def accuracy(y_pred, y_test): return np.sum(y_pred==y_test)/len(y_test) # create 3 classifiers, one for each dummy target classifier0 = LogisticRegression() classifier1 = LogisticRegression() classifier2 = LogisticRegression() # ----- train classifiers for each binary dummy target ----- # --- Binary classification problem 1 --- print("Binary classifier 1:") classifier0.fit(X_train , Y_train0) y_pred0 = classifier0.predict(X_test) print("real :",Y_test0) print("predicts: ",y_pred0) print("--------------") # --- Binary classification problem 2 --- print("Binary classifier 2:") classifier1.fit(X_train , Y_train1) y_pred1 = classifier1.predict(X_test) print("real :",Y_test1) print("predicts: ",y_pred1) print("--------------") # --- Binary classification problem 3 --- print("Binary classifier 3:") classifier2.fit(X_train , Y_train2) y_pred2 = classifier2.predict(X_test) print("real :",Y_test2) print("predicts: ",y_pred2) print("--------------") # -------------------- ONE VS ALL --------------------- total_pred = np.array([y_pred0, y_pred1, y_pred2]) # take the max from each probability obtained from each classifier, and create an array with their indices: multi_pred = np.argmax(total_pred,axis=0) print(multi_pred) # compare the indices with the value of the target variable to get accuracy: acc = accuracy(multi_pred, Y_test_all) print(acc)请一行一行的解释代码

好的，我会逐行解释这段代码。

def accuracy(y_pred, y_test):
    return np.sum(y_pred==y_test)/len(y_test)

这是一个计算准确率的函数，输入预测结果和真实结果，输出准确率。

classifier0 = LogisticRegression()
classifier1 = LogisticRegression()
classifier2 = LogisticRegression()

这里定义了三个逻辑回归分类器，分别用于三个二元目标分类问题的训练。

print("Binary classifier 1:")
classifier0.fit(X_train , Y_train0)
y_pred0 = classifier0.predict(X_test)
print("real :",Y_test0)
print("predicts: ",y_pred0)
print("--------------")

这段代码训练了第一个二元分类器，用于预测一个二元目标变量 Y_train0。其中 X_train 和 X_test 是特征矩阵的训练集和测试集，Y_train0 和 Y_test0 是目标变量的训练集和测试集。训练完后，用测试集进行预测，并输出真实值和预测值。

total_pred = np.array([y_pred0, y_pred1, y_pred2])
multi_pred = np.argmax(total_pred,axis=0)
print(multi_pred)
acc = accuracy(multi_pred, Y_test_all)
print(acc)

这段代码中，首先将三个分类器的预测结果合并成一个数组 total_pred。然后，使用 np.argmax 函数取出每个样本的最大概率的类别所对应的索引，并生成一个新的数组 multi_pred。最后，将 multi_pred 与真实目标变量 Y_test_all 进行比较，计算准确率并输出。这个过程用的是一对多策略。