def draw_cov(self, axes=None):
# get results
cov = self.results.cov
tre = toytree.tree(self.results.tree)
# names spaced in order
lnames = toyplot.locator.Explicit(
locations=range(len(tre.get_tip_labels())),
labels=tre.get_tip_labels()[::-1],
)
# get a colormap and plot the matrix
cmap = toyplot.color.diverging.map(
"BlueRed",
cov.min(),
cov.max(),
)
canvas, table = toyplot.matrix(
(cov, cmap),
width=400,
height=400,
bshow=True,
tshow=False,
lshow=False,
rlocator=lnames,
blocator=lnames,
)
return canvas, table
def main(model):
store = pd.HDFStore(model)
from_ = store['from_'][0][0]
to = store['to'][0][0]
assert from_ == 0
trace_fpath = store['trace_fpath'][0][0]
Theta_zh = store['Theta_zh'].values
Psi_oz = store['Psi_sz'].values
count_z = store['count_z'].values[:, 0]
Psi_oz = Psi_oz / Psi_oz.sum(axis=0)
Psi_zo = (Psi_oz * count_z).T
Psi_zo = Psi_zo / Psi_zo.sum(axis=0)
obj2id = dict(store['source2id'].values)
hyper2id = dict(store['hyper2id'].values)
id2obj = dict((v, k) for k, v in obj2id.items())
ZtZ = Psi_zo.dot(Psi_oz)
ZtZ = ZtZ / ZtZ.sum(axis=0)
L = ZtZ
#ZtZ[ZtZ < (ZtZ.mean())] = 0
L[ZtZ >= 1.0 / (len(ZtZ))] = 1
L[L != 1] = 0
colormap = toyplot.color.brewer.map("Purples", domain_min=0, domain_max=1, reverse=True)
print(colormap)
canvas = toyplot.matrix((L.T, colormap), label="P[z' | z]", \
colorshow=False, tlabel="To z'", llabel="From")[0]
#canvas.axes(ylabel='From z', xlabel='To z\'')
toyplot.pdf.render(canvas, 'tmat.pdf')
model = SpectralCoclustering(n_clusters=3)
model.fit(L)
fit_data = L[np.argsort(model.row_labels_)]
fit_data = fit_data[:, np.argsort(model.column_labels_)]
canvas = toyplot.matrix((fit_data, colormap), label="P[z' | z']", \
colorshow=False)[0]
toyplot.pdf.render(canvas, 'tmat-cluster.pdf')
#AtA = Psi_oz.dot(Psi_zo)
#np.fill_diagonal(AtA, 0)
#AtA = AtA / AtA.sum(axis=0)
store.close()