def dm_to_pcoa(dm, sample_md, category):
title = "Samples colored by %s." % category
pcoa_results = PCoA(dm).scores()
_ = pcoa_results.plot(df=sample_md,
column=category,
axis_labels=['PC 1', 'PC 2', 'PC 3'],
title=title,
s=35)
'E': {
'body_site': 'tongue',
'subject': 's2'
},
'F': {
'body_site': 'skin',
'subject': 's2'
}
}
sample_md = pd.DataFrame.from_dict(sample_md, orient='index')
sample_md
# subject body_site
# A s1 gut
# B s1 skin
# C s1 tongue
# D s2 gut
# E s2 tongue
# F s2 skin
# <BLANKLINE>
# [6 rows x 2 columns]
# Now let's plot our PCoA results, coloring each sample by the subject it
# was taken from:
fig = bc_pc.plot(sample_md,
'subject',
axis_labels=('PC 1', 'PC 2', 'PC 3'),
title='Samples colored by subject',
cmap='jet',
s=50)
'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) )
n_samples, n_features = X.shape
n_neighbors = 2
# ----------------------------------------------------------------------
import pandas as pd
metadata = {
'A': {
'body_site': 'skin'
},
'B': {
'body_site': 'gut'
},
'C': {
'body_site': 'gut'
},
'D': {
'body_site': 'skin'
}
}
df = pd.DataFrame.from_dict(metadata, orient='index')
# Run principal coordinate analysis (PCoA) on the distance matrix:
from skbio.stats.ordination import PCoA
pcoa_results = PCoA(dm).scores()
# Plot the ordination results, where each site is colored by body site
# (a categorical variable):
fig = pcoa_results.plot(df=df,
column='body_site',
title='Sites colored by body site',
cmap='Set1',
s=50)
