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118 lines
3.9 KiB
Python
118 lines
3.9 KiB
Python
import itertools
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from sklearn.model_selection import train_test_split
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from keras import layers, models
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def get_state_vect_cols(prefix=''):
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if prefix:
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prefix += '_'
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vectors = ['r', 'v']
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components = ['x', 'y', 'z']
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col_names = [f'{prefix}{v}_{c}'
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for v, c
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in itertools.product(vectors, components)]
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return col_names
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def build_train_test_sets(df, test_size=0.2):
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# Features are the physics predicted state vectors and the amount of
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# time in seconds into the future the prediction was made
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feature_cols = ['elapsed_seconds'] + get_state_vect_cols('physics_pred') \
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+ get_state_vect_cols('start')
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# The target values are the errors between the physical model predictions
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# and the ground truth observations
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target_cols = get_state_vect_cols('physics_err')
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# Create feature and target matrices
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X = df[feature_cols]
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y = df[target_cols]
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# Split feature and target data into training and test sets
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data_keys = ['X_train', 'X_test', 'y_train', 'y_test']
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data_vals = train_test_split(X, y, test_size=test_size)
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train_test_data = dict(zip(data_keys, data_vals))
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return train_test_data
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def get_data(file_path):
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print('Loading physical model orbit prediction training data...')
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physics_pred_df = pd.read_parquet(file_path)
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print('Building training and test sets...')
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train_test_data = build_train_test_sets(physics_pred_df)
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x_train = train_test_data['X_train']
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x_test = train_test_data['X_test']
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y_train = train_test_data['y_train']
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y_test = train_test_data['y_test']
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return x_train, y_train, x_test, y_test
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# 模型定义
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def build_model():
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network = models.Sequential()
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network.add(layers.Dense(64, activation='relu', input_shape=(13, )))
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network.add(layers.Dense(64, activation='relu'))
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network.add(layers.Dense(1)) # 最后输出预测值，恒等函数
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#损失函数用mes(均方误差), 监控指标为mae(平均绝对误差, 返回误差绝对值)
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network.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
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return network
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file_path = r"ssa/traindata/physics_preds.parquet"
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train_data, train_labels, test_data, test_labels = get_data(file_path)
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# # 数据标准化，减去平均值再除以标准差(测试数据也用训练数据的标准差)
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# mean = train_data.mean(axis=0)
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# train_data -= mean
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# std = train_data.std(axis=0)
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# train_data /= std
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# test_data -= mean
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# test_data /= std
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x_train = np.array(train_data)
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print(x_train.shape)
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x_test = np.array(test_data)
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print(x_test.shape)
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features = ['physics_err_r_x', 'physics_err_r_y', 'physics_err_r_z',
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'physics_err_v_x', 'physics_err_v_y', 'physics_err_v_z']
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ave_r2 = 0.
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for i in range(6):
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y_train = np.array(train_labels[features[i]])
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print(y_train.shape)
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y_test = np.array(test_labels[features[i]])
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print(y_test.shape)
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network = build_model()
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network.summary()
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History = network.fit(x_train, y_train, epochs=100, batch_size=1)
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network.save('models/DNN{0}.h5'.format(i+1))
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# 用训练好的模型衡量测试数据精确度
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mse, mae = network.evaluate(x_test, y_test)
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rmse = mse**0.5
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r2 = 1-mse / np.var(y_test)
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print(features[i], ": mse:", mse, " rmse:",
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rmse, " mae:", mae, " r2:", r2)
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ave_r2 += r2
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# #用训练好的网络预测结果
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# y_p = network.predict(x_test)
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# 绘制图像
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history_dict = History.history
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print(history_dict.keys())
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metric_list = history_dict['mae']
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x = range(1, len(metric_list) + 1)
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plt.figure(i)
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plt.plot(x, metric_list)
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plt.title('Training_mae')
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plt.xlabel('Epoches')
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plt.ylabel('mean abs error')
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plt.legend()
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plt.show()
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print('ave_r2: ', ave_r2/6)
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