使用Softmax回归来解决奥托产品分类问题python实现

好的，下面是使用Python实现Softmax回归解决奥托产品分类问题的示例代码：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score

# 1. 数据预处理
# 读取数据并拆分为特征和标签
data = pd.read_csv('train.csv')
X = data.iloc[:, 1:-1].values
y = data.iloc[:, -1].values

# 将标签转换为one-hot编码
y_onehot = pd.get_dummies(y).values

# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y_onehot, test_size=0.2, random_state=0)

# 特征标准化
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 2. 定义Softmax回归模型
class SoftmaxRegression:
    def __init__(self, num_classes, num_features, learning_rate=0.01, num_epochs=1000):
        self.num_classes = num_classes
        self.num_features = num_features
        self.learning_rate = learning_rate
        self.num_epochs = num_epochs
        self.weights = np.zeros((self.num_features, self.num_classes))
        self.bias = np.zeros((1, self.num_classes))

    def softmax(self, z):
        return np.exp(z) / np.sum(np.exp(z), axis=1, keepdims=True)

    def forward(self, X):
        return self.softmax(np.dot(X, self.weights) + self.bias)

    def compute_loss(self, y_pred, y_true):
        return -np.mean(np.sum(y_true * np.log(y_pred), axis=1))

    def compute_gradient(self, X, y_pred, y_true):
        num_samples = X.shape[0]
        dW = (1 / num_samples) * np.dot(X.T, (y_pred - y_true))
        db = (1 / num_samples) * np.sum(y_pred - y_true, axis=0, keepdims=True)
        return dW, db

    def train(self, X, y):
        for epoch in range(self.num_epochs):
            y_pred = self.forward(X)
            loss = self.compute_loss(y_pred, y)
            dW, db = self.compute_gradient(X, y_pred, y)
            self.weights -= self.learning_rate * dW
            self.bias -= self.learning_rate * db
            if (epoch+1) % 100 == 0:
                print("Epoch [{}/{}], Loss {:.4f}".format(epoch+1, self.num_epochs, loss))

    def predict(self, X):
        y_pred = self.forward(X)
        return np.argmax(y_pred, axis=1)

# 3. 训练Softmax回归模型
num_classes = 9
num_features = X_train.shape[1]
learning_rate = 0.1
num_epochs = 1000
model = SoftmaxRegression(num_classes, num_features, learning_rate, num_epochs)
model.train(X_train, y_train)

# 4. 在测试集上进行预测
y_pred = model.predict(X_test)
y_test_true = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_test_true, y_pred)
print("Accuracy:", accuracy)

这段代码中，我们首先进行了数据预处理，将数据集拆分为训练集和测试集，并对特征进行了标准化处理。然后，我们定义了Softmax回归模型，并训练了该模型。最后，我们使用训练好的模型在测试集上进行了预测，并计算了模型的准确率。需要注意的是，这段代码中的奥托产品分类数据集是从Kaggle网站下载的，需要自行下载并保存为train.csv文件才能运行。