""" Module for plotting. """
import matplotlib.pyplot as plt
import torch
from pina.callbacks import MetricTracker
from pina import LabelTensor
[docs]
class Plotter:
"""
Implementation of a plotter class, for easy visualizations.
"""
[docs]
def plot_samples(self, problem, variables=None, filename=None, **kwargs):
"""
Plot the training grid samples.
:param AbstractProblem problem: The PINA problem from where to plot
the domain.
:param list(str) variables: Variables to plot. If None, all variables
are plotted. If 'spatial', only spatial variables are plotted. If
'temporal', only temporal variables are plotted. Defaults to None.
:param str filename: The file name to save the plot. If None, the plot
is shown using the setted matplotlib frontend. Default is None.
.. todo::
- Add support for 3D plots.
- Fix support for more complex problems.
:Example:
>>> plotter = Plotter()
>>> plotter.plot_samples(problem=problem, variables='spatial')
"""
if variables is None:
variables = problem.domain.variables
elif variables == "spatial":
variables = problem.spatial_domain.variables
elif variables == "temporal":
variables = problem.temporal_domain.variables
if len(variables) not in [1, 2, 3]:
raise ValueError(
"Samples can be plotted only in " "dimensions 1, 2 and 3."
)
fig = plt.figure()
proj = "3d" if len(variables) == 3 else None
ax = fig.add_subplot(projection=proj)
for location in problem.input_pts:
coords = problem.input_pts[location].extract(variables).T.detach()
if len(variables) == 1: # 1D samples
ax.plot(
coords.flatten(),
torch.zeros(coords.flatten().shape),
".",
label=location,
**kwargs,
)
elif len(variables) == 2:
ax.plot(*coords, ".", label=location, **kwargs)
elif len(variables) == 3:
ax.scatter(*coords, ".", label=location, **kwargs)
ax.set_xlabel(variables[0])
try:
ax.set_ylabel(variables[1])
except (IndexError, AttributeError):
pass
try:
ax.set_zlabel(variables[2])
except (IndexError, AttributeError):
pass
plt.legend()
if filename:
plt.savefig(filename)
plt.close()
else:
plt.show()
def _1d_plot(self, pts, pred, v, method, truth_solution=None, **kwargs):
"""Plot solution for one dimensional function
:param pts: Points to plot the solution.
:type pts: torch.Tensor
:param pred: SolverInterface solution evaluated at 'pts'.
:type pred: torch.Tensor
:param v: Fixed variables when plotting the solution.
:type v: torch.Tensor
:param method: Not used, kept for code compatibility
:type method: None
:param truth_solution: Real solution evaluated at 'pts',
defaults to None.
:type truth_solution: torch.Tensor, optional
"""
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 8))
ax.plot(pts.extract(v), pred, label="Neural Network solution", **kwargs)
if truth_solution:
truth_output = truth_solution(pts).detach()
ax.plot(
pts.extract(v), truth_output, label="True solution", **kwargs
)
# TODO: pred is a torch.Tensor, so no labels is available
# extra variable for labels should be
# passed in the function arguments.
# plt.ylabel(pred.labels[0])
plt.legend()
def _2d_plot(
self, pts, pred, v, res, method, truth_solution=None, **kwargs
):
"""Plot solution for two dimensional function
:param pts: Points to plot the solution.
:type pts: torch.Tensor
:param pred: ``SolverInterface`` solution evaluated at 'pts'.
:type pred: torch.Tensor
:param v: Fixed variables when plotting the solution.
:type v: torch.Tensor
:param method: Matplotlib method to plot 2-dimensional data,
see https://matplotlib.org/stable/api/axes_api.html for
reference.
:type method: str
:param truth_solution: Real solution evaluated at 'pts',
defaults to None.
:type truth_solution: torch.Tensor, optional
"""
grids = [p_.reshape(res, res) for p_ in pts.extract(v).T]
pred_output = pred.reshape(res, res)
if truth_solution:
truth_output = (
truth_solution(pts)
.float()
.reshape(res, res)
.as_subclass(torch.Tensor)
)
fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(16, 6))
cb = getattr(ax[0], method)(*grids, pred_output, **kwargs)
fig.colorbar(cb, ax=ax[0])
ax[0].title.set_text("Neural Network prediction")
cb = getattr(ax[1], method)(*grids, truth_output, **kwargs)
fig.colorbar(cb, ax=ax[1])
ax[1].title.set_text("True solution")
cb = getattr(ax[2], method)(
*grids, (truth_output - pred_output), **kwargs
)
fig.colorbar(cb, ax=ax[2])
ax[2].title.set_text("Residual")
else:
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 6))
cb = getattr(ax, method)(*grids, pred_output, **kwargs)
fig.colorbar(cb, ax=ax)
ax.title.set_text("Neural Network prediction")
[docs]
def plot(
self,
solver,
components=None,
fixed_variables={},
method="contourf",
res=256,
filename=None,
title=None,
**kwargs,
):
"""
Plot sample of SolverInterface output.
:param SolverInterface solver: The ``SolverInterface`` object instance.
:param str | list(str) components: The output variable(s) to plot.
If None, all the output variables of the problem are selected.
Default value is None.
:param dict fixed_variables: A dictionary with all the variables that
should be kept fixed during the plot. The keys of the dictionary
are the variables name whereas the values are the corresponding
values of the variables. Defaults is `dict()`.
:param str method: The matplotlib method to use for
plotting the solution. Available methods are {'contourf', 'pcolor'}.
Default is 'contourf'.
:param int res: The resolution, aka the number of points used for
plotting in each axis. Default is 256.
:param str title: The title for the plot. If None, the plot
is shown without a title. Default is None.
:param str filename: The file name to save the plot. If None, the plot
is shown using the setted matplotlib frontend. Default is None.
"""
if components is None:
components = solver.problem.output_variables
if isinstance(components, str):
components = [components]
if not isinstance(components, list):
raise NotImplementedError(
"Output variables must be passed"
"as a string or a list of strings."
)
if len(components) > 1:
raise NotImplementedError(
"Multidimensional plots are not implemented, "
"set components to an available components of"
" the problem."
)
v = [
var
for var in solver.problem.input_variables
if var not in fixed_variables.keys()
]
pts = solver.problem.domain.sample(res, "grid", variables=v)
fixed_pts = torch.ones(pts.shape[0], len(fixed_variables))
fixed_pts *= torch.tensor(list(fixed_variables.values()))
fixed_pts = fixed_pts.as_subclass(LabelTensor)
fixed_pts.labels = list(fixed_variables.keys())
pts = pts.append(fixed_pts)
pts = pts.to(device=solver.device)
# computing soluting and sending to cpu
predicted_output = solver.forward(pts).extract(components)
predicted_output = (
predicted_output.as_subclass(torch.Tensor).cpu().detach()
)
pts = pts.cpu()
truth_solution = getattr(solver.problem, "truth_solution", None)
if len(v) == 1:
self._1d_plot(
pts, predicted_output, v, method, truth_solution, **kwargs
)
elif len(v) == 2:
self._2d_plot(
pts, predicted_output, v, res, method, truth_solution, **kwargs
)
plt.tight_layout()
if title is not None:
plt.title(title)
if filename:
plt.savefig(filename)
plt.close()
else:
plt.show()
[docs]
def plot_loss(
self,
trainer,
metrics=None,
logy=False,
logx=False,
filename=None,
**kwargs,
):
"""
Plot the loss function values during traininig.
:param trainer: the PINA Trainer object instance.
:type trainer: Trainer
:param str | list(str) metric: The metrics to use in the y axis. If None, the mean loss
is plotted.
:param bool logy: If True, the y axis is in log scale. Default is
True.
:param bool logx: If True, the x axis is in log scale. Default is
True.
:param str filename: The file name to save the plot. If None, the plot
is shown using the setted matplotlib frontend. Default is None.
"""
# check that MetricTracker has been used
list_ = [
idx
for idx, s in enumerate(trainer.callbacks)
if isinstance(s, MetricTracker)
]
if not bool(list_):
raise FileNotFoundError(
"MetricTracker should be used as a callback during training to"
" use this method."
)
# extract trainer metrics
trainer_metrics = trainer.callbacks[list_[0]].metrics
if metrics is None:
metrics = ["mean_loss"]
elif not isinstance(metrics, list):
raise ValueError("metrics must be class list.")
# loop over metrics to plot
for metric in metrics:
if metric not in trainer_metrics:
raise ValueError(
f"{metric} not a valid metric. Available metrics are {list(trainer_metrics.keys())}."
)
loss = trainer_metrics[metric]
epochs = range(len(loss))
plt.plot(epochs, loss.cpu(), **kwargs)
# plotting
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend()
# log axis
if logy:
plt.yscale("log")
if logx:
plt.xscale("log")
# saving in file
if filename:
plt.savefig(filename)
plt.close()
