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<p>我正在用两种方法用我的研究数据构建一个神经网络:用统计程序(SPSS)和python。
<strong>我正在使用scikit学习MLPREGESSOR。我的问题是,虽然我的代码显然写得很好(因为它可以运行),但结果却毫无意义。R2分数应该在0.70左右(它是-4147.64),图中表示的相关性应该几乎是线性的。(它只是一条与X轴保持恒定距离的直线)</strong>。此外,x轴和y轴的值应在0到180之间,情况并非如此(x轴从20到100,y轴从-4100到-3500)</p>
<p>如果你们中有人能帮忙,我将非常感激。
谢谢你</p>
<pre><code>import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import r2_score
vhdata = pd.read_csv('vhrawdata.csv')
vhdata.head()
X = vhdata[['PA NH4', 'PH NH4', 'PA K', 'PH K', 'PA NH4 + PA K', 'PH NH4 + PH K', 'PA IS', 'PH IS']]
y = vhdata['PMI']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
X_train_norm = scaler.transform(X_train)
X_test_norm = scaler.transform(X_test)
nnref = MLPRegressor(hidden_layer_sizes = [4], activation = 'logistic', solver = 'sgd', alpha = 1,
learning_rate= 'constant', learning_rate_init= 0.6, max_iter=40000, momentum=
0.3).fit(X_train, y_train)
y_predictions= nnref.predict(X_test)
print('Accuracy of NN classifier on training set (R2 score): {:.2f}'.format(nnref.score(X_train_norm, y_train)))
print('Accuracy of NN classifier on test set (R2 score): {:.2f}'.format(nnref.score(X_test_norm, y_test)))
plt.figure()
plt.scatter(y_test,y_predictions, marker = 'o', color='red')
plt.xlabel('PMI expected (hrs)')
plt.ylabel('PMI predicted (hrs)')
plt.title('Correlation of PMI predicted by MLP regressor and the actual PMI')
plt.show()
</code></pre>