def transform(self, stims, *args, **kwargs):
if isinstance(stims, string_types):
stims = load_stims(stims)
# If stims is a CompoundStim and the Transformer is expecting a single
# input type, extract all matching stims
if isinstance(stims, CompoundStim) and not isinstance(
self._input_type, tuple):
stims = stims.get_stim(self._input_type, return_all=True)
if not stims:
raise ValueError("No stims of class %s found in the provided"
"CompoundStim instance." % self._input_type)
# If stims is an iterable, naively loop over elements, removing
# invalid results if needed
if isiterable(stims):
iters = self._iterate(stims, *args, **kwargs)
if config.drop_bad_extractor_results:
iters = (i for i in iters if i is not None)
return progress_bar_wrapper(iters, desc='Stim')
# Validate stim, and then either pass it directly to the Transformer
# or, if a conversion occurred, recurse.
else:
validated_stim = self._validate(stims)
# If a conversion occurred during validation, we recurse
if stims is not validated_stim:
return self.transform(validated_stim, *args, **kwargs)
else:
result = self._transform(validated_stim, *args, **kwargs)
result = _log_transformation(validated_stim, result, self)
if isgenerator(result):
result = list(result)
return result
def wrapper(self, stim, *args, **kwargs):
use_cache = config.cache_transformers and isinstance(stim, Stim)
if use_cache:
key = hash((hash(self), id(stim)))
if key in _cache:
return _cache[key]
result = transform(self, stim, *args, **kwargs)
if use_cache:
if isgenerator(result):
result = list(result)
_cache[key] = result
return result
def wrapper(self, stim, *args, **kwargs):
use_cache = config.get_option('cache_transformers') \
and isinstance(stim, (Stim, string_types))
if use_cache:
key = hash((hash(self), hash(stim)))
if key in _cache:
return _cache[key]
result = transform(self, stim, *args, **kwargs)
if use_cache:
if isgenerator(result):
result = list(result)
_cache[key] = result
return result
def run_node(self, node, stim):
if isinstance(node, string_types):
node = self.nodes[node]
result = node.transformer.transform(stim)
if isinstance(node.transformer, Extractor):
return listify(result)
stim = result
# If result is a generator, the first child will destroy the
# iterable, so cache via list conversion
if len(node.children) > 1 and isgenerator(stim):
stim = list(stim)
return list(chain(*[self.run_node(c, stim) for c in node.children]))
def run_node(self, node, stim):
''' Executes the Transformer at a specific node.
Args:
node (str, Node): If a string, the name of the Node in the current
Graph. Otherwise the Node instance to execute.
stim (str, stim, list): Any valid input to the Transformer stored
at the target node.
'''
if isinstance(node, string_types):
node = self.nodes[node]
result = node.transformer.transform(stim)
if node.is_leaf():
return listify(result)
stim = result
# If result is a generator, the first child will destroy the
# iterable, so cache via list conversion
if len(node.children) > 1 and isgenerator(stim):
stim = list(stim)
return list(chain(*[self.run_node(c, stim) for c in node.children]))
def run_node(self, node, stim):
''' Executes the Transformer at a specific node.
Args:
node (str, Node): If a string, the name of the Node in the current
Graph. Otherwise the Node instance to execute.
stim (str, stim, list): Any valid input to the Transformer stored
at the target node.
'''
if isinstance(node, string_types):
node = self.nodes[node]
result = node.transformer.transform(stim)
if node.is_leaf():
return listify(result)
stim = result
# If result is a generator, the first child will destroy the
# iterable, so cache via list conversion
if len(node.children) > 1 and isgenerator(stim):
stim = list(stim)
return list(chain(*[self.run_node(c, stim) for c in node.children]))
def _iterate(self, stims, validation='strict', *args, **kwargs):
batches = batch_iterable(stims, self._batch_size)
results = []
for batch in progress_bar_wrapper(batches):
use_cache = config.get_option('cache_transformers')
target_inds = {}
non_cached = []
for stim in batch:
key = hash((hash(self), hash(stim)))
# If using the cache, only transform stims that aren't in the
# cache and haven't already appeared in the batch
if not (use_cache and (key in _cache or key in target_inds)):
target_inds[key] = len(non_cached)
non_cached.append(stim)
# _transform will likely fail if given an empty list
if len(non_cached) > 0:
batch_results = self._transform(non_cached, *args, **kwargs)
else:
batch_results = []
for i, stim in enumerate(batch):
key = hash((hash(self), hash(stim)))
# Use the target index to get the result from batch_results
if key in target_inds:
result = batch_results[target_inds[key]]
result = _log_transformation(stim, result, self)
self._propagate_context(stim, result)
if use_cache:
if isgenerator(result):
result = list(result)
_cache[key] = result
results.append(result)
# Otherwise, the result should be in the cache
else:
results.append(_cache[key])
return results
def transform(self, stim, *args, **kwargs):
result = super(Extractor, self).transform(stim, *args, **kwargs)
return list(result) if isgenerator(result) else result
def transform(self, stims, validation='strict', *args, **kwargs):
''' Executes the transformation on the passed stim(s).
Args:
stims (str, Stim, list): One or more stimuli to process. Must be
one of:
- A string giving the path to a file that can be read in
as a Stim (e.g., a .txt file, .jpg image, etc.)
- A Stim instance of any type.
- An iterable of stims, where each element is either a
string or a Stim.
validation (str): String specifying how validation errors should
be handled. Must be one of:
- 'strict': Raise an exception on any validation error
- 'warn': Issue a warning for all validation errors
- 'loose': Silently ignore all validation errors
args: Optional positional arguments to pass onto the internal
_transform call.
kwargs: Optional positional arguments to pass onto the internal
_transform call.
'''
if isinstance(stims, str):
stims = load_stims(stims)
# If stims is a CompoundStim and the Transformer is expecting a single
# input type, extract all matching stims
if isinstance(stims, CompoundStim) and not isinstance(
self._input_type, tuple):
stims = stims.get_stim(self._input_type, return_all=True)
if not stims:
raise ValueError("No stims of class %s found in the provided"
"CompoundStim instance." % self._input_type)
# If stims is an iterable, naively loop over elements, removing
# invalid results if needed
if isiterable(stims):
iters = self._iterate(stims,
validation=validation,
*args,
**kwargs)
if config.get_option('drop_bad_extractor_results'):
iters = (i for i in iters if i is not None)
iters = progress_bar_wrapper(iters, desc='Stim')
return set_iterable_type(iters)
# Validate stim, and then either pass it directly to the Transformer
# or, if a conversion occurred, recurse.
else:
try:
validated_stim = self._validate(stims)
except TypeError as err:
if validation == 'strict':
raise err
elif validation == 'warn':
logging.warning(str(err))
return
elif validation == 'loose':
return
# If a conversion occurred during validation, we recurse
if stims is not validated_stim:
return self.transform(validated_stim, *args, **kwargs)
else:
result = self._transform(validated_stim, *args, **kwargs)
result = _log_transformation(validated_stim, result, self)
if isgenerator(result):
result = list(result)
self._propagate_context(validated_stim, result)
return result
def transform(self, stim, *args, **kwargs):
result = super(Extractor, self).transform(stim, *args, **kwargs)
return list(result) if isgenerator(result) else result
