def _convert_to_numpy(*args):
r"""Convert a list of array to numpy if they are torch tensors"""
for arr in args:
if isinstance(arr, torch.Tensor):
yield torch_to_numpy(arr)
else:
yield arr
def __init__(self, base_callback, dataset, representations):
"""
Constructor of the class.
:param base_callback: The base callback.
:param dataset: The dataset being used.
:param representations: The representations to use.
"""
representations = [
representation.lower() for representation in representations
]
assert all([
representation in projections_list
for representation in representations
])
super().__init__()
self.base_callback = base_callback
self.dataset = dataset
self.data = dataset.data
self.targets = torch_to_numpy(dataset.targets)
self.record = self.base_callback.record
self.experiment = self.base_callback.experiment
self.projections = {
representation:
projections_list[representation](n_components=self.data.shape[1])
for representation in representations
}
self.representations = {
representation: dict()
for representation in representations
}
for representation, content in self.representations.items():
X_plot = self._get_x_plot(torch_to_numpy(self.data),
representation)
grid_xy = self._get_mesh_grid(X_plot)
content['X_plot'] = X_plot
content['grid_xy'] = grid_xy
content['grid'] = self._get_grid(grid_xy, representation)
self.experiment['data'] = torch_to_numpy(dataset.data)
self.experiment['targets'] = torch_to_numpy(dataset.targets)
self.experiment['representations'] = self.representations
def __init__(self, base_callback, dataset, batch_size):
"""
Constructor of the class.
:param base_callback: The base callback.
:param dataset: The dataset being used.
:param batch_size: The size of a batch.
"""
super().__init__()
self.base_callback = base_callback
self.dataset = dataset
self.data = dataset.data
self.targets = torch_to_numpy(dataset.targets)
self.record = self.base_callback.record
self.experiment = self.base_callback.experiment
self.experiment['data'] = torch_to_numpy(dataset.data)
self.experiment['targets'] = torch_to_numpy(dataset.targets)
self.experiment['feature_names'] = dataset.feature_names
self.experiment['target_names'] = dataset.feature_names
self.experiment['dataset_name'] = dataset.name
self.experiment['batch_size'] = batch_size
def forward(self, y_pred, y_true):
self.preds.append(torch_to_numpy(y_pred))
self.targets.append(torch_to_numpy(y_true))