partition stringclasses 3 values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1 value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | get_root_resource | Returns the root resource. | nefertari/resource.py | def get_root_resource(config):
"""Returns the root resource."""
app_package_name = get_app_package_name(config)
return config.registry._root_resources.setdefault(
app_package_name, Resource(config)) | def get_root_resource(config):
"""Returns the root resource."""
app_package_name = get_app_package_name(config)
return config.registry._root_resources.setdefault(
app_package_name, Resource(config)) | [
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train | add_resource_routes | ``view`` is a dotted name of (or direct reference to) a
Python view class,
e.g. ``'my.package.views.MyView'``.
``member_name`` should be the appropriate singular version of the resource
given your locale and used with members of the collection.
``collection_name`` will be used to refer to the resource collection
methods and should be a plural version of the member_name argument.
All keyword arguments are optional.
``path_prefix``
Prepends the URL path for the Route with the path_prefix
given. This is most useful for cases where you want to mix
resources or relations between resources.
``name_prefix``
Prepends the route names that are generated with the
name_prefix given. Combined with the path_prefix option,
it's easy to generate route names and paths that represent
resources that are in relations.
Example::
config.add_resource_routes(
'myproject.views:CategoryView', 'message', 'messages',
path_prefix='/category/{category_id}',
name_prefix="category_")
# GET /category/7/messages/1
# has named route "category_message" | nefertari/resource.py | def add_resource_routes(config, view, member_name, collection_name, **kwargs):
"""
``view`` is a dotted name of (or direct reference to) a
Python view class,
e.g. ``'my.package.views.MyView'``.
``member_name`` should be the appropriate singular version of the resource
given your locale and used with members of the collection.
``collection_name`` will be used to refer to the resource collection
methods and should be a plural version of the member_name argument.
All keyword arguments are optional.
``path_prefix``
Prepends the URL path for the Route with the path_prefix
given. This is most useful for cases where you want to mix
resources or relations between resources.
``name_prefix``
Prepends the route names that are generated with the
name_prefix given. Combined with the path_prefix option,
it's easy to generate route names and paths that represent
resources that are in relations.
Example::
config.add_resource_routes(
'myproject.views:CategoryView', 'message', 'messages',
path_prefix='/category/{category_id}',
name_prefix="category_")
# GET /category/7/messages/1
# has named route "category_message"
"""
view = maybe_dotted(view)
path_prefix = kwargs.pop('path_prefix', '')
name_prefix = kwargs.pop('name_prefix', '')
if config.route_prefix:
name_prefix = "%s_%s" % (config.route_prefix, name_prefix)
if collection_name:
id_name = '/{%s}' % (kwargs.pop('id_name', None) or DEFAULT_ID_NAME)
else:
id_name = ''
path = path_prefix.strip('/') + '/' + (collection_name or member_name)
_factory = kwargs.pop('factory', None)
# If factory is not set, than auth should be False
_auth = kwargs.pop('auth', None) and _factory
_traverse = (kwargs.pop('traverse', None) or id_name) if _factory else None
action_route = {}
added_routes = {}
def add_route_and_view(config, action, route_name, path, request_method,
**route_kwargs):
if route_name not in added_routes:
config.add_route(
route_name, path, factory=_factory,
request_method=['GET', 'POST', 'PUT', 'PATCH', 'DELETE',
'OPTIONS'],
**route_kwargs)
added_routes[route_name] = path
action_route[action] = route_name
if _auth:
permission = PERMISSIONS[action]
else:
permission = None
config.add_view(view=view, attr=action, route_name=route_name,
request_method=request_method,
permission=permission,
**kwargs)
config.commit()
if collection_name == member_name:
collection_name = collection_name + '_collection'
if collection_name:
add_route_and_view(
config, 'index', name_prefix + collection_name, path,
'GET')
add_route_and_view(
config, 'collection_options', name_prefix + collection_name, path,
'OPTIONS')
add_route_and_view(
config, 'show', name_prefix + member_name, path + id_name,
'GET', traverse=_traverse)
add_route_and_view(
config, 'item_options', name_prefix + member_name, path + id_name,
'OPTIONS', traverse=_traverse)
add_route_and_view(
config, 'replace', name_prefix + member_name, path + id_name,
'PUT', traverse=_traverse)
add_route_and_view(
config, 'update', name_prefix + member_name, path + id_name,
'PATCH', traverse=_traverse)
add_route_and_view(
config, 'create', name_prefix + (collection_name or member_name), path,
'POST')
add_route_and_view(
config, 'delete', name_prefix + member_name, path + id_name,
'DELETE', traverse=_traverse)
if collection_name:
add_route_and_view(
config, 'update_many',
name_prefix + (collection_name or member_name),
path, 'PUT', traverse=_traverse)
add_route_and_view(
config, 'update_many',
name_prefix + (collection_name or member_name),
path, 'PATCH', traverse=_traverse)
add_route_and_view(
config, 'delete_many',
name_prefix + (collection_name or member_name),
path, 'DELETE', traverse=_traverse)
return action_route | def add_resource_routes(config, view, member_name, collection_name, **kwargs):
"""
``view`` is a dotted name of (or direct reference to) a
Python view class,
e.g. ``'my.package.views.MyView'``.
``member_name`` should be the appropriate singular version of the resource
given your locale and used with members of the collection.
``collection_name`` will be used to refer to the resource collection
methods and should be a plural version of the member_name argument.
All keyword arguments are optional.
``path_prefix``
Prepends the URL path for the Route with the path_prefix
given. This is most useful for cases where you want to mix
resources or relations between resources.
``name_prefix``
Prepends the route names that are generated with the
name_prefix given. Combined with the path_prefix option,
it's easy to generate route names and paths that represent
resources that are in relations.
Example::
config.add_resource_routes(
'myproject.views:CategoryView', 'message', 'messages',
path_prefix='/category/{category_id}',
name_prefix="category_")
# GET /category/7/messages/1
# has named route "category_message"
"""
view = maybe_dotted(view)
path_prefix = kwargs.pop('path_prefix', '')
name_prefix = kwargs.pop('name_prefix', '')
if config.route_prefix:
name_prefix = "%s_%s" % (config.route_prefix, name_prefix)
if collection_name:
id_name = '/{%s}' % (kwargs.pop('id_name', None) or DEFAULT_ID_NAME)
else:
id_name = ''
path = path_prefix.strip('/') + '/' + (collection_name or member_name)
_factory = kwargs.pop('factory', None)
# If factory is not set, than auth should be False
_auth = kwargs.pop('auth', None) and _factory
_traverse = (kwargs.pop('traverse', None) or id_name) if _factory else None
action_route = {}
added_routes = {}
def add_route_and_view(config, action, route_name, path, request_method,
**route_kwargs):
if route_name not in added_routes:
config.add_route(
route_name, path, factory=_factory,
request_method=['GET', 'POST', 'PUT', 'PATCH', 'DELETE',
'OPTIONS'],
**route_kwargs)
added_routes[route_name] = path
action_route[action] = route_name
if _auth:
permission = PERMISSIONS[action]
else:
permission = None
config.add_view(view=view, attr=action, route_name=route_name,
request_method=request_method,
permission=permission,
**kwargs)
config.commit()
if collection_name == member_name:
collection_name = collection_name + '_collection'
if collection_name:
add_route_and_view(
config, 'index', name_prefix + collection_name, path,
'GET')
add_route_and_view(
config, 'collection_options', name_prefix + collection_name, path,
'OPTIONS')
add_route_and_view(
config, 'show', name_prefix + member_name, path + id_name,
'GET', traverse=_traverse)
add_route_and_view(
config, 'item_options', name_prefix + member_name, path + id_name,
'OPTIONS', traverse=_traverse)
add_route_and_view(
config, 'replace', name_prefix + member_name, path + id_name,
'PUT', traverse=_traverse)
add_route_and_view(
config, 'update', name_prefix + member_name, path + id_name,
'PATCH', traverse=_traverse)
add_route_and_view(
config, 'create', name_prefix + (collection_name or member_name), path,
'POST')
add_route_and_view(
config, 'delete', name_prefix + member_name, path + id_name,
'DELETE', traverse=_traverse)
if collection_name:
add_route_and_view(
config, 'update_many',
name_prefix + (collection_name or member_name),
path, 'PUT', traverse=_traverse)
add_route_and_view(
config, 'update_many',
name_prefix + (collection_name or member_name),
path, 'PATCH', traverse=_traverse)
add_route_and_view(
config, 'delete_many',
name_prefix + (collection_name or member_name),
path, 'DELETE', traverse=_traverse)
return action_route | [
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train | get_default_view_path | Returns the dotted path to the default view class. | nefertari/resource.py | def get_default_view_path(resource):
"Returns the dotted path to the default view class."
parts = [a.member_name for a in resource.ancestors] +\
[resource.collection_name or resource.member_name]
if resource.prefix:
parts.insert(-1, resource.prefix)
view_file = '%s' % '_'.join(parts)
view = '%s:%sView' % (view_file, snake2camel(view_file))
app_package_name = get_app_package_name(resource.config)
return '%s.views.%s' % (app_package_name, view) | def get_default_view_path(resource):
"Returns the dotted path to the default view class."
parts = [a.member_name for a in resource.ancestors] +\
[resource.collection_name or resource.member_name]
if resource.prefix:
parts.insert(-1, resource.prefix)
view_file = '%s' % '_'.join(parts)
view = '%s:%sView' % (view_file, snake2camel(view_file))
app_package_name = get_app_package_name(resource.config)
return '%s.views.%s' % (app_package_name, view) | [
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train | Resource.get_ancestors | Returns the list of ancestor resources. | nefertari/resource.py | def get_ancestors(self):
"Returns the list of ancestor resources."
if self._ancestors:
return self._ancestors
if not self.parent:
return []
obj = self.resource_map.get(self.parent.uid)
while obj and obj.member_name:
self._ancestors.append(obj)
obj = obj.parent
self._ancestors.reverse()
return self._ancestors | def get_ancestors(self):
"Returns the list of ancestor resources."
if self._ancestors:
return self._ancestors
if not self.parent:
return []
obj = self.resource_map.get(self.parent.uid)
while obj and obj.member_name:
self._ancestors.append(obj)
obj = obj.parent
self._ancestors.reverse()
return self._ancestors | [
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train | Resource.add | :param member_name: singular name of the resource. It should be the
appropriate singular version of the resource given your locale
and used with members of the collection.
:param collection_name: plural name of the resource. It will be used
to refer to the resource collection methods and should be a
plural version of the ``member_name`` argument.
Note: if collection_name is empty, it means resource is singular
:param parent: parent resource name or object.
:param uid: unique name for the resource
:param kwargs:
view: custom view to overwrite the default one.
the rest of the keyward arguments are passed to
add_resource_routes call.
:return: ResourceMap object | nefertari/resource.py | def add(self, member_name, collection_name='', parent=None, uid='',
**kwargs):
"""
:param member_name: singular name of the resource. It should be the
appropriate singular version of the resource given your locale
and used with members of the collection.
:param collection_name: plural name of the resource. It will be used
to refer to the resource collection methods and should be a
plural version of the ``member_name`` argument.
Note: if collection_name is empty, it means resource is singular
:param parent: parent resource name or object.
:param uid: unique name for the resource
:param kwargs:
view: custom view to overwrite the default one.
the rest of the keyward arguments are passed to
add_resource_routes call.
:return: ResourceMap object
"""
# self is the parent resource on which this method is called.
parent = (self.resource_map.get(parent) if type(parent)
is str else parent or self)
prefix = kwargs.pop('prefix', '')
uid = (uid or
':'.join(filter(bool, [parent.uid, prefix, member_name])))
if uid in self.resource_map:
raise ValueError('%s already exists in resource map' % uid)
# Use id_name of parent for singular views to make url generation
# easier
id_name = kwargs.get('id_name', '')
if not id_name and parent:
id_name = parent.id_name
new_resource = Resource(self.config, member_name=member_name,
collection_name=collection_name,
parent=parent, uid=uid,
id_name=id_name,
prefix=prefix)
view = maybe_dotted(
kwargs.pop('view', None) or get_default_view_path(new_resource))
for name, val in kwargs.pop('view_args', {}).items():
setattr(view, name, val)
root_resource = self.config.get_root_resource()
view.root_resource = root_resource
new_resource.view = view
path_segs = []
kwargs['path_prefix'] = ''
for res in new_resource.ancestors:
if not res.is_singular:
if res.id_name:
id_full = res.id_name
else:
id_full = "%s_%s" % (res.member_name, DEFAULT_ID_NAME)
path_segs.append('%s/{%s}' % (res.collection_name, id_full))
else:
path_segs.append(res.member_name)
if path_segs:
kwargs['path_prefix'] = '/'.join(path_segs)
if prefix:
kwargs['path_prefix'] += '/' + prefix
name_segs = [a.member_name for a in new_resource.ancestors]
name_segs.insert(1, prefix)
name_segs = [seg for seg in name_segs if seg]
if name_segs:
kwargs['name_prefix'] = '_'.join(name_segs) + ':'
new_resource.renderer = kwargs.setdefault(
'renderer', view._default_renderer)
kwargs.setdefault('auth', root_resource.auth)
kwargs.setdefault('factory', root_resource.default_factory)
_factory = maybe_dotted(kwargs['factory'])
kwargs['auth'] = kwargs.get('auth', root_resource.auth)
kwargs['http_cache'] = kwargs.get(
'http_cache', root_resource.http_cache)
new_resource.action_route_map = add_resource_routes(
self.config, view, member_name, collection_name,
**kwargs)
self.resource_map[uid] = new_resource
# add all route names for this resource as keys in the dict,
# so its easy to find it in the view.
self.resource_map.update(dict.fromkeys(
list(new_resource.action_route_map.values()),
new_resource))
# Store resources in {modelName: resource} map if:
# * Its view has Model defined
# * It's not singular
# * Its parent is root or it's not already stored
model = new_resource.view.Model
is_collection = model is not None and not new_resource.is_singular
if is_collection:
is_needed = (model.__name__ not in self.model_collections or
new_resource.parent is root_resource)
if is_needed:
self.model_collections[model.__name__] = new_resource
parent.children.append(new_resource)
view._resource = new_resource
view._factory = _factory
return new_resource | def add(self, member_name, collection_name='', parent=None, uid='',
**kwargs):
"""
:param member_name: singular name of the resource. It should be the
appropriate singular version of the resource given your locale
and used with members of the collection.
:param collection_name: plural name of the resource. It will be used
to refer to the resource collection methods and should be a
plural version of the ``member_name`` argument.
Note: if collection_name is empty, it means resource is singular
:param parent: parent resource name or object.
:param uid: unique name for the resource
:param kwargs:
view: custom view to overwrite the default one.
the rest of the keyward arguments are passed to
add_resource_routes call.
:return: ResourceMap object
"""
# self is the parent resource on which this method is called.
parent = (self.resource_map.get(parent) if type(parent)
is str else parent or self)
prefix = kwargs.pop('prefix', '')
uid = (uid or
':'.join(filter(bool, [parent.uid, prefix, member_name])))
if uid in self.resource_map:
raise ValueError('%s already exists in resource map' % uid)
# Use id_name of parent for singular views to make url generation
# easier
id_name = kwargs.get('id_name', '')
if not id_name and parent:
id_name = parent.id_name
new_resource = Resource(self.config, member_name=member_name,
collection_name=collection_name,
parent=parent, uid=uid,
id_name=id_name,
prefix=prefix)
view = maybe_dotted(
kwargs.pop('view', None) or get_default_view_path(new_resource))
for name, val in kwargs.pop('view_args', {}).items():
setattr(view, name, val)
root_resource = self.config.get_root_resource()
view.root_resource = root_resource
new_resource.view = view
path_segs = []
kwargs['path_prefix'] = ''
for res in new_resource.ancestors:
if not res.is_singular:
if res.id_name:
id_full = res.id_name
else:
id_full = "%s_%s" % (res.member_name, DEFAULT_ID_NAME)
path_segs.append('%s/{%s}' % (res.collection_name, id_full))
else:
path_segs.append(res.member_name)
if path_segs:
kwargs['path_prefix'] = '/'.join(path_segs)
if prefix:
kwargs['path_prefix'] += '/' + prefix
name_segs = [a.member_name for a in new_resource.ancestors]
name_segs.insert(1, prefix)
name_segs = [seg for seg in name_segs if seg]
if name_segs:
kwargs['name_prefix'] = '_'.join(name_segs) + ':'
new_resource.renderer = kwargs.setdefault(
'renderer', view._default_renderer)
kwargs.setdefault('auth', root_resource.auth)
kwargs.setdefault('factory', root_resource.default_factory)
_factory = maybe_dotted(kwargs['factory'])
kwargs['auth'] = kwargs.get('auth', root_resource.auth)
kwargs['http_cache'] = kwargs.get(
'http_cache', root_resource.http_cache)
new_resource.action_route_map = add_resource_routes(
self.config, view, member_name, collection_name,
**kwargs)
self.resource_map[uid] = new_resource
# add all route names for this resource as keys in the dict,
# so its easy to find it in the view.
self.resource_map.update(dict.fromkeys(
list(new_resource.action_route_map.values()),
new_resource))
# Store resources in {modelName: resource} map if:
# * Its view has Model defined
# * It's not singular
# * Its parent is root or it's not already stored
model = new_resource.view.Model
is_collection = model is not None and not new_resource.is_singular
if is_collection:
is_needed = (model.__name__ not in self.model_collections or
new_resource.parent is root_resource)
if is_needed:
self.model_collections[model.__name__] = new_resource
parent.children.append(new_resource)
view._resource = new_resource
view._factory = _factory
return new_resource | [
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train | Resource.add_from_child | Add a resource with its all children resources to the current
resource. | nefertari/resource.py | def add_from_child(self, resource, **kwargs):
""" Add a resource with its all children resources to the current
resource.
"""
new_resource = self.add(
resource.member_name, resource.collection_name, **kwargs)
for child in resource.children:
new_resource.add_from_child(child, **kwargs) | def add_from_child(self, resource, **kwargs):
""" Add a resource with its all children resources to the current
resource.
"""
new_resource = self.add(
resource.member_name, resource.collection_name, **kwargs)
for child in resource.children:
new_resource.add_from_child(child, **kwargs) | [
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"to",
"the",
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"resource",
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] | ramses-tech/nefertari | python | https://github.com/ramses-tech/nefertari/blob/c7caffe11576c11aa111adbdbadeff70ce66b1dd/nefertari/resource.py#L381-L389 | [
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train | DatasetContainer.add | Add the path of a data set to the list of available sets
NOTE: a data set is assumed to be a pickled
and gzip compressed Pandas DataFrame
Parameters
----------
path : str | opengrid/datasets/datasets.py | def add(self, path):
"""
Add the path of a data set to the list of available sets
NOTE: a data set is assumed to be a pickled
and gzip compressed Pandas DataFrame
Parameters
----------
path : str
"""
name_with_ext = os.path.split(path)[1] # split directory and filename
name = name_with_ext.split('.')[0] # remove extension
self.list.update({name: path}) | def add(self, path):
"""
Add the path of a data set to the list of available sets
NOTE: a data set is assumed to be a pickled
and gzip compressed Pandas DataFrame
Parameters
----------
path : str
"""
name_with_ext = os.path.split(path)[1] # split directory and filename
name = name_with_ext.split('.')[0] # remove extension
self.list.update({name: path}) | [
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] | opengridcc/opengrid | python | https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/datasets/datasets.py#L27-L40 | [
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train | DatasetContainer.unpack | Unpacks a data set to a Pandas DataFrame
Parameters
----------
name : str
call `.list` to see all availble datasets
Returns
-------
pd.DataFrame | opengrid/datasets/datasets.py | def unpack(self, name):
"""
Unpacks a data set to a Pandas DataFrame
Parameters
----------
name : str
call `.list` to see all availble datasets
Returns
-------
pd.DataFrame
"""
path = self.list[name]
df = pd.read_pickle(path, compression='gzip')
return df | def unpack(self, name):
"""
Unpacks a data set to a Pandas DataFrame
Parameters
----------
name : str
call `.list` to see all availble datasets
Returns
-------
pd.DataFrame
"""
path = self.list[name]
df = pd.read_pickle(path, compression='gzip')
return df | [
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] | opengridcc/opengrid | python | https://github.com/opengridcc/opengrid/blob/69b8da3c8fcea9300226c45ef0628cd6d4307651/opengrid/datasets/datasets.py#L42-L57 | [
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train | six_frame | translate each sequence into six reading frames | ctbBio/sixframe.py | def six_frame(genome, table, minimum = 10):
"""
translate each sequence into six reading frames
"""
for seq in parse_fasta(genome):
dna = Seq(seq[1].upper().replace('U', 'T'), IUPAC.ambiguous_dna)
counter = 0
for sequence in ['f', dna], ['rc', dna.reverse_complement()]:
direction, sequence = sequence
for frame in range(0, 3):
for prot in \
sequence[frame:].\
translate(table = table, to_stop = False).split('*'):
if len(prot) < minimum:
continue
counter += 1
header = '%s_%s table=%s frame=%s-%s %s' % \
(seq[0].split()[0], counter, table, frame+1, \
direction, ' '.join(seq[0].split()[1:]))
yield [header, prot] | def six_frame(genome, table, minimum = 10):
"""
translate each sequence into six reading frames
"""
for seq in parse_fasta(genome):
dna = Seq(seq[1].upper().replace('U', 'T'), IUPAC.ambiguous_dna)
counter = 0
for sequence in ['f', dna], ['rc', dna.reverse_complement()]:
direction, sequence = sequence
for frame in range(0, 3):
for prot in \
sequence[frame:].\
translate(table = table, to_stop = False).split('*'):
if len(prot) < minimum:
continue
counter += 1
header = '%s_%s table=%s frame=%s-%s %s' % \
(seq[0].split()[0], counter, table, frame+1, \
direction, ' '.join(seq[0].split()[1:]))
yield [header, prot] | [
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"sequence",
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] | christophertbrown/bioscripts | python | https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/sixframe.py#L13-L32 | [
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... | 83b2566b3a5745437ec651cd6cafddd056846240 |
train | publish_processed_network_packets | # Redis/RabbitMQ/SQS messaging endpoints for pub-sub
routing_key = ev("PUBLISH_EXCHANGE",
"reporting.accounts")
queue_name = ev("PUBLISH_QUEUE",
"reporting.accounts")
auth_url = ev("PUB_BROKER_URL",
"redis://localhost:6379/15")
serializer = "json" | network_pipeline/scripts/network_agent.py | def publish_processed_network_packets(
name="not-set",
task_queue=None,
result_queue=None,
need_response=False,
shutdown_msg="SHUTDOWN"):
"""
# Redis/RabbitMQ/SQS messaging endpoints for pub-sub
routing_key = ev("PUBLISH_EXCHANGE",
"reporting.accounts")
queue_name = ev("PUBLISH_QUEUE",
"reporting.accounts")
auth_url = ev("PUB_BROKER_URL",
"redis://localhost:6379/15")
serializer = "json"
"""
# these keys need to be cycled to prevent
# exploiting static keys
filter_key = ev("IGNORE_KEY",
INCLUDED_IGNORE_KEY)
forward_host = ev("FORWARD_HOST", "127.0.0.1")
forward_port = int(ev("FORWARD_PORT", "80"))
include_filter_key = ev("FILTER_KEY", "")
if not include_filter_key and filter_key:
include_filter_key = filter_key
filter_keys = [filter_key]
log.info(("START consumer={} "
"forward={}:{} with "
"key={} filters={}")
.format(name,
forward_host,
forward_port,
include_filter_key,
filter_key))
forward_skt = None
not_done = True
while not_done:
if not forward_skt:
forward_skt = connect_forwarder(
forward_host=forward_host,
forward_port=forward_port)
next_task = task_queue.get()
if next_task:
if str(next_task) == shutdown_msg:
# Poison pill for shutting down
log.info(("{}: DONE CALLBACK "
"Exiting msg={}")
.format(name,
next_task))
task_queue.task_done()
break
# end of handling shutdown case
try:
log.debug(("{} parsing")
.format(name))
source = next_task.source
packet = next_task.payload
if not packet:
log.error(("{} invalid task found "
"{} missing payload")
.format(name,
next_task))
break
log.debug(("{} found msg from src={}")
.format(name,
source))
network_data = parse_network_data(
data_packet=packet,
include_filter_key=include_filter_key,
filter_keys=filter_keys)
if network_data["status"] == VALID:
if network_data["data_type"] == TCP \
or network_data["data_type"] == UDP \
or network_data["data_type"] == ARP \
or network_data["data_type"] == ICMP:
log.info(("{} valid={} packet={} "
"data={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["target_data"]))
if not forward_skt:
forward_skt = connect_forwarder(
forward_host=forward_host,
forward_port=forward_port)
if forward_skt:
if network_data["stream"]:
sent = False
while not sent:
try:
log.info("sending={}".format(
network_data["stream"]))
send_msg(
forward_skt,
network_data["stream"]
.encode("utf-8"))
sent = True
except Exception as e:
sent = False
time.sleep(0.5)
try:
forward_skt.close()
forward_skt = None
except Exception as w:
forward_skt = None
forward_skt = connect_forwarder(
forward_host=forward_host,
forward_port=forward_port)
# end of reconnecting
log.info("sent={}".format(
network_data["stream"]))
if need_response:
log.info("receiving")
cdr_res = forward_skt.recv(1024)
log.info(("cdr - res{}")
.format(cdr_res))
else:
log.info(("{} EMPTY stream={} "
"error={} status={}")
.format(
name,
network_data["stream"],
network_data["err"],
network_data["status"]))
else:
log.info(("{} not_supported valid={} "
"packet data_type={} status={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["status"]))
elif network_data["status"] == FILTERED:
log.info(("{} filtered={} status={}")
.format(name,
network_data["filtered"],
network_data["status"]))
else:
if network_data["status"] == INVALID:
log.info(("{} invalid={} packet={} "
"error={} status={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["error"],
network_data["status"]))
else:
log.info(("{} unknown={} packet={} "
"error={} status={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["error"],
network_data["status"]))
# end of if valid or not data
except KeyboardInterrupt as k:
log.info(("{} stopping")
.format(name))
break
except Exception as e:
log.error(("{} failed packaging packet to forward "
"with ex={}")
.format(name,
e))
break
# end of try/ex during payload processing
# end of if found a next_task
log.info(("Consumer: {} {}")
.format(name, next_task))
task_queue.task_done()
if need_response:
answer = "processed: {}".format(next_task())
result_queue.put(answer)
# end of while
if forward_skt:
try:
forward_skt.close()
log.info("CLOSED connection")
forward_skt = None
except Exception:
log.info("CLOSED connection")
# end of cleaning up forwarding socket
log.info("{} Done".format(name))
return | def publish_processed_network_packets(
name="not-set",
task_queue=None,
result_queue=None,
need_response=False,
shutdown_msg="SHUTDOWN"):
"""
# Redis/RabbitMQ/SQS messaging endpoints for pub-sub
routing_key = ev("PUBLISH_EXCHANGE",
"reporting.accounts")
queue_name = ev("PUBLISH_QUEUE",
"reporting.accounts")
auth_url = ev("PUB_BROKER_URL",
"redis://localhost:6379/15")
serializer = "json"
"""
# these keys need to be cycled to prevent
# exploiting static keys
filter_key = ev("IGNORE_KEY",
INCLUDED_IGNORE_KEY)
forward_host = ev("FORWARD_HOST", "127.0.0.1")
forward_port = int(ev("FORWARD_PORT", "80"))
include_filter_key = ev("FILTER_KEY", "")
if not include_filter_key and filter_key:
include_filter_key = filter_key
filter_keys = [filter_key]
log.info(("START consumer={} "
"forward={}:{} with "
"key={} filters={}")
.format(name,
forward_host,
forward_port,
include_filter_key,
filter_key))
forward_skt = None
not_done = True
while not_done:
if not forward_skt:
forward_skt = connect_forwarder(
forward_host=forward_host,
forward_port=forward_port)
next_task = task_queue.get()
if next_task:
if str(next_task) == shutdown_msg:
# Poison pill for shutting down
log.info(("{}: DONE CALLBACK "
"Exiting msg={}")
.format(name,
next_task))
task_queue.task_done()
break
# end of handling shutdown case
try:
log.debug(("{} parsing")
.format(name))
source = next_task.source
packet = next_task.payload
if not packet:
log.error(("{} invalid task found "
"{} missing payload")
.format(name,
next_task))
break
log.debug(("{} found msg from src={}")
.format(name,
source))
network_data = parse_network_data(
data_packet=packet,
include_filter_key=include_filter_key,
filter_keys=filter_keys)
if network_data["status"] == VALID:
if network_data["data_type"] == TCP \
or network_data["data_type"] == UDP \
or network_data["data_type"] == ARP \
or network_data["data_type"] == ICMP:
log.info(("{} valid={} packet={} "
"data={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["target_data"]))
if not forward_skt:
forward_skt = connect_forwarder(
forward_host=forward_host,
forward_port=forward_port)
if forward_skt:
if network_data["stream"]:
sent = False
while not sent:
try:
log.info("sending={}".format(
network_data["stream"]))
send_msg(
forward_skt,
network_data["stream"]
.encode("utf-8"))
sent = True
except Exception as e:
sent = False
time.sleep(0.5)
try:
forward_skt.close()
forward_skt = None
except Exception as w:
forward_skt = None
forward_skt = connect_forwarder(
forward_host=forward_host,
forward_port=forward_port)
# end of reconnecting
log.info("sent={}".format(
network_data["stream"]))
if need_response:
log.info("receiving")
cdr_res = forward_skt.recv(1024)
log.info(("cdr - res{}")
.format(cdr_res))
else:
log.info(("{} EMPTY stream={} "
"error={} status={}")
.format(
name,
network_data["stream"],
network_data["err"],
network_data["status"]))
else:
log.info(("{} not_supported valid={} "
"packet data_type={} status={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["status"]))
elif network_data["status"] == FILTERED:
log.info(("{} filtered={} status={}")
.format(name,
network_data["filtered"],
network_data["status"]))
else:
if network_data["status"] == INVALID:
log.info(("{} invalid={} packet={} "
"error={} status={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["error"],
network_data["status"]))
else:
log.info(("{} unknown={} packet={} "
"error={} status={}")
.format(name,
network_data["id"],
network_data["data_type"],
network_data["error"],
network_data["status"]))
# end of if valid or not data
except KeyboardInterrupt as k:
log.info(("{} stopping")
.format(name))
break
except Exception as e:
log.error(("{} failed packaging packet to forward "
"with ex={}")
.format(name,
e))
break
# end of try/ex during payload processing
# end of if found a next_task
log.info(("Consumer: {} {}")
.format(name, next_task))
task_queue.task_done()
if need_response:
answer = "processed: {}".format(next_task())
result_queue.put(answer)
# end of while
if forward_skt:
try:
forward_skt.close()
log.info("CLOSED connection")
forward_skt = None
except Exception:
log.info("CLOSED connection")
# end of cleaning up forwarding socket
log.info("{} Done".format(name))
return | [
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... | jay-johnson/network-pipeline | python | https://github.com/jay-johnson/network-pipeline/blob/4e53ae13fe12085e0cf2e5e1aff947368f4f1ffa/network_pipeline/scripts/network_agent.py#L35-L244 | [
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train | run_main | run_main
start the packet consumers and the packet processors
:param need_response: should send response back to publisher
:param callback: handler method | network_pipeline/scripts/network_agent.py | def run_main(
need_response=False,
callback=None):
"""run_main
start the packet consumers and the packet processors
:param need_response: should send response back to publisher
:param callback: handler method
"""
stop_file = ev("STOP_FILE",
"/opt/stop_recording")
num_workers = int(ev("NUM_WORKERS",
"1"))
shutdown_msg = "SHUTDOWN"
log.info("Start - {}".format(name))
log.info("Creating multiprocessing queue")
tasks = multiprocessing.JoinableQueue()
queue_to_consume = multiprocessing.Queue()
host = "localhost"
# Start consumers
log.info("Starting Consumers to process queued tasks")
consumers = start_consumers_for_queue(
num_workers=num_workers,
tasks=tasks,
queue_to_consume=queue_to_consume,
shutdown_msg=shutdown_msg,
consumer_class=WorkerToProcessPackets,
callback=callback)
log.info("creating socket")
skt = create_layer_2_socket()
log.info("socket created")
not_done = True
while not_done:
if not skt:
log.info("Failed to create layer 2 socket")
log.info("Please make sure to run as root")
not_done = False
break
try:
if os.path.exists(stop_file):
log.info(("Detected stop_file={}")
.format(stop_file))
not_done = False
break
# stop if the file exists
# Only works on linux
packet = skt.recvfrom(65565)
if os.path.exists(stop_file):
log.info(("Detected stop_file={}")
.format(stop_file))
not_done = False
break
# stop if the file was created during a wait loop
tasks.put(NetworkPacketTask(source=host,
payload=packet))
except KeyboardInterrupt as k:
log.info("Stopping")
not_done = False
break
except Exception as e:
log.error(("Failed reading socket with ex={}")
.format(e))
not_done = False
break
# end of try/ex during socket receving
# end of while processing network packets
log.info(("Shutting down consumers={}")
.format(len(consumers)))
shutdown_consumers(num_workers=num_workers,
tasks=tasks)
# Wait for all of the tasks to finish
if need_response:
log.info("Waiting for tasks to finish")
tasks.join()
log.info("Done waiting for tasks to finish") | def run_main(
need_response=False,
callback=None):
"""run_main
start the packet consumers and the packet processors
:param need_response: should send response back to publisher
:param callback: handler method
"""
stop_file = ev("STOP_FILE",
"/opt/stop_recording")
num_workers = int(ev("NUM_WORKERS",
"1"))
shutdown_msg = "SHUTDOWN"
log.info("Start - {}".format(name))
log.info("Creating multiprocessing queue")
tasks = multiprocessing.JoinableQueue()
queue_to_consume = multiprocessing.Queue()
host = "localhost"
# Start consumers
log.info("Starting Consumers to process queued tasks")
consumers = start_consumers_for_queue(
num_workers=num_workers,
tasks=tasks,
queue_to_consume=queue_to_consume,
shutdown_msg=shutdown_msg,
consumer_class=WorkerToProcessPackets,
callback=callback)
log.info("creating socket")
skt = create_layer_2_socket()
log.info("socket created")
not_done = True
while not_done:
if not skt:
log.info("Failed to create layer 2 socket")
log.info("Please make sure to run as root")
not_done = False
break
try:
if os.path.exists(stop_file):
log.info(("Detected stop_file={}")
.format(stop_file))
not_done = False
break
# stop if the file exists
# Only works on linux
packet = skt.recvfrom(65565)
if os.path.exists(stop_file):
log.info(("Detected stop_file={}")
.format(stop_file))
not_done = False
break
# stop if the file was created during a wait loop
tasks.put(NetworkPacketTask(source=host,
payload=packet))
except KeyboardInterrupt as k:
log.info("Stopping")
not_done = False
break
except Exception as e:
log.error(("Failed reading socket with ex={}")
.format(e))
not_done = False
break
# end of try/ex during socket receving
# end of while processing network packets
log.info(("Shutting down consumers={}")
.format(len(consumers)))
shutdown_consumers(num_workers=num_workers,
tasks=tasks)
# Wait for all of the tasks to finish
if need_response:
log.info("Waiting for tasks to finish")
tasks.join()
log.info("Done waiting for tasks to finish") | [
"run_main"
] | jay-johnson/network-pipeline | python | https://github.com/jay-johnson/network-pipeline/blob/4e53ae13fe12085e0cf2e5e1aff947368f4f1ffa/network_pipeline/scripts/network_agent.py#L248-L341 | [
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train | best_model | determine the best model: archaea, bacteria, eukarya (best score) | ctbBio/16SfromHMM.py | def best_model(seq2hmm):
"""
determine the best model: archaea, bacteria, eukarya (best score)
"""
for seq in seq2hmm:
best = []
for model in seq2hmm[seq]:
best.append([model, sorted([i[-1] for i in seq2hmm[seq][model]], reverse = True)[0]])
best_model = sorted(best, key = itemgetter(1), reverse = True)[0][0]
seq2hmm[seq] = [best_model] + [seq2hmm[seq][best_model]]
return seq2hmm | def best_model(seq2hmm):
"""
determine the best model: archaea, bacteria, eukarya (best score)
"""
for seq in seq2hmm:
best = []
for model in seq2hmm[seq]:
best.append([model, sorted([i[-1] for i in seq2hmm[seq][model]], reverse = True)[0]])
best_model = sorted(best, key = itemgetter(1), reverse = True)[0][0]
seq2hmm[seq] = [best_model] + [seq2hmm[seq][best_model]]
return seq2hmm | [
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train | check_gaps | check for large gaps between alignment windows | ctbBio/16SfromHMM.py | def check_gaps(matches, gap_threshold = 0):
"""
check for large gaps between alignment windows
"""
gaps = []
prev = None
for match in sorted(matches, key = itemgetter(0)):
if prev is None:
prev = match
continue
if match[0] - prev[1] >= gap_threshold:
gaps.append([prev, match])
prev = match
return [[i[0][1], i[1][0]] for i in gaps] | def check_gaps(matches, gap_threshold = 0):
"""
check for large gaps between alignment windows
"""
gaps = []
prev = None
for match in sorted(matches, key = itemgetter(0)):
if prev is None:
prev = match
continue
if match[0] - prev[1] >= gap_threshold:
gaps.append([prev, match])
prev = match
return [[i[0][1], i[1][0]] for i in gaps] | [
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train | check_overlap | determine if sequence has already hit the same part of the model,
indicating that this hit is for another 16S rRNA gene | ctbBio/16SfromHMM.py | def check_overlap(current, hit, overlap = 200):
"""
determine if sequence has already hit the same part of the model,
indicating that this hit is for another 16S rRNA gene
"""
for prev in current:
p_coords = prev[2:4]
coords = hit[2:4]
if get_overlap(coords, p_coords) >= overlap:
return True
return False | def check_overlap(current, hit, overlap = 200):
"""
determine if sequence has already hit the same part of the model,
indicating that this hit is for another 16S rRNA gene
"""
for prev in current:
p_coords = prev[2:4]
coords = hit[2:4]
if get_overlap(coords, p_coords) >= overlap:
return True
return False | [
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train | check_order | determine if hits are sequential on model and on the
same strand
* if not, they should be split into different groups | ctbBio/16SfromHMM.py | def check_order(current, hit, overlap = 200):
"""
determine if hits are sequential on model and on the
same strand
* if not, they should be split into different groups
"""
prev_model = current[-1][2:4]
prev_strand = current[-1][-2]
hit_model = hit[2:4]
hit_strand = hit[-2]
# make sure they are on the same strand
if prev_strand != hit_strand:
return False
# check for sequential hits on + strand
if prev_strand == '+' and (prev_model[1] - hit_model[0] >= overlap):
return False
# check for sequential hits on - strand
if prev_strand == '-' and (hit_model[1] - prev_model[0] >= overlap):
return False
else:
return True | def check_order(current, hit, overlap = 200):
"""
determine if hits are sequential on model and on the
same strand
* if not, they should be split into different groups
"""
prev_model = current[-1][2:4]
prev_strand = current[-1][-2]
hit_model = hit[2:4]
hit_strand = hit[-2]
# make sure they are on the same strand
if prev_strand != hit_strand:
return False
# check for sequential hits on + strand
if prev_strand == '+' and (prev_model[1] - hit_model[0] >= overlap):
return False
# check for sequential hits on - strand
if prev_strand == '-' and (hit_model[1] - prev_model[0] >= overlap):
return False
else:
return True | [
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train | hit_groups | * each sequence may have more than one 16S rRNA gene
* group hits for each gene | ctbBio/16SfromHMM.py | def hit_groups(hits):
"""
* each sequence may have more than one 16S rRNA gene
* group hits for each gene
"""
groups = []
current = False
for hit in sorted(hits, key = itemgetter(0)):
if current is False:
current = [hit]
elif check_overlap(current, hit) is True or check_order(current, hit) is False:
groups.append(current)
current = [hit]
else:
current.append(hit)
groups.append(current)
return groups | def hit_groups(hits):
"""
* each sequence may have more than one 16S rRNA gene
* group hits for each gene
"""
groups = []
current = False
for hit in sorted(hits, key = itemgetter(0)):
if current is False:
current = [hit]
elif check_overlap(current, hit) is True or check_order(current, hit) is False:
groups.append(current)
current = [hit]
else:
current.append(hit)
groups.append(current)
return groups | [
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train | find_coordinates | find 16S rRNA gene sequence coordinates | ctbBio/16SfromHMM.py | def find_coordinates(hmms, bit_thresh):
"""
find 16S rRNA gene sequence coordinates
"""
# get coordinates from cmsearch output
seq2hmm = parse_hmm(hmms, bit_thresh)
seq2hmm = best_model(seq2hmm)
group2hmm = {} # group2hmm[seq][group] = [model, strand, coordinates, matches, gaps]
for seq, info in list(seq2hmm.items()):
group2hmm[seq] = {}
# info = [model, [[hit1], [hit2], ...]]
for group_num, group in enumerate(hit_groups(info[1])):
# group is a group of hits to a single 16S gene
# determine matching strand based on best hit
best = sorted(group, reverse = True, key = itemgetter(-1))[0]
strand = best[5]
coordinates = [i[0] for i in group] + [i[1] for i in group]
coordinates = [min(coordinates), max(coordinates), strand]
# make sure all hits are to the same strand
matches = [i for i in group if i[5] == strand]
# gaps = [[gstart, gend], [gstart2, gend2]]
gaps = check_gaps(matches)
group2hmm[seq][group_num] = [info[0], strand, coordinates, matches, gaps]
return group2hmm | def find_coordinates(hmms, bit_thresh):
"""
find 16S rRNA gene sequence coordinates
"""
# get coordinates from cmsearch output
seq2hmm = parse_hmm(hmms, bit_thresh)
seq2hmm = best_model(seq2hmm)
group2hmm = {} # group2hmm[seq][group] = [model, strand, coordinates, matches, gaps]
for seq, info in list(seq2hmm.items()):
group2hmm[seq] = {}
# info = [model, [[hit1], [hit2], ...]]
for group_num, group in enumerate(hit_groups(info[1])):
# group is a group of hits to a single 16S gene
# determine matching strand based on best hit
best = sorted(group, reverse = True, key = itemgetter(-1))[0]
strand = best[5]
coordinates = [i[0] for i in group] + [i[1] for i in group]
coordinates = [min(coordinates), max(coordinates), strand]
# make sure all hits are to the same strand
matches = [i for i in group if i[5] == strand]
# gaps = [[gstart, gend], [gstart2, gend2]]
gaps = check_gaps(matches)
group2hmm[seq][group_num] = [info[0], strand, coordinates, matches, gaps]
return group2hmm | [
"find",
"16S",
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] | christophertbrown/bioscripts | python | https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/16SfromHMM.py#L103-L126 | [
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train | get_info | get info from either ssu-cmsearch or cmsearch output | ctbBio/16SfromHMM.py | def get_info(line, bit_thresh):
"""
get info from either ssu-cmsearch or cmsearch output
"""
if len(line) >= 18: # output is from cmsearch
id, model, bit, inc = line[0].split()[0], line[2], float(line[14]), line[16]
sstart, send, strand = int(line[7]), int(line[8]), line[9]
mstart, mend = int(line[5]), int(line[6])
elif len(line) == 9: # output is from ssu-cmsearch
if bit_thresh == 0:
print('# ssu-cmsearch does not include a model-specific inclusion threshold, ', file=sys.stderr)
print('# please specify a bit score threshold', file=sys.stderr)
exit()
id, model, bit = line[1].split()[0], line[0], float(line[6])
inc = '!' # this is not a feature of ssu-cmsearch
sstart, send = int(line[2]), int(line[3])
mstart, mend = int(4), int(5)
if send >= sstart:
strand = '+'
else:
strand = '-'
else:
print('# unsupported hmm format:', file=sys.stderr)
print('# provide tabular output from ssu-cmsearch and cmsearch supported', file=sys.stderr)
exit()
coords = [sstart, send]
sstart, send = min(coords), max(coords)
mcoords = [mstart, mend]
mstart, mend = min(mcoords), max(mcoords)
return id, model, bit, sstart, send, mstart, mend, strand, inc | def get_info(line, bit_thresh):
"""
get info from either ssu-cmsearch or cmsearch output
"""
if len(line) >= 18: # output is from cmsearch
id, model, bit, inc = line[0].split()[0], line[2], float(line[14]), line[16]
sstart, send, strand = int(line[7]), int(line[8]), line[9]
mstart, mend = int(line[5]), int(line[6])
elif len(line) == 9: # output is from ssu-cmsearch
if bit_thresh == 0:
print('# ssu-cmsearch does not include a model-specific inclusion threshold, ', file=sys.stderr)
print('# please specify a bit score threshold', file=sys.stderr)
exit()
id, model, bit = line[1].split()[0], line[0], float(line[6])
inc = '!' # this is not a feature of ssu-cmsearch
sstart, send = int(line[2]), int(line[3])
mstart, mend = int(4), int(5)
if send >= sstart:
strand = '+'
else:
strand = '-'
else:
print('# unsupported hmm format:', file=sys.stderr)
print('# provide tabular output from ssu-cmsearch and cmsearch supported', file=sys.stderr)
exit()
coords = [sstart, send]
sstart, send = min(coords), max(coords)
mcoords = [mstart, mend]
mstart, mend = min(mcoords), max(mcoords)
return id, model, bit, sstart, send, mstart, mend, strand, inc | [
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train | check_buffer | check to see how much of the buffer is being used | ctbBio/16SfromHMM.py | def check_buffer(coords, length, buffer):
"""
check to see how much of the buffer is being used
"""
s = min(coords[0], buffer)
e = min(length - coords[1], buffer)
return [s, e] | def check_buffer(coords, length, buffer):
"""
check to see how much of the buffer is being used
"""
s = min(coords[0], buffer)
e = min(length - coords[1], buffer)
return [s, e] | [
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train | convert_parser_to | :return: a parser of type parser_or_type, initialized with the properties of parser. If parser_or_type
is a type, an instance of it must contain a update method. The update method must also process
the set of properties supported by MetadataParser for the conversion to have any affect.
:param parser: the parser (or content or parser type) to convert to new_type
:param parser_or_type: a parser (or content) or type of parser to return
:see: get_metadata_parser(metadata_container) for more on how parser_or_type is treated | gis_metadata/metadata_parser.py | def convert_parser_to(parser, parser_or_type, metadata_props=None):
"""
:return: a parser of type parser_or_type, initialized with the properties of parser. If parser_or_type
is a type, an instance of it must contain a update method. The update method must also process
the set of properties supported by MetadataParser for the conversion to have any affect.
:param parser: the parser (or content or parser type) to convert to new_type
:param parser_or_type: a parser (or content) or type of parser to return
:see: get_metadata_parser(metadata_container) for more on how parser_or_type is treated
"""
old_parser = parser if isinstance(parser, MetadataParser) else get_metadata_parser(parser)
new_parser = get_metadata_parser(parser_or_type)
for prop in (metadata_props or _supported_props):
setattr(new_parser, prop, deepcopy(getattr(old_parser, prop, u'')))
new_parser.update()
return new_parser | def convert_parser_to(parser, parser_or_type, metadata_props=None):
"""
:return: a parser of type parser_or_type, initialized with the properties of parser. If parser_or_type
is a type, an instance of it must contain a update method. The update method must also process
the set of properties supported by MetadataParser for the conversion to have any affect.
:param parser: the parser (or content or parser type) to convert to new_type
:param parser_or_type: a parser (or content) or type of parser to return
:see: get_metadata_parser(metadata_container) for more on how parser_or_type is treated
"""
old_parser = parser if isinstance(parser, MetadataParser) else get_metadata_parser(parser)
new_parser = get_metadata_parser(parser_or_type)
for prop in (metadata_props or _supported_props):
setattr(new_parser, prop, deepcopy(getattr(old_parser, prop, u'')))
new_parser.update()
return new_parser | [
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train | get_metadata_parser | Takes a metadata_container, which may be a type or instance of a parser, a dict, string, or file.
:return: a new instance of a parser corresponding to the standard represented by metadata_container
:see: get_parsed_content(metdata_content) for more on types of content that can be parsed | gis_metadata/metadata_parser.py | def get_metadata_parser(metadata_container, **metadata_defaults):
"""
Takes a metadata_container, which may be a type or instance of a parser, a dict, string, or file.
:return: a new instance of a parser corresponding to the standard represented by metadata_container
:see: get_parsed_content(metdata_content) for more on types of content that can be parsed
"""
parser_type = None
if isinstance(metadata_container, MetadataParser):
parser_type = type(metadata_container)
elif isinstance(metadata_container, type):
parser_type = metadata_container
metadata_container = metadata_container().update(**metadata_defaults)
xml_root, xml_tree = get_parsed_content(metadata_container)
# The get_parsed_content method ensures only these roots will be returned
parser = None
if parser_type is not None:
parser = parser_type(xml_tree, **metadata_defaults)
elif xml_root in ISO_ROOTS:
parser = IsoParser(xml_tree, **metadata_defaults)
else:
has_arcgis_data = any(element_exists(xml_tree, e) for e in ARCGIS_NODES)
if xml_root == FGDC_ROOT and not has_arcgis_data:
parser = FgdcParser(xml_tree, **metadata_defaults)
elif xml_root in ARCGIS_ROOTS:
parser = ArcGISParser(xml_tree, **metadata_defaults)
return parser | def get_metadata_parser(metadata_container, **metadata_defaults):
"""
Takes a metadata_container, which may be a type or instance of a parser, a dict, string, or file.
:return: a new instance of a parser corresponding to the standard represented by metadata_container
:see: get_parsed_content(metdata_content) for more on types of content that can be parsed
"""
parser_type = None
if isinstance(metadata_container, MetadataParser):
parser_type = type(metadata_container)
elif isinstance(metadata_container, type):
parser_type = metadata_container
metadata_container = metadata_container().update(**metadata_defaults)
xml_root, xml_tree = get_parsed_content(metadata_container)
# The get_parsed_content method ensures only these roots will be returned
parser = None
if parser_type is not None:
parser = parser_type(xml_tree, **metadata_defaults)
elif xml_root in ISO_ROOTS:
parser = IsoParser(xml_tree, **metadata_defaults)
else:
has_arcgis_data = any(element_exists(xml_tree, e) for e in ARCGIS_NODES)
if xml_root == FGDC_ROOT and not has_arcgis_data:
parser = FgdcParser(xml_tree, **metadata_defaults)
elif xml_root in ARCGIS_ROOTS:
parser = ArcGISParser(xml_tree, **metadata_defaults)
return parser | [
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train | get_parsed_content | Parses any of the following types of content:
1. XML string or file object: parses XML content
2. MetadataParser instance: deep copies xml_tree
3. Dictionary with nested objects containing:
- name (required): the name of the element tag
- text: the text contained by element
- tail: text immediately following the element
- attributes: a Dictionary containing element attributes
- children: a List of converted child elements
:raises InvalidContent: if the XML is invalid or does not conform to a supported metadata standard
:raises NoContent: If the content passed in is null or otherwise empty
:return: the XML root along with an XML Tree parsed by and compatible with element_utils | gis_metadata/metadata_parser.py | def get_parsed_content(metadata_content):
"""
Parses any of the following types of content:
1. XML string or file object: parses XML content
2. MetadataParser instance: deep copies xml_tree
3. Dictionary with nested objects containing:
- name (required): the name of the element tag
- text: the text contained by element
- tail: text immediately following the element
- attributes: a Dictionary containing element attributes
- children: a List of converted child elements
:raises InvalidContent: if the XML is invalid or does not conform to a supported metadata standard
:raises NoContent: If the content passed in is null or otherwise empty
:return: the XML root along with an XML Tree parsed by and compatible with element_utils
"""
_import_parsers() # Prevents circular dependencies between modules
xml_tree = None
if metadata_content is None:
raise NoContent('Metadata has no data')
else:
if isinstance(metadata_content, MetadataParser):
xml_tree = deepcopy(metadata_content._xml_tree)
elif isinstance(metadata_content, dict):
xml_tree = get_element_tree(metadata_content)
else:
try:
# Strip name spaces from file or XML content
xml_tree = get_element_tree(metadata_content)
except Exception:
xml_tree = None # Several exceptions possible, outcome is the same
if xml_tree is None:
raise InvalidContent(
'Cannot instantiate a {parser_type} parser with invalid content to parse',
parser_type=type(metadata_content).__name__
)
xml_root = get_element_name(xml_tree)
if xml_root is None:
raise NoContent('Metadata contains no data')
elif xml_root not in VALID_ROOTS:
content = type(metadata_content).__name__
raise InvalidContent('Invalid root element for {content}: {xml_root}', content=content, xml_root=xml_root)
return xml_root, xml_tree | def get_parsed_content(metadata_content):
"""
Parses any of the following types of content:
1. XML string or file object: parses XML content
2. MetadataParser instance: deep copies xml_tree
3. Dictionary with nested objects containing:
- name (required): the name of the element tag
- text: the text contained by element
- tail: text immediately following the element
- attributes: a Dictionary containing element attributes
- children: a List of converted child elements
:raises InvalidContent: if the XML is invalid or does not conform to a supported metadata standard
:raises NoContent: If the content passed in is null or otherwise empty
:return: the XML root along with an XML Tree parsed by and compatible with element_utils
"""
_import_parsers() # Prevents circular dependencies between modules
xml_tree = None
if metadata_content is None:
raise NoContent('Metadata has no data')
else:
if isinstance(metadata_content, MetadataParser):
xml_tree = deepcopy(metadata_content._xml_tree)
elif isinstance(metadata_content, dict):
xml_tree = get_element_tree(metadata_content)
else:
try:
# Strip name spaces from file or XML content
xml_tree = get_element_tree(metadata_content)
except Exception:
xml_tree = None # Several exceptions possible, outcome is the same
if xml_tree is None:
raise InvalidContent(
'Cannot instantiate a {parser_type} parser with invalid content to parse',
parser_type=type(metadata_content).__name__
)
xml_root = get_element_name(xml_tree)
if xml_root is None:
raise NoContent('Metadata contains no data')
elif xml_root not in VALID_ROOTS:
content = type(metadata_content).__name__
raise InvalidContent('Invalid root element for {content}: {xml_root}', content=content, xml_root=xml_root)
return xml_root, xml_tree | [
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train | _import_parsers | Lazy imports to prevent circular dependencies between this module and utils | gis_metadata/metadata_parser.py | def _import_parsers():
""" Lazy imports to prevent circular dependencies between this module and utils """
global ARCGIS_NODES
global ARCGIS_ROOTS
global ArcGISParser
global FGDC_ROOT
global FgdcParser
global ISO_ROOTS
global IsoParser
global VALID_ROOTS
if ARCGIS_NODES is None or ARCGIS_ROOTS is None or ArcGISParser is None:
from gis_metadata.arcgis_metadata_parser import ARCGIS_NODES
from gis_metadata.arcgis_metadata_parser import ARCGIS_ROOTS
from gis_metadata.arcgis_metadata_parser import ArcGISParser
if FGDC_ROOT is None or FgdcParser is None:
from gis_metadata.fgdc_metadata_parser import FGDC_ROOT
from gis_metadata.fgdc_metadata_parser import FgdcParser
if ISO_ROOTS is None or IsoParser is None:
from gis_metadata.iso_metadata_parser import ISO_ROOTS
from gis_metadata.iso_metadata_parser import IsoParser
if VALID_ROOTS is None:
VALID_ROOTS = {FGDC_ROOT}.union(ARCGIS_ROOTS + ISO_ROOTS) | def _import_parsers():
""" Lazy imports to prevent circular dependencies between this module and utils """
global ARCGIS_NODES
global ARCGIS_ROOTS
global ArcGISParser
global FGDC_ROOT
global FgdcParser
global ISO_ROOTS
global IsoParser
global VALID_ROOTS
if ARCGIS_NODES is None or ARCGIS_ROOTS is None or ArcGISParser is None:
from gis_metadata.arcgis_metadata_parser import ARCGIS_NODES
from gis_metadata.arcgis_metadata_parser import ARCGIS_ROOTS
from gis_metadata.arcgis_metadata_parser import ArcGISParser
if FGDC_ROOT is None or FgdcParser is None:
from gis_metadata.fgdc_metadata_parser import FGDC_ROOT
from gis_metadata.fgdc_metadata_parser import FgdcParser
if ISO_ROOTS is None or IsoParser is None:
from gis_metadata.iso_metadata_parser import ISO_ROOTS
from gis_metadata.iso_metadata_parser import IsoParser
if VALID_ROOTS is None:
VALID_ROOTS = {FGDC_ROOT}.union(ARCGIS_ROOTS + ISO_ROOTS) | [
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train | MetadataParser._init_metadata | Dynamically sets attributes from a Dictionary passed in by children.
The Dictionary will contain the name of each attribute as keys, and
either an XPATH mapping to a text value in _xml_tree, or a function
that takes no parameters and returns the intended value. | gis_metadata/metadata_parser.py | def _init_metadata(self):
"""
Dynamically sets attributes from a Dictionary passed in by children.
The Dictionary will contain the name of each attribute as keys, and
either an XPATH mapping to a text value in _xml_tree, or a function
that takes no parameters and returns the intended value.
"""
if self._data_map is None:
self._init_data_map()
validate_properties(self._data_map, self._metadata_props)
# Parse attribute values and assign them: key = parse(val)
for prop in self._data_map:
setattr(self, prop, parse_property(self._xml_tree, None, self._data_map, prop))
self.has_data = any(getattr(self, prop) for prop in self._data_map) | def _init_metadata(self):
"""
Dynamically sets attributes from a Dictionary passed in by children.
The Dictionary will contain the name of each attribute as keys, and
either an XPATH mapping to a text value in _xml_tree, or a function
that takes no parameters and returns the intended value.
"""
if self._data_map is None:
self._init_data_map()
validate_properties(self._data_map, self._metadata_props)
# Parse attribute values and assign them: key = parse(val)
for prop in self._data_map:
setattr(self, prop, parse_property(self._xml_tree, None, self._data_map, prop))
self.has_data = any(getattr(self, prop) for prop in self._data_map) | [
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train | MetadataParser._init_data_map | Default data map initialization: MUST be overridden in children | gis_metadata/metadata_parser.py | def _init_data_map(self):
""" Default data map initialization: MUST be overridden in children """
if self._data_map is None:
self._data_map = {'_root': None}
self._data_map.update({}.fromkeys(self._metadata_props)) | def _init_data_map(self):
""" Default data map initialization: MUST be overridden in children """
if self._data_map is None:
self._data_map = {'_root': None}
self._data_map.update({}.fromkeys(self._metadata_props)) | [
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train | MetadataParser._get_template | Iterate over items metadata_defaults {prop: val, ...} to populate template | gis_metadata/metadata_parser.py | def _get_template(self, root=None, **metadata_defaults):
""" Iterate over items metadata_defaults {prop: val, ...} to populate template """
if root is None:
if self._data_map is None:
self._init_data_map()
root = self._xml_root = self._data_map['_root']
template_tree = self._xml_tree = create_element_tree(root)
for prop, val in iteritems(metadata_defaults):
path = self._data_map.get(prop)
if path and val:
setattr(self, prop, val)
update_property(template_tree, None, path, prop, val)
return template_tree | def _get_template(self, root=None, **metadata_defaults):
""" Iterate over items metadata_defaults {prop: val, ...} to populate template """
if root is None:
if self._data_map is None:
self._init_data_map()
root = self._xml_root = self._data_map['_root']
template_tree = self._xml_tree = create_element_tree(root)
for prop, val in iteritems(metadata_defaults):
path = self._data_map.get(prop)
if path and val:
setattr(self, prop, val)
update_property(template_tree, None, path, prop, val)
return template_tree | [
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train | MetadataParser._get_xpath_for | :return: the configured xpath for a given property | gis_metadata/metadata_parser.py | def _get_xpath_for(self, prop):
""" :return: the configured xpath for a given property """
xpath = self._data_map.get(prop)
return getattr(xpath, 'xpath', xpath) | def _get_xpath_for(self, prop):
""" :return: the configured xpath for a given property """
xpath = self._data_map.get(prop)
return getattr(xpath, 'xpath', xpath) | [
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train | MetadataParser._parse_complex | Default parsing operation for a complex struct | gis_metadata/metadata_parser.py | def _parse_complex(self, prop):
""" Default parsing operation for a complex struct """
xpath_root = None
xpath_map = self._data_structures[prop]
return parse_complex(self._xml_tree, xpath_root, xpath_map, prop) | def _parse_complex(self, prop):
""" Default parsing operation for a complex struct """
xpath_root = None
xpath_map = self._data_structures[prop]
return parse_complex(self._xml_tree, xpath_root, xpath_map, prop) | [
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train | MetadataParser._parse_complex_list | Default parsing operation for lists of complex structs | gis_metadata/metadata_parser.py | def _parse_complex_list(self, prop):
""" Default parsing operation for lists of complex structs """
xpath_root = self._get_xroot_for(prop)
xpath_map = self._data_structures[prop]
return parse_complex_list(self._xml_tree, xpath_root, xpath_map, prop) | def _parse_complex_list(self, prop):
""" Default parsing operation for lists of complex structs """
xpath_root = self._get_xroot_for(prop)
xpath_map = self._data_structures[prop]
return parse_complex_list(self._xml_tree, xpath_root, xpath_map, prop) | [
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train | MetadataParser._parse_dates | Creates and returns a Date Types data structure parsed from the metadata | gis_metadata/metadata_parser.py | def _parse_dates(self, prop=DATES):
""" Creates and returns a Date Types data structure parsed from the metadata """
return parse_dates(self._xml_tree, self._data_structures[prop]) | def _parse_dates(self, prop=DATES):
""" Creates and returns a Date Types data structure parsed from the metadata """
return parse_dates(self._xml_tree, self._data_structures[prop]) | [
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train | MetadataParser._update_complex | Default update operation for a complex struct | gis_metadata/metadata_parser.py | def _update_complex(self, **update_props):
""" Default update operation for a complex struct """
prop = update_props['prop']
xpath_root = self._get_xroot_for(prop)
xpath_map = self._data_structures[prop]
return update_complex(xpath_root=xpath_root, xpath_map=xpath_map, **update_props) | def _update_complex(self, **update_props):
""" Default update operation for a complex struct """
prop = update_props['prop']
xpath_root = self._get_xroot_for(prop)
xpath_map = self._data_structures[prop]
return update_complex(xpath_root=xpath_root, xpath_map=xpath_map, **update_props) | [
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train | MetadataParser._update_complex_list | Default update operation for lists of complex structs | gis_metadata/metadata_parser.py | def _update_complex_list(self, **update_props):
""" Default update operation for lists of complex structs """
prop = update_props['prop']
xpath_root = self._get_xroot_for(prop)
xpath_map = self._data_structures[prop]
return update_complex_list(xpath_root=xpath_root, xpath_map=xpath_map, **update_props) | def _update_complex_list(self, **update_props):
""" Default update operation for lists of complex structs """
prop = update_props['prop']
xpath_root = self._get_xroot_for(prop)
xpath_map = self._data_structures[prop]
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train | MetadataParser._update_dates | Default update operation for Dates metadata
:see: gis_metadata.utils._complex_definitions[DATES] | gis_metadata/metadata_parser.py | def _update_dates(self, xpath_root=None, **update_props):
"""
Default update operation for Dates metadata
:see: gis_metadata.utils._complex_definitions[DATES]
"""
tree_to_update = update_props['tree_to_update']
prop = update_props['prop']
values = (update_props['values'] or {}).get(DATE_VALUES) or u''
xpaths = self._data_structures[prop]
if not self.dates:
date_xpaths = xpath_root
elif self.dates[DATE_TYPE] != DATE_TYPE_RANGE:
date_xpaths = xpaths.get(self.dates[DATE_TYPE], u'')
else:
date_xpaths = [
xpaths[DATE_TYPE_RANGE_BEGIN],
xpaths[DATE_TYPE_RANGE_END]
]
if xpath_root:
remove_element(tree_to_update, xpath_root)
return update_property(tree_to_update, xpath_root, date_xpaths, prop, values) | def _update_dates(self, xpath_root=None, **update_props):
"""
Default update operation for Dates metadata
:see: gis_metadata.utils._complex_definitions[DATES]
"""
tree_to_update = update_props['tree_to_update']
prop = update_props['prop']
values = (update_props['values'] or {}).get(DATE_VALUES) or u''
xpaths = self._data_structures[prop]
if not self.dates:
date_xpaths = xpath_root
elif self.dates[DATE_TYPE] != DATE_TYPE_RANGE:
date_xpaths = xpaths.get(self.dates[DATE_TYPE], u'')
else:
date_xpaths = [
xpaths[DATE_TYPE_RANGE_BEGIN],
xpaths[DATE_TYPE_RANGE_END]
]
if xpath_root:
remove_element(tree_to_update, xpath_root)
return update_property(tree_to_update, xpath_root, date_xpaths, prop, values) | [
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train | MetadataParser.write | Validates instance properties, updates an XML tree with them, and writes the content to a file.
:param use_template: if True, updates a new template XML tree; otherwise the original XML tree
:param out_file_or_path: optionally override self.out_file_or_path with a custom file path
:param encoding: optionally use another encoding instead of UTF-8 | gis_metadata/metadata_parser.py | def write(self, use_template=False, out_file_or_path=None, encoding=DEFAULT_ENCODING):
"""
Validates instance properties, updates an XML tree with them, and writes the content to a file.
:param use_template: if True, updates a new template XML tree; otherwise the original XML tree
:param out_file_or_path: optionally override self.out_file_or_path with a custom file path
:param encoding: optionally use another encoding instead of UTF-8
"""
if not out_file_or_path:
out_file_or_path = self.out_file_or_path
if not out_file_or_path:
# FileNotFoundError doesn't exist in Python 2
raise IOError('Output file path has not been provided')
write_element(self.update(use_template), out_file_or_path, encoding) | def write(self, use_template=False, out_file_or_path=None, encoding=DEFAULT_ENCODING):
"""
Validates instance properties, updates an XML tree with them, and writes the content to a file.
:param use_template: if True, updates a new template XML tree; otherwise the original XML tree
:param out_file_or_path: optionally override self.out_file_or_path with a custom file path
:param encoding: optionally use another encoding instead of UTF-8
"""
if not out_file_or_path:
out_file_or_path = self.out_file_or_path
if not out_file_or_path:
# FileNotFoundError doesn't exist in Python 2
raise IOError('Output file path has not been provided')
write_element(self.update(use_template), out_file_or_path, encoding) | [
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train | MetadataParser.validate | Default validation for updated properties: MAY be overridden in children | gis_metadata/metadata_parser.py | def validate(self):
""" Default validation for updated properties: MAY be overridden in children """
validate_properties(self._data_map, self._metadata_props)
for prop in self._data_map:
validate_any(prop, getattr(self, prop), self._data_structures.get(prop))
return self | def validate(self):
""" Default validation for updated properties: MAY be overridden in children """
validate_properties(self._data_map, self._metadata_props)
for prop in self._data_map:
validate_any(prop, getattr(self, prop), self._data_structures.get(prop))
return self | [
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train | _search_regex | Search order:
* specified regexps
* operators sorted from longer to shorter | sugartex/sugartex_filter.py | def _search_regex(ops: dict, regex_pat: str):
"""
Search order:
* specified regexps
* operators sorted from longer to shorter
"""
custom_regexps = list(filter(None, [dic['regex'] for op, dic in ops.items() if 'regex' in dic]))
op_names = [op for op, dic in ops.items() if 'regex' not in dic]
regex = [regex_pat.format(_ops_regex(op_names))] if len(op_names) > 0 else []
return re.compile('|'.join(custom_regexps + regex)) | def _search_regex(ops: dict, regex_pat: str):
"""
Search order:
* specified regexps
* operators sorted from longer to shorter
"""
custom_regexps = list(filter(None, [dic['regex'] for op, dic in ops.items() if 'regex' in dic]))
op_names = [op for op, dic in ops.items() if 'regex' not in dic]
regex = [regex_pat.format(_ops_regex(op_names))] if len(op_names) > 0 else []
return re.compile('|'.join(custom_regexps + regex)) | [
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train | Styles.spec | Return prefix unary operators list | sugartex/sugartex_filter.py | def spec(self, postf_un_ops: str) -> list:
"""Return prefix unary operators list"""
spec = [(l + op, {'pat': self.pat(pat),
'postf': self.postf(r, postf_un_ops),
'regex': None})
for op, pat in self.styles.items()
for l, r in self.brackets]
spec[0][1]['regex'] = self.regex_pat.format(
_ops_regex(l for l, r in self.brackets),
_ops_regex(self.styles.keys())
)
return spec | def spec(self, postf_un_ops: str) -> list:
"""Return prefix unary operators list"""
spec = [(l + op, {'pat': self.pat(pat),
'postf': self.postf(r, postf_un_ops),
'regex': None})
for op, pat in self.styles.items()
for l, r in self.brackets]
spec[0][1]['regex'] = self.regex_pat.format(
_ops_regex(l for l, r in self.brackets),
_ops_regex(self.styles.keys())
)
return spec | [
"Return",
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train | PrefUnGreedy.spec | Returns prefix unary operators list.
Sets only one regex for all items in the dict. | sugartex/sugartex_filter.py | def spec(self) -> list:
"""Returns prefix unary operators list.
Sets only one regex for all items in the dict."""
spec = [item
for op, pat in self.ops.items()
for item in [('{' + op, {'pat': pat, 'postf': self.postf, 'regex': None}),
('˱' + op, {'pat': pat, 'postf': self.postf, 'regex': None})]
]
spec[0][1]['regex'] = self.regex_pat.format(_ops_regex(self.ops.keys()))
return spec | def spec(self) -> list:
"""Returns prefix unary operators list.
Sets only one regex for all items in the dict."""
spec = [item
for op, pat in self.ops.items()
for item in [('{' + op, {'pat': pat, 'postf': self.postf, 'regex': None}),
('˱' + op, {'pat': pat, 'postf': self.postf, 'regex': None})]
]
spec[0][1]['regex'] = self.regex_pat.format(_ops_regex(self.ops.keys()))
return spec | [
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train | PrefUnOps.fill | Insert:
* math styles
* other styles
* unary prefix operators without brackets
* defaults | sugartex/sugartex_filter.py | def fill(self, postf_un_ops: str):
"""
Insert:
* math styles
* other styles
* unary prefix operators without brackets
* defaults
"""
for op, dic in self.ops.items():
if 'postf' not in dic:
dic['postf'] = self.postf
self.ops = OrderedDict(
self.styles.spec(postf_un_ops) +
self.other_styles.spec(postf_un_ops) +
self.pref_un_greedy.spec() +
list(self.ops.items())
)
for op, dic in self.ops.items():
dic['postf'] = re.compile(dic['postf'])
self.regex = _search_regex(self.ops, self.regex_pat) | def fill(self, postf_un_ops: str):
"""
Insert:
* math styles
* other styles
* unary prefix operators without brackets
* defaults
"""
for op, dic in self.ops.items():
if 'postf' not in dic:
dic['postf'] = self.postf
self.ops = OrderedDict(
self.styles.spec(postf_un_ops) +
self.other_styles.spec(postf_un_ops) +
self.pref_un_greedy.spec() +
list(self.ops.items())
)
for op, dic in self.ops.items():
dic['postf'] = re.compile(dic['postf'])
self.regex = _search_regex(self.ops, self.regex_pat) | [
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train | PostfUnOps.one_symbol_ops_str | Regex-escaped string with all one-symbol operators | sugartex/sugartex_filter.py | def one_symbol_ops_str(self) -> str:
"""Regex-escaped string with all one-symbol operators"""
return re.escape(''.join((key for key in self.ops.keys() if len(key) == 1))) | def one_symbol_ops_str(self) -> str:
"""Regex-escaped string with all one-symbol operators"""
return re.escape(''.join((key for key in self.ops.keys() if len(key) == 1))) | [
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train | SugarTeX._su_scripts_regex | :return:
[compiled regex, function] | sugartex/sugartex_filter.py | def _su_scripts_regex(self):
"""
:return:
[compiled regex, function]
"""
sups = re.escape(''.join([k for k in self.superscripts.keys()]))
subs = re.escape(''.join([k for k in self.subscripts.keys()])) # language=PythonRegExp
su_regex = (r'\\([{su_}])|([{sub}]+|‹[{sub}]+›|˹[{sub}]+˺)' +
r'|([{sup}]+)(?=√)|([{sup}]+(?!√)|‹[{sup}]+›|˹[{sup}]+˺)').format(
su_=subs + sups, sub=subs, sup=sups)
su_regex = re.compile(su_regex)
def su_replace(m):
esc, sub, root_sup, sup = m.groups()
if esc is not None:
return esc
elif sub is not None:
return '_{' + ''.join([c if (c in ['‹', '›', '˹', '˺']) else self.subscripts[c] for c in sub]) + '}'
elif root_sup is not None:
return ''.join([self.superscripts[c] for c in root_sup])
elif sup is not None:
return '^{' + ''.join([c if (c in ['‹', '›', '˹', '˺']) else self.superscripts[c] for c in sup]) + '}'
else:
raise TypeError("Regex bug: this should never be reached")
return [su_regex, su_replace] | def _su_scripts_regex(self):
"""
:return:
[compiled regex, function]
"""
sups = re.escape(''.join([k for k in self.superscripts.keys()]))
subs = re.escape(''.join([k for k in self.subscripts.keys()])) # language=PythonRegExp
su_regex = (r'\\([{su_}])|([{sub}]+|‹[{sub}]+›|˹[{sub}]+˺)' +
r'|([{sup}]+)(?=√)|([{sup}]+(?!√)|‹[{sup}]+›|˹[{sup}]+˺)').format(
su_=subs + sups, sub=subs, sup=sups)
su_regex = re.compile(su_regex)
def su_replace(m):
esc, sub, root_sup, sup = m.groups()
if esc is not None:
return esc
elif sub is not None:
return '_{' + ''.join([c if (c in ['‹', '›', '˹', '˺']) else self.subscripts[c] for c in sub]) + '}'
elif root_sup is not None:
return ''.join([self.superscripts[c] for c in root_sup])
elif sup is not None:
return '^{' + ''.join([c if (c in ['‹', '›', '˹', '˺']) else self.superscripts[c] for c in sup]) + '}'
else:
raise TypeError("Regex bug: this should never be reached")
return [su_regex, su_replace] | [
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train | SugarTeX._local_map | :param match:
:param loc: str
"l" or "r" or "lr"
turns on/off left/right local area calculation
:return: list
list of the same size as the string + 2
it's the local map that counted { and }
list can contain: None or int>=0
from the left of the operator match:
in `b}a` if a:0 then }:0 and b:1
in `b{a` if a:0 then {:0 and b:-1(None)
from the right of the operator match:
in `a{b` if a:0 then {:0 and b:1
in `a}b` if a:0 then }:0 and b:-1(None)
Map for +1 (needed for r'$') and -1 (needed for r'^')
characters is also stored: +1 -> +1, -1 -> +2 | sugartex/sugartex_filter.py | def _local_map(match, loc: str = 'lr') -> list:
"""
:param match:
:param loc: str
"l" or "r" or "lr"
turns on/off left/right local area calculation
:return: list
list of the same size as the string + 2
it's the local map that counted { and }
list can contain: None or int>=0
from the left of the operator match:
in `b}a` if a:0 then }:0 and b:1
in `b{a` if a:0 then {:0 and b:-1(None)
from the right of the operator match:
in `a{b` if a:0 then {:0 and b:1
in `a}b` if a:0 then }:0 and b:-1(None)
Map for +1 (needed for r'$') and -1 (needed for r'^')
characters is also stored: +1 -> +1, -1 -> +2
"""
s = match.string
map_ = [None] * (len(s) + 2)
if loc == 'l' or loc == 'lr':
balance = 0
for i in reversed(range(0, match.start())):
map_[i] = balance
c, prev = s[i], (s[i - 1] if i > 0 else '')
if (c == '}' or c == '˲') and prev != '\\':
balance += 1
elif (c == '{' or c == '˱') and prev != '\\':
balance -= 1
if balance < 0:
break
map_[-1] = balance
if loc == 'r' or loc == 'lr':
balance = 0
for i in range(match.end(), len(s)):
map_[i] = balance
c, prev = s[i], s[i - 1]
if (c == '{' or c == '˱') and prev != '\\':
balance += 1
elif (c == '}' or c == '˲') and prev != '\\':
balance -= 1
if balance < 0:
break
map_[len(s)] = balance
return map_ | def _local_map(match, loc: str = 'lr') -> list:
"""
:param match:
:param loc: str
"l" or "r" or "lr"
turns on/off left/right local area calculation
:return: list
list of the same size as the string + 2
it's the local map that counted { and }
list can contain: None or int>=0
from the left of the operator match:
in `b}a` if a:0 then }:0 and b:1
in `b{a` if a:0 then {:0 and b:-1(None)
from the right of the operator match:
in `a{b` if a:0 then {:0 and b:1
in `a}b` if a:0 then }:0 and b:-1(None)
Map for +1 (needed for r'$') and -1 (needed for r'^')
characters is also stored: +1 -> +1, -1 -> +2
"""
s = match.string
map_ = [None] * (len(s) + 2)
if loc == 'l' or loc == 'lr':
balance = 0
for i in reversed(range(0, match.start())):
map_[i] = balance
c, prev = s[i], (s[i - 1] if i > 0 else '')
if (c == '}' or c == '˲') and prev != '\\':
balance += 1
elif (c == '{' or c == '˱') and prev != '\\':
balance -= 1
if balance < 0:
break
map_[-1] = balance
if loc == 'r' or loc == 'lr':
balance = 0
for i in range(match.end(), len(s)):
map_[i] = balance
c, prev = s[i], s[i - 1]
if (c == '{' or c == '˱') and prev != '\\':
balance += 1
elif (c == '}' or c == '˲') and prev != '\\':
balance -= 1
if balance < 0:
break
map_[len(s)] = balance
return map_ | [
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... | kiwi0fruit/sugartex | python | https://github.com/kiwi0fruit/sugartex/blob/9eb13703cb02d3e2163c9c5f29df280f6bf49cec/sugartex/sugartex_filter.py#L708-L753 | [
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... | 9eb13703cb02d3e2163c9c5f29df280f6bf49cec |
train | SugarTeX._operators_replace | Searches for first unary or binary operator (via self.op_regex
that has only one group that contain operator)
then replaces it (or escapes it if brackets do not match).
Everything until:
* space ' '
* begin/end of the string
* bracket from outer scope (like '{a/b}': term1=a term2=b)
is considered a term (contents of matching brackets '{}' are
ignored).
Attributes
----------
string: str
string to replace | sugartex/sugartex_filter.py | def _operators_replace(self, string: str) -> str:
"""
Searches for first unary or binary operator (via self.op_regex
that has only one group that contain operator)
then replaces it (or escapes it if brackets do not match).
Everything until:
* space ' '
* begin/end of the string
* bracket from outer scope (like '{a/b}': term1=a term2=b)
is considered a term (contents of matching brackets '{}' are
ignored).
Attributes
----------
string: str
string to replace
"""
# noinspection PyShadowingNames
def replace(string: str, start: int, end: int, substring: str) -> str:
return string[0:start] + substring + string[end:len(string)]
# noinspection PyShadowingNames
def sub_pat(pat: Callable[[list], str] or str, terms: list) -> str:
if isinstance(pat, str):
return pat.format(*terms)
else:
return pat(terms)
count = 0
def check():
nonlocal count
count += 1
if count > self.max_while:
raise RuntimeError('Presumably while loop is stuck')
# noinspection PyShadowingNames
def null_replace(match) -> str:
regex_terms = [gr for gr in match.groups() if gr is not None]
op = regex_terms[0]
terms = regex_terms[1:]
return sub_pat(self.null_ops.ops[op]['pat'], terms)
string = self.null_ops.regex.sub(null_replace, string)
for ops, loc in [(self.pref_un_ops, 'r'), (self.postf_un_ops, 'l'),
(self.bin_centr_ops, 'lr')]:
count = 0
match = ops.regex.search(string)
while match:
check()
regex_terms = [gr for gr in match.groups() if gr is not None]
op = regex_terms[0]
loc_map = self._local_map(match, loc)
lmatch, rmatch = None, None
if loc == 'l' or loc == 'lr':
for m in ops.ops[op]['pref'].finditer(string):
if m.end() <= match.start() and loc_map[m.end() - 1] == 0:
lmatch = m
if lmatch is None:
string = replace(string, match.start(), match.end(), match.group(0).replace(op, '\\' + op))
match = ops.regex.search(string)
continue
else:
term1 = string[lmatch.end():match.start()]
if loc == 'r' or loc == 'lr':
for m in ops.ops[op]['postf'].finditer(string):
if m.start() >= match.end() and loc_map[m.start()] == 0:
rmatch = m
break
if rmatch is None:
string = replace(string, match.start(), match.end(), match.group(0).replace(op, '\\' + op))
match = ops.regex.search(string)
continue
else:
term2 = string[match.end():rmatch.start()]
if loc == 'l':
# noinspection PyUnboundLocalVariable
terms = list(lmatch.groups()) + [term1] + regex_terms[1:]
start, end = lmatch.start(), match.end()
elif loc == 'r':
# noinspection PyUnboundLocalVariable
terms = regex_terms[1:] + [term2] + list(rmatch.groups())
start, end = match.start(), rmatch.end()
elif loc == 'lr':
terms = list(lmatch.groups()) + [term1] + regex_terms[1:] + [term2] + list(rmatch.groups())
start, end = lmatch.start(), rmatch.end()
else: # this never happen
terms = regex_terms[1:]
start, end = match.start(), match.end()
string = replace(string, start, end, sub_pat(ops.ops[op]['pat'], terms))
match = ops.regex.search(string)
return string | def _operators_replace(self, string: str) -> str:
"""
Searches for first unary or binary operator (via self.op_regex
that has only one group that contain operator)
then replaces it (or escapes it if brackets do not match).
Everything until:
* space ' '
* begin/end of the string
* bracket from outer scope (like '{a/b}': term1=a term2=b)
is considered a term (contents of matching brackets '{}' are
ignored).
Attributes
----------
string: str
string to replace
"""
# noinspection PyShadowingNames
def replace(string: str, start: int, end: int, substring: str) -> str:
return string[0:start] + substring + string[end:len(string)]
# noinspection PyShadowingNames
def sub_pat(pat: Callable[[list], str] or str, terms: list) -> str:
if isinstance(pat, str):
return pat.format(*terms)
else:
return pat(terms)
count = 0
def check():
nonlocal count
count += 1
if count > self.max_while:
raise RuntimeError('Presumably while loop is stuck')
# noinspection PyShadowingNames
def null_replace(match) -> str:
regex_terms = [gr for gr in match.groups() if gr is not None]
op = regex_terms[0]
terms = regex_terms[1:]
return sub_pat(self.null_ops.ops[op]['pat'], terms)
string = self.null_ops.regex.sub(null_replace, string)
for ops, loc in [(self.pref_un_ops, 'r'), (self.postf_un_ops, 'l'),
(self.bin_centr_ops, 'lr')]:
count = 0
match = ops.regex.search(string)
while match:
check()
regex_terms = [gr for gr in match.groups() if gr is not None]
op = regex_terms[0]
loc_map = self._local_map(match, loc)
lmatch, rmatch = None, None
if loc == 'l' or loc == 'lr':
for m in ops.ops[op]['pref'].finditer(string):
if m.end() <= match.start() and loc_map[m.end() - 1] == 0:
lmatch = m
if lmatch is None:
string = replace(string, match.start(), match.end(), match.group(0).replace(op, '\\' + op))
match = ops.regex.search(string)
continue
else:
term1 = string[lmatch.end():match.start()]
if loc == 'r' or loc == 'lr':
for m in ops.ops[op]['postf'].finditer(string):
if m.start() >= match.end() and loc_map[m.start()] == 0:
rmatch = m
break
if rmatch is None:
string = replace(string, match.start(), match.end(), match.group(0).replace(op, '\\' + op))
match = ops.regex.search(string)
continue
else:
term2 = string[match.end():rmatch.start()]
if loc == 'l':
# noinspection PyUnboundLocalVariable
terms = list(lmatch.groups()) + [term1] + regex_terms[1:]
start, end = lmatch.start(), match.end()
elif loc == 'r':
# noinspection PyUnboundLocalVariable
terms = regex_terms[1:] + [term2] + list(rmatch.groups())
start, end = match.start(), rmatch.end()
elif loc == 'lr':
terms = list(lmatch.groups()) + [term1] + regex_terms[1:] + [term2] + list(rmatch.groups())
start, end = lmatch.start(), rmatch.end()
else: # this never happen
terms = regex_terms[1:]
start, end = match.start(), match.end()
string = replace(string, start, end, sub_pat(ops.ops[op]['pat'], terms))
match = ops.regex.search(string)
return string | [
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")... | 9eb13703cb02d3e2163c9c5f29df280f6bf49cec |
train | SugarTeX.replace | Extends LaTeX syntax via regex preprocess
:param src: str
LaTeX string
:return: str
New LaTeX string | sugartex/sugartex_filter.py | def replace(self, src: str) -> str:
"""
Extends LaTeX syntax via regex preprocess
:param src: str
LaTeX string
:return: str
New LaTeX string
"""
if not self.readied:
self.ready()
# Brackets + simple pre replacements:
src = self._dict_replace(self.simple_pre, src)
# Superscripts and subscripts + pre regexps:
for regex, replace in self.regex_pre:
src = regex.sub(replace, src)
# Unary and binary operators:
src = self._operators_replace(src)
# Loop regexps:
src_prev = src
for i in range(self.max_iter):
for regex, replace in self.loop_regexps:
src = regex.sub(replace, src)
if src_prev == src:
break
else:
src_prev = src
# Post regexps:
for regex, replace in self.regex_post:
src = regex.sub(replace, src)
# Simple post replacements:
src = self._dict_replace(self.simple_post, src)
# Escape characters:
src = self.escapes_regex.sub(r'\1', src)
return src | def replace(self, src: str) -> str:
"""
Extends LaTeX syntax via regex preprocess
:param src: str
LaTeX string
:return: str
New LaTeX string
"""
if not self.readied:
self.ready()
# Brackets + simple pre replacements:
src = self._dict_replace(self.simple_pre, src)
# Superscripts and subscripts + pre regexps:
for regex, replace in self.regex_pre:
src = regex.sub(replace, src)
# Unary and binary operators:
src = self._operators_replace(src)
# Loop regexps:
src_prev = src
for i in range(self.max_iter):
for regex, replace in self.loop_regexps:
src = regex.sub(replace, src)
if src_prev == src:
break
else:
src_prev = src
# Post regexps:
for regex, replace in self.regex_post:
src = regex.sub(replace, src)
# Simple post replacements:
src = self._dict_replace(self.simple_post, src)
# Escape characters:
src = self.escapes_regex.sub(r'\1', src)
return src | [
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":",
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] | kiwi0fruit/sugartex | python | https://github.com/kiwi0fruit/sugartex/blob/9eb13703cb02d3e2163c9c5f29df280f6bf49cec/sugartex/sugartex_filter.py#L863-L904 | [
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... | 9eb13703cb02d3e2163c9c5f29df280f6bf49cec |
train | plot_gaps | plot % of gaps at each position | ctbBio/strip_align.py | def plot_gaps(plot, columns):
"""
plot % of gaps at each position
"""
from plot_window import window_plot_convolve as plot_window
# plot_window([columns], len(columns)*.01, plot)
plot_window([[100 - i for i in columns]], len(columns)*.01, plot) | def plot_gaps(plot, columns):
"""
plot % of gaps at each position
"""
from plot_window import window_plot_convolve as plot_window
# plot_window([columns], len(columns)*.01, plot)
plot_window([[100 - i for i in columns]], len(columns)*.01, plot) | [
"plot",
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"at",
"each",
"position"
] | christophertbrown/bioscripts | python | https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_align.py#L11-L17 | [
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... | 83b2566b3a5745437ec651cd6cafddd056846240 |
train | strip_msa_100 | strip out columns of a MSA that represent gaps for X percent (threshold) of sequences | ctbBio/strip_align.py | def strip_msa_100(msa, threshold, plot = False):
"""
strip out columns of a MSA that represent gaps for X percent (threshold) of sequences
"""
msa = [seq for seq in parse_fasta(msa)]
columns = [[0, 0] for pos in msa[0][1]] # [[#bases, #gaps], [#bases, #gaps], ...]
for seq in msa:
for position, base in enumerate(seq[1]):
if base == '-' or base == '.':
columns[position][1] += 1
else:
columns[position][0] += 1
columns = [float(float(g)/float(g+b)*100) for b, g in columns] # convert to percent gaps
for seq in msa:
stripped = []
for position, base in enumerate(seq[1]):
if columns[position] < threshold:
stripped.append(base)
yield [seq[0], ''.join(stripped)]
if plot is not False:
plot_gaps(plot, columns) | def strip_msa_100(msa, threshold, plot = False):
"""
strip out columns of a MSA that represent gaps for X percent (threshold) of sequences
"""
msa = [seq for seq in parse_fasta(msa)]
columns = [[0, 0] for pos in msa[0][1]] # [[#bases, #gaps], [#bases, #gaps], ...]
for seq in msa:
for position, base in enumerate(seq[1]):
if base == '-' or base == '.':
columns[position][1] += 1
else:
columns[position][0] += 1
columns = [float(float(g)/float(g+b)*100) for b, g in columns] # convert to percent gaps
for seq in msa:
stripped = []
for position, base in enumerate(seq[1]):
if columns[position] < threshold:
stripped.append(base)
yield [seq[0], ''.join(stripped)]
if plot is not False:
plot_gaps(plot, columns) | [
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] | christophertbrown/bioscripts | python | https://github.com/christophertbrown/bioscripts/blob/83b2566b3a5745437ec651cd6cafddd056846240/ctbBio/strip_align.py#L19-L39 | [
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... | 83b2566b3a5745437ec651cd6cafddd056846240 |
train | sample_group | Iterate through all categories in an OrderedDict and return category name if SampleID
present in that category.
:type sid: str
:param sid: SampleID from dataset.
:type groups: OrderedDict
:param groups: Returned dict from phylotoast.util.gather_categories() function.
:return type: str
:return: Category name used to classify `sid`. | bin/extract_shared_or_unique_otuids.py | def sample_group(sid, groups):
"""
Iterate through all categories in an OrderedDict and return category name if SampleID
present in that category.
:type sid: str
:param sid: SampleID from dataset.
:type groups: OrderedDict
:param groups: Returned dict from phylotoast.util.gather_categories() function.
:return type: str
:return: Category name used to classify `sid`.
"""
for name in groups:
if sid in groups[name].sids:
return name | def sample_group(sid, groups):
"""
Iterate through all categories in an OrderedDict and return category name if SampleID
present in that category.
:type sid: str
:param sid: SampleID from dataset.
:type groups: OrderedDict
:param groups: Returned dict from phylotoast.util.gather_categories() function.
:return type: str
:return: Category name used to classify `sid`.
"""
for name in groups:
if sid in groups[name].sids:
return name | [
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"present",
"in",
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"."
] | smdabdoub/phylotoast | python | https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/extract_shared_or_unique_otuids.py#L22-L38 | [
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] | 0b74ef171e6a84761710548501dfac71285a58a3 |
train | combine_sets | Combine multiple sets to create a single larger set. | bin/extract_shared_or_unique_otuids.py | def combine_sets(*sets):
"""
Combine multiple sets to create a single larger set.
"""
combined = set()
for s in sets:
combined.update(s)
return combined | def combine_sets(*sets):
"""
Combine multiple sets to create a single larger set.
"""
combined = set()
for s in sets:
combined.update(s)
return combined | [
"Combine",
"multiple",
"sets",
"to",
"create",
"a",
"single",
"larger",
"set",
"."
] | smdabdoub/phylotoast | python | https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/extract_shared_or_unique_otuids.py#L41-L48 | [
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"(",
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")",
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] | 0b74ef171e6a84761710548501dfac71285a58a3 |
train | unique_otuids | Get unique OTUIDs of each category.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on category name and unique OTUIDs as values. | bin/extract_shared_or_unique_otuids.py | def unique_otuids(groups):
"""
Get unique OTUIDs of each category.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on category name and unique OTUIDs as values.
"""
uniques = {key: set() for key in groups}
for i, group in enumerate(groups):
to_combine = groups.values()[:i]+groups.values()[i+1:]
combined = combine_sets(*to_combine)
uniques[group] = groups[group].difference(combined)
return uniques | def unique_otuids(groups):
"""
Get unique OTUIDs of each category.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on category name and unique OTUIDs as values.
"""
uniques = {key: set() for key in groups}
for i, group in enumerate(groups):
to_combine = groups.values()[:i]+groups.values()[i+1:]
combined = combine_sets(*to_combine)
uniques[group] = groups[group].difference(combined)
return uniques | [
"Get",
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"OTUIDs",
"of",
"each",
"category",
"."
] | smdabdoub/phylotoast | python | https://github.com/smdabdoub/phylotoast/blob/0b74ef171e6a84761710548501dfac71285a58a3/bin/extract_shared_or_unique_otuids.py#L51-L66 | [
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... | 0b74ef171e6a84761710548501dfac71285a58a3 |
train | shared_otuids | Get shared OTUIDs between all unique combinations of groups.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on group combination and their shared OTUIDs as values. | bin/extract_shared_or_unique_otuids.py | def shared_otuids(groups):
"""
Get shared OTUIDs between all unique combinations of groups.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on group combination and their shared OTUIDs as values.
"""
for g in sorted(groups):
print("Number of OTUs in {0}: {1}".format(g, len(groups[g].results["otuids"])))
number_of_categories = len(groups)
shared = defaultdict()
for i in range(2, number_of_categories+1):
for j in combinations(sorted(groups), i):
combo_name = " & ".join(list(j))
for grp in j:
# initialize combo values
shared[combo_name] = groups[j[0]].results["otuids"].copy()
"""iterate through all groups and keep updating combo OTUIDs with set
intersection_update"""
for grp in j[1:]:
shared[combo_name].intersection_update(groups[grp].results["otuids"])
return shared | def shared_otuids(groups):
"""
Get shared OTUIDs between all unique combinations of groups.
:type groups: Dict
:param groups: {Category name: OTUIDs in category}
:return type: dict
:return: Dict keyed on group combination and their shared OTUIDs as values.
"""
for g in sorted(groups):
print("Number of OTUs in {0}: {1}".format(g, len(groups[g].results["otuids"])))
number_of_categories = len(groups)
shared = defaultdict()
for i in range(2, number_of_categories+1):
for j in combinations(sorted(groups), i):
combo_name = " & ".join(list(j))
for grp in j:
# initialize combo values
shared[combo_name] = groups[j[0]].results["otuids"].copy()
"""iterate through all groups and keep updating combo OTUIDs with set
intersection_update"""
for grp in j[1:]:
shared[combo_name].intersection_update(groups[grp].results["otuids"])
return shared | [
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train | write_uniques | Given a path, the method writes out one file for each group name in the
uniques dictionary with the file name in the pattern
PATH/prefix_group.txt
with each file containing the unique OTUIDs found when comparing that group
to all the other groups in uniques.
:type path: str
:param path: Output files will be saved in this PATH.
:type prefix: str
:param prefix: Prefix name added in front of output filename.
:type uniques: dict
:param uniques: Output from unique_otus() function. | bin/extract_shared_or_unique_otuids.py | def write_uniques(path, prefix, uniques):
"""
Given a path, the method writes out one file for each group name in the
uniques dictionary with the file name in the pattern
PATH/prefix_group.txt
with each file containing the unique OTUIDs found when comparing that group
to all the other groups in uniques.
:type path: str
:param path: Output files will be saved in this PATH.
:type prefix: str
:param prefix: Prefix name added in front of output filename.
:type uniques: dict
:param uniques: Output from unique_otus() function.
"""
for group in uniques:
fp = osp.join(path, "{}_{}.txt".format(prefix, group))
with open(fp, "w") as outf:
outf.write("\n".join(uniques[group])) | def write_uniques(path, prefix, uniques):
"""
Given a path, the method writes out one file for each group name in the
uniques dictionary with the file name in the pattern
PATH/prefix_group.txt
with each file containing the unique OTUIDs found when comparing that group
to all the other groups in uniques.
:type path: str
:param path: Output files will be saved in this PATH.
:type prefix: str
:param prefix: Prefix name added in front of output filename.
:type uniques: dict
:param uniques: Output from unique_otus() function.
"""
for group in uniques:
fp = osp.join(path, "{}_{}.txt".format(prefix, group))
with open(fp, "w") as outf:
outf.write("\n".join(uniques[group])) | [
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"open"... | 0b74ef171e6a84761710548501dfac71285a58a3 |
train | storeFASTA | Parse the records in a FASTA-format file by first reading the entire file into memory.
:type source: path to FAST file or open file handle
:param source: The data source from which to parse the FASTA records. Expects the
input to resolve to a collection that can be iterated through, such as
an open file handle.
:rtype: tuple
:return: FASTA records containing entries for id, description and data. | phylotoast/util.py | def storeFASTA(fastaFNH):
"""
Parse the records in a FASTA-format file by first reading the entire file into memory.
:type source: path to FAST file or open file handle
:param source: The data source from which to parse the FASTA records. Expects the
input to resolve to a collection that can be iterated through, such as
an open file handle.
:rtype: tuple
:return: FASTA records containing entries for id, description and data.
"""
fasta = file_handle(fastaFNH).read()
return [FASTARecord(rec[0].split()[0], rec[0].split(None, 1)[1], "".join(rec[1:]))
for rec in (x.strip().split("\n") for x in fasta.split(">")[1:])] | def storeFASTA(fastaFNH):
"""
Parse the records in a FASTA-format file by first reading the entire file into memory.
:type source: path to FAST file or open file handle
:param source: The data source from which to parse the FASTA records. Expects the
input to resolve to a collection that can be iterated through, such as
an open file handle.
:rtype: tuple
:return: FASTA records containing entries for id, description and data.
"""
fasta = file_handle(fastaFNH).read()
return [FASTARecord(rec[0].split()[0], rec[0].split(None, 1)[1], "".join(rec[1:]))
for rec in (x.strip().split("\n") for x in fasta.split(">")[1:])] | [
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train | parseFASTA | Parse the records in a FASTA-format file keeping the file open, and reading through
one line at a time.
:type source: path to FAST file or open file handle
:param source: The data source from which to parse the FASTA records.
Expects the input to resolve to a collection that can be iterated
through, such as an open file handle.
:rtype: tuple
:return: FASTA records containing entries for id, description and data. | phylotoast/util.py | def parseFASTA(fastaFNH):
"""
Parse the records in a FASTA-format file keeping the file open, and reading through
one line at a time.
:type source: path to FAST file or open file handle
:param source: The data source from which to parse the FASTA records.
Expects the input to resolve to a collection that can be iterated
through, such as an open file handle.
:rtype: tuple
:return: FASTA records containing entries for id, description and data.
"""
recs = []
seq = []
seqID = ""
descr = ""
for line in file_handle(fastaFNH):
line = line.strip()
if line[0] == ";":
continue
if line[0] == ">":
# conclude previous record
if seq:
recs.append(FASTARecord(seqID, descr, "".join(seq)))
seq = []
# start new record
line = line[1:].split(None, 1)
seqID, descr = line[0], line[1]
else:
seq.append(line)
# catch last seq in file
if seq:
recs.append(FASTARecord(seqID, descr, "".join(seq)))
return recs | def parseFASTA(fastaFNH):
"""
Parse the records in a FASTA-format file keeping the file open, and reading through
one line at a time.
:type source: path to FAST file or open file handle
:param source: The data source from which to parse the FASTA records.
Expects the input to resolve to a collection that can be iterated
through, such as an open file handle.
:rtype: tuple
:return: FASTA records containing entries for id, description and data.
"""
recs = []
seq = []
seqID = ""
descr = ""
for line in file_handle(fastaFNH):
line = line.strip()
if line[0] == ";":
continue
if line[0] == ">":
# conclude previous record
if seq:
recs.append(FASTARecord(seqID, descr, "".join(seq)))
seq = []
# start new record
line = line[1:].split(None, 1)
seqID, descr = line[0], line[1]
else:
seq.append(line)
# catch last seq in file
if seq:
recs.append(FASTARecord(seqID, descr, "".join(seq)))
return recs | [
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"... | 0b74ef171e6a84761710548501dfac71285a58a3 |
train | parse_map_file | Opens a QIIME mapping file and stores the contents in a dictionary keyed on SampleID
(default) or a user-supplied one. The only required fields are SampleID,
BarcodeSequence, LinkerPrimerSequence (in that order), and Description
(which must be the final field).
:type mapFNH: str
:param mapFNH: Either the full path to the map file or an open file handle
:rtype: tuple, dict
:return: A tuple of header line for mapping file and a map associating each line of
the mapping file with the appropriate sample ID (each value of the map also
contains the sample ID). An OrderedDict is used for mapping so the returned
map is guaranteed to have the same order as the input file.
Example data:
#SampleID BarcodeSequence LinkerPrimerSequence State Description
11.V13 ACGCTCGACA GTTTGATCCTGGCTCAG Disease Rat_Oral | phylotoast/util.py | def parse_map_file(mapFNH):
"""
Opens a QIIME mapping file and stores the contents in a dictionary keyed on SampleID
(default) or a user-supplied one. The only required fields are SampleID,
BarcodeSequence, LinkerPrimerSequence (in that order), and Description
(which must be the final field).
:type mapFNH: str
:param mapFNH: Either the full path to the map file or an open file handle
:rtype: tuple, dict
:return: A tuple of header line for mapping file and a map associating each line of
the mapping file with the appropriate sample ID (each value of the map also
contains the sample ID). An OrderedDict is used for mapping so the returned
map is guaranteed to have the same order as the input file.
Example data:
#SampleID BarcodeSequence LinkerPrimerSequence State Description
11.V13 ACGCTCGACA GTTTGATCCTGGCTCAG Disease Rat_Oral
"""
m = OrderedDict()
map_header = None
with file_handle(mapFNH) as mapF:
for line in mapF:
if line.startswith("#SampleID"):
map_header = line.strip().split("\t")
if line.startswith("#") or not line:
continue
line = line.strip().split("\t")
m[line[0]] = line
return map_header, m | def parse_map_file(mapFNH):
"""
Opens a QIIME mapping file and stores the contents in a dictionary keyed on SampleID
(default) or a user-supplied one. The only required fields are SampleID,
BarcodeSequence, LinkerPrimerSequence (in that order), and Description
(which must be the final field).
:type mapFNH: str
:param mapFNH: Either the full path to the map file or an open file handle
:rtype: tuple, dict
:return: A tuple of header line for mapping file and a map associating each line of
the mapping file with the appropriate sample ID (each value of the map also
contains the sample ID). An OrderedDict is used for mapping so the returned
map is guaranteed to have the same order as the input file.
Example data:
#SampleID BarcodeSequence LinkerPrimerSequence State Description
11.V13 ACGCTCGACA GTTTGATCCTGGCTCAG Disease Rat_Oral
"""
m = OrderedDict()
map_header = None
with file_handle(mapFNH) as mapF:
for line in mapF:
if line.startswith("#SampleID"):
map_header = line.strip().split("\t")
if line.startswith("#") or not line:
continue
line = line.strip().split("\t")
m[line[0]] = line
return map_header, m | [
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valid | learn | Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function. | baselines/deepq/deepq.py | def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, _ = env.step(env_action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act | def learn(env,
network,
seed=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=100,
checkpoint_freq=10000,
checkpoint_path=None,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
param_noise=False,
callback=None,
load_path=None,
**network_kwargs
):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = total_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * total_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
reset = True
with tempfile.TemporaryDirectory() as td:
td = checkpoint_path or td
model_file = os.path.join(td, "model")
model_saved = False
if tf.train.latest_checkpoint(td) is not None:
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
for t in range(total_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(env.action_space.n))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
action = act(np.array(obs)[None], update_eps=update_eps, **kwargs)[0]
env_action = action
reset = False
new_obs, rew, done, _ = env.step(env_action)
# Store transition in the replay buffer.
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
episode_rewards[-1] += rew
if done:
obs = env.reset()
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
load_variables(model_file)
return act | [
"Train",
"a",
"deepq",
"model",
"."
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L95-L333 | [
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... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | ActWrapper.save_act | Save model to a pickle located at `path` | baselines/deepq/deepq.py | def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_variables(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f) | def save_act(self, path=None):
"""Save model to a pickle located at `path`"""
if path is None:
path = os.path.join(logger.get_dir(), "model.pkl")
with tempfile.TemporaryDirectory() as td:
save_variables(os.path.join(td, "model"))
arc_name = os.path.join(td, "packed.zip")
with zipfile.ZipFile(arc_name, 'w') as zipf:
for root, dirs, files in os.walk(td):
for fname in files:
file_path = os.path.join(root, fname)
if file_path != arc_name:
zipf.write(file_path, os.path.relpath(file_path, td))
with open(arc_name, "rb") as f:
model_data = f.read()
with open(path, "wb") as f:
cloudpickle.dump((model_data, self._act_params), f) | [
"Save",
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"to",
"a",
"pickle",
"located",
"at",
"path"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/deepq.py#L55-L72 | [
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"Temporar... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | nature_cnn | CNN from Nature paper. | baselines/common/models.py | def nature_cnn(unscaled_images, **conv_kwargs):
"""
CNN from Nature paper.
"""
scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
activ = tf.nn.relu
h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
**conv_kwargs))
h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))
h3 = conv_to_fc(h3)
return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2))) | def nature_cnn(unscaled_images, **conv_kwargs):
"""
CNN from Nature paper.
"""
scaled_images = tf.cast(unscaled_images, tf.float32) / 255.
activ = tf.nn.relu
h = activ(conv(scaled_images, 'c1', nf=32, rf=8, stride=4, init_scale=np.sqrt(2),
**conv_kwargs))
h2 = activ(conv(h, 'c2', nf=64, rf=4, stride=2, init_scale=np.sqrt(2), **conv_kwargs))
h3 = activ(conv(h2, 'c3', nf=64, rf=3, stride=1, init_scale=np.sqrt(2), **conv_kwargs))
h3 = conv_to_fc(h3)
return activ(fc(h3, 'fc1', nh=512, init_scale=np.sqrt(2))) | [
"CNN",
"from",
"Nature",
"paper",
"."
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/models.py#L16-L27 | [
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"... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | mlp | Stack of fully-connected layers to be used in a policy / q-function approximator
Parameters:
----------
num_layers: int number of fully-connected layers (default: 2)
num_hidden: int size of fully-connected layers (default: 64)
activation: activation function (default: tf.tanh)
Returns:
-------
function that builds fully connected network with a given input tensor / placeholder | baselines/common/models.py | def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False):
"""
Stack of fully-connected layers to be used in a policy / q-function approximator
Parameters:
----------
num_layers: int number of fully-connected layers (default: 2)
num_hidden: int size of fully-connected layers (default: 64)
activation: activation function (default: tf.tanh)
Returns:
-------
function that builds fully connected network with a given input tensor / placeholder
"""
def network_fn(X):
h = tf.layers.flatten(X)
for i in range(num_layers):
h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2))
if layer_norm:
h = tf.contrib.layers.layer_norm(h, center=True, scale=True)
h = activation(h)
return h
return network_fn | def mlp(num_layers=2, num_hidden=64, activation=tf.tanh, layer_norm=False):
"""
Stack of fully-connected layers to be used in a policy / q-function approximator
Parameters:
----------
num_layers: int number of fully-connected layers (default: 2)
num_hidden: int size of fully-connected layers (default: 64)
activation: activation function (default: tf.tanh)
Returns:
-------
function that builds fully connected network with a given input tensor / placeholder
"""
def network_fn(X):
h = tf.layers.flatten(X)
for i in range(num_layers):
h = fc(h, 'mlp_fc{}'.format(i), nh=num_hidden, init_scale=np.sqrt(2))
if layer_norm:
h = tf.contrib.layers.layer_norm(h, center=True, scale=True)
h = activation(h)
return h
return network_fn | [
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"a",
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"/",
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"-",
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"approximator"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/models.py#L31-L59 | [
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... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | lstm | Builds LSTM (Long-Short Term Memory) network to be used in a policy.
Note that the resulting function returns not only the output of the LSTM
(i.e. hidden state of lstm for each step in the sequence), but also a dictionary
with auxiliary tensors to be set as policy attributes.
Specifically,
S is a placeholder to feed current state (LSTM state has to be managed outside policy)
M is a placeholder for the mask (used to mask out observations after the end of the episode, but can be used for other purposes too)
initial_state is a numpy array containing initial lstm state (usually zeros)
state is the output LSTM state (to be fed into S at the next call)
An example of usage of lstm-based policy can be found here: common/tests/test_doc_examples.py/test_lstm_example
Parameters:
----------
nlstm: int LSTM hidden state size
layer_norm: bool if True, layer-normalized version of LSTM is used
Returns:
-------
function that builds LSTM with a given input tensor / placeholder | baselines/common/models.py | def lstm(nlstm=128, layer_norm=False):
"""
Builds LSTM (Long-Short Term Memory) network to be used in a policy.
Note that the resulting function returns not only the output of the LSTM
(i.e. hidden state of lstm for each step in the sequence), but also a dictionary
with auxiliary tensors to be set as policy attributes.
Specifically,
S is a placeholder to feed current state (LSTM state has to be managed outside policy)
M is a placeholder for the mask (used to mask out observations after the end of the episode, but can be used for other purposes too)
initial_state is a numpy array containing initial lstm state (usually zeros)
state is the output LSTM state (to be fed into S at the next call)
An example of usage of lstm-based policy can be found here: common/tests/test_doc_examples.py/test_lstm_example
Parameters:
----------
nlstm: int LSTM hidden state size
layer_norm: bool if True, layer-normalized version of LSTM is used
Returns:
-------
function that builds LSTM with a given input tensor / placeholder
"""
def network_fn(X, nenv=1):
nbatch = X.shape[0]
nsteps = nbatch // nenv
h = tf.layers.flatten(X)
M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states
xs = batch_to_seq(h, nenv, nsteps)
ms = batch_to_seq(M, nenv, nsteps)
if layer_norm:
h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
else:
h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)
h = seq_to_batch(h5)
initial_state = np.zeros(S.shape.as_list(), dtype=float)
return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
return network_fn | def lstm(nlstm=128, layer_norm=False):
"""
Builds LSTM (Long-Short Term Memory) network to be used in a policy.
Note that the resulting function returns not only the output of the LSTM
(i.e. hidden state of lstm for each step in the sequence), but also a dictionary
with auxiliary tensors to be set as policy attributes.
Specifically,
S is a placeholder to feed current state (LSTM state has to be managed outside policy)
M is a placeholder for the mask (used to mask out observations after the end of the episode, but can be used for other purposes too)
initial_state is a numpy array containing initial lstm state (usually zeros)
state is the output LSTM state (to be fed into S at the next call)
An example of usage of lstm-based policy can be found here: common/tests/test_doc_examples.py/test_lstm_example
Parameters:
----------
nlstm: int LSTM hidden state size
layer_norm: bool if True, layer-normalized version of LSTM is used
Returns:
-------
function that builds LSTM with a given input tensor / placeholder
"""
def network_fn(X, nenv=1):
nbatch = X.shape[0]
nsteps = nbatch // nenv
h = tf.layers.flatten(X)
M = tf.placeholder(tf.float32, [nbatch]) #mask (done t-1)
S = tf.placeholder(tf.float32, [nenv, 2*nlstm]) #states
xs = batch_to_seq(h, nenv, nsteps)
ms = batch_to_seq(M, nenv, nsteps)
if layer_norm:
h5, snew = utils.lnlstm(xs, ms, S, scope='lnlstm', nh=nlstm)
else:
h5, snew = utils.lstm(xs, ms, S, scope='lstm', nh=nlstm)
h = seq_to_batch(h5)
initial_state = np.zeros(S.shape.as_list(), dtype=float)
return h, {'S':S, 'M':M, 'state':snew, 'initial_state':initial_state}
return network_fn | [
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... | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/models.py#L84-L135 | [
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"="... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | conv_only | convolutions-only net
Parameters:
----------
conv: list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
Returns:
function that takes tensorflow tensor as input and returns the output of the last convolutional layer | baselines/common/models.py | def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
'''
convolutions-only net
Parameters:
----------
conv: list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
Returns:
function that takes tensorflow tensor as input and returns the output of the last convolutional layer
'''
def network_fn(X):
out = tf.cast(X, tf.float32) / 255.
with tf.variable_scope("convnet"):
for num_outputs, kernel_size, stride in convs:
out = layers.convolution2d(out,
num_outputs=num_outputs,
kernel_size=kernel_size,
stride=stride,
activation_fn=tf.nn.relu,
**conv_kwargs)
return out
return network_fn | def conv_only(convs=[(32, 8, 4), (64, 4, 2), (64, 3, 1)], **conv_kwargs):
'''
convolutions-only net
Parameters:
----------
conv: list of triples (filter_number, filter_size, stride) specifying parameters for each layer.
Returns:
function that takes tensorflow tensor as input and returns the output of the last convolutional layer
'''
def network_fn(X):
out = tf.cast(X, tf.float32) / 255.
with tf.variable_scope("convnet"):
for num_outputs, kernel_size, stride in convs:
out = layers.convolution2d(out,
num_outputs=num_outputs,
kernel_size=kernel_size,
stride=stride,
activation_fn=tf.nn.relu,
**conv_kwargs)
return out
return network_fn | [
"convolutions",
"-",
"only",
"net"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/models.py#L171-L198 | [
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valid | get_network_builder | If you want to register your own network outside models.py, you just need:
Usage Example:
-------------
from baselines.common.models import register
@register("your_network_name")
def your_network_define(**net_kwargs):
...
return network_fn | baselines/common/models.py | def get_network_builder(name):
"""
If you want to register your own network outside models.py, you just need:
Usage Example:
-------------
from baselines.common.models import register
@register("your_network_name")
def your_network_define(**net_kwargs):
...
return network_fn
"""
if callable(name):
return name
elif name in mapping:
return mapping[name]
else:
raise ValueError('Unknown network type: {}'.format(name)) | def get_network_builder(name):
"""
If you want to register your own network outside models.py, you just need:
Usage Example:
-------------
from baselines.common.models import register
@register("your_network_name")
def your_network_define(**net_kwargs):
...
return network_fn
"""
if callable(name):
return name
elif name in mapping:
return mapping[name]
else:
raise ValueError('Unknown network type: {}'.format(name)) | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/models.py#L206-L224 | [
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valid | mlp | This model takes as input an observation and returns values of all actions.
Parameters
----------
hiddens: [int]
list of sizes of hidden layers
layer_norm: bool
if true applies layer normalization for every layer
as described in https://arxiv.org/abs/1607.06450
Returns
-------
q_func: function
q_function for DQN algorithm. | baselines/deepq/models.py | def mlp(hiddens=[], layer_norm=False):
"""This model takes as input an observation and returns values of all actions.
Parameters
----------
hiddens: [int]
list of sizes of hidden layers
layer_norm: bool
if true applies layer normalization for every layer
as described in https://arxiv.org/abs/1607.06450
Returns
-------
q_func: function
q_function for DQN algorithm.
"""
return lambda *args, **kwargs: _mlp(hiddens, layer_norm=layer_norm, *args, **kwargs) | def mlp(hiddens=[], layer_norm=False):
"""This model takes as input an observation and returns values of all actions.
Parameters
----------
hiddens: [int]
list of sizes of hidden layers
layer_norm: bool
if true applies layer normalization for every layer
as described in https://arxiv.org/abs/1607.06450
Returns
-------
q_func: function
q_function for DQN algorithm.
"""
return lambda *args, **kwargs: _mlp(hiddens, layer_norm=layer_norm, *args, **kwargs) | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/models.py#L17-L33 | [
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valid | cnn_to_mlp | This model takes as input an observation and returns values of all actions.
Parameters
----------
convs: [(int, int, int)]
list of convolutional layers in form of
(num_outputs, kernel_size, stride)
hiddens: [int]
list of sizes of hidden layers
dueling: bool
if true double the output MLP to compute a baseline
for action scores
layer_norm: bool
if true applies layer normalization for every layer
as described in https://arxiv.org/abs/1607.06450
Returns
-------
q_func: function
q_function for DQN algorithm. | baselines/deepq/models.py | def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
"""This model takes as input an observation and returns values of all actions.
Parameters
----------
convs: [(int, int, int)]
list of convolutional layers in form of
(num_outputs, kernel_size, stride)
hiddens: [int]
list of sizes of hidden layers
dueling: bool
if true double the output MLP to compute a baseline
for action scores
layer_norm: bool
if true applies layer normalization for every layer
as described in https://arxiv.org/abs/1607.06450
Returns
-------
q_func: function
q_function for DQN algorithm.
"""
return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs) | def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
"""This model takes as input an observation and returns values of all actions.
Parameters
----------
convs: [(int, int, int)]
list of convolutional layers in form of
(num_outputs, kernel_size, stride)
hiddens: [int]
list of sizes of hidden layers
dueling: bool
if true double the output MLP to compute a baseline
for action scores
layer_norm: bool
if true applies layer normalization for every layer
as described in https://arxiv.org/abs/1607.06450
Returns
-------
q_func: function
q_function for DQN algorithm.
"""
return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs) | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/deepq/models.py#L73-L96 | [
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",",... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | make_vec_env | Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo. | baselines/common/cmd_util.py | def make_vec_env(env_id, env_type, num_env, seed,
wrapper_kwargs=None,
start_index=0,
reward_scale=1.0,
flatten_dict_observations=True,
gamestate=None):
"""
Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo.
"""
wrapper_kwargs = wrapper_kwargs or {}
mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
seed = seed + 10000 * mpi_rank if seed is not None else None
logger_dir = logger.get_dir()
def make_thunk(rank):
return lambda: make_env(
env_id=env_id,
env_type=env_type,
mpi_rank=mpi_rank,
subrank=rank,
seed=seed,
reward_scale=reward_scale,
gamestate=gamestate,
flatten_dict_observations=flatten_dict_observations,
wrapper_kwargs=wrapper_kwargs,
logger_dir=logger_dir
)
set_global_seeds(seed)
if num_env > 1:
return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])
else:
return DummyVecEnv([make_thunk(start_index)]) | def make_vec_env(env_id, env_type, num_env, seed,
wrapper_kwargs=None,
start_index=0,
reward_scale=1.0,
flatten_dict_observations=True,
gamestate=None):
"""
Create a wrapped, monitored SubprocVecEnv for Atari and MuJoCo.
"""
wrapper_kwargs = wrapper_kwargs or {}
mpi_rank = MPI.COMM_WORLD.Get_rank() if MPI else 0
seed = seed + 10000 * mpi_rank if seed is not None else None
logger_dir = logger.get_dir()
def make_thunk(rank):
return lambda: make_env(
env_id=env_id,
env_type=env_type,
mpi_rank=mpi_rank,
subrank=rank,
seed=seed,
reward_scale=reward_scale,
gamestate=gamestate,
flatten_dict_observations=flatten_dict_observations,
wrapper_kwargs=wrapper_kwargs,
logger_dir=logger_dir
)
set_global_seeds(seed)
if num_env > 1:
return SubprocVecEnv([make_thunk(i + start_index) for i in range(num_env)])
else:
return DummyVecEnv([make_thunk(start_index)]) | [
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"and",
"MuJoCo",
"."
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cmd_util.py#L21-L52 | [
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valid | make_mujoco_env | Create a wrapped, monitored gym.Env for MuJoCo. | baselines/common/cmd_util.py | def make_mujoco_env(env_id, seed, reward_scale=1.0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
rank = MPI.COMM_WORLD.Get_rank()
myseed = seed + 1000 * rank if seed is not None else None
set_global_seeds(myseed)
env = gym.make(env_id)
logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank))
env = Monitor(env, logger_path, allow_early_resets=True)
env.seed(seed)
if reward_scale != 1.0:
from baselines.common.retro_wrappers import RewardScaler
env = RewardScaler(env, reward_scale)
return env | def make_mujoco_env(env_id, seed, reward_scale=1.0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
rank = MPI.COMM_WORLD.Get_rank()
myseed = seed + 1000 * rank if seed is not None else None
set_global_seeds(myseed)
env = gym.make(env_id)
logger_path = None if logger.get_dir() is None else os.path.join(logger.get_dir(), str(rank))
env = Monitor(env, logger_path, allow_early_resets=True)
env.seed(seed)
if reward_scale != 1.0:
from baselines.common.retro_wrappers import RewardScaler
env = RewardScaler(env, reward_scale)
return env | [
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"monitored",
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cmd_util.py#L88-L102 | [
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valid | make_robotics_env | Create a wrapped, monitored gym.Env for MuJoCo. | baselines/common/cmd_util.py | def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success',))
env.seed(seed)
return env | def make_robotics_env(env_id, seed, rank=0):
"""
Create a wrapped, monitored gym.Env for MuJoCo.
"""
set_global_seeds(seed)
env = gym.make(env_id)
env = FlattenDictWrapper(env, ['observation', 'desired_goal'])
env = Monitor(
env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)),
info_keywords=('is_success',))
env.seed(seed)
return env | [
"Create",
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"."
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cmd_util.py#L104-L115 | [
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valid | common_arg_parser | Create an argparse.ArgumentParser for run_mujoco.py. | baselines/common/cmd_util.py | def common_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be automatically determined', type=str)
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
parser.add_argument('--num_timesteps', type=float, default=1e6),
parser.add_argument('--network', help='network type (mlp, cnn, lstm, cnn_lstm, conv_only)', default=None)
parser.add_argument('--gamestate', help='game state to load (so far only used in retro games)', default=None)
parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int)
parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float)
parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str)
parser.add_argument('--save_video_interval', help='Save video every x steps (0 = disabled)', default=0, type=int)
parser.add_argument('--save_video_length', help='Length of recorded video. Default: 200', default=200, type=int)
parser.add_argument('--play', default=False, action='store_true')
return parser | def common_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='Reacher-v2')
parser.add_argument('--env_type', help='type of environment, used when the environment type cannot be automatically determined', type=str)
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--alg', help='Algorithm', type=str, default='ppo2')
parser.add_argument('--num_timesteps', type=float, default=1e6),
parser.add_argument('--network', help='network type (mlp, cnn, lstm, cnn_lstm, conv_only)', default=None)
parser.add_argument('--gamestate', help='game state to load (so far only used in retro games)', default=None)
parser.add_argument('--num_env', help='Number of environment copies being run in parallel. When not specified, set to number of cpus for Atari, and to 1 for Mujoco', default=None, type=int)
parser.add_argument('--reward_scale', help='Reward scale factor. Default: 1.0', default=1.0, type=float)
parser.add_argument('--save_path', help='Path to save trained model to', default=None, type=str)
parser.add_argument('--save_video_interval', help='Save video every x steps (0 = disabled)', default=0, type=int)
parser.add_argument('--save_video_length', help='Length of recorded video. Default: 200', default=200, type=int)
parser.add_argument('--play', default=False, action='store_true')
return parser | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cmd_util.py#L135-L153 | [
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valid | robotics_arg_parser | Create an argparse.ArgumentParser for run_mujoco.py. | baselines/common/cmd_util.py | def robotics_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0')
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--num-timesteps', type=int, default=int(1e6))
return parser | def robotics_arg_parser():
"""
Create an argparse.ArgumentParser for run_mujoco.py.
"""
parser = arg_parser()
parser.add_argument('--env', help='environment ID', type=str, default='FetchReach-v0')
parser.add_argument('--seed', help='RNG seed', type=int, default=None)
parser.add_argument('--num-timesteps', type=int, default=int(1e6))
return parser | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cmd_util.py#L155-L163 | [
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valid | parse_unknown_args | Parse arguments not consumed by arg parser into a dicitonary | baselines/common/cmd_util.py | def parse_unknown_args(args):
"""
Parse arguments not consumed by arg parser into a dicitonary
"""
retval = {}
preceded_by_key = False
for arg in args:
if arg.startswith('--'):
if '=' in arg:
key = arg.split('=')[0][2:]
value = arg.split('=')[1]
retval[key] = value
else:
key = arg[2:]
preceded_by_key = True
elif preceded_by_key:
retval[key] = arg
preceded_by_key = False
return retval | def parse_unknown_args(args):
"""
Parse arguments not consumed by arg parser into a dicitonary
"""
retval = {}
preceded_by_key = False
for arg in args:
if arg.startswith('--'):
if '=' in arg:
key = arg.split('=')[0][2:]
value = arg.split('=')[1]
retval[key] = value
else:
key = arg[2:]
preceded_by_key = True
elif preceded_by_key:
retval[key] = arg
preceded_by_key = False
return retval | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cmd_util.py#L166-L185 | [
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valid | clear_mpi_env_vars | from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
Processes. | baselines/common/vec_env/vec_env.py | def clear_mpi_env_vars():
"""
from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
Processes.
"""
removed_environment = {}
for k, v in list(os.environ.items()):
for prefix in ['OMPI_', 'PMI_']:
if k.startswith(prefix):
removed_environment[k] = v
del os.environ[k]
try:
yield
finally:
os.environ.update(removed_environment) | def clear_mpi_env_vars():
"""
from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
Processes.
"""
removed_environment = {}
for k, v in list(os.environ.items()):
for prefix in ['OMPI_', 'PMI_']:
if k.startswith(prefix):
removed_environment[k] = v
del os.environ[k]
try:
yield
finally:
os.environ.update(removed_environment) | [
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... | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/vec_env/vec_env.py#L204-L219 | [
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valid | learn | Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers. | baselines/ppo2/ppo2.py | def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
save_interval=0, load_path=None, model_fn=None, **network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
policy = build_policy(env, network, **network_kwargs)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
if model_fn is None:
from baselines.ppo2.model import Model
model_fn = Model
model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,
nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if eval_env is not None:
eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
epinfobuf = deque(maxlen=100)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=100)
# Start total timer
tfirststart = time.perf_counter()
nupdates = total_timesteps//nbatch
for update in range(1, nupdates+1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.perf_counter()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
# Calculate the cliprange
cliprangenow = cliprange(frac)
# Get minibatch
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
if states is None: # nonrecurrent version
# Index of each element of batch_size
# Create the indices array
inds = np.arange(nbatch)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow, *slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# End timer
tnow = time.perf_counter()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update*nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update*nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
if eval_env is not None:
logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i'%update)
print('Saving to', savepath)
model.save(savepath)
return model | def learn(*, network, env, total_timesteps, eval_env = None, seed=None, nsteps=2048, ent_coef=0.0, lr=3e-4,
vf_coef=0.5, max_grad_norm=0.5, gamma=0.99, lam=0.95,
log_interval=10, nminibatches=4, noptepochs=4, cliprange=0.2,
save_interval=0, load_path=None, model_fn=None, **network_kwargs):
'''
Learn policy using PPO algorithm (https://arxiv.org/abs/1707.06347)
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See common/models.py/lstm for more details on using recurrent nets in policies
env: baselines.common.vec_env.VecEnv environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int number of timesteps (i.e. number of actions taken in the environment)
ent_coef: float policy entropy coefficient in the optimization objective
lr: float or function learning rate, constant or a schedule function [0,1] -> R+ where 1 is beginning of the
training and 0 is the end of the training.
vf_coef: float value function loss coefficient in the optimization objective
max_grad_norm: float or None gradient norm clipping coefficient
gamma: float discounting factor
lam: float advantage estimation discounting factor (lambda in the paper)
log_interval: int number of timesteps between logging events
nminibatches: int number of training minibatches per update. For recurrent policies,
should be smaller or equal than number of environments run in parallel.
noptepochs: int number of training epochs per update
cliprange: float or function clipping range, constant or schedule function [0,1] -> R+ where 1 is beginning of the training
and 0 is the end of the training
save_interval: int number of timesteps between saving events
load_path: str path to load the model from
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
if isinstance(lr, float): lr = constfn(lr)
else: assert callable(lr)
if isinstance(cliprange, float): cliprange = constfn(cliprange)
else: assert callable(cliprange)
total_timesteps = int(total_timesteps)
policy = build_policy(env, network, **network_kwargs)
# Get the nb of env
nenvs = env.num_envs
# Get state_space and action_space
ob_space = env.observation_space
ac_space = env.action_space
# Calculate the batch_size
nbatch = nenvs * nsteps
nbatch_train = nbatch // nminibatches
# Instantiate the model object (that creates act_model and train_model)
if model_fn is None:
from baselines.ppo2.model import Model
model_fn = Model
model = model_fn(policy=policy, ob_space=ob_space, ac_space=ac_space, nbatch_act=nenvs, nbatch_train=nbatch_train,
nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env=env, model=model, nsteps=nsteps, gamma=gamma, lam=lam)
if eval_env is not None:
eval_runner = Runner(env = eval_env, model = model, nsteps = nsteps, gamma = gamma, lam= lam)
epinfobuf = deque(maxlen=100)
if eval_env is not None:
eval_epinfobuf = deque(maxlen=100)
# Start total timer
tfirststart = time.perf_counter()
nupdates = total_timesteps//nbatch
for update in range(1, nupdates+1):
assert nbatch % nminibatches == 0
# Start timer
tstart = time.perf_counter()
frac = 1.0 - (update - 1.0) / nupdates
# Calculate the learning rate
lrnow = lr(frac)
# Calculate the cliprange
cliprangenow = cliprange(frac)
# Get minibatch
obs, returns, masks, actions, values, neglogpacs, states, epinfos = runner.run() #pylint: disable=E0632
if eval_env is not None:
eval_obs, eval_returns, eval_masks, eval_actions, eval_values, eval_neglogpacs, eval_states, eval_epinfos = eval_runner.run() #pylint: disable=E0632
epinfobuf.extend(epinfos)
if eval_env is not None:
eval_epinfobuf.extend(eval_epinfos)
# Here what we're going to do is for each minibatch calculate the loss and append it.
mblossvals = []
if states is None: # nonrecurrent version
# Index of each element of batch_size
# Create the indices array
inds = np.arange(nbatch)
for _ in range(noptepochs):
# Randomize the indexes
np.random.shuffle(inds)
# 0 to batch_size with batch_train_size step
for start in range(0, nbatch, nbatch_train):
end = start + nbatch_train
mbinds = inds[start:end]
slices = (arr[mbinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mblossvals.append(model.train(lrnow, cliprangenow, *slices))
else: # recurrent version
assert nenvs % nminibatches == 0
envsperbatch = nenvs // nminibatches
envinds = np.arange(nenvs)
flatinds = np.arange(nenvs * nsteps).reshape(nenvs, nsteps)
for _ in range(noptepochs):
np.random.shuffle(envinds)
for start in range(0, nenvs, envsperbatch):
end = start + envsperbatch
mbenvinds = envinds[start:end]
mbflatinds = flatinds[mbenvinds].ravel()
slices = (arr[mbflatinds] for arr in (obs, returns, masks, actions, values, neglogpacs))
mbstates = states[mbenvinds]
mblossvals.append(model.train(lrnow, cliprangenow, *slices, mbstates))
# Feedforward --> get losses --> update
lossvals = np.mean(mblossvals, axis=0)
# End timer
tnow = time.perf_counter()
# Calculate the fps (frame per second)
fps = int(nbatch / (tnow - tstart))
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, returns)
logger.logkv("serial_timesteps", update*nsteps)
logger.logkv("nupdates", update)
logger.logkv("total_timesteps", update*nbatch)
logger.logkv("fps", fps)
logger.logkv("explained_variance", float(ev))
logger.logkv('eprewmean', safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.logkv('eplenmean', safemean([epinfo['l'] for epinfo in epinfobuf]))
if eval_env is not None:
logger.logkv('eval_eprewmean', safemean([epinfo['r'] for epinfo in eval_epinfobuf]) )
logger.logkv('eval_eplenmean', safemean([epinfo['l'] for epinfo in eval_epinfobuf]) )
logger.logkv('time_elapsed', tnow - tfirststart)
for (lossval, lossname) in zip(lossvals, model.loss_names):
logger.logkv(lossname, lossval)
if MPI is None or MPI.COMM_WORLD.Get_rank() == 0:
logger.dumpkvs()
if save_interval and (update % save_interval == 0 or update == 1) and logger.get_dir() and (MPI is None or MPI.COMM_WORLD.Get_rank() == 0):
checkdir = osp.join(logger.get_dir(), 'checkpoints')
os.makedirs(checkdir, exist_ok=True)
savepath = osp.join(checkdir, '%.5i'%update)
print('Saving to', savepath)
model.save(savepath)
return model | [
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valid | cg | Demmel p 312 | baselines/common/cg.py | def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
"""
Demmel p 312
"""
p = b.copy()
r = b.copy()
x = np.zeros_like(b)
rdotr = r.dot(r)
fmtstr = "%10i %10.3g %10.3g"
titlestr = "%10s %10s %10s"
if verbose: print(titlestr % ("iter", "residual norm", "soln norm"))
for i in range(cg_iters):
if callback is not None:
callback(x)
if verbose: print(fmtstr % (i, rdotr, np.linalg.norm(x)))
z = f_Ax(p)
v = rdotr / p.dot(z)
x += v*p
r -= v*z
newrdotr = r.dot(r)
mu = newrdotr/rdotr
p = r + mu*p
rdotr = newrdotr
if rdotr < residual_tol:
break
if callback is not None:
callback(x)
if verbose: print(fmtstr % (i+1, rdotr, np.linalg.norm(x))) # pylint: disable=W0631
return x | def cg(f_Ax, b, cg_iters=10, callback=None, verbose=False, residual_tol=1e-10):
"""
Demmel p 312
"""
p = b.copy()
r = b.copy()
x = np.zeros_like(b)
rdotr = r.dot(r)
fmtstr = "%10i %10.3g %10.3g"
titlestr = "%10s %10s %10s"
if verbose: print(titlestr % ("iter", "residual norm", "soln norm"))
for i in range(cg_iters):
if callback is not None:
callback(x)
if verbose: print(fmtstr % (i, rdotr, np.linalg.norm(x)))
z = f_Ax(p)
v = rdotr / p.dot(z)
x += v*p
r -= v*z
newrdotr = r.dot(r)
mu = newrdotr/rdotr
p = r + mu*p
rdotr = newrdotr
if rdotr < residual_tol:
break
if callback is not None:
callback(x)
if verbose: print(fmtstr % (i+1, rdotr, np.linalg.norm(x))) # pylint: disable=W0631
return x | [
"Demmel",
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/cg.py#L2-L34 | [
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valid | observation_placeholder | Create placeholder to feed observations into of the size appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
batch_size: int size of the batch to be fed into input. Can be left None in most cases.
name: str name of the placeholder
Returns:
-------
tensorflow placeholder tensor | baselines/common/input.py | def observation_placeholder(ob_space, batch_size=None, name='Ob'):
'''
Create placeholder to feed observations into of the size appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
batch_size: int size of the batch to be fed into input. Can be left None in most cases.
name: str name of the placeholder
Returns:
-------
tensorflow placeholder tensor
'''
assert isinstance(ob_space, Discrete) or isinstance(ob_space, Box) or isinstance(ob_space, MultiDiscrete), \
'Can only deal with Discrete and Box observation spaces for now'
dtype = ob_space.dtype
if dtype == np.int8:
dtype = np.uint8
return tf.placeholder(shape=(batch_size,) + ob_space.shape, dtype=dtype, name=name) | def observation_placeholder(ob_space, batch_size=None, name='Ob'):
'''
Create placeholder to feed observations into of the size appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
batch_size: int size of the batch to be fed into input. Can be left None in most cases.
name: str name of the placeholder
Returns:
-------
tensorflow placeholder tensor
'''
assert isinstance(ob_space, Discrete) or isinstance(ob_space, Box) or isinstance(ob_space, MultiDiscrete), \
'Can only deal with Discrete and Box observation spaces for now'
dtype = ob_space.dtype
if dtype == np.int8:
dtype = np.uint8
return tf.placeholder(shape=(batch_size,) + ob_space.shape, dtype=dtype, name=name) | [
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valid | observation_input | Create placeholder to feed observations into of the size appropriate to the observation space, and add input
encoder of the appropriate type. | baselines/common/input.py | def observation_input(ob_space, batch_size=None, name='Ob'):
'''
Create placeholder to feed observations into of the size appropriate to the observation space, and add input
encoder of the appropriate type.
'''
placeholder = observation_placeholder(ob_space, batch_size, name)
return placeholder, encode_observation(ob_space, placeholder) | def observation_input(ob_space, batch_size=None, name='Ob'):
'''
Create placeholder to feed observations into of the size appropriate to the observation space, and add input
encoder of the appropriate type.
'''
placeholder = observation_placeholder(ob_space, batch_size, name)
return placeholder, encode_observation(ob_space, placeholder) | [
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valid | encode_observation | Encode input in the way that is appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
placeholder: tf.placeholder observation input placeholder | baselines/common/input.py | def encode_observation(ob_space, placeholder):
'''
Encode input in the way that is appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
placeholder: tf.placeholder observation input placeholder
'''
if isinstance(ob_space, Discrete):
return tf.to_float(tf.one_hot(placeholder, ob_space.n))
elif isinstance(ob_space, Box):
return tf.to_float(placeholder)
elif isinstance(ob_space, MultiDiscrete):
placeholder = tf.cast(placeholder, tf.int32)
one_hots = [tf.to_float(tf.one_hot(placeholder[..., i], ob_space.nvec[i])) for i in range(placeholder.shape[-1])]
return tf.concat(one_hots, axis=-1)
else:
raise NotImplementedError | def encode_observation(ob_space, placeholder):
'''
Encode input in the way that is appropriate to the observation space
Parameters:
----------
ob_space: gym.Space observation space
placeholder: tf.placeholder observation input placeholder
'''
if isinstance(ob_space, Discrete):
return tf.to_float(tf.one_hot(placeholder, ob_space.n))
elif isinstance(ob_space, Box):
return tf.to_float(placeholder)
elif isinstance(ob_space, MultiDiscrete):
placeholder = tf.cast(placeholder, tf.int32)
one_hots = [tf.to_float(tf.one_hot(placeholder[..., i], ob_space.nvec[i])) for i in range(placeholder.shape[-1])]
return tf.concat(one_hots, axis=-1)
else:
raise NotImplementedError | [
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valid | RolloutWorker.generate_rollouts | Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current
policy acting on it accordingly. | baselines/her/rollout.py | def generate_rollouts(self):
"""Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current
policy acting on it accordingly.
"""
self.reset_all_rollouts()
# compute observations
o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32) # observations
ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32) # achieved goals
o[:] = self.initial_o
ag[:] = self.initial_ag
# generate episodes
obs, achieved_goals, acts, goals, successes = [], [], [], [], []
dones = []
info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]
Qs = []
for t in range(self.T):
policy_output = self.policy.get_actions(
o, ag, self.g,
compute_Q=self.compute_Q,
noise_eps=self.noise_eps if not self.exploit else 0.,
random_eps=self.random_eps if not self.exploit else 0.,
use_target_net=self.use_target_net)
if self.compute_Q:
u, Q = policy_output
Qs.append(Q)
else:
u = policy_output
if u.ndim == 1:
# The non-batched case should still have a reasonable shape.
u = u.reshape(1, -1)
o_new = np.empty((self.rollout_batch_size, self.dims['o']))
ag_new = np.empty((self.rollout_batch_size, self.dims['g']))
success = np.zeros(self.rollout_batch_size)
# compute new states and observations
obs_dict_new, _, done, info = self.venv.step(u)
o_new = obs_dict_new['observation']
ag_new = obs_dict_new['achieved_goal']
success = np.array([i.get('is_success', 0.0) for i in info])
if any(done):
# here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done
# trick with using vecenvs is not to add the obs from the environments that are "done", because those are already observations
# after a reset
break
for i, info_dict in enumerate(info):
for idx, key in enumerate(self.info_keys):
info_values[idx][t, i] = info[i][key]
if np.isnan(o_new).any():
self.logger.warn('NaN caught during rollout generation. Trying again...')
self.reset_all_rollouts()
return self.generate_rollouts()
dones.append(done)
obs.append(o.copy())
achieved_goals.append(ag.copy())
successes.append(success.copy())
acts.append(u.copy())
goals.append(self.g.copy())
o[...] = o_new
ag[...] = ag_new
obs.append(o.copy())
achieved_goals.append(ag.copy())
episode = dict(o=obs,
u=acts,
g=goals,
ag=achieved_goals)
for key, value in zip(self.info_keys, info_values):
episode['info_{}'.format(key)] = value
# stats
successful = np.array(successes)[-1, :]
assert successful.shape == (self.rollout_batch_size,)
success_rate = np.mean(successful)
self.success_history.append(success_rate)
if self.compute_Q:
self.Q_history.append(np.mean(Qs))
self.n_episodes += self.rollout_batch_size
return convert_episode_to_batch_major(episode) | def generate_rollouts(self):
"""Performs `rollout_batch_size` rollouts in parallel for time horizon `T` with the current
policy acting on it accordingly.
"""
self.reset_all_rollouts()
# compute observations
o = np.empty((self.rollout_batch_size, self.dims['o']), np.float32) # observations
ag = np.empty((self.rollout_batch_size, self.dims['g']), np.float32) # achieved goals
o[:] = self.initial_o
ag[:] = self.initial_ag
# generate episodes
obs, achieved_goals, acts, goals, successes = [], [], [], [], []
dones = []
info_values = [np.empty((self.T - 1, self.rollout_batch_size, self.dims['info_' + key]), np.float32) for key in self.info_keys]
Qs = []
for t in range(self.T):
policy_output = self.policy.get_actions(
o, ag, self.g,
compute_Q=self.compute_Q,
noise_eps=self.noise_eps if not self.exploit else 0.,
random_eps=self.random_eps if not self.exploit else 0.,
use_target_net=self.use_target_net)
if self.compute_Q:
u, Q = policy_output
Qs.append(Q)
else:
u = policy_output
if u.ndim == 1:
# The non-batched case should still have a reasonable shape.
u = u.reshape(1, -1)
o_new = np.empty((self.rollout_batch_size, self.dims['o']))
ag_new = np.empty((self.rollout_batch_size, self.dims['g']))
success = np.zeros(self.rollout_batch_size)
# compute new states and observations
obs_dict_new, _, done, info = self.venv.step(u)
o_new = obs_dict_new['observation']
ag_new = obs_dict_new['achieved_goal']
success = np.array([i.get('is_success', 0.0) for i in info])
if any(done):
# here we assume all environments are done is ~same number of steps, so we terminate rollouts whenever any of the envs returns done
# trick with using vecenvs is not to add the obs from the environments that are "done", because those are already observations
# after a reset
break
for i, info_dict in enumerate(info):
for idx, key in enumerate(self.info_keys):
info_values[idx][t, i] = info[i][key]
if np.isnan(o_new).any():
self.logger.warn('NaN caught during rollout generation. Trying again...')
self.reset_all_rollouts()
return self.generate_rollouts()
dones.append(done)
obs.append(o.copy())
achieved_goals.append(ag.copy())
successes.append(success.copy())
acts.append(u.copy())
goals.append(self.g.copy())
o[...] = o_new
ag[...] = ag_new
obs.append(o.copy())
achieved_goals.append(ag.copy())
episode = dict(o=obs,
u=acts,
g=goals,
ag=achieved_goals)
for key, value in zip(self.info_keys, info_values):
episode['info_{}'.format(key)] = value
# stats
successful = np.array(successes)[-1, :]
assert successful.shape == (self.rollout_batch_size,)
success_rate = np.mean(successful)
self.success_history.append(success_rate)
if self.compute_Q:
self.Q_history.append(np.mean(Qs))
self.n_episodes += self.rollout_batch_size
return convert_episode_to_batch_major(episode) | [
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valid | RolloutWorker.save_policy | Pickles the current policy for later inspection. | baselines/her/rollout.py | def save_policy(self, path):
"""Pickles the current policy for later inspection.
"""
with open(path, 'wb') as f:
pickle.dump(self.policy, f) | def save_policy(self, path):
"""Pickles the current policy for later inspection.
"""
with open(path, 'wb') as f:
pickle.dump(self.policy, f) | [
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valid | RolloutWorker.logs | Generates a dictionary that contains all collected statistics. | baselines/her/rollout.py | def logs(self, prefix='worker'):
"""Generates a dictionary that contains all collected statistics.
"""
logs = []
logs += [('success_rate', np.mean(self.success_history))]
if self.compute_Q:
logs += [('mean_Q', np.mean(self.Q_history))]
logs += [('episode', self.n_episodes)]
if prefix != '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs | def logs(self, prefix='worker'):
"""Generates a dictionary that contains all collected statistics.
"""
logs = []
logs += [('success_rate', np.mean(self.success_history))]
if self.compute_Q:
logs += [('mean_Q', np.mean(self.Q_history))]
logs += [('episode', self.n_episodes)]
if prefix != '' and not prefix.endswith('/'):
return [(prefix + '/' + key, val) for key, val in logs]
else:
return logs | [
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"that",
"contains",
"all",
"collected",
"statistics",
"."
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/her/rollout.py#L157-L169 | [
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... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | smooth | Smooth signal y, where radius is determines the size of the window
mode='twosided':
average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
mode='causal':
average over the window [max(index - radius, 0), index]
valid_only: put nan in entries where the full-sized window is not available | baselines/common/plot_util.py | def smooth(y, radius, mode='two_sided', valid_only=False):
'''
Smooth signal y, where radius is determines the size of the window
mode='twosided':
average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
mode='causal':
average over the window [max(index - radius, 0), index]
valid_only: put nan in entries where the full-sized window is not available
'''
assert mode in ('two_sided', 'causal')
if len(y) < 2*radius+1:
return np.ones_like(y) * y.mean()
elif mode == 'two_sided':
convkernel = np.ones(2 * radius+1)
out = np.convolve(y, convkernel,mode='same') / np.convolve(np.ones_like(y), convkernel, mode='same')
if valid_only:
out[:radius] = out[-radius:] = np.nan
elif mode == 'causal':
convkernel = np.ones(radius)
out = np.convolve(y, convkernel,mode='full') / np.convolve(np.ones_like(y), convkernel, mode='full')
out = out[:-radius+1]
if valid_only:
out[:radius] = np.nan
return out | def smooth(y, radius, mode='two_sided', valid_only=False):
'''
Smooth signal y, where radius is determines the size of the window
mode='twosided':
average over the window [max(index - radius, 0), min(index + radius, len(y)-1)]
mode='causal':
average over the window [max(index - radius, 0), index]
valid_only: put nan in entries where the full-sized window is not available
'''
assert mode in ('two_sided', 'causal')
if len(y) < 2*radius+1:
return np.ones_like(y) * y.mean()
elif mode == 'two_sided':
convkernel = np.ones(2 * radius+1)
out = np.convolve(y, convkernel,mode='same') / np.convolve(np.ones_like(y), convkernel, mode='same')
if valid_only:
out[:radius] = out[-radius:] = np.nan
elif mode == 'causal':
convkernel = np.ones(radius)
out = np.convolve(y, convkernel,mode='full') / np.convolve(np.ones_like(y), convkernel, mode='full')
out = out[:-radius+1]
if valid_only:
out[:radius] = np.nan
return out | [
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"is",
"determines",
"the",
"size",
"of",
"the",
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/plot_util.py#L11-L37 | [
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... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | one_sided_ema | perform one-sided (causal) EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arguments:
xolds: array or list - x values of data. Needs to be sorted in ascending order
yolds: array of list - y values of data. Has to have the same length as xolds
low: float - min value of the new x grid. By default equals to xolds[0]
high: float - max value of the new x grid. By default equals to xolds[-1]
n: int - number of points in new x grid
decay_steps: float - EMA decay factor, expressed in new x grid steps.
low_counts_threshold: float or int
- y values with counts less than this value will be set to NaN
Returns:
tuple sum_ys, count_ys where
xs - array with new x grid
ys - array of EMA of y at each point of the new x grid
count_ys - array of EMA of y counts at each point of the new x grid | baselines/common/plot_util.py | def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
'''
perform one-sided (causal) EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arguments:
xolds: array or list - x values of data. Needs to be sorted in ascending order
yolds: array of list - y values of data. Has to have the same length as xolds
low: float - min value of the new x grid. By default equals to xolds[0]
high: float - max value of the new x grid. By default equals to xolds[-1]
n: int - number of points in new x grid
decay_steps: float - EMA decay factor, expressed in new x grid steps.
low_counts_threshold: float or int
- y values with counts less than this value will be set to NaN
Returns:
tuple sum_ys, count_ys where
xs - array with new x grid
ys - array of EMA of y at each point of the new x grid
count_ys - array of EMA of y counts at each point of the new x grid
'''
low = xolds[0] if low is None else low
high = xolds[-1] if high is None else high
assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0])
assert xolds[-1] >= high, 'high = {} > xolds[-1] = {} - extrapolation not permitted!'.format(high, xolds[-1])
assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds))
xolds = xolds.astype('float64')
yolds = yolds.astype('float64')
luoi = 0 # last unused old index
sum_y = 0.
count_y = 0.
xnews = np.linspace(low, high, n)
decay_period = (high - low) / (n - 1) * decay_steps
interstep_decay = np.exp(- 1. / decay_steps)
sum_ys = np.zeros_like(xnews)
count_ys = np.zeros_like(xnews)
for i in range(n):
xnew = xnews[i]
sum_y *= interstep_decay
count_y *= interstep_decay
while True:
xold = xolds[luoi]
if xold <= xnew:
decay = np.exp(- (xnew - xold) / decay_period)
sum_y += decay * yolds[luoi]
count_y += decay
luoi += 1
else:
break
if luoi >= len(xolds):
break
sum_ys[i] = sum_y
count_ys[i] = count_y
ys = sum_ys / count_ys
ys[count_ys < low_counts_threshold] = np.nan
return xnews, ys, count_ys | def one_sided_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
'''
perform one-sided (causal) EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arguments:
xolds: array or list - x values of data. Needs to be sorted in ascending order
yolds: array of list - y values of data. Has to have the same length as xolds
low: float - min value of the new x grid. By default equals to xolds[0]
high: float - max value of the new x grid. By default equals to xolds[-1]
n: int - number of points in new x grid
decay_steps: float - EMA decay factor, expressed in new x grid steps.
low_counts_threshold: float or int
- y values with counts less than this value will be set to NaN
Returns:
tuple sum_ys, count_ys where
xs - array with new x grid
ys - array of EMA of y at each point of the new x grid
count_ys - array of EMA of y counts at each point of the new x grid
'''
low = xolds[0] if low is None else low
high = xolds[-1] if high is None else high
assert xolds[0] <= low, 'low = {} < xolds[0] = {} - extrapolation not permitted!'.format(low, xolds[0])
assert xolds[-1] >= high, 'high = {} > xolds[-1] = {} - extrapolation not permitted!'.format(high, xolds[-1])
assert len(xolds) == len(yolds), 'length of xolds ({}) and yolds ({}) do not match!'.format(len(xolds), len(yolds))
xolds = xolds.astype('float64')
yolds = yolds.astype('float64')
luoi = 0 # last unused old index
sum_y = 0.
count_y = 0.
xnews = np.linspace(low, high, n)
decay_period = (high - low) / (n - 1) * decay_steps
interstep_decay = np.exp(- 1. / decay_steps)
sum_ys = np.zeros_like(xnews)
count_ys = np.zeros_like(xnews)
for i in range(n):
xnew = xnews[i]
sum_y *= interstep_decay
count_y *= interstep_decay
while True:
xold = xolds[luoi]
if xold <= xnew:
decay = np.exp(- (xnew - xold) / decay_period)
sum_y += decay * yolds[luoi]
count_y += decay
luoi += 1
else:
break
if luoi >= len(xolds):
break
sum_ys[i] = sum_y
count_ys[i] = count_y
ys = sum_ys / count_ys
ys[count_ys < low_counts_threshold] = np.nan
return xnews, ys, count_ys | [
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... | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/plot_util.py#L39-L109 | [
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"if"... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | symmetric_ema | perform symmetric EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arguments:
xolds: array or list - x values of data. Needs to be sorted in ascending order
yolds: array of list - y values of data. Has to have the same length as xolds
low: float - min value of the new x grid. By default equals to xolds[0]
high: float - max value of the new x grid. By default equals to xolds[-1]
n: int - number of points in new x grid
decay_steps: float - EMA decay factor, expressed in new x grid steps.
low_counts_threshold: float or int
- y values with counts less than this value will be set to NaN
Returns:
tuple sum_ys, count_ys where
xs - array with new x grid
ys - array of EMA of y at each point of the new x grid
count_ys - array of EMA of y counts at each point of the new x grid | baselines/common/plot_util.py | def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
'''
perform symmetric EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arguments:
xolds: array or list - x values of data. Needs to be sorted in ascending order
yolds: array of list - y values of data. Has to have the same length as xolds
low: float - min value of the new x grid. By default equals to xolds[0]
high: float - max value of the new x grid. By default equals to xolds[-1]
n: int - number of points in new x grid
decay_steps: float - EMA decay factor, expressed in new x grid steps.
low_counts_threshold: float or int
- y values with counts less than this value will be set to NaN
Returns:
tuple sum_ys, count_ys where
xs - array with new x grid
ys - array of EMA of y at each point of the new x grid
count_ys - array of EMA of y counts at each point of the new x grid
'''
xs, ys1, count_ys1 = one_sided_ema(xolds, yolds, low, high, n, decay_steps, low_counts_threshold=0)
_, ys2, count_ys2 = one_sided_ema(-xolds[::-1], yolds[::-1], -high, -low, n, decay_steps, low_counts_threshold=0)
ys2 = ys2[::-1]
count_ys2 = count_ys2[::-1]
count_ys = count_ys1 + count_ys2
ys = (ys1 * count_ys1 + ys2 * count_ys2) / count_ys
ys[count_ys < low_counts_threshold] = np.nan
return xs, ys, count_ys | def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1., low_counts_threshold=1e-8):
'''
perform symmetric EMA (exponential moving average)
smoothing and resampling to an even grid with n points.
Does not do extrapolation, so we assume
xolds[0] <= low && high <= xolds[-1]
Arguments:
xolds: array or list - x values of data. Needs to be sorted in ascending order
yolds: array of list - y values of data. Has to have the same length as xolds
low: float - min value of the new x grid. By default equals to xolds[0]
high: float - max value of the new x grid. By default equals to xolds[-1]
n: int - number of points in new x grid
decay_steps: float - EMA decay factor, expressed in new x grid steps.
low_counts_threshold: float or int
- y values with counts less than this value will be set to NaN
Returns:
tuple sum_ys, count_ys where
xs - array with new x grid
ys - array of EMA of y at each point of the new x grid
count_ys - array of EMA of y counts at each point of the new x grid
'''
xs, ys1, count_ys1 = one_sided_ema(xolds, yolds, low, high, n, decay_steps, low_counts_threshold=0)
_, ys2, count_ys2 = one_sided_ema(-xolds[::-1], yolds[::-1], -high, -low, n, decay_steps, low_counts_threshold=0)
ys2 = ys2[::-1]
count_ys2 = count_ys2[::-1]
count_ys = count_ys1 + count_ys2
ys = (ys1 * count_ys1 + ys2 * count_ys2) / count_ys
ys[count_ys < low_counts_threshold] = np.nan
return xs, ys, count_ys | [
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... | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/plot_util.py#L111-L147 | [
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... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | load_results | load summaries of runs from a list of directories (including subdirectories)
Arguments:
enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: True
enable_monitor: bool - if True, will attempt to load data from monitor.csv files (data saved by Monitor environment wrapper). Default: True
verbose: bool - if True, will print out list of directories from which the data is loaded. Default: False
Returns:
List of Result objects with the following fields:
- dirname - path to the directory data was loaded from
- metadata - run metadata (such as command-line arguments and anything else in metadata.json file
- monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory)
- progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file | baselines/common/plot_util.py | def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False):
'''
load summaries of runs from a list of directories (including subdirectories)
Arguments:
enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: True
enable_monitor: bool - if True, will attempt to load data from monitor.csv files (data saved by Monitor environment wrapper). Default: True
verbose: bool - if True, will print out list of directories from which the data is loaded. Default: False
Returns:
List of Result objects with the following fields:
- dirname - path to the directory data was loaded from
- metadata - run metadata (such as command-line arguments and anything else in metadata.json file
- monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory)
- progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file
'''
import re
if isinstance(root_dir_or_dirs, str):
rootdirs = [osp.expanduser(root_dir_or_dirs)]
else:
rootdirs = [osp.expanduser(d) for d in root_dir_or_dirs]
allresults = []
for rootdir in rootdirs:
assert osp.exists(rootdir), "%s doesn't exist"%rootdir
for dirname, dirs, files in os.walk(rootdir):
if '-proc' in dirname:
files[:] = []
continue
monitor_re = re.compile(r'(\d+\.)?(\d+\.)?monitor\.csv')
if set(['metadata.json', 'monitor.json', 'progress.json', 'progress.csv']).intersection(files) or \
any([f for f in files if monitor_re.match(f)]): # also match monitor files like 0.1.monitor.csv
# used to be uncommented, which means do not go deeper than current directory if any of the data files
# are found
# dirs[:] = []
result = {'dirname' : dirname}
if "metadata.json" in files:
with open(osp.join(dirname, "metadata.json"), "r") as fh:
result['metadata'] = json.load(fh)
progjson = osp.join(dirname, "progress.json")
progcsv = osp.join(dirname, "progress.csv")
if enable_progress:
if osp.exists(progjson):
result['progress'] = pandas.DataFrame(read_json(progjson))
elif osp.exists(progcsv):
try:
result['progress'] = read_csv(progcsv)
except pandas.errors.EmptyDataError:
print('skipping progress file in ', dirname, 'empty data')
else:
if verbose: print('skipping %s: no progress file'%dirname)
if enable_monitor:
try:
result['monitor'] = pandas.DataFrame(monitor.load_results(dirname))
except monitor.LoadMonitorResultsError:
print('skipping %s: no monitor files'%dirname)
except Exception as e:
print('exception loading monitor file in %s: %s'%(dirname, e))
if result.get('monitor') is not None or result.get('progress') is not None:
allresults.append(Result(**result))
if verbose:
print('successfully loaded %s'%dirname)
if verbose: print('loaded %i results'%len(allresults))
return allresults | def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False):
'''
load summaries of runs from a list of directories (including subdirectories)
Arguments:
enable_progress: bool - if True, will attempt to load data from progress.csv files (data saved by logger). Default: True
enable_monitor: bool - if True, will attempt to load data from monitor.csv files (data saved by Monitor environment wrapper). Default: True
verbose: bool - if True, will print out list of directories from which the data is loaded. Default: False
Returns:
List of Result objects with the following fields:
- dirname - path to the directory data was loaded from
- metadata - run metadata (such as command-line arguments and anything else in metadata.json file
- monitor - if enable_monitor is True, this field contains pandas dataframe with loaded monitor.csv file (or aggregate of all *.monitor.csv files in the directory)
- progress - if enable_progress is True, this field contains pandas dataframe with loaded progress.csv file
'''
import re
if isinstance(root_dir_or_dirs, str):
rootdirs = [osp.expanduser(root_dir_or_dirs)]
else:
rootdirs = [osp.expanduser(d) for d in root_dir_or_dirs]
allresults = []
for rootdir in rootdirs:
assert osp.exists(rootdir), "%s doesn't exist"%rootdir
for dirname, dirs, files in os.walk(rootdir):
if '-proc' in dirname:
files[:] = []
continue
monitor_re = re.compile(r'(\d+\.)?(\d+\.)?monitor\.csv')
if set(['metadata.json', 'monitor.json', 'progress.json', 'progress.csv']).intersection(files) or \
any([f for f in files if monitor_re.match(f)]): # also match monitor files like 0.1.monitor.csv
# used to be uncommented, which means do not go deeper than current directory if any of the data files
# are found
# dirs[:] = []
result = {'dirname' : dirname}
if "metadata.json" in files:
with open(osp.join(dirname, "metadata.json"), "r") as fh:
result['metadata'] = json.load(fh)
progjson = osp.join(dirname, "progress.json")
progcsv = osp.join(dirname, "progress.csv")
if enable_progress:
if osp.exists(progjson):
result['progress'] = pandas.DataFrame(read_json(progjson))
elif osp.exists(progcsv):
try:
result['progress'] = read_csv(progcsv)
except pandas.errors.EmptyDataError:
print('skipping progress file in ', dirname, 'empty data')
else:
if verbose: print('skipping %s: no progress file'%dirname)
if enable_monitor:
try:
result['monitor'] = pandas.DataFrame(monitor.load_results(dirname))
except monitor.LoadMonitorResultsError:
print('skipping %s: no monitor files'%dirname)
except Exception as e:
print('exception loading monitor file in %s: %s'%(dirname, e))
if result.get('monitor') is not None or result.get('progress') is not None:
allresults.append(Result(**result))
if verbose:
print('successfully loaded %s'%dirname)
if verbose: print('loaded %i results'%len(allresults))
return allresults | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/plot_util.py#L152-L220 | [
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"=... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | plot_results | Plot multiple Results objects
xy_fn: function Result -> x,y - function that converts results objects into tuple of x and y values.
By default, x is cumsum of episode lengths, and y is episode rewards
split_fn: function Result -> hashable - function that converts results objects into keys to split curves into sub-panels by.
That is, the results r for which split_fn(r) is different will be put on different sub-panels.
By default, the portion of r.dirname between last / and -<digits> is returned. The sub-panels are
stacked vertically in the figure.
group_fn: function Result -> hashable - function that converts results objects into keys to group curves by.
That is, the results r for which group_fn(r) is the same will be put into the same group.
Curves in the same group have the same color (if average_group is False), or averaged over
(if average_group is True). The default value is the same as default value for split_fn
average_group: bool - if True, will average the curves in the same group and plot the mean. Enables resampling
(if resample = 0, will use 512 steps)
shaded_std: bool - if True (default), the shaded region corresponding to standard deviation of the group of curves will be
shown (only applicable if average_group = True)
shaded_err: bool - if True (default), the shaded region corresponding to error in mean estimate of the group of curves
(that is, standard deviation divided by square root of number of curves) will be
shown (only applicable if average_group = True)
figsize: tuple or None - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of
sub-panels.
legend_outside: bool - if True, will place the legend outside of the sub-panels.
resample: int - if not zero, size of the uniform grid in x direction to resample onto. Resampling is performed via symmetric
EMA smoothing (see the docstring for symmetric_ema).
Default is zero (no resampling). Note that if average_group is True, resampling is necessary; in that case, default
value is 512.
smooth_step: float - when resampling (i.e. when resample > 0 or average_group is True), use this EMA decay parameter (in units of the new grid step).
See docstrings for decay_steps in symmetric_ema or one_sided_ema functions. | baselines/common/plot_util.py | def plot_results(
allresults, *,
xy_fn=default_xy_fn,
split_fn=default_split_fn,
group_fn=default_split_fn,
average_group=False,
shaded_std=True,
shaded_err=True,
figsize=None,
legend_outside=False,
resample=0,
smooth_step=1.0
):
'''
Plot multiple Results objects
xy_fn: function Result -> x,y - function that converts results objects into tuple of x and y values.
By default, x is cumsum of episode lengths, and y is episode rewards
split_fn: function Result -> hashable - function that converts results objects into keys to split curves into sub-panels by.
That is, the results r for which split_fn(r) is different will be put on different sub-panels.
By default, the portion of r.dirname between last / and -<digits> is returned. The sub-panels are
stacked vertically in the figure.
group_fn: function Result -> hashable - function that converts results objects into keys to group curves by.
That is, the results r for which group_fn(r) is the same will be put into the same group.
Curves in the same group have the same color (if average_group is False), or averaged over
(if average_group is True). The default value is the same as default value for split_fn
average_group: bool - if True, will average the curves in the same group and plot the mean. Enables resampling
(if resample = 0, will use 512 steps)
shaded_std: bool - if True (default), the shaded region corresponding to standard deviation of the group of curves will be
shown (only applicable if average_group = True)
shaded_err: bool - if True (default), the shaded region corresponding to error in mean estimate of the group of curves
(that is, standard deviation divided by square root of number of curves) will be
shown (only applicable if average_group = True)
figsize: tuple or None - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of
sub-panels.
legend_outside: bool - if True, will place the legend outside of the sub-panels.
resample: int - if not zero, size of the uniform grid in x direction to resample onto. Resampling is performed via symmetric
EMA smoothing (see the docstring for symmetric_ema).
Default is zero (no resampling). Note that if average_group is True, resampling is necessary; in that case, default
value is 512.
smooth_step: float - when resampling (i.e. when resample > 0 or average_group is True), use this EMA decay parameter (in units of the new grid step).
See docstrings for decay_steps in symmetric_ema or one_sided_ema functions.
'''
if split_fn is None: split_fn = lambda _ : ''
if group_fn is None: group_fn = lambda _ : ''
sk2r = defaultdict(list) # splitkey2results
for result in allresults:
splitkey = split_fn(result)
sk2r[splitkey].append(result)
assert len(sk2r) > 0
assert isinstance(resample, int), "0: don't resample. <integer>: that many samples"
nrows = len(sk2r)
ncols = 1
figsize = figsize or (6, 6 * nrows)
f, axarr = plt.subplots(nrows, ncols, sharex=False, squeeze=False, figsize=figsize)
groups = list(set(group_fn(result) for result in allresults))
default_samples = 512
if average_group:
resample = resample or default_samples
for (isplit, sk) in enumerate(sorted(sk2r.keys())):
g2l = {}
g2c = defaultdict(int)
sresults = sk2r[sk]
gresults = defaultdict(list)
ax = axarr[isplit][0]
for result in sresults:
group = group_fn(result)
g2c[group] += 1
x, y = xy_fn(result)
if x is None: x = np.arange(len(y))
x, y = map(np.asarray, (x, y))
if average_group:
gresults[group].append((x,y))
else:
if resample:
x, y, counts = symmetric_ema(x, y, x[0], x[-1], resample, decay_steps=smooth_step)
l, = ax.plot(x, y, color=COLORS[groups.index(group) % len(COLORS)])
g2l[group] = l
if average_group:
for group in sorted(groups):
xys = gresults[group]
if not any(xys):
continue
color = COLORS[groups.index(group) % len(COLORS)]
origxs = [xy[0] for xy in xys]
minxlen = min(map(len, origxs))
def allequal(qs):
return all((q==qs[0]).all() for q in qs[1:])
if resample:
low = max(x[0] for x in origxs)
high = min(x[-1] for x in origxs)
usex = np.linspace(low, high, resample)
ys = []
for (x, y) in xys:
ys.append(symmetric_ema(x, y, low, high, resample, decay_steps=smooth_step)[1])
else:
assert allequal([x[:minxlen] for x in origxs]),\
'If you want to average unevenly sampled data, set resample=<number of samples you want>'
usex = origxs[0]
ys = [xy[1][:minxlen] for xy in xys]
ymean = np.mean(ys, axis=0)
ystd = np.std(ys, axis=0)
ystderr = ystd / np.sqrt(len(ys))
l, = axarr[isplit][0].plot(usex, ymean, color=color)
g2l[group] = l
if shaded_err:
ax.fill_between(usex, ymean - ystderr, ymean + ystderr, color=color, alpha=.4)
if shaded_std:
ax.fill_between(usex, ymean - ystd, ymean + ystd, color=color, alpha=.2)
# https://matplotlib.org/users/legend_guide.html
plt.tight_layout()
if any(g2l.keys()):
ax.legend(
g2l.values(),
['%s (%i)'%(g, g2c[g]) for g in g2l] if average_group else g2l.keys(),
loc=2 if legend_outside else None,
bbox_to_anchor=(1,1) if legend_outside else None)
ax.set_title(sk)
return f, axarr | def plot_results(
allresults, *,
xy_fn=default_xy_fn,
split_fn=default_split_fn,
group_fn=default_split_fn,
average_group=False,
shaded_std=True,
shaded_err=True,
figsize=None,
legend_outside=False,
resample=0,
smooth_step=1.0
):
'''
Plot multiple Results objects
xy_fn: function Result -> x,y - function that converts results objects into tuple of x and y values.
By default, x is cumsum of episode lengths, and y is episode rewards
split_fn: function Result -> hashable - function that converts results objects into keys to split curves into sub-panels by.
That is, the results r for which split_fn(r) is different will be put on different sub-panels.
By default, the portion of r.dirname between last / and -<digits> is returned. The sub-panels are
stacked vertically in the figure.
group_fn: function Result -> hashable - function that converts results objects into keys to group curves by.
That is, the results r for which group_fn(r) is the same will be put into the same group.
Curves in the same group have the same color (if average_group is False), or averaged over
(if average_group is True). The default value is the same as default value for split_fn
average_group: bool - if True, will average the curves in the same group and plot the mean. Enables resampling
(if resample = 0, will use 512 steps)
shaded_std: bool - if True (default), the shaded region corresponding to standard deviation of the group of curves will be
shown (only applicable if average_group = True)
shaded_err: bool - if True (default), the shaded region corresponding to error in mean estimate of the group of curves
(that is, standard deviation divided by square root of number of curves) will be
shown (only applicable if average_group = True)
figsize: tuple or None - size of the resulting figure (including sub-panels). By default, width is 6 and height is 6 times number of
sub-panels.
legend_outside: bool - if True, will place the legend outside of the sub-panels.
resample: int - if not zero, size of the uniform grid in x direction to resample onto. Resampling is performed via symmetric
EMA smoothing (see the docstring for symmetric_ema).
Default is zero (no resampling). Note that if average_group is True, resampling is necessary; in that case, default
value is 512.
smooth_step: float - when resampling (i.e. when resample > 0 or average_group is True), use this EMA decay parameter (in units of the new grid step).
See docstrings for decay_steps in symmetric_ema or one_sided_ema functions.
'''
if split_fn is None: split_fn = lambda _ : ''
if group_fn is None: group_fn = lambda _ : ''
sk2r = defaultdict(list) # splitkey2results
for result in allresults:
splitkey = split_fn(result)
sk2r[splitkey].append(result)
assert len(sk2r) > 0
assert isinstance(resample, int), "0: don't resample. <integer>: that many samples"
nrows = len(sk2r)
ncols = 1
figsize = figsize or (6, 6 * nrows)
f, axarr = plt.subplots(nrows, ncols, sharex=False, squeeze=False, figsize=figsize)
groups = list(set(group_fn(result) for result in allresults))
default_samples = 512
if average_group:
resample = resample or default_samples
for (isplit, sk) in enumerate(sorted(sk2r.keys())):
g2l = {}
g2c = defaultdict(int)
sresults = sk2r[sk]
gresults = defaultdict(list)
ax = axarr[isplit][0]
for result in sresults:
group = group_fn(result)
g2c[group] += 1
x, y = xy_fn(result)
if x is None: x = np.arange(len(y))
x, y = map(np.asarray, (x, y))
if average_group:
gresults[group].append((x,y))
else:
if resample:
x, y, counts = symmetric_ema(x, y, x[0], x[-1], resample, decay_steps=smooth_step)
l, = ax.plot(x, y, color=COLORS[groups.index(group) % len(COLORS)])
g2l[group] = l
if average_group:
for group in sorted(groups):
xys = gresults[group]
if not any(xys):
continue
color = COLORS[groups.index(group) % len(COLORS)]
origxs = [xy[0] for xy in xys]
minxlen = min(map(len, origxs))
def allequal(qs):
return all((q==qs[0]).all() for q in qs[1:])
if resample:
low = max(x[0] for x in origxs)
high = min(x[-1] for x in origxs)
usex = np.linspace(low, high, resample)
ys = []
for (x, y) in xys:
ys.append(symmetric_ema(x, y, low, high, resample, decay_steps=smooth_step)[1])
else:
assert allequal([x[:minxlen] for x in origxs]),\
'If you want to average unevenly sampled data, set resample=<number of samples you want>'
usex = origxs[0]
ys = [xy[1][:minxlen] for xy in xys]
ymean = np.mean(ys, axis=0)
ystd = np.std(ys, axis=0)
ystderr = ystd / np.sqrt(len(ys))
l, = axarr[isplit][0].plot(usex, ymean, color=color)
g2l[group] = l
if shaded_err:
ax.fill_between(usex, ymean - ystderr, ymean + ystderr, color=color, alpha=.4)
if shaded_std:
ax.fill_between(usex, ymean - ystd, ymean + ystd, color=color, alpha=.2)
# https://matplotlib.org/users/legend_guide.html
plt.tight_layout()
if any(g2l.keys()):
ax.legend(
g2l.values(),
['%s (%i)'%(g, g2c[g]) for g in g2l] if average_group else g2l.keys(),
loc=2 if legend_outside else None,
bbox_to_anchor=(1,1) if legend_outside else None)
ax.set_title(sk)
return f, axarr | [
"Plot",
"multiple",
"Results",
"objects"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/plot_util.py#L240-L375 | [
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"... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | check_synced | It's common to forget to initialize your variables to the same values, or
(less commonly) if you update them in some other way than adam, to get them out of sync.
This function checks that variables on all MPI workers are the same, and raises
an AssertionError otherwise
Arguments:
comm: MPI communicator
localval: list of local variables (list of variables on current worker to be compared with the other workers) | baselines/common/mpi_adam_optimizer.py | def check_synced(localval, comm=None):
"""
It's common to forget to initialize your variables to the same values, or
(less commonly) if you update them in some other way than adam, to get them out of sync.
This function checks that variables on all MPI workers are the same, and raises
an AssertionError otherwise
Arguments:
comm: MPI communicator
localval: list of local variables (list of variables on current worker to be compared with the other workers)
"""
comm = comm or MPI.COMM_WORLD
vals = comm.gather(localval)
if comm.rank == 0:
assert all(val==vals[0] for val in vals[1:]) | def check_synced(localval, comm=None):
"""
It's common to forget to initialize your variables to the same values, or
(less commonly) if you update them in some other way than adam, to get them out of sync.
This function checks that variables on all MPI workers are the same, and raises
an AssertionError otherwise
Arguments:
comm: MPI communicator
localval: list of local variables (list of variables on current worker to be compared with the other workers)
"""
comm = comm or MPI.COMM_WORLD
vals = comm.gather(localval)
if comm.rank == 0:
assert all(val==vals[0] for val in vals[1:]) | [
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valid | copy_obs_dict | Deep-copy an observation dict. | baselines/common/vec_env/util.py | def copy_obs_dict(obs):
"""
Deep-copy an observation dict.
"""
return {k: np.copy(v) for k, v in obs.items()} | def copy_obs_dict(obs):
"""
Deep-copy an observation dict.
"""
return {k: np.copy(v) for k, v in obs.items()} | [
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valid | obs_space_info | Get dict-structured information about a gym.Space.
Returns:
A tuple (keys, shapes, dtypes):
keys: a list of dict keys.
shapes: a dict mapping keys to shapes.
dtypes: a dict mapping keys to dtypes. | baselines/common/vec_env/util.py | def obs_space_info(obs_space):
"""
Get dict-structured information about a gym.Space.
Returns:
A tuple (keys, shapes, dtypes):
keys: a list of dict keys.
shapes: a dict mapping keys to shapes.
dtypes: a dict mapping keys to dtypes.
"""
if isinstance(obs_space, gym.spaces.Dict):
assert isinstance(obs_space.spaces, OrderedDict)
subspaces = obs_space.spaces
else:
subspaces = {None: obs_space}
keys = []
shapes = {}
dtypes = {}
for key, box in subspaces.items():
keys.append(key)
shapes[key] = box.shape
dtypes[key] = box.dtype
return keys, shapes, dtypes | def obs_space_info(obs_space):
"""
Get dict-structured information about a gym.Space.
Returns:
A tuple (keys, shapes, dtypes):
keys: a list of dict keys.
shapes: a dict mapping keys to shapes.
dtypes: a dict mapping keys to dtypes.
"""
if isinstance(obs_space, gym.spaces.Dict):
assert isinstance(obs_space.spaces, OrderedDict)
subspaces = obs_space.spaces
else:
subspaces = {None: obs_space}
keys = []
shapes = {}
dtypes = {}
for key, box in subspaces.items():
keys.append(key)
shapes[key] = box.shape
dtypes[key] = box.dtype
return keys, shapes, dtypes | [
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".... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | q_retrace | Calculates q_retrace targets
:param R: Rewards
:param D: Dones
:param q_i: Q values for actions taken
:param v: V values
:param rho_i: Importance weight for each action
:return: Q_retrace values | baselines/acer/acer.py | def q_retrace(R, D, q_i, v, rho_i, nenvs, nsteps, gamma):
"""
Calculates q_retrace targets
:param R: Rewards
:param D: Dones
:param q_i: Q values for actions taken
:param v: V values
:param rho_i: Importance weight for each action
:return: Q_retrace values
"""
rho_bar = batch_to_seq(tf.minimum(1.0, rho_i), nenvs, nsteps, True) # list of len steps, shape [nenvs]
rs = batch_to_seq(R, nenvs, nsteps, True) # list of len steps, shape [nenvs]
ds = batch_to_seq(D, nenvs, nsteps, True) # list of len steps, shape [nenvs]
q_is = batch_to_seq(q_i, nenvs, nsteps, True)
vs = batch_to_seq(v, nenvs, nsteps + 1, True)
v_final = vs[-1]
qret = v_final
qrets = []
for i in range(nsteps - 1, -1, -1):
check_shape([qret, ds[i], rs[i], rho_bar[i], q_is[i], vs[i]], [[nenvs]] * 6)
qret = rs[i] + gamma * qret * (1.0 - ds[i])
qrets.append(qret)
qret = (rho_bar[i] * (qret - q_is[i])) + vs[i]
qrets = qrets[::-1]
qret = seq_to_batch(qrets, flat=True)
return qret | def q_retrace(R, D, q_i, v, rho_i, nenvs, nsteps, gamma):
"""
Calculates q_retrace targets
:param R: Rewards
:param D: Dones
:param q_i: Q values for actions taken
:param v: V values
:param rho_i: Importance weight for each action
:return: Q_retrace values
"""
rho_bar = batch_to_seq(tf.minimum(1.0, rho_i), nenvs, nsteps, True) # list of len steps, shape [nenvs]
rs = batch_to_seq(R, nenvs, nsteps, True) # list of len steps, shape [nenvs]
ds = batch_to_seq(D, nenvs, nsteps, True) # list of len steps, shape [nenvs]
q_is = batch_to_seq(q_i, nenvs, nsteps, True)
vs = batch_to_seq(v, nenvs, nsteps + 1, True)
v_final = vs[-1]
qret = v_final
qrets = []
for i in range(nsteps - 1, -1, -1):
check_shape([qret, ds[i], rs[i], rho_bar[i], q_is[i], vs[i]], [[nenvs]] * 6)
qret = rs[i] + gamma * qret * (1.0 - ds[i])
qrets.append(qret)
qret = (rho_bar[i] * (qret - q_is[i])) + vs[i]
qrets = qrets[::-1]
qret = seq_to_batch(qrets, flat=True)
return qret | [
"Calculates",
"q_retrace",
"targets"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/acer/acer.py#L25-L51 | [
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valid | learn | Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
Train an agent with given network architecture on a given environment using ACER.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel) (default: 20)
nstack: int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
(last image dimension) (default: 4)
total_timesteps: int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
q_coef: float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
ent_coef: float, policy entropy coefficient in the optimization objective (default: 0.01)
max_grad_norm: float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
rprop_alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting factor (default: 0.99)
log_interval: int, number of updates between logging events (default: 100)
buffer_size: int, size of the replay buffer (default: 50k)
replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
c: float, importance weight clipping factor (default: 10)
trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True)
delta: float, max KL divergence between the old policy and updated policy (default: 1)
alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
load_path: str, path to load the model from (default: None)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers. | baselines/acer/acer.py | def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_region=True, alpha=0.99, delta=1, load_path=None, **network_kwargs):
'''
Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
Train an agent with given network architecture on a given environment using ACER.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel) (default: 20)
nstack: int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
(last image dimension) (default: 4)
total_timesteps: int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
q_coef: float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
ent_coef: float, policy entropy coefficient in the optimization objective (default: 0.01)
max_grad_norm: float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
rprop_alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting factor (default: 0.99)
log_interval: int, number of updates between logging events (default: 100)
buffer_size: int, size of the replay buffer (default: 50k)
replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
c: float, importance weight clipping factor (default: 10)
trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True)
delta: float, max KL divergence between the old policy and updated policy (default: 1)
alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
load_path: str, path to load the model from (default: None)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
print("Running Acer Simple")
print(locals())
set_global_seeds(seed)
if not isinstance(env, VecFrameStack):
env = VecFrameStack(env, 1)
policy = build_policy(env, network, estimate_q=True, **network_kwargs)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nstack = env.nstack
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps,
ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
trust_region=trust_region, alpha=alpha, delta=delta)
runner = Runner(env=env, model=model, nsteps=nsteps)
if replay_ratio > 0:
buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size)
else:
buffer = None
nbatch = nenvs*nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
return model | def learn(network, env, seed=None, nsteps=20, total_timesteps=int(80e6), q_coef=0.5, ent_coef=0.01,
max_grad_norm=10, lr=7e-4, lrschedule='linear', rprop_epsilon=1e-5, rprop_alpha=0.99, gamma=0.99,
log_interval=100, buffer_size=50000, replay_ratio=4, replay_start=10000, c=10.0,
trust_region=True, alpha=0.99, delta=1, load_path=None, **network_kwargs):
'''
Main entrypoint for ACER (Actor-Critic with Experience Replay) algorithm (https://arxiv.org/pdf/1611.01224.pdf)
Train an agent with given network architecture on a given environment using ACER.
Parameters:
----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: environment. Needs to be vectorized for parallel environment simulation.
The environments produced by gym.make can be wrapped using baselines.common.vec_env.DummyVecEnv class.
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel) (default: 20)
nstack: int, size of the frame stack, i.e. number of the frames passed to the step model. Frames are stacked along channel dimension
(last image dimension) (default: 4)
total_timesteps: int, number of timesteps (i.e. number of actions taken in the environment) (default: 80M)
q_coef: float, value function loss coefficient in the optimization objective (analog of vf_coef for other actor-critic methods)
ent_coef: float, policy entropy coefficient in the optimization objective (default: 0.01)
max_grad_norm: float, gradient norm clipping coefficient. If set to None, no clipping. (default: 10),
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
rprop_epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
rprop_alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting factor (default: 0.99)
log_interval: int, number of updates between logging events (default: 100)
buffer_size: int, size of the replay buffer (default: 50k)
replay_ratio: int, now many (on average) batches of data to sample from the replay buffer take after batch from the environment (default: 4)
replay_start: int, the sampling from the replay buffer does not start until replay buffer has at least that many samples (default: 10k)
c: float, importance weight clipping factor (default: 10)
trust_region bool, whether or not algorithms estimates the gradient KL divergence between the old and updated policy and uses it to determine step size (default: True)
delta: float, max KL divergence between the old policy and updated policy (default: 1)
alpha: float, momentum factor in the Polyak (exponential moving average) averaging of the model parameters (default: 0.99)
load_path: str, path to load the model from (default: None)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
print("Running Acer Simple")
print(locals())
set_global_seeds(seed)
if not isinstance(env, VecFrameStack):
env = VecFrameStack(env, 1)
policy = build_policy(env, network, estimate_q=True, **network_kwargs)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
nstack = env.nstack
model = Model(policy=policy, ob_space=ob_space, ac_space=ac_space, nenvs=nenvs, nsteps=nsteps,
ent_coef=ent_coef, q_coef=q_coef, gamma=gamma,
max_grad_norm=max_grad_norm, lr=lr, rprop_alpha=rprop_alpha, rprop_epsilon=rprop_epsilon,
total_timesteps=total_timesteps, lrschedule=lrschedule, c=c,
trust_region=trust_region, alpha=alpha, delta=delta)
runner = Runner(env=env, model=model, nsteps=nsteps)
if replay_ratio > 0:
buffer = Buffer(env=env, nsteps=nsteps, size=buffer_size)
else:
buffer = None
nbatch = nenvs*nsteps
acer = Acer(runner, model, buffer, log_interval)
acer.tstart = time.time()
for acer.steps in range(0, total_timesteps, nbatch): #nbatch samples, 1 on_policy call and multiple off-policy calls
acer.call(on_policy=True)
if replay_ratio > 0 and buffer.has_atleast(replay_start):
n = np.random.poisson(replay_ratio)
for _ in range(n):
acer.call(on_policy=False) # no simulation steps in this
return model | [
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... | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/acer/acer.py#L274-L377 | [
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valid | KfacOptimizer.apply_stats | compute stats and update/apply the new stats to the running average | baselines/acktr/kfac.py | def apply_stats(self, statsUpdates):
""" compute stats and update/apply the new stats to the running average
"""
def updateAccumStats():
if self._full_stats_init:
return tf.cond(tf.greater(self.sgd_step, self._cold_iter), lambda: tf.group(*self._apply_stats(statsUpdates, accumulate=True, accumulateCoeff=1. / self._stats_accum_iter)), tf.no_op)
else:
return tf.group(*self._apply_stats(statsUpdates, accumulate=True, accumulateCoeff=1. / self._stats_accum_iter))
def updateRunningAvgStats(statsUpdates, fac_iter=1):
# return tf.cond(tf.greater_equal(self.factor_step,
# tf.convert_to_tensor(fac_iter)), lambda:
# tf.group(*self._apply_stats(stats_list, varlist)), tf.no_op)
return tf.group(*self._apply_stats(statsUpdates))
if self._async_stats:
# asynchronous stats update
update_stats = self._apply_stats(statsUpdates)
queue = tf.FIFOQueue(1, [item.dtype for item in update_stats], shapes=[
item.get_shape() for item in update_stats])
enqueue_op = queue.enqueue(update_stats)
def dequeue_stats_op():
return queue.dequeue()
self.qr_stats = tf.train.QueueRunner(queue, [enqueue_op])
update_stats_op = tf.cond(tf.equal(queue.size(), tf.convert_to_tensor(
0)), tf.no_op, lambda: tf.group(*[dequeue_stats_op(), ]))
else:
# synchronous stats update
update_stats_op = tf.cond(tf.greater_equal(
self.stats_step, self._stats_accum_iter), lambda: updateRunningAvgStats(statsUpdates), updateAccumStats)
self._update_stats_op = update_stats_op
return update_stats_op | def apply_stats(self, statsUpdates):
""" compute stats and update/apply the new stats to the running average
"""
def updateAccumStats():
if self._full_stats_init:
return tf.cond(tf.greater(self.sgd_step, self._cold_iter), lambda: tf.group(*self._apply_stats(statsUpdates, accumulate=True, accumulateCoeff=1. / self._stats_accum_iter)), tf.no_op)
else:
return tf.group(*self._apply_stats(statsUpdates, accumulate=True, accumulateCoeff=1. / self._stats_accum_iter))
def updateRunningAvgStats(statsUpdates, fac_iter=1):
# return tf.cond(tf.greater_equal(self.factor_step,
# tf.convert_to_tensor(fac_iter)), lambda:
# tf.group(*self._apply_stats(stats_list, varlist)), tf.no_op)
return tf.group(*self._apply_stats(statsUpdates))
if self._async_stats:
# asynchronous stats update
update_stats = self._apply_stats(statsUpdates)
queue = tf.FIFOQueue(1, [item.dtype for item in update_stats], shapes=[
item.get_shape() for item in update_stats])
enqueue_op = queue.enqueue(update_stats)
def dequeue_stats_op():
return queue.dequeue()
self.qr_stats = tf.train.QueueRunner(queue, [enqueue_op])
update_stats_op = tf.cond(tf.equal(queue.size(), tf.convert_to_tensor(
0)), tf.no_op, lambda: tf.group(*[dequeue_stats_op(), ]))
else:
# synchronous stats update
update_stats_op = tf.cond(tf.greater_equal(
self.stats_step, self._stats_accum_iter), lambda: updateRunningAvgStats(statsUpdates), updateAccumStats)
self._update_stats_op = update_stats_op
return update_stats_op | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/acktr/kfac.py#L440-L474 | [
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"... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | tile_images | Tile N images into one big PxQ image
(P,Q) are chosen to be as close as possible, and if N
is square, then P=Q.
input: img_nhwc, list or array of images, ndim=4 once turned into array
n = batch index, h = height, w = width, c = channel
returns:
bigim_HWc, ndarray with ndim=3 | baselines/common/tile_images.py | def tile_images(img_nhwc):
"""
Tile N images into one big PxQ image
(P,Q) are chosen to be as close as possible, and if N
is square, then P=Q.
input: img_nhwc, list or array of images, ndim=4 once turned into array
n = batch index, h = height, w = width, c = channel
returns:
bigim_HWc, ndarray with ndim=3
"""
img_nhwc = np.asarray(img_nhwc)
N, h, w, c = img_nhwc.shape
H = int(np.ceil(np.sqrt(N)))
W = int(np.ceil(float(N)/H))
img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)])
img_HWhwc = img_nhwc.reshape(H, W, h, w, c)
img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4)
img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c)
return img_Hh_Ww_c | def tile_images(img_nhwc):
"""
Tile N images into one big PxQ image
(P,Q) are chosen to be as close as possible, and if N
is square, then P=Q.
input: img_nhwc, list or array of images, ndim=4 once turned into array
n = batch index, h = height, w = width, c = channel
returns:
bigim_HWc, ndarray with ndim=3
"""
img_nhwc = np.asarray(img_nhwc)
N, h, w, c = img_nhwc.shape
H = int(np.ceil(np.sqrt(N)))
W = int(np.ceil(float(N)/H))
img_nhwc = np.array(list(img_nhwc) + [img_nhwc[0]*0 for _ in range(N, H*W)])
img_HWhwc = img_nhwc.reshape(H, W, h, w, c)
img_HhWwc = img_HWhwc.transpose(0, 2, 1, 3, 4)
img_Hh_Ww_c = img_HhWwc.reshape(H*h, W*w, c)
return img_Hh_Ww_c | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/tile_images.py#L3-L22 | [
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valid | SumSegmentTree.sum | Returns arr[start] + ... + arr[end] | baselines/common/segment_tree.py | def sum(self, start=0, end=None):
"""Returns arr[start] + ... + arr[end]"""
return super(SumSegmentTree, self).reduce(start, end) | def sum(self, start=0, end=None):
"""Returns arr[start] + ... + arr[end]"""
return super(SumSegmentTree, self).reduce(start, end) | [
"Returns",
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"]",
"+",
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"arr",
"[",
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/segment_tree.py#L101-L103 | [
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")"
] | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | SumSegmentTree.find_prefixsum_idx | Find the highest index `i` in the array such that
sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
if array values are probabilities, this function
allows to sample indexes according to the discrete
probability efficiently.
Parameters
----------
perfixsum: float
upperbound on the sum of array prefix
Returns
-------
idx: int
highest index satisfying the prefixsum constraint | baselines/common/segment_tree.py | def find_prefixsum_idx(self, prefixsum):
"""Find the highest index `i` in the array such that
sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
if array values are probabilities, this function
allows to sample indexes according to the discrete
probability efficiently.
Parameters
----------
perfixsum: float
upperbound on the sum of array prefix
Returns
-------
idx: int
highest index satisfying the prefixsum constraint
"""
assert 0 <= prefixsum <= self.sum() + 1e-5
idx = 1
while idx < self._capacity: # while non-leaf
if self._value[2 * idx] > prefixsum:
idx = 2 * idx
else:
prefixsum -= self._value[2 * idx]
idx = 2 * idx + 1
return idx - self._capacity | def find_prefixsum_idx(self, prefixsum):
"""Find the highest index `i` in the array such that
sum(arr[0] + arr[1] + ... + arr[i - i]) <= prefixsum
if array values are probabilities, this function
allows to sample indexes according to the discrete
probability efficiently.
Parameters
----------
perfixsum: float
upperbound on the sum of array prefix
Returns
-------
idx: int
highest index satisfying the prefixsum constraint
"""
assert 0 <= prefixsum <= self.sum() + 1e-5
idx = 1
while idx < self._capacity: # while non-leaf
if self._value[2 * idx] > prefixsum:
idx = 2 * idx
else:
prefixsum -= self._value[2 * idx]
idx = 2 * idx + 1
return idx - self._capacity | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/segment_tree.py#L105-L131 | [
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valid | MinSegmentTree.min | Returns min(arr[start], ..., arr[end]) | baselines/common/segment_tree.py | def min(self, start=0, end=None):
"""Returns min(arr[start], ..., arr[end])"""
return super(MinSegmentTree, self).reduce(start, end) | def min(self, start=0, end=None):
"""Returns min(arr[start], ..., arr[end])"""
return super(MinSegmentTree, self).reduce(start, end) | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/segment_tree.py#L142-L145 | [
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")"
] | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | PiecewiseSchedule.value | See Schedule.value | baselines/common/schedules.py | def value(self, t):
"""See Schedule.value"""
for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]):
if l_t <= t and t < r_t:
alpha = float(t - l_t) / (r_t - l_t)
return self._interpolation(l, r, alpha)
# t does not belong to any of the pieces, so doom.
assert self._outside_value is not None
return self._outside_value | def value(self, t):
"""See Schedule.value"""
for (l_t, l), (r_t, r) in zip(self._endpoints[:-1], self._endpoints[1:]):
if l_t <= t and t < r_t:
alpha = float(t - l_t) / (r_t - l_t)
return self._interpolation(l, r, alpha)
# t does not belong to any of the pieces, so doom.
assert self._outside_value is not None
return self._outside_value | [
"See",
"Schedule",
".",
"value"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/schedules.py#L64-L73 | [
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"... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | _subproc_worker | Control a single environment instance using IPC and
shared memory. | baselines/common/vec_env/shmem_vec_env.py | def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
"""
Control a single environment instance using IPC and
shared memory.
"""
def _write_obs(maybe_dict_obs):
flatdict = obs_to_dict(maybe_dict_obs)
for k in keys:
dst = obs_bufs[k].get_obj()
dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k]) # pylint: disable=W0212
np.copyto(dst_np, flatdict[k])
env = env_fn_wrapper.x()
parent_pipe.close()
try:
while True:
cmd, data = pipe.recv()
if cmd == 'reset':
pipe.send(_write_obs(env.reset()))
elif cmd == 'step':
obs, reward, done, info = env.step(data)
if done:
obs = env.reset()
pipe.send((_write_obs(obs), reward, done, info))
elif cmd == 'render':
pipe.send(env.render(mode='rgb_array'))
elif cmd == 'close':
pipe.send(None)
break
else:
raise RuntimeError('Got unrecognized cmd %s' % cmd)
except KeyboardInterrupt:
print('ShmemVecEnv worker: got KeyboardInterrupt')
finally:
env.close() | def _subproc_worker(pipe, parent_pipe, env_fn_wrapper, obs_bufs, obs_shapes, obs_dtypes, keys):
"""
Control a single environment instance using IPC and
shared memory.
"""
def _write_obs(maybe_dict_obs):
flatdict = obs_to_dict(maybe_dict_obs)
for k in keys:
dst = obs_bufs[k].get_obj()
dst_np = np.frombuffer(dst, dtype=obs_dtypes[k]).reshape(obs_shapes[k]) # pylint: disable=W0212
np.copyto(dst_np, flatdict[k])
env = env_fn_wrapper.x()
parent_pipe.close()
try:
while True:
cmd, data = pipe.recv()
if cmd == 'reset':
pipe.send(_write_obs(env.reset()))
elif cmd == 'step':
obs, reward, done, info = env.step(data)
if done:
obs = env.reset()
pipe.send((_write_obs(obs), reward, done, info))
elif cmd == 'render':
pipe.send(env.render(mode='rgb_array'))
elif cmd == 'close':
pipe.send(None)
break
else:
raise RuntimeError('Got unrecognized cmd %s' % cmd)
except KeyboardInterrupt:
print('ShmemVecEnv worker: got KeyboardInterrupt')
finally:
env.close() | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/vec_env/shmem_vec_env.py#L105-L139 | [
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valid | learn | Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
Parameters:
-----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int, total number of timesteps to train on (default: 80M)
vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting parameter (default: 0.99)
log_interval: int, specifies how frequently the logs are printed out (default: 100)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers. | baselines/a2c/a2c.py | def learn(
network,
env,
seed=None,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100,
load_path=None,
**network_kwargs):
'''
Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
Parameters:
-----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int, total number of timesteps to train on (default: 80M)
vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting parameter (default: 0.99)
log_interval: int, specifies how frequently the logs are printed out (default: 100)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
# Get the nb of env
nenvs = env.num_envs
policy = build_policy(env, network, **network_kwargs)
# Instantiate the model object (that creates step_model and train_model)
model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
epinfobuf = deque(maxlen=100)
# Calculate the batch_size
nbatch = nenvs*nsteps
# Start total timer
tstart = time.time()
for update in range(1, total_timesteps//nbatch+1):
# Get mini batch of experiences
obs, states, rewards, masks, actions, values, epinfos = runner.run()
epinfobuf.extend(epinfos)
policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
nseconds = time.time()-tstart
# Calculate the fps (frame per second)
fps = int((update*nbatch)/nseconds)
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.dump_tabular()
return model | def learn(
network,
env,
seed=None,
nsteps=5,
total_timesteps=int(80e6),
vf_coef=0.5,
ent_coef=0.01,
max_grad_norm=0.5,
lr=7e-4,
lrschedule='linear',
epsilon=1e-5,
alpha=0.99,
gamma=0.99,
log_interval=100,
load_path=None,
**network_kwargs):
'''
Main entrypoint for A2C algorithm. Train a policy with given network architecture on a given environment using a2c algorithm.
Parameters:
-----------
network: policy network architecture. Either string (mlp, lstm, lnlstm, cnn_lstm, cnn, cnn_small, conv_only - see baselines.common/models.py for full list)
specifying the standard network architecture, or a function that takes tensorflow tensor as input and returns
tuple (output_tensor, extra_feed) where output tensor is the last network layer output, extra_feed is None for feed-forward
neural nets, and extra_feed is a dictionary describing how to feed state into the network for recurrent neural nets.
See baselines.common/policies.py/lstm for more details on using recurrent nets in policies
env: RL environment. Should implement interface similar to VecEnv (baselines.common/vec_env) or be wrapped with DummyVecEnv (baselines.common/vec_env/dummy_vec_env.py)
seed: seed to make random number sequence in the alorightm reproducible. By default is None which means seed from system noise generator (not reproducible)
nsteps: int, number of steps of the vectorized environment per update (i.e. batch size is nsteps * nenv where
nenv is number of environment copies simulated in parallel)
total_timesteps: int, total number of timesteps to train on (default: 80M)
vf_coef: float, coefficient in front of value function loss in the total loss function (default: 0.5)
ent_coef: float, coeffictiant in front of the policy entropy in the total loss function (default: 0.01)
max_gradient_norm: float, gradient is clipped to have global L2 norm no more than this value (default: 0.5)
lr: float, learning rate for RMSProp (current implementation has RMSProp hardcoded in) (default: 7e-4)
lrschedule: schedule of learning rate. Can be 'linear', 'constant', or a function [0..1] -> [0..1] that takes fraction of the training progress as input and
returns fraction of the learning rate (specified as lr) as output
epsilon: float, RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
alpha: float, RMSProp decay parameter (default: 0.99)
gamma: float, reward discounting parameter (default: 0.99)
log_interval: int, specifies how frequently the logs are printed out (default: 100)
**network_kwargs: keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
For instance, 'mlp' network architecture has arguments num_hidden and num_layers.
'''
set_global_seeds(seed)
# Get the nb of env
nenvs = env.num_envs
policy = build_policy(env, network, **network_kwargs)
# Instantiate the model object (that creates step_model and train_model)
model = Model(policy=policy, env=env, nsteps=nsteps, ent_coef=ent_coef, vf_coef=vf_coef,
max_grad_norm=max_grad_norm, lr=lr, alpha=alpha, epsilon=epsilon, total_timesteps=total_timesteps, lrschedule=lrschedule)
if load_path is not None:
model.load(load_path)
# Instantiate the runner object
runner = Runner(env, model, nsteps=nsteps, gamma=gamma)
epinfobuf = deque(maxlen=100)
# Calculate the batch_size
nbatch = nenvs*nsteps
# Start total timer
tstart = time.time()
for update in range(1, total_timesteps//nbatch+1):
# Get mini batch of experiences
obs, states, rewards, masks, actions, values, epinfos = runner.run()
epinfobuf.extend(epinfos)
policy_loss, value_loss, policy_entropy = model.train(obs, states, rewards, masks, actions, values)
nseconds = time.time()-tstart
# Calculate the fps (frame per second)
fps = int((update*nbatch)/nseconds)
if update % log_interval == 0 or update == 1:
# Calculates if value function is a good predicator of the returns (ev > 1)
# or if it's just worse than predicting nothing (ev =< 0)
ev = explained_variance(values, rewards)
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update*nbatch)
logger.record_tabular("fps", fps)
logger.record_tabular("policy_entropy", float(policy_entropy))
logger.record_tabular("value_loss", float(value_loss))
logger.record_tabular("explained_variance", float(ev))
logger.record_tabular("eprewmean", safemean([epinfo['r'] for epinfo in epinfobuf]))
logger.record_tabular("eplenmean", safemean([epinfo['l'] for epinfo in epinfobuf]))
logger.dump_tabular()
return model | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/a2c/a2c.py#L119-L231 | [
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"l... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | sf01 | swap and then flatten axes 0 and 1 | baselines/ppo2/runner.py | def sf01(arr):
"""
swap and then flatten axes 0 and 1
"""
s = arr.shape
return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:]) | def sf01(arr):
"""
swap and then flatten axes 0 and 1
"""
s = arr.shape
return arr.swapaxes(0, 1).reshape(s[0] * s[1], *s[2:]) | [
"swap",
"and",
"then",
"flatten",
"axes",
"0",
"and",
"1"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/ppo2/runner.py#L69-L74 | [
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"]",
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] | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | PolicyWithValue.step | Compute next action(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
Returns:
-------
(action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple | baselines/common/policies.py | def step(self, observation, **extra_feed):
"""
Compute next action(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
Returns:
-------
(action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple
"""
a, v, state, neglogp = self._evaluate([self.action, self.vf, self.state, self.neglogp], observation, **extra_feed)
if state.size == 0:
state = None
return a, v, state, neglogp | def step(self, observation, **extra_feed):
"""
Compute next action(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
Returns:
-------
(action, value estimate, next state, negative log likelihood of the action under current policy parameters) tuple
"""
a, v, state, neglogp = self._evaluate([self.action, self.vf, self.state, self.neglogp], observation, **extra_feed)
if state.size == 0:
state = None
return a, v, state, neglogp | [
"Compute",
"next",
"action",
"(",
"s",
")",
"given",
"the",
"observation",
"(",
"s",
")"
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/policies.py#L77-L96 | [
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",",
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".",
"state",... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | PolicyWithValue.value | Compute value estimate(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
Returns:
-------
value estimate | baselines/common/policies.py | def value(self, ob, *args, **kwargs):
"""
Compute value estimate(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
Returns:
-------
value estimate
"""
return self._evaluate(self.vf, ob, *args, **kwargs) | def value(self, ob, *args, **kwargs):
"""
Compute value estimate(s) given the observation(s)
Parameters:
----------
observation observation data (either single or a batch)
**extra_feed additional data such as state or mask (names of the arguments should match the ones in constructor, see __init__)
Returns:
-------
value estimate
"""
return self._evaluate(self.vf, ob, *args, **kwargs) | [
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] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/policies.py#L98-L113 | [
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] | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
valid | pretty_eta | Print the number of seconds in human readable format.
Examples:
2 days
2 hours and 37 minutes
less than a minute
Paramters
---------
seconds_left: int
Number of seconds to be converted to the ETA
Returns
-------
eta: str
String representing the pretty ETA. | baselines/common/misc_util.py | def pretty_eta(seconds_left):
"""Print the number of seconds in human readable format.
Examples:
2 days
2 hours and 37 minutes
less than a minute
Paramters
---------
seconds_left: int
Number of seconds to be converted to the ETA
Returns
-------
eta: str
String representing the pretty ETA.
"""
minutes_left = seconds_left // 60
seconds_left %= 60
hours_left = minutes_left // 60
minutes_left %= 60
days_left = hours_left // 24
hours_left %= 24
def helper(cnt, name):
return "{} {}{}".format(str(cnt), name, ('s' if cnt > 1 else ''))
if days_left > 0:
msg = helper(days_left, 'day')
if hours_left > 0:
msg += ' and ' + helper(hours_left, 'hour')
return msg
if hours_left > 0:
msg = helper(hours_left, 'hour')
if minutes_left > 0:
msg += ' and ' + helper(minutes_left, 'minute')
return msg
if minutes_left > 0:
return helper(minutes_left, 'minute')
return 'less than a minute' | def pretty_eta(seconds_left):
"""Print the number of seconds in human readable format.
Examples:
2 days
2 hours and 37 minutes
less than a minute
Paramters
---------
seconds_left: int
Number of seconds to be converted to the ETA
Returns
-------
eta: str
String representing the pretty ETA.
"""
minutes_left = seconds_left // 60
seconds_left %= 60
hours_left = minutes_left // 60
minutes_left %= 60
days_left = hours_left // 24
hours_left %= 24
def helper(cnt, name):
return "{} {}{}".format(str(cnt), name, ('s' if cnt > 1 else ''))
if days_left > 0:
msg = helper(days_left, 'day')
if hours_left > 0:
msg += ' and ' + helper(hours_left, 'hour')
return msg
if hours_left > 0:
msg = helper(hours_left, 'hour')
if minutes_left > 0:
msg += ' and ' + helper(minutes_left, 'minute')
return msg
if minutes_left > 0:
return helper(minutes_left, 'minute')
return 'less than a minute' | [
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"number",
"of",
"seconds",
"in",
"human",
"readable",
"format",
"."
] | openai/baselines | python | https://github.com/openai/baselines/blob/3301089b48c42b87b396e246ea3f56fa4bfc9678/baselines/common/misc_util.py#L65-L104 | [
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... | 3301089b48c42b87b396e246ea3f56fa4bfc9678 |
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