/Data/criteo/origin_data/test.csv
@@ -0,0 +1,401 @@
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diff --git a/Data/criteo/origin_data/train.csv b/Data/criteo/origin_data/train.csv
new file mode 100644
index 0000000..da4b570
--- /dev/null
+++ b/Data/criteo/origin_data/train.csv
@@ -0,0 +1,1600 @@
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diff --git a/Data/criteo/processed_da/.gitkeep b/Data/criteo/processed_da/.gitkeep
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index 0000000..e69de29
diff --git a/Data/criteo/processed_da/fea_col.npy b/Data/criteo/processed_da/fea_col.npy
new file mode 100644
index 0000000000000000000000000000000000000000..03577ea0a879e5608c6899e004c852604845e183
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diff --git a/Data/criteo/processed_da/test_set.csv b/Data/criteo/processed_da/test_set.csv
new file mode 100644
index 0000000..a209190
--- /dev/null
+++ b/Data/criteo/processed_da/test_set.csv
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diff --git a/Data/criteo/processed_da/train_set.csv b/Data/criteo/processed_da/train_set.csv
new file mode 100644
index 0000000..7ffa862
--- /dev/null
+++ b/Data/criteo/processed_da/train_set.csv
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diff --git a/Data/criteo/processed_da/val_set.csv b/Data/criteo/processed_da/val_set.csv
new file mode 100644
index 0000000..8292e1f
--- /dev/null
+++ b/Data/criteo/processed_da/val_set.csv
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diff --git a/TrainedModels/.gitkeep b/TrainedModels/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/TrainedModels/WideDeep.model b/TrainedModels/WideDeep.model
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diff --git a/WideDeep/criteo_loader.py b/WideDeep/criteo_loader.py
new file mode 100644
index 0000000..7415684
--- /dev/null
+++ b/WideDeep/criteo_loader.py
@@ -0,0 +1,79 @@
+import numpy as np
+import pandas as pd
+
+from sklearn.preprocessing import MinMaxScaler, LabelEncoder
+from sklearn.model_selection import train_test_split
+
+def DataPreprocess():
+    # import data
+    train_df = pd.read_csv('../Data/criteo/origin_data/train.csv')
+    test_df = pd.read_csv('../Data/criteo/origin_data/test.csv')
+
+    print(train_df.shape, test_df.shape)
+    # 先将label这一列保存起来，再从train中删除
+    label = train_df['Label']
+    del train_df['Label']
+
+    # 进行数据合并，为了同时对train和test数据进行预处理
+    data_df = pd.concat((train_df, test_df))
+
+    del data_df['Id']
+
+    print(data_df.columns)
+
+    # 特征分开类别
+    sparse_feas = [col for col in data_df.columns if col[0] == 'C']
+    dense_feas = [col for col in data_df.columns if col[0] == 'I']
+
+    # 填充缺失值
+    data_df[sparse_feas] = data_df[sparse_feas].fillna('-1')
+    data_df[dense_feas] = data_df[dense_feas].fillna(0)
+
+    # 进行编码  类别特征编码
+    for feat in sparse_feas:
+        le = LabelEncoder()
+        data_df[feat] = le.fit_transform(data_df[feat])
+
+    # 数值特征归一化
+    mms = MinMaxScaler()
+    data_df[dense_feas] = mms.fit_transform(data_df[dense_feas])
+
+    # 分开测试集和训练集
+    train = data_df[:train_df.shape[0]]
+    test = data_df[train_df.shape[0]:]
+
+    train['Label'] = label
+
+    train_set, val_set = train_test_split(train, test_size = 0.2, random_state=2020)
+
+    print(train_set['Label'].value_counts())
+    print(val_set['Label'].value_counts())
+
+    # 保存文件
+    train_set.reset_index(drop=True, inplace=True)
+    val_set.reset_index(drop=True, inplace=True)
+
+    train_set.to_csv('../Data/criteo/processed_data/train_set.csv', index=0)
+    val_set.to_csv('../Data/criteo/processed_data/val_set.csv', index=0)
+    test.to_csv('../Data/criteo/processed_data/test_set.csv', index=0)
+
+def getTrainData(filename, feafile):
+    df = pd.read_csv(filename)
+    print(df.columns)
+
+    # C开头的列代表稀疏特征，I开头的列代表的是稠密特征
+    dense_features_col = [col for col in df.columns if col[0] == 'I']
+
+    # 这个文件里面存储了稀疏特征的最大范围，用于设置Embedding的输入维度
+    fea_col = np.load(feafile, allow_pickle=True)
+    sparse_features_col = []
+    for f in fea_col[1]:
+        sparse_features_col.append(f['feat_num'])
+
+    data, labels = df.drop(columns='Label').values, df['Label'].values
+
+    return data, labels, dense_features_col, sparse_features_col
+
+def getTestData(filename):
+    df = pd.read_csv(filename)
+    return df.to_numpy()
diff --git a/WideDeep/network.py b/WideDeep/network.py
new file mode 100644
index 0000000..7a802db
--- /dev/null
+++ b/WideDeep/network.py
@@ -0,0 +1,76 @@
+import torch
+import torch.nn as nn
+
+class Wide(nn.Module):
+    def __init__(self, input_dim):
+        super(Wide, self).__init__()
+        # hand-crafted cross-product features
+        self.linear = nn.Linear(in_features=input_dim, out_features=1)
+
+    def forward(self, x):
+        return self.linear(x)
+
+class Deep(nn.Module):
+    def __init__(self, config, hidden_layers):
+        super(Deep, self).__init__()
+        self.dnn = nn.ModuleList([nn.Linear(layer[0], layer[1]) for layer in list(zip(hidden_layers[:-1], hidden_layers[1:]))])
+        self.dropout = nn.Dropout(p=config['deep_dropout'])
+
+    def forward(self, x):
+        for layer in self.dnn:
+            x = layer(x)
+            # 如果输出层大小是1的话，这里再使用了个ReLU激活函数，可能导致输出全变成0，即造成了梯度消失，导致Loss不收敛
+            x = torch.relu(x)
+        x = self.dropout(x)
+        return x
+
+class WideDeep(nn.Module):
+    def __init__(self, config, dense_features_cols, sparse_features_cols):
+        super(WideDeep, self).__init__()
+        self._config = config
+        # 稠密特征的数量
+        self._num_of_dense_feature = dense_features_cols.__len__()
+        # 稠密特征
+        self.sparse_features_cols = sparse_features_cols
+
+        self.embedding_layers = nn.ModuleList([
+            # 根据稀疏特征的个数创建对应个数的Embedding层，Embedding输入大小是稀疏特征的类别总数，输出稠密向量的维度由config文件配置
+            nn.Embedding(num_embeddings = num_feat, embedding_dim=config['embed_dim'])
+                for num_feat in self.sparse_features_cols
+        ])
+
+        # Deep hidden layers
+        self._deep_hidden_layers = config['hidden_layers']
+        self._deep_hidden_layers.insert(0, self._num_of_dense_feature + config['embed_dim'] * len(self.sparse_features_cols))
+
+        self._wide = Wide(self._num_of_dense_feature)
+        self._deep = Deep(config, self._deep_hidden_layers)
+        # 之前直接将这个final_layer加入到了Deep模块里面，想着反正输出都是1，结果没注意到Deep没经过一个Linear层都会经过Relu激活函数，如果
+        # 最后输出层大小是1的话，再经过ReLU之后，很可能变为了0，造成梯度消失问题，导致Loss怎么样都降不下来。
+        self._final_linear = nn.Linear(self._deep_hidden_layers[-1], 1)
+
+    def forward(self, x):
+        # 先区分出稀疏特征和稠密特征，这里是按照列来划分的，即所有的行都要进行筛选
+        dense_input, sparse_inputs = x[:, :self._num_of_dense_feature], x[:, self._num_of_dense_feature:]
+        sparse_inputs = sparse_inputs.long()
+
+        sparse_embeds = [self.embedding_layers[i](sparse_inputs[:, i]) for i in range(sparse_inputs.shape[1])]
+        sparse_embeds = torch.cat(sparse_embeds, axis=-1)
+        # Deep模块的输入是稠密特征和稀疏特征经过Embedding产生的稠密特征的
+        deep_input = torch.cat([sparse_embeds, dense_input], axis=-1)
+
+        wide_out = self._wide(dense_input)
+        deep_out = self._deep(deep_input)
+        deep_out = self._final_linear(deep_out)
+
+        assert (wide_out.shape == deep_out.shape)
+
+        outputs = torch.sigmoid(0.5 * (wide_out + deep_out))
+        return outputs
+
+    def saveModel(self):
+        torch.save(self.state_dict(), self._config['model_name'])
+
+    def loadModel(self, map_location):
+        state_dict = torch.load(self._config['model_name'], map_location=map_location)
+        self.load_state_dict(state_dict, strict=False)
diff --git a/WideDeep/trainer.py b/WideDeep/trainer.py
new file mode 100644
index 0000000..3329582
--- /dev/null
+++ b/WideDeep/trainer.py
@@ -0,0 +1,69 @@
+import torch
+from torch.autograd import Variable
+from torch.utils.data import DataLoader
+from sklearn.metrics import roc_auc_score
+
+def auc(y_pred, y_true):
+    pred = y_pred.data
+    y = y_true.data
+    return roc_auc_score(y, pred)
+
+class Trainer(object):
+    def __init__(self, model, config):
+        self._model = model
+        self._config = config
+        self._optimizer = torch.optim.Adam(self._model.parameters(), lr=config['lr'], weight_decay=config['l2_regularization'])
+        self._loss_func = torch.nn.BCELoss()
+
+    def _train_single_batch(self, x, labels):
+        """
+        对单个小批量数据进行训练
+        """
+        self._optimizer.zero_grad()
+        y_predict = self._model(x)
+        loss = self._loss_func(y_predict.view(-1), labels)
+
+        loss.backward()
+        self._optimizer.step()
+
+        loss = loss.item()
+        return loss, y_predict
+
+    def _train_an_epoch(self, train_loader, epoch_id):
+        """
+        训练一个Epoch，即将训练集中的所有样本全部都过一遍
+        """
+        # 设置模型为训练模式，启用dropout以及batch normalization
+        self._model.train()
+
+        total = 0
+        # 从DataLoader中获取小批量的id以及数据
+        for batch_id, (x, labels) in enumerate(train_loader):
+            x = Variable(x)
+            labels = Variable(labels)
+            if self._config['use_cuda'] is True:
+                x, labels = x.cuda(), labels.cuda()
+
+            loss, predicted = self._train_single_batch(x, labels)
+
+            total += loss
+            print('[Training Epoch: {}] Batch: {}, Loss: {}'.format(epoch_id, batch_id, loss))
+        print("Training Epoch: %d, total loss: %f" % (epoch_id, total))
+
+    def train(self, train_dataset):
+        self.use_cuda()
+        for epoch in range(self._config['num_epoch']):
+            print('-' * 20 + ' Epoch {} starts '.format(epoch) + '-' * 20)
+            # 构造DataLoader
+            data_loader = DataLoader(dataset=train_dataset, batch_size=self._config['batch_size'], shuffle=True)
+            # 训练一个轮次
+            self._train_an_epoch(data_loader, epoch_id=epoch)
+
+    def use_cuda(self):
+        if self._config['use_cuda'] is True:
+            assert torch.cuda.is_available(), 'CUDA is not available'
+            torch.cuda.set_device(self._config['device_id'])
+            self._model.cuda()
+
+    def save(self):
+        self._model.saveModel()
diff --git a/tool/train_test.py b/tool/train_test.py
new file mode 100644
index 0000000..ef1a78d
--- /dev/null
+++ b/tool/train_test.py
@@ -0,0 +1,61 @@
+import torch
+from WideDeep.trainer import Trainer
+from WideDeep.network import WideDeep
+from WideDeep.criteo_loader import getTestData, getTrainData
+import torch.utils.data as Data
+
+import numpy as np
+
+widedeep_config = \
+{
+    'deep_dropout': 0,
+    'embed_dim': 8, # 用于控制稀疏特征经过Embedding层后的稠密特征大小
+    'hidden_layers': [256,128,64],
+    'num_epoch': 10,
+    'batch_size': 32,
+    'lr': 1e-3,
+    'l2_regularization': 1e-4,
+    'device_id': 0,
+    'use_cuda': True,
+    'train_file': '../Data/criteo/processed_data/train_set.csv',
+    'fea_file': '../Data/criteo/processed_data/fea_col.npy',
+    'validate_file': '../Data/criteo/processed_data/val_set.csv',
+    'test_file': '../Data/criteo/processed_data/test_set.csv',
+    'model_name': '../TrainedModels/WideDeep.model'
+}
+
+if __name__ == "__main__":
+    ####################################################################################
+    # WideDeep 模型
+    ####################################################################################
+    training_data, training_label, dense_features_col, sparse_features_col = getTrainData(widedeep_config['train_file'], widedeep_config['fea_file'])
+    train_dataset = Data.TensorDataset(torch.tensor(training_data).float(), torch.tensor(training_label).float())
+    test_data = getTestData(widedeep_config['test_file'])
+    test_dataset = Data.TensorDataset(torch.tensor(test_data).float())
+
+    wideDeep = WideDeep(widedeep_config, dense_features_cols=dense_features_col, sparse_features_cols=sparse_features_col)
+
+    ####################################################################################
+    # 模型训练阶段
+    ####################################################################################
+    # # 实例化模型训练器
+    trainer = Trainer(model=wideDeep, config=widedeep_config)
+    # 训练
+    trainer.train(train_dataset)
+    # 保存模型
+    trainer.save()
+
+    ####################################################################################
+    # 模型测试阶段
+    ####################################################################################
+    wideDeep.eval()
+    if widedeep_config['use_cuda']:
+        wideDeep.loadModel(map_location=lambda storage, loc: storage.cuda(widedeep_config['device_id']))
+        resNet = wideDeep.cuda()
+    else:
+        wideDeep.loadModel(map_location=torch.device('cpu'))
+
+    y_pred_probs = wideDeep(torch.tensor(test_data).float().cuda())
+    y_pred = torch.where(y_pred_probs>0.5, torch.ones_like(y_pred_probs), torch.zeros_like(y_pred_probs))
+    print("Test Data CTR Predict...\n ", y_pred.view(-1))
+