def test_values(self):
"""Adapted from cogent's `test_principal_coordinate_analysis`:
"I took the example in the book (see intro info), and did the
principal coordinates analysis, plotted the data and it looked
right"."""
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=RuntimeWarning)
ordination = PCoA(self.dist_matrix)
scores = ordination.scores()
exp_eigvals = np.array([
0.73599103, 0.26260032, 0.14926222, 0.06990457, 0.02956972,
0.01931184, 0., 0., 0., 0., 0., 0., 0., 0.
])
exp_site = np.loadtxt(get_data_path('exp_PCoAzeros_site'))
exp_prop_expl = np.array([
0.58105792, 0.20732046, 0.1178411, 0.05518899, 0.02334502,
0.01524651, 0., 0., 0., 0., 0., 0., 0., 0.
])
exp_site_ids = [
'0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',
'13'
]
# Note the absolute value because column can have signs swapped
npt.assert_almost_equal(scores.eigvals, exp_eigvals)
npt.assert_almost_equal(np.abs(scores.site), exp_site)
npt.assert_almost_equal(scores.proportion_explained, exp_prop_expl)
npt.assert_equal(scores.site_ids, exp_site_ids)
def setup(self):
dist_matrix = DistanceMatrix.read(get_data_path('PCoA_sample_data_3'))
self.ordination = PCoA(dist_matrix)
self.ids = [
'PC.636', 'PC.635', 'PC.356', 'PC.481', 'PC.354', 'PC.593',
'PC.355', 'PC.607', 'PC.634'
]
def pcoa(lines):
"""Run PCoA on the distance matrix present on lines"""
# Parse the distance matrix
dist_mtx = DistanceMatrix.read(lines)
# Create the PCoA object
pcoa_obj = PCoA(dist_mtx)
# Get the PCoA results and return them
return pcoa_obj.scores()
def setup(self):
with open(get_data_path('PCoA_sample_data_3'), 'U') as lines:
dist_matrix = DistanceMatrix.from_file(lines)
self.ordination = PCoA(dist_matrix)
self.ids = [
'PC.636', 'PC.635', 'PC.356', 'PC.481', 'PC.354', 'PC.593',
'PC.355', 'PC.607', 'PC.634'
]
def js_PCoA(distributions):
"""Dimension reduction via Jensen-Shannon Divergence & Principal Components
Parameters
----------
distributions : array-like, shape (`n_dists`, `k`)
Matrix of distributions probabilities.
Returns
-------
pcoa : array, shape (`n_dists`, 2)
"""
dist_matrix = DistanceMatrix(dist.squareform(dist.pdist(distributions.values, _jensen_shannon)))
pcoa = PCoA(dist_matrix).scores()
return pcoa.site[:,0:2]
def test_input(self):
with npt.assert_raises(TypeError):
PCoA([[1, 2], [3, 4]])
def setup(self):
matrix = np.loadtxt(get_data_path('PCoA_sample_data_2'))
self.ids = [str(i) for i in range(matrix.shape[0])]
dist_matrix = DistanceMatrix(matrix, self.ids)
self.ordination = PCoA(dist_matrix)
},
'B': {
'Méthode': 's2'
},
'C': {
'Méthode': 's3'
},
'D': {
'Méthode': 's4'
},
'E': {
'Méthode': 's5'
}
}
df = pd.DataFrame.from_dict(metadata, orient='index')
pcoa_results = PCoA(dm).scores()
print(pcoa_results)
fig = pcoa_results.plot(
df=df,
column='Méthode',
title='Estimation methods projected on 3 first principal components',
cmap='Set1',
s=500)
plt.show()
"""
digits = datasets.load_digits()
X = np.array([[ 0. ,35.57933426 ,17.75168991 ,32.03273392 ,33.87740707],[35.57933426 , 0. ,17.86463547 , 7.161726 , 5.87323952], [17.75168991 ,17.86463547 , 0. ,14.88137054 ,16.6187191 ], [32.03273392 , 7.161726 ,14.88137054 , 0. ,3.63054395], [33.87740707 , 5.87323952 ,16.6187191 , 3.63054395 ,0. ]] )
print(type(X) )
y = np.array( [1, 2, 3, 4, 5])
print(y)
print(type(y) )
# Determine if the resulting distance matrices are significantly correlated
# by computing the Mantel correlation between them. Then determine if the
# p-value is significant based on an alpha of 0.05:
from skbio.stats.distance import mantel
r, p_value, n = mantel(j_dm, bc_dm)
print(r)
# -0.209362157621
print(p_value < 0.05)
# False
# Compute PCoA for both distance matrices, and then find the Procrustes
# M-squared value that results from comparing the coordinate matrices.
from skbio.stats.ordination import PCoA
bc_pc = PCoA(bc_dm).scores()
j_pc = PCoA(j_dm).scores()
from skbio.stats.spatial import procrustes
print(procrustes(bc_pc.site, j_pc.site)[2])
# 0.466134984787
# All of this only gets interesting in the context of sample metadata, so
# let's define some:
import pandas as pd
try:
# not necessary for normal use
pd.set_option('show_dimensions', True)
except KeyError:
pass
sample_md = {
def pcoa(adist, cluster_members=None):
from skbio import DistanceMatrix
from skbio.stats.ordination import PCoA
pcoa_results = PCoA(DistanceMatrix(adist)).scores()
