如何获得`skbio` PCoA(主要坐标分析)结果？

Question

我看attributes的skbio's PCoA方法(见下表).我是新来这个API,我希望能够得到eigenvectors投射到新中轴线和原始点相似.fit_transform的sklearn.decomposition.PCA,所以我可以创造一些PC_1 vs PC_2式的情节.我想出了如何获得eigvals,proportion_explained但features回来了None.

这是因为它处于测试阶段吗？

如果有任何教程使用它,那将非常感激.我是一个狂热的粉丝,scikit-learn并希望开始使用更多的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)

Answer 1

您可以使用访问转换的样本坐标OrdinationResults.samples.这将返回pandas.DataFrame按样本ID索引的行(即距离矩阵中的ID).由于主坐标分析对样本的距离矩阵进行操作,因此变换的特征坐标(OrdinationResults.features)不可用.scikit-bio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变换特征坐标(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.