def sweep_components(X, n_max=None, n_min=2):
"""
Sweeps over all values of n_components and returns a plot of losses vs n_components
Parameters
----------
X : Tensor
n_max : int
Max n in search, default X.shape[0]
n_min : int
Min n in search, default 2
Returns
-------
fig
"""
from constrainednmf.nmf.models import NMF
mpl.rcParams.update({"font.size": fontsize, "lines.linewidth": linewidth})
if n_max is None:
n_max = X.shape[0]
losses = list()
kl_losses = list()
for n_components in range(n_min, n_max + 1):
nmf = NMF(X.shape, n_components)
nmf.fit(X, beta=2, tol=1e-8, max_iter=500)
losses.append(nmf.loss(X, beta=2))
kl_losses.append(nmf.loss(X, beta=1))
fig = plt.figure(
figsize=(one_column_width, one_column_width / 2), tight_layout=True
) # create a figure object
ax = fig.add_subplot(1, 1, 1)
axes = [ax, ax.twinx()]
x = list(range(n_min, n_max + 1))
axes[0].plot(x, losses, color="C0", label="MSE Loss")
axes[0].set_ylabel("MSE Loss", color="C0")
axes[1].plot(x, kl_losses, color="C1", label="KL Loss")
axes[1].set_ylabel("KL Loss", color="C1")
axes[1].ticklabel_format(style='sci', scilimits=(-2, 2))
ax.set_xlabel("Number of components")
return fig, axes
def constrained_nmf(X, components):
input_H = [
torch.tensor(component[None, :], dtype=torch.float)
for component in components
]
nmf = NMF(
X.shape,
n_components=3,
initial_components=input_H,
fix_components=[True for _ in range(len(input_H))],
)
n_iter = nmf.fit(torch.tensor(X), beta=2)
return nmf
def constrained_nmf(X):
ideal_weights = np.array(
list(zip(*[(x, 1 - x) for x in np.linspace(0, 1, X.shape[0])])))
input_W = [
torch.tensor(w[None, :], dtype=torch.float) for w in ideal_weights.T
]
nmf = NMF(
X.shape,
n_components=2,
initial_weights=input_W,
fix_weights=[True for _ in range(len(input_W))],
)
n_iter = nmf.fit(torch.tensor(X), beta=2)
return nmf
def partial_constrained_nmf(X,
components,
fix_components=(True for _ in range(3))):
input_H = [
torch.tensor(component[None, :], dtype=torch.float)
if fix_components[i] else torch.rand(1, X.shape[-1])
for i, component in enumerate(components)
]
nmf = NMF(
X.shape,
n_components=3,
initial_components=input_H,
fix_components=fix_components,
)
_ = nmf.fit(torch.tensor(X), beta=2)
return nmf
def standard_nmf(X, n_components=4):
nmf = NMF(X.shape, n_components)
n_iter = nmf.fit(X, beta=2, tol=1e-8, max_iter=1000)
return nmf
def standard_nmf(X):
nmf = NMF(X.shape, n_components=2)
n_iter = nmf.fit(torch.tensor(X), beta=2)
return nmf
def standard_nmf(X):
nmf = NMF(X.shape, 4)
n_iter = nmf.fit(X, beta=2, tol=1e-8, max_iter=1000)
return nmf