mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
synced 2025-11-09 06:44:02 +00:00
87 lines
3.6 KiB
Python
87 lines
3.6 KiB
Python
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
|
|
from sklearn.svm import SVR
|
|
from sklearn.neighbors import KNeighborsRegressor
|
|
from sklearn.model_selection import train_test_split
|
|
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
|
|
from spec_read import all_spectral_data
|
|
import joblib
|
|
|
|
# def prepare_data(data):
|
|
# """Reshape data and select specified spectral bands."""
|
|
# reshaped_data = data.reshape(100, -1)
|
|
# selected_bands = [8, 9, 10, 48, 49, 50, 77, 80, 103, 108, 115, 143, 145]
|
|
# return reshaped_data[:, selected_bands]
|
|
|
|
def prepare_data(data):
|
|
"""Reshape data and select specified spectral bands."""
|
|
selected_bands = [8, 9, 10, 48, 49, 50, 77, 80, 103, 108, 115, 143, 145]
|
|
# 筛选特定的波段
|
|
data_selected = data[:, :25, :, selected_bands]
|
|
print(f'筛选后的数据尺寸：{data_selected.shape}')
|
|
# 将筛选后的数据重塑为二维数组，每行代表一个样本
|
|
reshaped_data = data_selected.reshape(-1, 25 * 30 * len(selected_bands))
|
|
return reshaped_data
|
|
|
|
|
|
def split_data(X, y, test_size=0.20, random_state=12):
|
|
"""Split data into training and test sets."""
|
|
return train_test_split(X, y, test_size=test_size, random_state=random_state)
|
|
|
|
|
|
def evaluate_model(model, X_test, y_test):
|
|
"""Evaluate the model and return multiple metrics and predictions."""
|
|
y_pred = model.predict(X_test)
|
|
mse = mean_squared_error(y_test, y_pred)
|
|
mae = mean_absolute_error(y_test, y_pred)
|
|
r2 = r2_score(y_test, y_pred)
|
|
return mse, mae, r2, y_pred
|
|
|
|
|
|
def print_predictions(y_test, y_pred, model_name):
|
|
"""Print actual and predicted values."""
|
|
print(f"Test Set Predictions for {model_name}:")
|
|
for i, (real, pred) in enumerate(zip(y_test, y_pred)):
|
|
print(f"Sample {i + 1}: True Value = {real:.2f}, Predicted Value = {pred:.2f}")
|
|
|
|
def main():
|
|
sweetness_acidity = np.array([
|
|
16.2, 16.1, 17, 16.9, 16.8, 17.8, 18.1, 17.2, 17, 17.2, 17.1, 17.2,
|
|
17.2, 17.2, 18.1, 17, 17.6, 17.4, 17.1, 17.1, 16.9, 17.6, 17.3, 16.3,
|
|
16.5, 18.7, 17.6, 16.2, 16.8, 17.2, 16.8, 17.3, 16, 16.6, 16.7, 16.7,
|
|
17.3, 16.3, 16.8, 17.4, 17.3, 16.3, 16.1, 17.2, 18.6, 16.8, 16.1, 17.2,
|
|
18.3, 16.5, 16.6, 17, 17, 17.8, 16.4, 18, 17.7, 17, 18.3, 16.8, 17.5,
|
|
17.7, 18.5, 18, 17.7, 17, 18.3, 18.1, 17.4, 17.7, 17.8, 16.3, 17.1, 16.8,
|
|
17.2, 17.5, 16.6, 17.7, 17.1, 17.7, 19.4, 20.3, 17.3, 15.8, 18, 17.7,
|
|
17.2, 15.2, 18, 18.4, 18.3, 15.7, 17.2, 18.6, 15.6, 17, 16.9, 17.4, 17.8,
|
|
16.5
|
|
])
|
|
|
|
X = prepare_data(all_spectral_data)
|
|
print(f'原数据尺寸：{all_spectral_data.shape};训练数据尺寸：{X.shape}')
|
|
X_train, X_test, y_train, y_test = split_data(X, sweetness_acidity)
|
|
|
|
models = {
|
|
"RandomForest": RandomForestRegressor(n_estimators=100),
|
|
"GradientBoosting": GradientBoostingRegressor(n_estimators=100),
|
|
"SVR": SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1),
|
|
}
|
|
|
|
for model_name, model in models.items():
|
|
model.fit(X_train, y_train)
|
|
if model_name == "RandomForest":
|
|
joblib.dump(model,
|
|
r'D:\project\supermachine--tomato-passion_fruit\20240529RGBtest3\models\passion_fruit.joblib')
|
|
|
|
mse, mae, r2, y_pred = evaluate_model(model, X_test, y_test)
|
|
print(f"Model: {model_name}")
|
|
print(f"MSE on the test set: {mse}")
|
|
print(f"MAE on the test set: {mae}")
|
|
print(f"R² score on the test set: {r2}")
|
|
print_predictions(y_test, y_pred, model_name)
|
|
print("\n" + "-" * 50 + "\n")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main() |