mirror of
https://github.com/NanjingForestryUniversity/supermachine--tomato-passion_fruit.git
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111 lines
4.3 KiB
Python
111 lines
4.3 KiB
Python
import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
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from sklearn.svm import SVR
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from sklearn.neighbors import KNeighborsRegressor
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from sklearn.model_selection import train_test_split, GridSearchCV
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from spec_read import all_spectral_data
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import joblib
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def prepare_data(data):
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"""Reshape data and select specified spectral bands."""
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selected_bands = [8, 9, 10, 48, 49, 50, 77, 80, 103, 108, 115, 143, 145]
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# 筛选特定的波段
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data_selected = data[:, :, :, selected_bands]
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# 将筛选后的数据重塑为二维数组，每行代表一个样本
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reshaped_data = data_selected.reshape(-1, 30 * 30 * len(selected_bands))
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return reshaped_data
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def split_data(X, y, test_size=0.20, random_state=12):
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"""Split data into training and test sets."""
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return train_test_split(X, y, test_size=test_size, random_state=random_state)
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def evaluate_model(model, X_test, y_test):
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"""Evaluate the model and return multiple metrics and predictions."""
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y_pred = model.predict(X_test)
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mse = mean_squared_error(y_test, y_pred)
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mae = mean_absolute_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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return mse, mae, r2, y_pred
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def print_predictions(y_test, y_pred, model_name):
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"""Print actual and predicted values."""
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print(f"Test Set Predictions for {model_name}:")
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for i, (real, pred) in enumerate(zip(y_test, y_pred)):
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print(f"Sample {i + 1}: True Value = {real:.2f}, Predicted Value = {pred:.2f}")
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def main():
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sweetness_acidity = np.array([
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16.2, 16.1, 17, 16.9, 16.8, 17.8, 18.1, 17.2, 17, 17.2, 17.1, 17.2,
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17.2, 17.2, 18.1, 17, 17.6, 17.4, 17.1, 17.1, 16.9, 17.6, 17.3, 16.3,
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16.5, 18.7, 17.6, 16.2, 16.8, 17.2, 16.8, 17.3, 16, 16.6, 16.7, 16.7,
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17.3, 16.3, 16.8, 17.4, 17.3, 16.3, 16.1, 17.2, 18.6, 16.8, 16.1, 17.2,
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18.3, 16.5, 16.6, 17, 17, 17.8, 16.4, 18, 17.7, 17, 18.3, 16.8, 17.5,
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17.7, 18.5, 18, 17.7, 17, 18.3, 18.1, 17.4, 17.7, 17.8, 16.3, 17.1, 16.8,
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17.2, 17.5, 16.6, 17.7, 17.1, 17.7, 19.4, 20.3, 17.3, 15.8, 18, 17.7,
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17.2, 15.2, 18, 18.4, 18.3, 15.7, 17.2, 18.6, 15.6, 17, 16.9, 17.4, 17.8,
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16.5
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])
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X = prepare_data(all_spectral_data)
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print(f'原数据尺寸：{all_spectral_data.shape};训练数据尺寸：{X.shape}')
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X_train, X_test, y_train, y_test = split_data(X, sweetness_acidity)
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models_params = {
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"RandomForest": {
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'model': RandomForestRegressor(),
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'params': {
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'n_estimators': [100, 200, 300],
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'max_depth': [None, 10, 20],
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'min_samples_split': [2, 5],
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'min_samples_leaf': [1, 2],
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'random_state': [42]
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}
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},
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"GradientBoosting": {
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'model': GradientBoostingRegressor(),
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'params': {
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'n_estimators': [100, 200, 300],
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'learning_rate': [0.01, 0.1, 0.2],
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'max_depth': [3, 5, 7],
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'min_samples_split': [2, 5],
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'min_samples_leaf': [1, 2],
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'random_state': [42]
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}
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},
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"SVR": {
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'model': SVR(),
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'params': {
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'C': [0.1, 1, 10, 100],
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'gamma': ['scale', 'auto', 0.01, 0.1],
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'epsilon': [0.01, 0.1, 0.5]
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}
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}
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}
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best_models = {}
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for model_name, mp in models_params.items():
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grid_search = GridSearchCV(mp['model'], mp['params'], cv=5, scoring='r2', verbose=2)
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grid_search.fit(X_train, y_train)
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best_models[model_name] = grid_search.best_estimator_
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mse, mae, r2, y_pred = evaluate_model(grid_search.best_estimator_, X_test, y_test)
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print(f"Best {model_name} parameters: {grid_search.best_params_}")
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print(f"Model: {model_name}")
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print(f"MSE on the test set: {mse}")
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print(f"MAE on the test set: {mae}")
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print(f"R² score on the test set: {r2}")
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print_predictions(y_test, y_pred, model_name)
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print("\n" + "-" * 50 + "\n")
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# Optionally save the best model for each type
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joblib.dump(grid_search.best_estimator_, f'{model_name}_best_model.joblib')
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if __name__ == "__main__":
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main() |