v_p = self.df.iloc[index]['Protein']是什么
时间: 2023-05-11 18:01:45 浏览: 67
意思?
这是一个Python代码中的一行,其中self.df是一个数据框,iloc[index]表示选取数据框中的第index行,['Protein']表示选取该行中名为'Protein'的列,v_p则表示将该列的值赋给变量v_p。
相关问题
修正代码 for k in range(self.n_fold): est=self.init_estimator() train_id, val_id=cv[k] x=pd.DataFrame(x) y=pd.DataFrame(y) # x_train, x_test = x.iloc[train_id], x.iloc[test_id] # y_train, y_test = y.iloc[train_id], y.iloc[test_id] # print(x[train_id]) x_train= x.iloc[train_id] y_train= y.iloc[train_id] est.fit(x_train, y_train) x_proba=est.predict_proba(x.iloc[val_id]) print(x_proba) print(x_probas[val_id]) y_pre=est.predict(x.iloc[val_id]) acc=accuracy_score(y.iloc[val_id],y_pre) f1=f1_score(y.iloc[val_id],y_pre,average="macro") LOGGER_2.info("{}, n_fold{},Accuracy={:.4f}, f1_macro={:.4f}".format(self.name,k,acc,f1)) x_probas[val_id]=x_proba
修正后的代码应该是这样的:
```
for k in range(self.n_fold):
est = self.init_estimator()
train_id, val_id = cv[k]
x = pd.DataFrame(x)
y = pd.DataFrame(y)
x_train = x.iloc[train_id]
y_train = y.iloc[train_id]
est.fit(x_train, y_train)
x_proba = est.predict_proba(x.iloc[val_id])
print(x_proba)
print(x_probas[val_id])
y_pre = est.predict(x.iloc[val_id])
acc = accuracy_score(y.iloc[val_id], y_pre)
f1 = f1_score(y.iloc[val_id], y_pre, average="macro")
LOGGER_2.info("{}, n_fold{},Accuracy={:.4f}, f1_macro={:.4f}".format(self.name, k, acc, f1))
x_probas[val_id] = x_proba
```
在原有代码的基础上,主要修改了以下两点:
1. 将 `x`、`y` 转化为 DataFrame 类型。
2. 将 `x_train` 和 `y_train` 的赋值方式修改为 `x_train = x.iloc[train_id]`、`y_train = y.iloc[train_id]`,以保证对应行的数据匹配。
def get_data(train_df): train_df = train_df[['user_id', 'behavior_type']] train_df=pd.pivot_table(train_df,index=['user_id'],columns=['behavior_type'],aggfunc={'behavior_type':'count'}) train_df.fillna(0,inplace=True) train_df=train_df.reset_index(drop=True) train_df.columns=train_df.columns.droplevel(0) x_train=train_df.iloc[:,:3] y_train=train_df.iloc[:,-1] type=torch.float32 x_train=torch.tensor(x_train.values,dtype=type) y_train=torch.tensor(y_train.values,dtype=type) print(x_train) print(y_train) return x_train ,y_train x_train,y_train=get_data(train_df) x_test,y_test=get_data(test_df) print(x_test) #创建模型 class Order_pre(nn.Module): def __init__(self): super(Order_pre, self).__init__() self.ln1=nn.LayerNorm(3) self.fc1=nn.Linear(3,6) self.fc2 = nn.Linear(6, 12) self.fc3 = nn.Linear(12, 24) self.dropout=nn.Dropout(0.5) self.fc4 = nn.Linear(24, 48) self.fc5 = nn.Linear(48, 96) self.fc6 = nn.Linear(96, 1) def forward(self,x): x=self.ln1(x) x=self.fc1(x) x = nn.functional.relu(x) x = self.fc2(x) x = nn.functional.relu(x) x = self.fc3(x) x = self.dropout(x) x = nn.functional.relu(x) x = self.fc4(x) x = nn.functional.relu(x) x = self.fc5(x) x = nn.functional.relu(x) x = self.fc6(x) return x #定义模型、损失函数和优化器 model=Order_pre() loss_fn=nn.MSELoss() optimizer=torch.optim.SGD(model.parameters(),lr=0.05) #开始跑数据 for epoch in range(1,50): #预测值 y_pred=model(x_train) #损失值 loss=loss_fn(y_pred,y_train) #反向传播 optimizer.zero_grad() loss.backward() optimizer.step() print('epoch',epoch,'loss',loss) # 开始预测y值 y_test_pred=model(x_test) y_test_pred=y_test_pred.detach().numpy() y_test=y_test.detach().numpy() y_test_pred=pd.DataFrame(y_test_pred) y_test=pd.DataFrame(y_test) dfy=pd.concat([y_test,y_test_pred],axis=1) print(dfy) dfy.to_csv('resulty.csv') 如果我想要使用学习率调度器应该怎么操作
如果您想使用学习率调度器,可以先创建一个学习率调度器对象,例如StepLR或ReduceLROnPlateau,然后在每个epoch或一定的步骤之后,通过调用学习率调度器对象的step方法来更新优化器的学习率。例如,对于StepLR,可以按照以下方式操作:
```
# 创建学习率调度器对象
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
for epoch in range(1,50):
# 预测值
y_pred=model(x_train)
# 损失值
loss=loss_fn(y_pred,y_train)
# 反向传播
optimizer.zero_grad()
loss.backward()
# 更新学习率
scheduler.step()
optimizer.step()
print('epoch',epoch,'loss',loss)
```
在这个例子中,我们使用StepLR来每10个epoch将学习率乘以0.1。您可以根据需要自定义step_size和gamma值。
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