def preprocess_dataset(self, ds):
"""
Preprocesses the dataset.
:param ds: dataset
:return: preprocessed dataset
"""
if self.cfg.has('dataset_input') and self.cfg.dataset_input != 'input':
ds['input'] = ds[self.cfg.dataset_input]
del ds[self.cfg.dataset_input]
if self.cfg.has('dataset_target') and self.cfg.dataset_target != 'target':
ds['target'] = ds[self.cfg.dataset_target]
del ds[self.cfg.dataset_target]
if ds['input'].ndim == 3:
# ensure that n_steps and valid are both in the dataset
if 'n_steps' in ds and 'valid' not in ds:
ds['valid'] = n_steps_to_valid(ds['n_steps'], ds['input'].shape[1])
elif 'valid' in ds and 'n_steps' not in ds:
ds['n_steps'] = valid_to_n_steps(ds['valid'])
if self.cfg.has('dataset_samples'):
ds['input'] = ds['input'][..., 0:self.cfg.dataset_samples]
ds['target'] = ds['target'][..., 0:self.cfg.dataset_samples]
print "Using only %d samples from dataset" % ds['input'].shape[-1]
if self.cfg.get('no_negative_data'):
minval = np.min(ds['input'])
if minval < 0:
print "Adding %.3f to dataset inputs to ensure positive values." % (-minval)
ds['input'] -= minval
else:
print "Dataset inputs are already positive."
if self.cfg.has('preprocess_pca'):
ds['orig_input'] = np.copy(ds['input'])
if ds['input'].ndim == 2:
res = pca_white(ds['input'], n_components=self.cfg.preprocess_pca, return_axes=True)
elif ds['input'].ndim == 3:
res = for_step_data(pca_white)(ds['n_steps'], ds['input'], n_components=self.cfg.preprocess_pca, return_axes=True)
else:
raise ValueError("unrecognized dimensionality of input variable")
ds['input'], ds['meta_pca_vars'], ds['meta_pca_axes'], ds['meta_pca_means'] = res
print "Keeping %d principal components (PCA) with variances:" % self.cfg.preprocess_pca
print ds['meta_pca_vars']
np.savez_compressed(join(self.cfg.out_dir, "pca.npz"),
pca_vars=ds['meta_pca_vars'],
pca_axes=ds['meta_pca_axes'],
pca_means=ds['meta_pca_means'])
if self.cfg.get('use_training_as_validation'):
ds['meta_use_training_as_validation'] = self.cfg.use_training_as_validation
return ds
def perform_pca(self, data):
"""
Performs the same PCA whitening as done during preprocessing.
:param data: data[feature, smpl] or data[feature, step, smpl]
:return: whitened[comp, smpl] or whitened[comp, step, smpl]
"""
if self.cfg.has('preprocess_pca'):
if data.ndim == 2:
return pca_white(data,
variances=self.dataset.meta_pca_vars,
axes=self.dataset.meta_pca_axes,
means=self.dataset.meta_pca_means)
elif data.ndim == 3:
n_steps = np.full((data.shape[2],), data.shape[1], dtype=int)
return for_step_data(pca_white)(n_steps, data,
variances=self.dataset.meta_pca_vars,
axes=self.dataset.meta_pca_axes,
means=self.dataset.meta_pca_means)
else:
return data
def perform_pca(self, data):
"""
Performs the same PCA whitening as done during preprocessing.
:param data: data[feature, smpl] or data[feature, step, smpl]
:return: whitened[comp, smpl] or whitened[comp, step, smpl]
"""
if self.cfg.has('preprocess_pca'):
if data.ndim == 2:
return pca_white(data,
variances=self.dataset.meta_pca_vars,
axes=self.dataset.meta_pca_axes,
means=self.dataset.meta_pca_means)
elif data.ndim == 3:
n_steps = np.full((data.shape[2], ), data.shape[1], dtype=int)
return for_step_data(pca_white)(
n_steps,
data,
variances=self.dataset.meta_pca_vars,
axes=self.dataset.meta_pca_axes,
means=self.dataset.meta_pca_means)
else:
return data
def preprocess_dataset(self, ds):
"""
Preprocesses the dataset.
:param ds: dataset
:return: preprocessed dataset
"""
if self.cfg.has('dataset_input') and self.cfg.dataset_input != 'input':
ds['input'] = ds[self.cfg.dataset_input]
del ds[self.cfg.dataset_input]
if self.cfg.has(
'dataset_target') and self.cfg.dataset_target != 'target':
ds['target'] = ds[self.cfg.dataset_target]
del ds[self.cfg.dataset_target]
if ds['input'].ndim == 3:
# ensure that n_steps and valid are both in the dataset
if 'n_steps' in ds and 'valid' not in ds:
ds['valid'] = n_steps_to_valid(ds['n_steps'],
ds['input'].shape[1])
elif 'valid' in ds and 'n_steps' not in ds:
ds['n_steps'] = valid_to_n_steps(ds['valid'])
if self.cfg.has('dataset_samples'):
ds['input'] = ds['input'][..., 0:self.cfg.dataset_samples]
ds['target'] = ds['target'][..., 0:self.cfg.dataset_samples]
print "Using only %d samples from dataset" % ds['input'].shape[-1]
if self.cfg.get('no_negative_data'):
minval = np.min(ds['input'])
if minval < 0:
print "Adding %.3f to dataset inputs to ensure positive values." % (
-minval)
ds['input'] -= minval
else:
print "Dataset inputs are already positive."
if self.cfg.has('preprocess_pca'):
ds['orig_input'] = np.copy(ds['input'])
if ds['input'].ndim == 2:
res = pca_white(ds['input'],
n_components=self.cfg.preprocess_pca,
return_axes=True)
elif ds['input'].ndim == 3:
res = for_step_data(pca_white)(
ds['n_steps'],
ds['input'],
n_components=self.cfg.preprocess_pca,
return_axes=True)
else:
raise ValueError(
"unrecognized dimensionality of input variable")
ds['input'], ds['meta_pca_vars'], ds['meta_pca_axes'], ds[
'meta_pca_means'] = res
print "Keeping %d principal components (PCA) with variances:" % self.cfg.preprocess_pca
print ds['meta_pca_vars']
np.savez_compressed(join(self.cfg.out_dir, "pca.npz"),
pca_vars=ds['meta_pca_vars'],
pca_axes=ds['meta_pca_axes'],
pca_means=ds['meta_pca_means'])
if self.cfg.get('use_training_as_validation'):
ds['meta_use_training_as_validation'] = self.cfg.use_training_as_validation
return ds