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def _load_version(cls, state, version):
""" A function to load a previously saved SoundClassifier instance. """ |
from ._audio_feature_extractor import _get_feature_extractor
from .._mxnet import _mxnet_utils
state['_feature_extractor'] = _get_feature_extractor(state['feature_extractor_name'])
# Load the custom nerual network
num_classes = state['num_classes']
num_inputs = state['... |
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def classify(self, dataset, verbose=True, batch_size=64):
""" Return the classification for each examples in the ``dataset``. The output SFrame contains predicte... |
prob_vector = self.predict(dataset, output_type='probability_vector',
verbose=verbose, batch_size=batch_size)
id_to_label = self._id_to_class_label
return _tc.SFrame({
'class': prob_vector.apply(lambda v: id_to_label[_np.argmax(v)]),
'... |
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def _init_data(data, allow_empty, default_name):
"""Convert data into canonical form.""" |
assert (data is not None) or allow_empty
if data is None:
data = []
if isinstance(data, (np.ndarray, NDArray)):
data = [data]
if isinstance(data, list):
if not allow_empty:
assert(len(data) > 0)
if len(data) == 1:
data = OrderedDict([(default_nam... |
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def provide_data(self):
"""The name and shape of data provided by this iterator""" |
return [(k, tuple([self.batch_size] + list(v.shape[1:]))) for k, v in self.data] |
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def provide_label(self):
"""The name and shape of label provided by this iterator""" |
return [(k, tuple([self.batch_size] + list(v.shape[1:]))) for k, v in self.label] |
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def register (g):
""" Registers new generator instance 'g'. """ |
assert isinstance(g, Generator)
id = g.id()
__generators [id] = g
# A generator can produce several targets of the
# same type. We want unique occurence of that generator
# in .generators.$(t) in that case, otherwise, it will
# be tried twice and we'll get false ambiguity.
for t in se... |
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def override (overrider_id, overridee_id):
"""Make generator 'overrider-id' be preferred to 'overridee-id'. If, when searching for generators that could produce ... |
assert isinstance(overrider_id, basestring)
assert isinstance(overridee_id, basestring)
__overrides.setdefault(overrider_id, []).append(overridee_id) |
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def __viable_source_types_real (target_type):
""" Returns a list of source type which can possibly be converted to 'target_type' by some chain of generator invoc... |
assert isinstance(target_type, basestring)
generators = []
# 't0' is the initial list of target types we need to process to get a list
# of their viable source target types. New target types will not be added to
# this list.
t0 = type.all_bases (target_type)
# 't' is the list of target t... |
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def viable_source_types_for_generator (generator):
""" Caches the result of 'viable_source_types_for_generator'. """ |
assert isinstance(generator, Generator)
if generator not in __viable_source_types_cache:
__vstg_cached_generators.append(generator)
__viable_source_types_cache[generator] = viable_source_types_for_generator_real (generator)
return __viable_source_types_cache[generator] |
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def try_one_generator_really (project, name, generator, target_type, properties, sources):
""" Returns usage requirements + list of created targets. """ |
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(generator, Generator)
assert isinstance(target_type, basestring)
assert isinstance(properties, property_se... |
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def try_one_generator (project, name, generator, target_type, properties, sources):
""" Checks if generator invocation can be pruned, because it's guaranteed to ... |
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(generator, Generator)
assert isinstance(target_type, basestring)
assert isinstance(properties, property_se... |
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def __ensure_type (targets):
""" Ensures all 'targets' have types. If this is not so, exists with error. """ |
assert is_iterable_typed(targets, virtual_target.VirtualTarget)
for t in targets:
if not t.type ():
get_manager().errors()("target '%s' has no type" % str (t)) |
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def __construct_really (project, name, target_type, prop_set, sources):
""" Attempts to construct target by finding viable generators, running them and selecting... |
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(target_type, basestring)
assert isinstance(prop_set, property_set.PropertySet)
assert is_iterable_typed(so... |
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def match_rank (self, ps):
""" Returns true if the generator can be run with the specified properties. """ |
# See if generator's requirements are satisfied by
# 'properties'. Treat a feature name in requirements
# (i.e. grist-only element), as matching any value of the
# feature.
assert isinstance(ps, property_set.PropertySet)
all_requirements = self.requirements ()
... |
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def determine_output_name(self, sources):
"""Determine the name of the produced target from the names of the sources.""" |
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
# The simple case if when a name
# of source has single dot. Then, we take the part before
# dot. Several dots can be caused by:
# - Using source file like a.host.cpp
# - A type which suffix has a dot. Say,... |
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def generated_targets (self, sources, prop_set, project, name):
""" Constructs targets that are created after consuming 'sources'. The result will be the list of... |
if __debug__:
from .targets import ProjectTarget
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
assert isinstance(prop_set, property_set.PropertySet)
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or nam... |
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def convert_multiple_sources_to_consumable_types (self, project, prop_set, sources):
""" Converts several files to consumable types. """ |
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(prop_set, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
if not self.source_types_:
return... |
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def element_sub_sketch(self, keys = None):
""" Returns the sketch summary for the given set of keys. This is only applicable for sketch summary created from SArr... |
single_val = False
if keys is None:
keys = []
else:
if not isinstance(keys, list):
single_val = True
keys = [keys]
value_types = set([type(i) for i in keys])
if (len(value_types) > 1):
raise ValueErr... |
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def _get_global_dbapi_info(dbapi_module, conn):
""" Fetches all needed information from the top-level DBAPI module, guessing at the module if it wasn't passed as... |
module_given_msg = "The DBAPI2 module given ({0}) is missing the global\n"+\
"variable '{1}'. Please make sure you are supplying a module that\n"+\
"conforms to the DBAPI 2.0 standard (PEP 0249)."
module_not_given_msg = "Hello! I gave my best effort to find the\n"+\
"top-level module that the conne... |
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def read_csv_with_errors(cls, url, delimiter=',', header=True, comment_char='', escape_char='\\', double_quote=True, quote_char='\"', skip_initial_space=True, col... |
return cls._read_csv_impl(url,
delimiter=delimiter,
header=header,
error_bad_lines=False, # we are storing errors,
# thus we must not fail
... |
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def read_json(cls, url, orient='records'):
""" Reads a JSON file representing a table into an SFrame. Parameters url : string Location of the CSV file or directo... |
if orient == "records":
g = SArray.read_json(url)
if len(g) == 0:
return SFrame()
g = SFrame({'X1':g})
return g.unpack('X1','')
elif orient == "lines":
g = cls.read_csv(url, header=False,na_values=['null'],true_values=['true'],... |
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def to_sql(self, conn, table_name, dbapi_module=None, use_python_type_specifiers=False, use_exact_column_names=True):
""" Convert an SFrame to a single table in ... |
mod_info = _get_global_dbapi_info(dbapi_module, conn)
c = conn.cursor()
col_info = list(zip(self.column_names(), self.column_types()))
if not use_python_type_specifiers:
_pytype_to_printf = lambda x: 's'
# DBAPI2 standard allows for five different ways to specify ... |
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def print_rows(self, num_rows=10, num_columns=40, max_column_width=30, max_row_width=80, output_file=None):
""" Print the first M rows and N columns of the SFram... |
if output_file is None:
output_file = sys.stdout
max_row_width = max(max_row_width, max_column_width + 1)
printed_sf = self._imagecols_to_stringcols(num_rows)
row_of_tables = printed_sf.__get_pretty_tables__(wrap_text=False,
... |
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def _row_selector(self, other):
""" Where other is an SArray of identical length as the current Frame, this returns a selection of a subset of rows in the curren... |
if type(other) is SArray:
if self.__has_size__() and other.__has_size__() and len(other) != len(self):
raise IndexError("Cannot perform logical indexing on arrays of different length.")
with cython_context():
return SFrame(_proxy=self.__proxy__.logical_fi... |
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def to_dataframe(self):
""" Convert this SFrame to pandas.DataFrame. This operation will construct a pandas.DataFrame in memory. Care must be taken when size of ... |
assert HAS_PANDAS, 'pandas is not installed.'
df = pandas.DataFrame()
for i in range(self.num_columns()):
column_name = self.column_names()[i]
df[column_name] = list(self[column_name])
if len(df[column_name]) == 0:
df[column_name] = df[column_... |
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def to_numpy(self):
""" Converts this SFrame to a numpy array This operation will construct a numpy array in memory. Care must be taken when size of the returned... |
assert HAS_NUMPY, 'numpy is not installed.'
import numpy
return numpy.transpose(numpy.asarray([self[x] for x in self.column_names()])) |
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def flat_map(self, column_names, fn, column_types='auto', seed=None):
""" Map each row of the SFrame to multiple rows in a new SFrame via a function. will be a s... |
assert callable(fn), "Input must be callable"
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
# determine the column_types
if column_types == 'auto':
types = set()
sample = self[0:10]
results = [fn(row) for row in s... |
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def sample(self, fraction, seed=None, exact=False):
""" Sample a fraction of the current SFrame's rows. Parameters fraction : float Fraction of the rows to fetch... |
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
if (fraction > 1 or fraction < 0):
raise ValueError('Invalid sampling rate: ' + str(fraction))
if (self.num_rows() == 0 or self.num_columns() == 0):
return self
else:
... |
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def save(self, filename, format=None):
""" Save the SFrame to a file system for later use. Parameters filename : string The location to save the SFrame. Either a... |
if format is None:
if filename.endswith(('.csv', '.csv.gz')):
format = 'csv'
elif filename.endswith(('.json')):
format = 'json'
else:
format = 'binary'
else:
if format is 'csv':
if not filen... |
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def export_csv(self, filename, delimiter=',', line_terminator='\n', header=True, quote_level=csv.QUOTE_NONNUMERIC, double_quote=True, escape_char='\\', quote_char... |
# Pandas argument compatibility
if "sep" in kwargs:
delimiter = kwargs['sep']
del kwargs['sep']
if "quotechar" in kwargs:
quote_char = kwargs['quotechar']
del kwargs['quotechar']
if "doublequote" in kwargs:
double_quote = kwarg... |
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def export_json(self, filename, orient='records'):
""" Writes an SFrame to a JSON file. Parameters filename : string The location to save the JSON file. orient :... |
if orient == "records":
self.pack_columns(dtype=dict).export_csv(
filename, file_header='[', file_footer=']',
header=False, double_quote=False,
quote_level=csv.QUOTE_NONE,
line_prefix=',',
_no_prefix... |
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def _save_reference(self, filename):
""" Performs an incomplete save of an existing SFrame into a directory. This saved SFrame may reference SFrames in other loc... |
## Save the SFrame
url = _make_internal_url(filename)
with cython_context():
self.__proxy__.save_reference(url) |
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def add_column(self, data, column_name="", inplace=False):
""" Returns an SFrame with a new column. The number of elements in the data given must match the lengt... |
# Check type for pandas dataframe or SArray?
if not isinstance(data, SArray):
if isinstance(data, _Iterable):
data = SArray(data)
else:
if self.num_columns() == 0:
data = SArray([data])
else:
... |
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def add_columns(self, data, column_names=None, inplace=False):
""" Returns an SFrame with multiple columns added. The number of elements in all columns must matc... |
datalist = data
if isinstance(data, SFrame):
other = data
datalist = [other.select_column(name) for name in other.column_names()]
column_names = other.column_names()
my_columns = set(self.column_names())
for name in column_names:
... |
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def remove_column(self, column_name, inplace=False):
""" Returns an SFrame with a column removed. If inplace == False (default) this operation does not modify th... |
column_name = str(column_name)
if column_name not in self.column_names():
raise KeyError('Cannot find column %s' % column_name)
colid = self.column_names().index(column_name)
if inplace:
ret = self
else:
ret = self.copy()
with cython... |
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def remove_columns(self, column_names, inplace=False):
""" Returns an SFrame with one or more columns removed. If inplace == False (default) this operation does ... |
column_names = list(column_names)
existing_columns = dict((k, i) for i, k in enumerate(self.column_names()))
for name in column_names:
if name not in existing_columns:
raise KeyError('Cannot find column %s' % name)
# Delete it going backwards so we don't in... |
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def swap_columns(self, column_name_1, column_name_2, inplace=False):
""" Returns an SFrame with two column positions swapped. If inplace == False (default) this ... |
colnames = self.column_names()
colid_1 = colnames.index(column_name_1)
colid_2 = colnames.index(column_name_2)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.swap_columns(colid_1, colid_2)
... |
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def rename(self, names, inplace=False):
""" Returns an SFrame with columns renamed. ``names`` is expected to be a dict specifying the old and new names. This cha... |
if (type(names) is not dict):
raise TypeError('names must be a dictionary: oldname -> newname')
all_columns = set(self.column_names())
for k in names:
if not k in all_columns:
raise ValueError('Cannot find column %s in the SFrame' % k)
if inplace... |
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def append(self, other):
""" Add the rows of an SFrame to the end of this SFrame. Both SFrames must have the same set of columns with the same column names and c... |
if type(other) is not SFrame:
raise RuntimeError("SFrame append can only work with SFrame")
left_empty = len(self.column_names()) == 0
right_empty = len(other.column_names()) == 0
if (left_empty and right_empty):
return SFrame()
if (left_empty or right... |
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def explore(self, title=None):
""" Explore the SFrame in an interactive GUI. Opens a new app window. Parameters title : str The plot title to show for the result... |
import sys
import os
if sys.platform != 'darwin' and sys.platform != 'linux2' and sys.platform != 'linux':
raise NotImplementedError('Visualization is currently supported only on macOS and Linux.')
path_to_client = _get_client_app_path()
if title is None:
... |
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def split_datetime(self, column_name, column_name_prefix=None, limit=None, timezone=False):
""" Splits a datetime column of SFrame to multiple columns, with each... |
if column_name not in self.column_names():
raise KeyError("column '" + column_name + "' does not exist in current SFrame")
if column_name_prefix is None:
column_name_prefix = column_name
new_sf = self[column_name].split_datetime(column_name_prefix, limit, timezone)
... |
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def stack(self, column_name, new_column_name=None, drop_na=False, new_column_type=None):
""" Convert a "wide" column of an SFrame to one or two "tall" columns by... |
# validate column_name
column_name = str(column_name)
if column_name not in self.column_names():
raise ValueError("Cannot find column '" + str(column_name) + "' in the SFrame.")
stack_column_type = self[column_name].dtype
if (stack_column_type not in [dict, array.a... |
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def unstack(self, column_names, new_column_name=None):
""" Concatenate values from one or two columns into one column, grouping by all other columns. The resulti... |
if (type(column_names) != str and len(column_names) != 2):
raise TypeError("'column_names' parameter has to be either a string or a list of two strings.")
with cython_context():
if type(column_names) == str:
key_columns = [i for i in self.column_names() if i != ... |
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def sort(self, key_column_names, ascending=True):
""" Sort current SFrame by the given columns, using the given sort order. Only columns that are type of str, in... |
sort_column_names = []
sort_column_orders = []
# validate key_column_names
if (type(key_column_names) == str):
sort_column_names = [key_column_names]
elif (type(key_column_names) == list):
if (len(key_column_names) == 0):
raise ValueError... |
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def dropna(self, columns=None, how='any'):
""" Remove missing values from an SFrame. A missing value is either ``None`` or ``NaN``. If ``how`` is 'any', a row wi... |
# If the user gives me an empty list (the indicator to use all columns)
# NA values being dropped would not be the expected behavior. This
# is a NOOP, so let's not bother the server
if type(columns) is list and len(columns) == 0:
return SFrame(_proxy=self.__proxy__)
... |
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def fillna(self, column_name, value):
""" Fill all missing values with a given value in a given column. If the ``value`` is not the same type as the values in ``... |
# Normal error checking
if type(column_name) is not str:
raise TypeError("column_name must be a str")
ret = self[self.column_names()]
ret[column_name] = ret[column_name].fillna(value)
return ret |
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Description:
def add_row_number(self, column_name='id', start=0, inplace=False):
""" Returns an SFrame with a new column that numbers each row sequentially. By default the co... |
if type(column_name) is not str:
raise TypeError("Must give column_name as strs")
if type(start) is not int:
raise TypeError("Must give start as int")
if column_name in self.column_names():
raise RuntimeError("Column '" + column_name + "' already exists in... |
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def AddSerializedFile(self, serialized_file_desc_proto):
"""Adds the FileDescriptorProto and its types to this pool. Args: serialized_file_desc_proto: A bytes st... |
# pylint: disable=g-import-not-at-top
from google.protobuf import descriptor_pb2
file_desc_proto = descriptor_pb2.FileDescriptorProto.FromString(
serialized_file_desc_proto)
self.Add(file_desc_proto) |
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def AddDescriptor(self, desc):
"""Adds a Descriptor to the pool, non-recursively. If the Descriptor contains nested messages or enums, the caller must explicitly... |
if not isinstance(desc, descriptor.Descriptor):
raise TypeError('Expected instance of descriptor.Descriptor.')
self._descriptors[desc.full_name] = desc
self._AddFileDescriptor(desc.file) |
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def AddServiceDescriptor(self, service_desc):
"""Adds a ServiceDescriptor to the pool. Args: service_desc: A ServiceDescriptor. """ |
if not isinstance(service_desc, descriptor.ServiceDescriptor):
raise TypeError('Expected instance of descriptor.ServiceDescriptor.')
self._service_descriptors[service_desc.full_name] = service_desc |
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def AddExtensionDescriptor(self, extension):
"""Adds a FieldDescriptor describing an extension to the pool. Args: extension: A FieldDescriptor. Raises: Assertion... |
if not (isinstance(extension, descriptor.FieldDescriptor) and
extension.is_extension):
raise TypeError('Expected an extension descriptor.')
if extension.extension_scope is None:
self._toplevel_extensions[extension.full_name] = extension
try:
existing_desc = self._extensions_... |
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def FindFileByName(self, file_name):
"""Gets a FileDescriptor by file name. Args: file_name: The path to the file to get a descriptor for. Returns: A FileDescrip... |
try:
return self._file_descriptors[file_name]
except KeyError:
pass
try:
file_proto = self._internal_db.FindFileByName(file_name)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileByName(file_name)
else:
raise erro... |
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def FindFileContainingSymbol(self, symbol):
"""Gets the FileDescriptor for the file containing the specified symbol. Args: symbol: The name of the symbol to sear... |
symbol = _NormalizeFullyQualifiedName(symbol)
try:
return self._descriptors[symbol].file
except KeyError:
pass
try:
return self._enum_descriptors[symbol].file
except KeyError:
pass
try:
return self._FindFileContainingSymbolInDb(symbol)
except KeyError:
... |
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def FindMessageTypeByName(self, full_name):
"""Loads the named descriptor from the pool. Args: full_name: The full name of the descriptor to load. Returns: The d... |
full_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._descriptors[full_name] |
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def FindEnumTypeByName(self, full_name):
"""Loads the named enum descriptor from the pool. Args: full_name: The full name of the enum descriptor to load. Returns... |
full_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._enum_descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._enum_descriptors[full_name] |
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def FindFieldByName(self, full_name):
"""Loads the named field descriptor from the pool. Args: full_name: The full name of the field descriptor to load. Returns:... |
full_name = _NormalizeFullyQualifiedName(full_name)
message_name, _, field_name = full_name.rpartition('.')
message_descriptor = self.FindMessageTypeByName(message_name)
return message_descriptor.fields_by_name[field_name] |
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def FindServiceByName(self, full_name):
"""Loads the named service descriptor from the pool. Args: full_name: The full name of the service descriptor to load. Re... |
full_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._service_descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._service_descriptors[full_name] |
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def _FindFileContainingSymbolInDb(self, symbol):
"""Finds the file in descriptor DB containing the specified symbol. Args: symbol: The name of the symbol to sear... |
try:
file_proto = self._internal_db.FindFileContainingSymbol(symbol)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileContainingSymbol(symbol)
else:
raise error
if not file_proto:
raise KeyError('Cannot find a file containing... |
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def _ConvertFileProtoToFileDescriptor(self, file_proto):
"""Creates a FileDescriptor from a proto or returns a cached copy. This method also has the side effect ... |
if file_proto.name not in self._file_descriptors:
built_deps = list(self._GetDeps(file_proto.dependency))
direct_deps = [self.FindFileByName(n) for n in file_proto.dependency]
public_deps = [direct_deps[i] for i in file_proto.public_dependency]
file_descriptor = descriptor.FileDescriptor(... |
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def _ConvertMessageDescriptor(self, desc_proto, package=None, file_desc=None, scope=None, syntax=None):
"""Adds the proto to the pool in the specified package. A... |
if package:
desc_name = '.'.join((package, desc_proto.name))
else:
desc_name = desc_proto.name
if file_desc is None:
file_name = None
else:
file_name = file_desc.name
if scope is None:
scope = {}
nested = [
self._ConvertMessageDescriptor(
ne... |
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def _SetAllFieldTypes(self, package, desc_proto, scope):
"""Sets all the descriptor's fields's types. This method also sets the containing types on any extension... |
package = _PrefixWithDot(package)
main_desc = self._GetTypeFromScope(package, desc_proto.name, scope)
if package == '.':
nested_package = _PrefixWithDot(desc_proto.name)
else:
nested_package = '.'.join([package, desc_proto.name])
for field_proto, field_desc in zip(desc_proto.field, ... |
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def _SetFieldType(self, field_proto, field_desc, package, scope):
"""Sets the field's type, cpp_type, message_type and enum_type. Args: field_proto: Data about t... |
if field_proto.type_name:
desc = self._GetTypeFromScope(package, field_proto.type_name, scope)
else:
desc = None
if not field_proto.HasField('type'):
if isinstance(desc, descriptor.Descriptor):
field_proto.type = descriptor.FieldDescriptor.TYPE_MESSAGE
else:
field_p... |
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def _MakeEnumValueDescriptor(self, value_proto, index):
"""Creates a enum value descriptor object from a enum value proto. Args: value_proto: The proto describin... |
return descriptor.EnumValueDescriptor(
name=value_proto.name,
index=index,
number=value_proto.number,
options=_OptionsOrNone(value_proto),
type=None) |
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def _MakeServiceDescriptor(self, service_proto, service_index, scope, package, file_desc):
"""Make a protobuf ServiceDescriptor given a ServiceDescriptorProto. A... |
if package:
service_name = '.'.join((package, service_proto.name))
else:
service_name = service_proto.name
methods = [self._MakeMethodDescriptor(method_proto, service_name, package,
scope, index)
for index, method_proto in enumerate(ser... |
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def _MakeMethodDescriptor(self, method_proto, service_name, package, scope, index):
"""Creates a method descriptor from a MethodDescriptorProto. Args: method_pro... |
full_name = '.'.join((service_name, method_proto.name))
input_type = self._GetTypeFromScope(
package, method_proto.input_type, scope)
output_type = self._GetTypeFromScope(
package, method_proto.output_type, scope)
return descriptor.MethodDescriptor(name=method_proto.name,
... |
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def _ExtractSymbols(self, descriptors):
"""Pulls out all the symbols from descriptor protos. Args: descriptors: The messages to extract descriptors from. Yields:... |
for desc in descriptors:
yield (_PrefixWithDot(desc.full_name), desc)
for symbol in self._ExtractSymbols(desc.nested_types):
yield symbol
for enum in desc.enum_types:
yield (_PrefixWithDot(enum.full_name), enum) |
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def _GetDeps(self, dependencies):
"""Recursively finds dependencies for file protos. Args: dependencies: The names of the files being depended on. Yields: Each d... |
for dependency in dependencies:
dep_desc = self.FindFileByName(dependency)
yield dep_desc
for parent_dep in dep_desc.dependencies:
yield parent_dep |
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def _GetTypeFromScope(self, package, type_name, scope):
"""Finds a given type name in the current scope. Args: package: The package the proto should be located i... |
if type_name not in scope:
components = _PrefixWithDot(package).split('.')
while components:
possible_match = '.'.join(components + [type_name])
if possible_match in scope:
type_name = possible_match
break
else:
components.pop(-1)
return scope[t... |
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def max_element (elements, ordered = None):
""" Returns the maximum number in 'elements'. Uses 'ordered' for comparisons, or '<' is none is provided. """ |
assert is_iterable(elements)
assert callable(ordered) or ordered is None
if not ordered: ordered = operator.lt
max = elements [0]
for e in elements [1:]:
if ordered (max, e):
max = e
return max |
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def select_highest_ranked (elements, ranks):
""" Returns all of 'elements' for which corresponding element in parallel list 'rank' is equal to the maximum value ... |
assert is_iterable(elements)
assert is_iterable(ranks)
if not elements:
return []
max_rank = max_element (ranks)
result = []
while elements:
if ranks [0] == max_rank:
result.append (elements [0])
elements = elements [1:]
ranks = ranks [1:]
ret... |
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def CopyFrom(self, other_msg):
"""Copies the content of the specified message into the current message. The method clears the current message and then merges the... |
if self is other_msg:
return
self.Clear()
self.MergeFrom(other_msg) |
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def convert_tree_ensemble(model, feature_names, target, force_32bit_float):
"""Convert a generic tree model to the protobuf spec. This currently supports: * Deci... |
if not(_HAS_XGBOOST):
raise RuntimeError('xgboost not found. xgboost conversion API is disabled.')
import json
import os
feature_map = None
if isinstance(model, (_xgboost.core.Booster, _xgboost.XGBRegressor)):
# Testing a few corner cases that we don't support
if isinstan... |
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def update_dimension(model, input_dimension):
""" Given a model that takes an array of dimension input_dimension, returns the output dimension. """ |
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'active_features_'))
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'n_values_'))
if model.categorical_features == 'all':... |
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def convert_elementwise_mul_scalar(net, node, module, builder):
"""Convert a scalar multiplication from mxnet to coreml. Parameters net: network A mxnet network ... |
import numpy
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
mult = literal_eval(param['scalar'])
builder.add_scale(name=name,
W=numpy.array([mult]),
b=0,
has_bias=False,
... |
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def convert_dense(net, node, module, builder):
"""Convert a dense layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer Node to... |
input_name, output_name = _get_input_output_name(net, node)
has_bias = True
name = node['name']
inputs = node['inputs']
args, _ = module.get_params()
W = args[_get_node_name(net, inputs[1][0])].asnumpy()
if has_bias:
Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
else:
... |
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def convert_padding(net, node, module, builder):
"""Convert a padding layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer Nod... |
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
pad = literal_eval(param['pad_width'])
pad_left = pad[4]
pad_top = pad[5]
pad_right = pad[6]
pad_bottom = pad[7]
if param['mode'] == 'reflect':
builder.add_padding(
... |
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def convert_upsample(net, node, module, builder):
"""Convert a UpSampling layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer... |
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
inputs = node['inputs']
args, _ = module.get_params()
scale = literal_eval(param['scale'])
#method
if 'sample_type' in param.keys():
method = param['sample_type']
if... |
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def convert_custom(net, node, module, builder):
"""Convert highly specific ops""" |
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
if param['op_type'] == 'special-darknet-maxpool':
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=2,
width=2,
... |
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def convert_embedding(net, node, model, builder):
"""Convert an embedding layer from mxnet to coreml. Parameters net: network A mxnet network object. node: layer... |
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
outputs = node['outputs']
arg_params, aux_params = model.get_params()
W = arg_params[_get_node_name(net, inputs[1][0])].asnumpy()
if not ONE_HOT_ENCODE_HACK:
nC, nB = W.shape
... |
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def convert_scalar_add(net, node, model, builder):
"""Convert a scalar add layer from mxnet to coreml. Parameters net: network A mxnet network object. node: laye... |
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
mode = 'ADD'
alpha = _np.array([float(param['scalar'])])
builder.add_scale(name = name, input_name = input_name,
output_name = output_name, W = _np.array([... |
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def convert_scalar_multiply(net, node, model, builder):
"""Convert a scalar multiply layer from mxnet to coreml. Parameters net: network A mxnet network object. ... |
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
alpha = _np.array([float(param['scalar'])])
builder.add_scale(name = name, input_name = input_name,
output_name = output_name, W = alpha, has_bias=False, b=Non... |
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def convert_instancenorm(net, node, model, builder):
"""Convert an instance norm layer from mxnet to coreml. Parameters net: network A mxnet network object. node... |
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
outputs = node['outputs']
data_blob_name = _get_node_name(net, inputs[0][0])
gamma_blob_name = _get_node_name(net, inputs[1][0])
beta_blob_name = _get_node_name... |
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def _get_aws_credentials():
""" Returns the values stored in the AWS credential environment variables. Returns the value stored in the AWS_ACCESS_KEY_ID environm... |
if (not 'AWS_ACCESS_KEY_ID' in _os.environ):
raise KeyError('No access key found. Please set the environment variable AWS_ACCESS_KEY_ID.')
if (not 'AWS_SECRET_ACCESS_KEY' in _os.environ):
raise KeyError('No secret key found. Please set the environment variable AWS_SECRET_ACCESS_KEY.')
retu... |
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def is_directory_archive(path):
""" Utility function that returns True if the path provided is a directory that has an SFrame or SGraph in it. SFrames are writte... |
if path is None:
return False
if not _os.path.isdir(path):
return False
ini_path = '/'.join([_convert_slashes(path), 'dir_archive.ini'])
if not _os.path.exists(ini_path):
return False
if _os.path.isfile(ini_path):
return True
return False |
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def get_archive_type(path):
""" Returns the contents type for the provided archive path. Parameters path : string Directory to evaluate. Returns ------- Returns ... |
if not is_directory_archive(path):
raise TypeError('Unable to determine the type of archive at path: %s' % path)
try:
ini_path = '/'.join([_convert_slashes(path), 'dir_archive.ini'])
parser = _ConfigParser.SafeConfigParser()
parser.read(ini_path)
contents = parser.get(... |
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def crossproduct(d):
""" Create an SFrame containing the crossproduct of all provided options. Parameters d : dict Each key is the name of an option, and each va... |
from .. import SArray
d = [list(zip(list(d.keys()), x)) for x in _itertools.product(*list(d.values()))]
sa = [{k:v for (k,v) in x} for x in d]
return SArray(sa).unpack(column_name_prefix='') |
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def _assert_sframe_equal(sf1, sf2, check_column_names=True, check_column_order=True, check_row_order=True, float_column_delta=None):
""" Assert the two SFrames a... |
from .. import SFrame as _SFrame
if (type(sf1) is not _SFrame) or (type(sf2) is not _SFrame):
raise TypeError("Cannot function on types other than SFrames.")
if not check_column_order and not check_column_names:
raise ValueError("Cannot ignore both column order and column names.")
sf1... |
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| def _make_temp_directory(prefix):
'''
Generate a temporary directory that would not live beyond the lifetime of
unity_server.
Caller is expected to clean up the temp file as soon as the directory is no
longer needed. But the directory will be cleaned as unity_server restarts
'''
temp_dir = ... |
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| def _make_temp_filename(prefix):
'''
Generate a temporary file that would not live beyond the lifetime of
unity_server.
Caller is expected to clean up the temp file as soon as the file is no
longer needed. But temp files created using this method will be cleaned up
when unity_server restarts
... |
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def _ParameterDecorator(naming_type, testcases):
"""Implementation of the parameterization decorators. Args: naming_type: The naming type. testcases: Testcase pa... |
def _Apply(obj):
if isinstance(obj, type):
_ModifyClass(
obj,
list(testcases) if not isinstance(testcases, collections.Sequence)
else testcases,
naming_type)
return obj
else:
return _ParameterizedTestIter(obj, testcases, naming_type)
if _IsSingleto... |
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def check_header_comment(filename):
"""Checks if the header-comment of the given file needs fixing.""" |
# Check input file.
name = os.path.basename( filename )
# Read content of input file.
sourcefile = open( filename, "rU" )
content = sourcefile.read()
sourcefile.close()
# Search content for '$Id$'.
match = re.search(r'\$Id\$', content)
if match == None:
# Make sure that the ... |
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def check_input_files_for_variadic_seq(headerDir, sourceDir):
"""Checks if files, used as input when pre-processing MPL-containers in their variadic form, need f... |
# Check input files in include/source-directories.
files = glob.glob( os.path.join( headerDir, "*.hpp" ) )
files += glob.glob( os.path.join( headerDir, "aux_", "*.hpp" ) )
files += glob.glob( os.path.join( sourceDir, "src", "*" ) )
for currentFile in sorted( files ):
if check_header_commen... |
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def check_input_files_for_numbered_seq(sourceDir, suffix, containers):
"""Check if files, used as input when pre-processing MPL-containers in their numbered form... |
# Check input files for each MPL-container type.
for container in containers:
files = glob.glob( os.path.join( sourceDir, container, container + '*' + suffix ) )
for currentFile in sorted( files ):
if check_header_comment( currentFile ):
return True
return False |
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def check_input_files(headerDir, sourceDir, containers=['vector', 'list', 'set', 'map'], seqType='both', verbose=False):
"""Checks if source- and header-files, u... |
# Check the input files for containers in their variadic form.
result1 = False
if seqType == "both" or seqType == "variadic":
if verbose:
print "Check if input files for pre-processing Boost.MPL variadic containers need fixing."
result1 = check_input_files_for_variadic_seq(heade... |
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def fix_header_comment(filename, timestamp):
"""Fixes the header-comment of the given file.""" |
# Fix input file.
name = os.path.basename( filename )
for line in fileinput.input( filename, inplace=1, mode="rU" ):
# If header-comment already contains anything for '$Id$', remove it.
line = re.sub(r'\$Id:[^$]+\$', r'$Id$', line.rstrip())
# Replace '$Id$' by a string containing th... |
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def fix_input_files_for_variadic_seq(headerDir, sourceDir, timestamp):
"""Fixes files used as input when pre-processing MPL-containers in their variadic form.""" |
# Fix files in include/source-directories.
files = glob.glob( os.path.join( headerDir, "*.hpp" ) )
files += glob.glob( os.path.join( headerDir, "aux_", "*.hpp" ) )
files += glob.glob( os.path.join( sourceDir, "src", "*" ) )
for currentFile in sorted( files ):
fix_header_comment( currentFil... |
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def fix_input_files_for_numbered_seq(sourceDir, suffix, timestamp, containers):
"""Fixes files used as input when pre-processing MPL-containers in their numbered... |
# Fix input files for each MPL-container type.
for container in containers:
files = glob.glob( os.path.join( sourceDir, container, container + '*' + suffix ) )
for currentFile in sorted( files ):
fix_header_comment( currentFile, timestamp ) |
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def fix_input_files(headerDir, sourceDir, containers=['vector', 'list', 'set', 'map'], seqType='both', verbose=False):
"""Fixes source- and header-files used as ... |
# The new modification time.
timestamp = datetime.datetime.now();
# Fix the input files for containers in their variadic form.
if seqType == "both" or seqType == "variadic":
if verbose:
print "Fix input files for pre-processing Boost.MPL variadic containers."
fix_input_files... |
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def to_existing_absolute_path(string):
"""Converts a path into its absolute path and verifies that it exists or throws an exception.""" |
value = os.path.abspath(string)
if not os.path.exists( value ) or not os.path.isdir( value ):
msg = '"%r" is not a valid path to a directory.' % string
raise argparse.ArgumentTypeError(msg)
return value |
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def query(self, dataset, label=None, k=5, radius=None, verbose=True, batch_size=64):
""" For each image, retrieve the nearest neighbors from the model's stored d... |
if not isinstance(dataset, (_tc.SFrame, _tc.SArray, _tc.Image)):
raise TypeError('dataset must be either an SFrame, SArray or turicreate.Image')
if(batch_size < 1):
raise ValueError("'batch_size' must be greater than or equal to 1")
if isinstance(dataset, _tc.SArray):
... |
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