def test_scaling2(self):
eigvals = pd.Series(np.array([0.09613302, 0.04094181]), self.pc_ids)
# p. 460 L&L 1998
features = pd.DataFrame(
np.array([
[0.40887, -0.06955], # F_hat
[-0.11539, 0.29977],
[-0.30997, -0.18739]
]),
self.feature_ids,
self.pc_ids)
samples = pd.DataFrame(
np.array([
[-0.84896, -0.88276], # V_hat
[-0.22046, 1.34482],
[1.66697, -0.47032]
]),
self.sample_ids,
self.pc_ids)
exp = OrdinationResults('CA',
'Correspondance Analysis',
eigvals=eigvals,
features=features,
samples=samples)
scores = ca(self.contingency, 2)
assert_ordination_results_equal(exp,
scores,
decimal=5,
ignore_directionality=True)
def test_scaling1(self):
eigvals = pd.Series(np.array([0.09613302, 0.04094181]), self.pc_ids)
# p. 458
features = pd.DataFrame(
np.array([
[1.31871, -0.34374], # V
[-0.37215, 1.48150],
[-0.99972, -0.92612]
]),
self.feature_ids,
self.pc_ids)
samples = pd.DataFrame(
np.array([
[-0.26322, -0.17862], # F
[-0.06835, 0.27211],
[0.51685, -0.09517]
]),
self.sample_ids,
self.pc_ids)
exp = OrdinationResults('CA',
'Correspondance Analysis',
eigvals=eigvals,
features=features,
samples=samples)
scores = ca(self.contingency, 1)
assert_ordination_results_equal(exp,
scores,
decimal=5,
ignore_directionality=True)
def test_scaling2(self):
eigvals = pd.Series(np.array([0.09613302, 0.04094181]), self.pc_ids)
# p. 460 L&L 1998
features = pd.DataFrame(np.array([[0.40887, -0.06955], # F_hat
[-0.11539, 0.29977],
[-0.30997, -0.18739]]),
self.feature_ids,
self.pc_ids)
samples = pd.DataFrame(np.array([[-0.84896, -0.88276], # V_hat
[-0.22046, 1.34482],
[1.66697, -0.47032]]),
self.sample_ids,
self.pc_ids)
proportion_explained = pd.Series(np.array([0.701318, 0.298682]),
self.pc_ids)
exp = OrdinationResults('CA', 'Correspondance Analysis',
eigvals=eigvals, features=features,
samples=samples,
proportion_explained=proportion_explained)
scores = ca(self.contingency, 2)
assert_ordination_results_equal(exp, scores, decimal=5,
ignore_directionality=True)
def test_maintain_chi_square_distance_scaling2(self):
"""In scaling 2, chi^2 distance among columns (features) is
equal to euclidean distance between them in transformed space."""
frequencies = self.X / self.X.sum()
chi2_distances = chi_square_distance(frequencies, between_rows=False)
transformed_species = ca(self.contingency, 2).features.values
euclidean_distances = pdist(transformed_species, 'euclidean')
npt.assert_almost_equal(chi2_distances, euclidean_distances)
def test_maintain_chi_square_distance_scaling1(self):
"""In scaling 1, chi^2 distance among rows (samples) is equal to
euclidean distance between them in transformed space."""
frequencies = self.X / self.X.sum()
chi2_distances = chi_square_distance(frequencies)
transformed_sites = ca(self.contingency, 1).samples.values
euclidean_distances = pdist(transformed_sites, 'euclidean')
npt.assert_almost_equal(chi2_distances, euclidean_distances)
def test_maintain_chi_square_distance_scaling2(self):
"""In scaling 2, chi^2 distance among columns (features) is
equal to euclidean distance between them in transformed space."""
frequencies = self.X / self.X.sum()
chi2_distances = chi_square_distance(frequencies, between_rows=False)
transformed_species = ca(self.contingency, 2).features.values
euclidean_distances = pdist(transformed_species, 'euclidean')
npt.assert_almost_equal(chi2_distances, euclidean_distances)
def test_maintain_chi_square_distance_scaling1(self):
"""In scaling 1, chi^2 distance among rows (samples) is equal to
euclidean distance between them in transformed space."""
frequencies = self.X / self.X.sum()
chi2_distances = chi_square_distance(frequencies)
transformed_sites = ca(self.contingency, 1).samples.values
euclidean_distances = pdist(transformed_sites, 'euclidean')
npt.assert_almost_equal(chi2_distances, euclidean_distances)
def test_scaling1(self):
eigvals = pd.Series(np.array([0.09613302, 0.04094181]), self.pc_ids)
# p. 458
features = pd.DataFrame(np.array([[1.31871, -0.34374], # V
[-0.37215, 1.48150],
[-0.99972, -0.92612]]),
self.feature_ids,
self.pc_ids)
samples = pd.DataFrame(np.array([[-0.26322, -0.17862], # F
[-0.06835, 0.27211],
[0.51685, -0.09517]]),
self.sample_ids,
self.pc_ids)
exp = OrdinationResults('CA', 'Correspondance Analysis',
eigvals=eigvals, features=features,
samples=samples)
scores = ca(self.contingency, 1)
assert_ordination_results_equal(exp, scores, decimal=5,
ignore_directionality=True)
alabels = [
"%4.2f" % pca.explained_variance_[0],
"%4.2f" % pca.explained_variance_[1]
]
lcoords = [coords[:, 0].tolist(), coords[:, 1].tolist()]
elif fmethod.find("PCoA") != -1:
M = pd.DataFrame(np.array(cdata), site_ids, site_ids)
ordres = ordination.pcoa(M)
alabels = [
"%5.3f" % ordres.eigvals["PC1"],
"%5.3f" % ordres.eigvals["PC2"]
]
lcoords = [ordres.samples["PC1"].tolist(), ordres.samples["PC2"].tolist()]
elif fmethod == "CA (skbio)":
M = pd.DataFrame(np.array(cdata), site_ids, site_ids)
ordres = ordination.ca(M)
alabels = [
"%5.3f" % ordres.eigvals["CA1"],
"%5.3f" % ordres.eigvals["CA2"]
]
lcoords = [ordres.samples["CA1"].tolist(), ordres.samples["CA2"].tolist()]
print """
<style>
.axis line, .axis path {
shape-rendering: crispEdges;
stroke: black;
fill: none;
}
circle {
def test_negative(self):
X = np.array([[1, 2], [-0.1, -2]])
with npt.assert_raises(ValueError):
ca(pd.DataFrame(X))
def test_negative(self):
X = np.array([[1, 2], [-0.1, -2]])
with npt.assert_raises(ValueError):
ca(pd.DataFrame(X))
