时间戳

首页 > 解决方案 > scikit-learn 中的高斯过程：在训练数据上表现良好，在测试数据上表现不佳

python - scikit-learn 中的高斯过程：在训练数据上表现良好，在测试数据上表现不佳

问题描述

我编写了一个 Python 脚本，用于scikit-learn将高斯过程拟合到某些数据。

简而言之：我面临的问题是，虽然高斯过程似乎对训练数据集学习得很好，但对测试数据集的预测却是错误的，在我看来，这背后存在归一化问题。

详细说明：我的训练数据集是一组1500时间序列。每个时间序列都有50时间分量。高斯过程学习的映射在一组三个坐标x,y,z（代表我的模型的参数）和一个时间序列之间。换句话说，x,y,z一个时间序列之间存在 1:1 的映射，而 GP 学习这种映射。这个想法是，通过为训练有素的 GP 提供新坐标，他们应该能够为我提供与这些坐标相关的预测时间序列。

这是我的代码：

from __future__ import division
import numpy as np
from matplotlib import pyplot as plt

from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern

coordinates_training = np.loadtxt(...) # read coordinates training x, y, z from file
coordinates_testing = np.loadtxt(..) # read coordinates testing x, y, z from file

# z-score of the coordinates for the training and testing data.
# Note I am using the mean and std of the training dataset ALSO to normalize the testing dataset

mean_coords_training = np.zeros(3)
std_coords_training = np.zeros(3)

for i in range(3):
    mean_coords_training[i] = coordinates_training[:, i].mean()
    std_coords_training[i] = coordinates_training[:, i].std()

    coordinates_training[:, i] = (coordinates_training[:, i] - mean_coords_training[i])/std_coords_training[i]
    coordinates_testing[:, i] = (coordinates_testing[:, i] - mean_coords_training[i])/std_coords_training[i]

time_series_training = np.loadtxt(...)# reading time series of training data from file
number_of_time_components = np.shape(time_series_training)[1] # 100 time components

# z_score of the time series
mean_time_series_training = np.zeros(number_of_time_components)
std_time_series_training = np.zeros(number_of_time_components)
for i in range(number_of_time_components):
    mean_time_series_training[i] = time_series_training[:, i].mean()
    std_time_series_training[i] = time_series_training[:, i].std()
    time_series_training[:, i] = (time_series_training[:, i] - mean_time_series_training[i])/std_time_series_training[i]

time_series_testing = np.loadtxt(...)# reading test data from file
# the number of time components is the same for training and testing dataset

# z-score of testing data, again using mean and std of training data
for i in range(number_of_time_components):
    time_series_testing[:, i] = (time_series_testing[:, i] - mean_time_series_training[i])/std_time_series_training[i]

# GPs        

pred_time_series_training = np.zeros((np.shape(time_series_training)))
pred_time_series_testing = np.zeros((np.shape(time_series_testing)))

# Instantiate a Gaussian Process model
kernel = 1.0 * Matern(nu=1.5)
gp = GaussianProcessRegressor(kernel=kernel)

for i in range(number_of_time_components):
    print("time component", i)

    # Fit to data using Maximum Likelihood Estimation of the parameters
    gp.fit(coordinates_training, time_series_training[:,i])

    # Make the prediction on the meshed x-axis (ask for MSE as well)
    y_pred_train, sigma_train = gp.predict(coordinates_train, return_std=True)
    y_pred_test, sigma_test = gp.predict(coordinates_test, return_std=True)

    pred_time_series_training[:,i] = y_pred_train*std_time_series_training[i] + mean_time_series_training[i]
    pred_time_series_testing[:,i] = y_pred_test*std_time_series_training[i] + mean_time_series_training[i]


# plot training
fig, ax = plt.subplots(5, figsize=(10,20))
for i in range(5):
        ax[i].plot(time_series_training[100*i], color='blue', label='Original training')
        ax[i].plot(pred_time_series_training[100*i], color='black', label='GP predicted - training')

# plot testing
fig, ax = plt.subplots(5, figsize=(10,20))
for i in range(5):
        ax[i].plot(features_time_series_testing[100*i], color='blue', label='Original testing')
        ax[i].plot(pred_time_series_testing[100*i], color='black', label='GP predicted - testing')

这里是训练数据的性能示例。 这里是测试数据的性能示例。测试

标签： pythonmachine-learningscikit-learnnormalization

解决方案

首先，您应该使用 sklearn 预处理工具来处理您的数据。

from sklearn.preprocessing import StandardScaler

还有其他有用的工具可以组织，但这个特定的工具可以规范化数据。其次，您应该使用相同的参数对训练集和测试集进行归一化。模型将拟合数据的“几何”来定义参数，如果您使用其他比例训练模型，则类似使用错误的单位制。

scale = StandardScaler()
training_set = scale.fit_tranform(data_train)
test_set = scale.transform(data_test)

这将在集合中使用相同的转换。

最后你需要规范化特征而不是traget，我的意思是规范化X条目而不是Y输出，规范化有助于模型更快地找到答案在优化过程中改变目标函数的拓扑输出不会影响这一点.

我希望这能回答你的问题。

推荐阅读