def test_not_a_pcoa(self):
self.ordination.short_method_name = 'RDA'
self.ordination.long_method_name = 'Redundancy Analysis'
with self.assertRaisesRegex(
ValueError, 'This biplot computation can'
' only be performed in a PCoA matrix.'):
pcoa_biplot(self.ordination, self.descriptors)
def test_not_a_pcoa(self):
self.ordination.short_method_name = 'RDA'
self.ordination.long_method_name = 'Redundancy Analysis'
with self.assertRaisesRegex(ValueError, 'This biplot computation can'
' only be performed in a '
'PCoA matrix.'):
pcoa_biplot(self.ordination, self.descriptors)
def test_mismatching_samples(self):
new_index = self.descriptors.index.tolist()
new_index[3] = 'Not.an.id'
self.descriptors.index = pd.Index(new_index)
with self.assertRaisesRegex(
ValueError, 'The eigenvectors and the '
'descriptors must describe the same '
'samples.'):
pcoa_biplot(self.ordination, self.descriptors)
def test_mismatching_samples(self):
new_index = self.descriptors.index.tolist()
new_index[3] = 'Not.an.id'
self.descriptors.index = pd.Index(new_index)
with self.assertRaisesRegex(ValueError, 'The eigenvectors and the '
'descriptors must describe '
'the same '
'samples.'):
pcoa_biplot(self.ordination, self.descriptors)
def test_from_seralized_results(self):
# the current implementation of ordination results loses some
# information, test that pcoa_biplot works fine regardless
results = OrdinationResults.read(get_data_path('PCoA_skbio'))
serialized = pcoa_biplot(results, self.descriptors)
in_memory = pcoa_biplot(self.ordination, self.descriptors)
assert_ordination_results_equal(serialized, in_memory,
ignore_directionality=True,
ignore_axis_labels=True,
ignore_method_names=True)
def test_pcoa_biplot_subset_input(self):
# create a 2D copy of the full ordination
two_dims = deepcopy(self.ordination)
two_dims.eigvals = two_dims.eigvals[:2]
two_dims.samples = two_dims.samples.iloc[:, :2]
two_dims.proportion_explained = two_dims.proportion_explained[:2]
# only look at the features
subset = pcoa_biplot(two_dims, self.descriptors).features
full = pcoa_biplot(self.ordination, self.descriptors).features
# the biplot should be identical regardless of the number of axes used
pd.util.testing.assert_almost_equal(subset, full.iloc[:, :2])
def test_pcoa_biplot_subset_input(self):
# create a 2D copy of the full ordination
two_dims = deepcopy(self.ordination)
two_dims.eigvals = two_dims.eigvals[:2]
two_dims.samples = two_dims.samples.iloc[:, :2]
two_dims.proportion_explained = two_dims.proportion_explained[:2]
# only look at the features
subset = pcoa_biplot(two_dims, self.descriptors).features
full = pcoa_biplot(self.ordination, self.descriptors).features
# the biplot should be identical regardless of the number of axes used
pd.util.testing.assert_almost_equal(subset, full.iloc[:, :2])
def test_pcoa_biplot_from_ape(self):
"""Test against a reference implementation from R's ape package
The test data was generated with the R script below and using a
modified version of pcoa.biplot that returns the U matrix.
library(ape)
# files can be found in the test data folder of the ordination module
y = t(read.table('PCoA_biplot_descriptors', row.names = 1, header = 1))
dm = read.table('PCoA_sample_data_3', row.names = 1, header = 1)
h = pcoa(dm)
# biplot.pcoa will only calculate the biplot for two axes at a time
acc = NULL
for (axes in c(1, 3, 5, 7)) {
new = biplot.pcoa(h, y, plot.axes=c(axes, axes+1),
rn = rep('.', length(colnames(dm))) )
if(is.null(acc)) {
acc = new
}
else {
b = acc
acc <- cbind(acc, new)
}
}
write.csv(acc, file='PCoA_biplot_projected_descriptors')
"""
obs = pcoa_biplot(self.ordination, self.descriptors)
# we'll build a dummy ordination results object based on the expected
# the main thing we'll compare and modify is the features dataframe
exp = deepcopy(obs)
fp = get_data_path('PCoA_biplot_projected_descriptors')
# R won't calculate the last dimension, so pad with zeros to make the
# arrays comparable
exp.features = pd.read_table(fp, sep=',', index_col=0)
exp.features['Axis.9'] = np.zeros_like(exp.features['Axis.8'])
# make the order comparable
exp.features = exp.features.reindex(obs.features.index)
assert_ordination_results_equal(obs,
exp,
ignore_directionality=True,
ignore_axis_labels=True)
def test_pcoa_biplot_from_ape(self):
"""Test against a reference implementation from R's ape package
The test data was generated with the R script below and using a
modified version of pcoa.biplot that returns the U matrix.
library(ape)
# files can be found in the test data folder of the ordination module
y = t(read.table('PCoA_biplot_descriptors', row.names = 1, header = 1))
dm = read.table('PCoA_sample_data_3', row.names = 1, header = 1)
h = pcoa(dm)
# biplot.pcoa will only calculate the biplot for two axes at a time
acc = NULL
for (axes in c(1, 3, 5, 7)) {
new = biplot.pcoa(h, y, plot.axes=c(axes, axes+1),
rn = rep('.', length(colnames(dm))) )
if(is.null(acc)) {
acc = new
}
else {
b = acc
acc <- cbind(acc, new)
}
}
write.csv(acc, file='PCoA_biplot_projected_descriptors')
"""
obs = pcoa_biplot(self.ordination, self.descriptors)
# we'll build a dummy ordination results object based on the expected
# the main thing we'll compare and modify is the features dataframe
exp = deepcopy(obs)
fp = get_data_path('PCoA_biplot_projected_descriptors')
# R won't calculate the last dimension, so pad with zeros to make the
# arrays comparable
exp.features = pd.read_table(fp, sep=',', index_col=0)
exp.features['Axis.9'] = np.zeros_like(exp.features['Axis.8'])
# make the order comparable
exp.features = exp.features.reindex(obs.features.index)
assert_ordination_results_equal(obs, exp, ignore_directionality=True,
ignore_axis_labels=True)
def _build_model(self, dism, data, base_model):
distance_matrix = DistanceMatrix(data=dism.values, ids=dism.nodes)
ordination_results = base_model(distance_matrix)
if data is not None:
ordination_results = pcoa_biplot(ordination_results, data)
return ordination_results
