Demo

# 声明数据schema
import torch
from rec_pangu.dataset import get_dataloader
from rec_pangu.models.ranking import WDL, DeepFM, NFM, FiBiNet, AFM, AFN, AOANet, AutoInt, CCPM, LR, FM, xDeepFM
from rec_pangu.trainer import RankTrainer
import pandas as pd

if __name__ == '__main__':
    df = pd.read_csv('sample_data/ranking_sample_data.csv')
    print(df.head())
    # 声明数据schema
    schema = {
        "sparse_cols": ['user_id', 'item_id', 'item_type', 'dayofweek', 'is_workday', 'city', 'county',
                        'town', 'village', 'lbs_city', 'lbs_district', 'hardware_platform', 'hardware_ischarging',
                        'os_type', 'network_type', 'position'],
        "dense_cols": ['item_expo_1d', 'item_expo_7d', 'item_expo_14d', 'item_expo_30d', 'item_clk_1d',
                       'item_clk_7d', 'item_clk_14d', 'item_clk_30d', 'use_duration'],
        "label_col": 'click',
    }
    # 准备数据,这里只选择了100条数据,所以没有切分数据集
    train_df = df
    valid_df = df
    test_df = df

    # 声明使用的device
    device = torch.device('cpu')
    # 获取dataloader
    train_loader, valid_loader, test_loader, enc_dict = get_dataloader(train_df, valid_df, test_df, schema)
    # 声明模型,排序模型目前支持：WDL, DeepFM, NFM, FiBiNet, AFM, AFN, AOANet, AutoInt, CCPM, LR, FM, xDeepFM
    model = xDeepFM(enc_dict=enc_dict)
    # 声明Trainer
    trainer = RankTrainer(num_task=1)
    # 训练模型
    trainer.fit(model, train_loader, valid_loader, epoch=5, lr=1e-3, device=device)
    # 保存模型权重
    trainer.save_model(model, './model_ckpt')
    # 模型验证
    test_metric = trainer.evaluate_model(model, test_loader, device=device)
    print('Test metric:{}'.format(test_metric))

import torch
from rec_pangu.dataset import get_dataloader
from rec_pangu.models.multi_task import AITM, ShareBottom, ESSM, MMOE, OMOE, MLMMOE
from rec_pangu.trainer import RankTrainer
import pandas as pd

if __name__ == '__main__':
    df = pd.read_csv('sample_data/multi_task_sample_data.csv')
    print(df.head())
    #声明数据schema
    schema = {
        "sparse_cols": ['user_id', 'item_id', 'item_type', 'dayofweek', 'is_workday', 'city', 'county',
                        'town', 'village', 'lbs_city', 'lbs_district', 'hardware_platform', 'hardware_ischarging',
                        'os_type', 'network_type', 'position'],
        "dense_cols": ['item_expo_1d', 'item_expo_7d', 'item_expo_14d', 'item_expo_30d', 'item_clk_1d',
                       'item_clk_7d', 'item_clk_14d', 'item_clk_30d', 'use_duration'],
        "label_col": ['click', 'scroll'],
    }
    #准备数据,这里只选择了100条数据,所以没有切分数据集
    train_df = df
    valid_df = df
    test_df = df

    #声明使用的device
    device = torch.device('cpu')
    #获取dataloader
    train_loader, valid_loader, test_loader, enc_dict = get_dataloader(train_df, valid_df, test_df, schema)
    #声明模型,多任务模型目前支持：AITM,ShareBottom,ESSM,MMOE,OMOE,MLMMOE
    model = AITM(enc_dict=enc_dict)
    #声明Trainer
    trainer = RankTrainer(num_task=2)
    #训练模型
    trainer.fit(model, train_loader, valid_loader, epoch=5, lr=1e-3, device=device)
    #保存模型权重
    trainer.save_model(model, './model_ckpt')
    #模型验证
    test_metric = trainer.evaluate_model(model, test_loader, device=device)
    print('Test metric:{}'.format(test_metric))