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)