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我正在查看attributesofskbio's PCoA方法（如下所列）。我对此API并不陌生，我希望能够将eigenvectors原始点投影到类似于.fit_transformin的新轴上，sklearn.decomposition.PCA这样我就可以创建一些PC_1 vs PC_2风格的图。我想出了如何获得eigvalsandproportion_explained但又features以None.

是因为它处于测试阶段吗？

如果有任何使用它的教程，那将不胜感激。我是一个超级粉丝scikit-learn并且想开始使用更多的scikit's产品。

|  Attributes
 |  ----------
 |  short_method_name : str
 |      Abbreviated ordination method name.
 |  long_method_name : str
 |      Ordination method name.
 |  eigvals : pd.Series
 |      The resulting eigenvalues.  The index corresponds to the ordination
 |      axis labels
 |  samples : pd.DataFrame
 |      The position of the samples in the ordination space, row-indexed by the
 |      sample id.
 |  features : pd.DataFrame
 |      The position of the features in the ordination space, row-indexed by
 |      the feature id.
 |  biplot_scores : pd.DataFrame
 |      Correlation coefficients of the samples with respect to the features.
 |  sample_constraints : pd.DataFrame
 |      Site constraints (linear combinations of constraining variables):
 |      coordinates of the sites in the space of the explanatory variables X.
 |      These are the fitted site scores
 |  proportion_explained : pd.Series
 |      Proportion explained by each of the dimensions in the ordination space.
 |      The index corresponds to the ordination axis labels

这是我生成principal component analysis对象的代码。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn import decomposition
import seaborn as sns; sns.set_style("whitegrid", {'axes.grid' : False})
import skbio
from scipy.spatial import distance

%matplotlib inline
np.random.seed(0)

# Iris dataset
DF_data = pd.DataFrame(load_iris().data, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])],
                       columns = load_iris().feature_names)
n,m = DF_data.shape
# print(n,m)
# 150 4

Se_targets = pd.Series(load_iris().target, 
                       index = ["iris_%d" % i for i in range(load_iris().data.shape[0])], 
                       name = "Species")

# Scaling mean = 0, var = 1
DF_standard = pd.DataFrame(StandardScaler().fit_transform(DF_data), 
                           index = DF_data.index,
                           columns = DF_data.columns)

# Distance Matrix
Ar_dist = distance.squareform(distance.pdist(DF_standard.T, metric="braycurtis")) # (m x m) distance measure
DM_dist = skbio.stats.distance.DistanceMatrix(Ar_dist, ids=DF_standard.columns)
PCoA = skbio.stats.ordination.pcoa(DM_dist)

在此处输入图像描述

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1 回答 1

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您可以使用 访问转换后的样本坐标OrdinationResults.samples。这将返回pandas.DataFrame由样本 ID（即距离矩阵中的 ID）索引的行。由于主坐标分析对样本的距离矩阵进行操作，因此转换后的特征坐标 ( OrdinationResults.features) 不可用。scikit-bio 中接受样本 x 特征表作为输入的其他排序方法将具有可用的转换特征坐标（例如 CA、CCA、RDA）。

旁注：distance.squareform调用是不必要的，因为skbio.DistanceMatrix支持方形或矢量形式的数组。

于 2016-07-14T21:51:10.467 回答