def test_scaling2(self):
scores = rda(self.Y, self.X, scaling=2)
mat = np.loadtxt(get_data_path('example2_biplot_scaling2'))
cropped_pc_ids = self.pc_ids[:mat.shape[1]]
biplot_scores = pd.DataFrame(mat,
index=self.env_ids,
columns=cropped_pc_ids)
sample_constraints = pd.DataFrame(np.loadtxt(
get_data_path('example2_sample_constraints_scaling2')))
# Load data as computed with vegan 2.0-8
vegan_features = pd.DataFrame(
np.loadtxt(get_data_path(
'example2_species_scaling2_from_vegan')),
index=self.feature_ids,
columns=self.pc_ids)
vegan_samples = pd.DataFrame(
np.loadtxt(get_data_path(
'example2_site_scaling2_from_vegan')),
index=self.sample_ids,
columns=self.pc_ids)
sample_constraints = pd.DataFrame(
np.loadtxt(get_data_path(
'example2_sample_constraints_scaling2')),
index=self.sample_ids,
columns=self.pc_ids)
mat = np.loadtxt(get_data_path(
'example2_biplot_scaling2'))
cropped_pc_ids = self.pc_ids[:mat.shape[1]]
biplot_scores = pd.DataFrame(mat,
index=self.env_ids,
columns=cropped_pc_ids)
proportion_explained = pd.Series([0.44275783, 0.25614586,
0.15280354, 0.10497021,
0.02873375, 0.00987052,
0.00471828],
index=self.pc_ids)
eigvals = pd.Series([25.897954, 14.982578, 8.937841, 6.139956,
1.680705, 0.577350, 0.275984],
index=self.pc_ids)
exp = OrdinationResults(
'RDA', 'Redundancy Analysis',
samples=vegan_samples,
features=vegan_features,
sample_constraints=sample_constraints,
biplot_scores=biplot_scores,
proportion_explained=proportion_explained,
eigvals=eigvals)
assert_ordination_results_equal(scores, exp,
ignore_directionality=True,
decimal=6)
def do_analysis(df, n_components=-1):
Y_cols = ["slump", "flow", "compressive_strength"]
X_cols = [
"cement", "slag", "fly_ash", "water", "superplasticizer",
"coarse_aggregate", "fine_aggregate"
]
Y = df[Y_cols]
X = df[X_cols]
if n_components == -1:
r2s = []
mses = []
rpds = []
xticks = np.arange(1, X.shape[1] + 1)
for n_comp in xticks:
y_cv, r2, mse, rpd = optimise_cca_cv(X, Y, n_comp)
r2s.append(r2)
mses.append(mse)
rpds.append(rpd)
plot_metrics(mses, 'MSE', 'min', xticks)
plot_metrics(r2s, 'R2', 'max', xticks)
#plot_metrics(rpds, 'RPD', 'max', xticks)
n_components = np.argmin(mses) + 1
cca = CCA(n_components=n_components, scale=True)
cca.fit(X, Y)
loadings = pd.DataFrame(cca.x_loadings_)
scores = pd.DataFrame(cca.x_scores_)
X_rows_dict = {i: X_cols[i] for i in range(0, len(X_cols))}
X_cols_dict = {i: 'LV' + str(i + 1) for i in range(0, n_components)}
loadings.rename(index=X_rows_dict, columns=X_cols_dict, inplace=True)
print(loadings)
rda_res = rda(Y, X, scale_Y=True)
print(rda_res)
print(rda_res.proportion_explained)
def test_biplot_score(self):
rda_ = rda(y=self.Y, x=self.X, scale_Y=False, scaling=1)
# Load data as computed with vegan 2.4-3:
# library(vegan)
# data(varechem)
# data(varespec)
# rda_ = rda(X=varespec, Y=varechem, scale=FALSE)
# write.table(summary(rda_, scaling=1)$biplot,
# 'vare_rda_biplot_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$sites,
# 'vare_rda_sites_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$species,
# 'vare_rda_species_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$constraints, #
# 'vare_rda_constraints_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$cont$importance[2, ],
# 'vare_rda_propexpl_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$cont$importance[1, ],
# 'vare_rda_eigvals_from_vegan.csv', sep=',')
vegan_features = pd.read_csv(
get_data_path('vare_rda_species_from_vegan.csv'))
vegan_samples = pd.read_csv(
get_data_path('vare_rda_sites_from_vegan.csv'))
vegan_biplot = pd.read_csv(
get_data_path('vare_rda_biplot_from_vegan.csv'))
vegan_constraints = pd.read_csv(
get_data_path('vare_rda_constraints_from_vegan.csv'))
vegan_propexpl = pd.read_csv(
get_data_path('vare_rda_propexpl_from_vegan.csv'))
vegan_propexpl = pd.Series(vegan_propexpl.x.values,
index=rda_.eigvals.index)
vegan_eigvals = pd.read_csv(
get_data_path('vare_rda_eigvals_from_vegan.csv'))
vegan_eigvals = pd.Series(vegan_eigvals.x.values,
index=rda_.eigvals.index)
# scikit-bio returns singular values, whereas vegan returns eigenvalues
vegan_eigvals = np.sqrt(vegan_eigvals * vegan_eigvals.shape[0])
vegan_propexpl = vegan_eigvals / vegan_eigvals.sum()
# transform the output of rda_ to match column selection of vegan
res_samples = rda_.samples.iloc[:, 0:6]
res_features = rda_.features.iloc[:, 0:6]
rda_ = OrdinationResults(
'RDA',
'Redundancy Analysis',
samples=res_samples,
features=res_features,
sample_constraints=rda_.sample_constraints.iloc[:, 0:6],
biplot_scores=rda_.biplot_scores.iloc[:, 0:6],
proportion_explained=rda_.proportion_explained,
eigvals=rda_.eigvals)
exp = OrdinationResults('RDA',
'Redundancy Analysis',
samples=vegan_samples,
features=vegan_features,
sample_constraints=vegan_constraints,
biplot_scores=vegan_biplot,
proportion_explained=vegan_propexpl,
eigvals=vegan_eigvals)
# This scaling constant is required to make skbio comparable to vegan.
scaling = (rda_.eigvals[0] / rda_.eigvals[:6])
exp.biplot_scores *= scaling
assert_ordination_results_equal(rda_,
exp,
ignore_directionality=False,
decimal=6)
def test_biplot_score(self):
rda_ = rda(y=self.Y, x=self.X, scale_Y=False, scaling=1)
# Load data as computed with vegan 2.4-3:
# library(vegan)
# data(varechem)
# data(varespec)
# rda_ = rda(X=varespec, Y=varechem, scale=FALSE)
# write.table(summary(rda_, scaling=1)$biplot,
# 'vare_rda_biplot_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$sites,
# 'vare_rda_sites_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$species,
# 'vare_rda_species_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$constraints, #
# 'vare_rda_constraints_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$cont$importance[2, ],
# 'vare_rda_propexpl_from_vegan.csv', sep=',')
# write.table(summary(rda_, scaling=1)$cont$importance[1, ],
# 'vare_rda_eigvals_from_vegan.csv', sep=',')
vegan_features = pd.read_csv(
get_data_path('vare_rda_species_from_vegan.csv'))
vegan_samples = pd.read_csv(
get_data_path('vare_rda_sites_from_vegan.csv'))
vegan_biplot = pd.read_csv(
get_data_path('vare_rda_biplot_from_vegan.csv'))
vegan_constraints = pd.read_csv(
get_data_path('vare_rda_constraints_from_vegan.csv'))
vegan_propexpl = pd.read_csv(
get_data_path('vare_rda_propexpl_from_vegan.csv'))
vegan_propexpl = pd.Series(
vegan_propexpl.x.values, index=rda_.eigvals.index)
vegan_eigvals = pd.read_csv(
get_data_path('vare_rda_eigvals_from_vegan.csv'))
vegan_eigvals = pd.Series(
vegan_eigvals.x.values, index=rda_.eigvals.index)
# scikit-bio returns singular values, whereas vegan returns eigenvalues
vegan_eigvals = np.sqrt(vegan_eigvals*vegan_eigvals.shape[0])
vegan_propexpl = vegan_eigvals/vegan_eigvals.sum()
# transform the output of rda_ to match column selection of vegan
res_samples = rda_.samples.iloc[:, 0:6]
res_features = rda_.features.iloc[:, 0:6]
rda_ = OrdinationResults(
'RDA', 'Redundancy Analysis',
samples=res_samples,
features=res_features,
sample_constraints=rda_.sample_constraints.iloc[:, 0:6],
biplot_scores=rda_.biplot_scores.iloc[:, 0:6],
proportion_explained=rda_.proportion_explained,
eigvals=rda_.eigvals)
exp = OrdinationResults(
'RDA', 'Redundancy Analysis',
samples=vegan_samples,
features=vegan_features,
sample_constraints=vegan_constraints,
biplot_scores=vegan_biplot,
proportion_explained=vegan_propexpl,
eigvals=vegan_eigvals)
pdt.assert_frame_equal(res_samples, vegan_samples)
# This scaling constant is required to make skbio comparable to vegan.
scaling = (rda_.eigvals[0] / rda_.eigvals[:6])
exp.biplot_scores *= scaling
assert_ordination_results_equal(
rda_, exp,
ignore_directionality=False,
decimal=6)