datasets.rardatasets.rar

import torch.nn as nn from itertools import count from collections import defaultdict from scipy.sparse import csr import numpy as np import pandas as pd import numpy as np from sklearn.feature_extraction import DictVectorizer import matplotlib.pyplot as plt import torch from torch.utils.data import Dataset, DataLoader # 数据处理的代码 def vectorize_dic(dic,ix=None,p=None,n=0,g=0): """ dic -- 特征列表字典，关键字是特征名 ix -- 索引 (default None) p -- 特征向量的维度 (number of columns in the sparse matrix) (default None) """ if ix==None: ix = dict() nz = n * g col_ix = np.empty(nz,dtype = int) # 随机生成一个大小为nz的数组，元素为整数 i = 0 for k,lis in dic.items(): # users和users的list，或者是items和items的list for t in range(len(lis)): # 为编号为t的user或者item赋值 ix[str(lis[t]) + str(k)] = ix.get(str(lis[t]) + str(k),0) + 1 col_ix[i+t*g] = ix[str(lis[t]) + str(k)] i += 1 row_ix = np.repeat(np.arange(0,n),g) data = np.ones(nz) if p == None: p = len(ix) ixx = np.where(col_ix < p) return csr.csr_matrix((data[ixx],(row_ix[ixx],col_ix[ixx])),shape=(n,p)),ix # 分批次训练模型 def batcher(X_, y_=None, batch_size=-1): n_samples = X_.shape[0] if batch_size == -1: batch_size = n_samples if batch_size < 1: raise ValueError('Parameter batch_size={} 是不支持的'.format(batch_size)) for i in range(0, n_samples, batch_size): upper_bound = min(i + batch_size, n_samples) ret_x = X_[i:upper_bound] ret_y = None if y_ is not None: ret_y = y_[i:i + batch_size] yield (ret_x, ret_y) cols = ['user','item','rating','timestamp'] train = pd.read_csv('/Users/cuixiyong/Desktop/开学/Model/FM-by-pytorch-master/ua.base',delimiter='\t',names = cols) test = pd.read_csv('/Users/cuixiyong/Desktop/开学/Model/FM-by-pytorch-master/ua.test',delimiter='\t',names = cols) x_train,ix = vectorize_dic({'users':train['user'].values, 'items':train['item'].values},n=len(train.index),g=2) x_test,ix = vectorize_dic({'users':test['user'].values, 'items':test['item'].values},ix,x_train.shape[1],n=len(test.index),g=2) y_train = train['rating'].values y_test = test['rating'].values x_train = x_train.todense() x_test = x_test.todense() print(x_train.shape) print(x_test.shape) #FM model n,p = x_train.shape k = 10 print("p,k = ",p,k) class FM_model(nn.Module): def __init__(self,p,k): super(FM_model,self).__init__() self.p = p self.k = k self.linear = nn.Linear(self.p,1,bias=True) self.v = nn.Parameter(torch.randn(self.k,self.p)) def fm_layer(self,x): linear_part = self.linear(x) inter_part1 = torch.mm(x,self.v.t()) inter_part2 = torch.mm(torch.pow(x,2),torch.pow(self.v,2).t()) output = linear_part + 0.5*torch.sum(torch.pow(inter_part1,2) - inter_part2) return output def forward(self,x): output = self.fm_layer(x) return output #train from tqdm import tqdm batch_size=64 model = FM_model(p,k) loss_dict = [] loss_fn =nn.MSELoss() optimer = torch.optim.SGD(model.parameters(),lr=0.0001,weight_decay=0.001) epochs = 100 epochs_dict = [] for epoch in range(epochs): loss_epoch = 0.0 loss_all = 0.0 perm = np.random.permutation(x_train.shape[0]) model.train() for x,y in tqdm(batcher(x_train[perm], y_train[perm], batch_size)): model.zero_grad() x = torch.as_tensor(np.array(x.tolist()), dtype=torch.float,device=torch.device('cpu')) y = torch.as_tensor(np.array(y.tolist()), dtype=torch.float,device=torch.device('cpu')) x = x.view(-1, p) y = y.view(-1, 1) preds = model(x) loss = loss_fn(preds,y) loss_all += loss.item() loss.backward() optimer.step() loss_epoch = loss_all/len(x) loss_dict.append(loss_epoch) epochs_dict.append(epoch) print(f"Epoch [{epoch}/{10}], " f"Loss: {loss_epoch:.8f} ") plt.plot(epochs_dict, loss_dict) plt.show() # model_save torch.save({'epoch': epochs , 'state_dict': model.state_dict(), 'best_loss': min(loss_dict), 'optimizer': optimer.state_dict()}, './temp/parameter.pkl')