X_train = X_train.iloc[:,3]

This code selects the fourth column (index 3) of the pandas DataFrame X_train and assigns it to a new DataFrame called X_train.

Note: The resulting DataFrame X_train will only have one column.

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。

优化这段代码 x_train_YS, y_train_YS = data.iloc[0:418+20*i,cols],data.iloc[0:418+20*i,95] x_test_YS, y_test_YS = data.iloc[418+20*i:438+20*i,cols],data.iloc[418+20*i:438+20*i,95] print(x_train_YS) x_train_YS_1,y_train_YS_1 = x_train_YS.values[0:418+20*i,1:85],y_train_YS.values[0:418+20*i,85]

可以将代码进行优化，避免多次索引和切片，可以使用数据切分函数train_test_split()来代替手动切分数据。

具体实现如下：

1. 首先需要导入train_test_split函数：from sklearn.model_selection import train_test_split

2. 对数据进行切分：

X = data.iloc[:, cols]
y = data.iloc[:, 95]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, random_state=42)

其中，test_size表示测试数据占比，random_state为随机种子，保证每次运行结果一致。

3. 对训练数据进行切分：

X_train_YS = X_train.iloc[:418+20*i, 1:85]
y_train_YS = y_train.iloc[:418+20*i]

4. 对测试数据进行切分：

X_test_YS = X_test.iloc[418+20*i:438+20*i, 1:85]
y_test_YS = y_test.iloc[418+20*i:438+20*i]

这样就可以得到和原代码相同的训练数据和测试数据了。