转为python代码

将您提供的MATLAB代码转换成Python代码，特别是涉及到深度学习和随机森林集成模型的部分，需要使用类似`tensorflow`或`keras`这样的深度学习框架以及`sklearn`中的随机森林回归器。下面是基于这些库的一个简化版本示例：

由于原始MATLAB脚本包含大量的细节，并且某些部分如绘图和特定格式的输出在Python中可能有所不同，这里主要提供一个基本的结构，用于说明如何用Python实现类似的流程（即数据预处理、构建卷积神经网络（CNN）模型并训练它，最后与随机森林集成）：

### Python 示例代码

首先安装必要的库:

pip install numpy pandas tensorflow scikit-learn matplotlib

然后编写如下Python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 mean_absolute_error, mean_squared_error, r2_score
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from tensorflow.keras.optimizers import Adam
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt


# Load data
data = pd.read_excel("BostonHousing.xlsx")
features = data.iloc[:, 1:].values
target = data.iloc[:, -1].values

# Split dataset into train/validation/test sets
X_train_val, X_test, y_train_val, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X_train_val, y_train_val, test_size=0.25, random_state=42)

# Data normalization
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_val_scaled = scaler.transform(X_val)
X_test_scaled = scaler.transform(X_test)


# Define and compile the model
model = Sequential([
    Conv2D(16, kernel_size=(2, 1), activation='relu', input_shape=(X_train.shape[1], 1, 1)),
    MaxPooling2D(pool_size=(2, 1)),
    Flatten(),
    Dense(1)
])

model.compile(optimizer=Adam(learning_rate=0.001),
              loss='mse',
              metrics=['mae'])

# Reshape inputs for CNN
X_train_cnn = X_train_scaled.reshape(-1, X_train_scaled.shape[-1], 1, 1)
X_val_cnn = X_val_scaled.reshape(-1, X_val_scaled.shape[-1], 1, 1)
X_test_cnn = X_test_scaled.reshape(-1, X_test_scaled.shape[-1], 1, 1)

# Train the model
history = model.fit(X_train_cnn, y_train,
                    epochs=70,
                    batch_size=50,
                    validation_data=(X_val_cnn, y_val))

# Evaluate predictions
def evaluate_predictions(y_true, y_pred):
    mae = mean_absolute_error(y_true, y_pred)
    mse = mean_squared_error(y_true, y_pred)
    r2 = r2_score(y_true, y_pred)
    return mae, mse, r2

predictions = model.predict(X_test_cnn).flatten()

train_mae, train_mse, train_r2 = evaluate_predictions(y_train, model.predict(X_train_cnn).flatten())
val_mae, val_mse, val_r2 = evaluate_predictions(y_val, model.predict(X_val_cnn).flatten())
test_mae, test_mse, test_r2 = evaluate_predictions(y_test, predictions)

print(f'Train MAE: {train_mae}, MSE: {train_mse}, R2: {train_r2}')
print(f'Val MAE: {val_mae}, MSE: {val_mse}, R2: {val_r2}')
print(f'Test MAE: {test_mae}, MSE: {test_mse}, R2: {test_r2}')


# Feature extraction from last pooling layer
last_pooling_layer = ...
get_activations = K.function([model.layers[0].input, K.learning_phase()], 
                             [model.get_layer(last_pooling_layer).output])
activation = get_activations([X_train_cnn, 0])[0]

# Train Random Forest using extracted features
rf_model = RandomForestRegressor(n_estimators=50)
rf_model.fit(activation, y_train)

# Make predictions with RF model
rf_preds = rf_model.predict(activation)
rf_train_mae, rf_train_mse, rf_train_r2 = evaluate_predictions(y_train, rf_preds)

print(f'RF Train MAE: {rf_train_mae}, MSE: {rf_train_mse}, R2: {rf_train_r2}')

# Plot results
plt.figure(figsize=(10, 5))
plt.plot(history.history['loss'], label='Training Loss')
plt.plot(history.history['val_loss'], label='Validation Loss')
plt.legend()
plt.show()

请注意，在实际应用时你需要根据自己具体的数据调整上述代码，并确保所有使用的库版本兼容。此外，上面的示例仅展示了一个基础架构，具体的超参数调优等步骤还需进一步完善。