总的来说,我对 scikit learn 和机器学习非常陌生。
I am currently designing a SVM to predict if a specific amino acid sequence will be cut by a protease. So far the the SVM method seems to be working quite well:
我想可视化两个类别(剪切和未剪切)之间的距离,因此我尝试使用线性判别分析,它类似于主成分分析,使用以下代码:
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda = LinearDiscriminantAnalysis(n_components=2)
targs = np.array([1 if _ else 0 for _ in XOR_list])
DATA = np.array(data_list)
X_r2 = lda.fit(DATA, targs).transform(DATA)
plt.figure()
for c, i, target_name in zip("rg", [1, 0],["Cleaved","Not Cleaved"]):
plt.scatter(X_r2[targs == i], X_r2[targs == i], c=c, label=target_name)
plt.legend()
plt.title('LDA of cleavage_site dataset')
然而,LDA 只给出一维结果
In: print X_r2[:5]
Out: [[ 6.74369996]
[ 4.14254941]
[ 5.19537896]
[ 7.00884032]
[ 3.54707676]]
然而,PCA 分析将根据我输入的数据给出 2 个维度:
pca = PCA(n_components=2)
X_r = pca.fit(DATA).transform(DATA)
print X_r[:5]
Out: [[ 0.05474151 0.38401203]
[ 0.39244191 0.74113729]
[-0.56785236 -0.30109694]
[-0.55633116 -0.30267444]
[ 0.41311866 -0.25501662]]
编辑:这里是两个带有输入数据的谷歌文档的链接。我没有使用序列信息,只是使用后面的数字信息。文件分为阳性和阴性对照数据。
输入数据:file1 https://drive.google.com/file/d/0B9fhTraU6SUkT0toQnFMQmFHVjQ/view?usp=sharing
file2 https://drive.google.com/file/d/0B9fhTraU6SUkZkdkbm0wa18wZG8/view?usp=sharing