INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
If obj can be serialized directly into memory (via cloudpickle) this
will return the serialized bytes.
Otherwise, gl_pickle is attempted and it will then
generates a temporary directory serializes an object into it, returning
the directory name. This directory will not have lifespan grea... | def _pickle_to_temp_location_or_memory(obj):
'''
If obj can be serialized directly into memory (via cloudpickle) this
will return the serialized bytes.
Otherwise, gl_pickle is attempted and it will then
generates a temporary directory serializes an object into it, returning
... |
Returns a list of dictionaries, with the following keys:
- index (integer, device index of the GPU)
- name (str, GPU name)
- memory_free (float, free memory in MiB)
- memory_total (float, total memory in MiB) | def _get_cuda_gpus():
"""
Returns a list of dictionaries, with the following keys:
- index (integer, device index of the GPU)
- name (str, GPU name)
- memory_free (float, free memory in MiB)
- memory_total (float, total memory in MiB)
"""
import subprocess
try:
output = subpr... |
Implementation of the parameterization decorators.
Args:
naming_type: The naming type.
testcases: Testcase parameters.
Returns:
A function for modifying the decorated object. | def _ParameterDecorator(naming_type, testcases):
"""Implementation of the parameterization decorators.
Args:
naming_type: The naming type.
testcases: Testcase parameters.
Returns:
A function for modifying the decorated object.
"""
def _Apply(obj):
if isinstance(obj, type):
_ModifyClass... |
Checks if the header-comment of the given file needs fixing. | 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... |
Checks if files, used as input when pre-processing MPL-containers in their variadic form, need fixing. | 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 fixing."""
# Check input files in include/source-directories.
files = glob.glob( os.path.join( headerDir, "*.hpp" ) )
files += glob.glob( os.pa... |
Check if files, used as input when pre-processing MPL-containers in their numbered form, need fixing. | 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, need fixing."""
# Check input files for each MPL-container type.
for container in containers:
files = glob.glob( os.path.join( sourceDir,... |
Fixes the header-comment of the given file. | 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 = ... |
Fixes files used as input when pre-processing MPL-containers in their variadic form. | 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( headerDi... |
Fixes files used as input when pre-processing MPL-containers in their numbered form. | def fix_input_files_for_numbered_seq(sourceDir, suffix, timestamp, containers):
"""Fixes files used as input when pre-processing MPL-containers in their numbered form."""
# Fix input files for each MPL-container type.
for container in containers:
files = glob.glob( os.path.join( sourceDir, container... |
Fixes source- and header-files used as input when pre-processing MPL-containers. | def fix_input_files(headerDir, sourceDir, containers=['vector', 'list', 'set', 'map'],
seqType='both', verbose=False):
"""Fixes source- and header-files used as input when pre-processing MPL-containers."""
# The new modification time.
timestamp = datetime.datetime.now();
# Fix the in... |
Converts a path into its absolute path and verifies that it exists or throws an exception. | 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
rais... |
The main function. | def main():
"""The main function."""
# Prepare and run cmdline-parser.
cmdlineParser = argparse.ArgumentParser(
description="Fixes the input files used for pre-processing of Boost.MPL headers.")
cmdlineParser.add_argument("-v", "--verbose", dest='verbose', action='store_true',
... |
Create a :class:`ImageSimilarityModel` model.
Parameters
----------
dataset : SFrame
Input data. The column named by the 'feature' parameter will be
extracted for modeling.
label : string
Name of the SFrame column with row labels to be used as uuid's to
identify the dat... | def create(dataset, label = None, feature = None, model = 'resnet-50', verbose = True,
batch_size = 64):
"""
Create a :class:`ImageSimilarityModel` model.
Parameters
----------
dataset : SFrame
Input data. The column named by the 'feature' parameter will be
extracted for ... |
A function to load a previously saved ImageClassifier
instance.
Parameters
----------
unpickler : GLUnpickler
A GLUnpickler file handler.
version : int
Version number maintained by the class writer. | def _load_version(cls, state, version):
"""
A function to load a previously saved ImageClassifier
instance.
Parameters
----------
unpickler : GLUnpickler
A GLUnpickler file handler.
version : int
Version number maintained by the class wri... |
For each image, retrieve the nearest neighbors from the model's stored
data. In general, the query dataset does not need to be the same as
the reference data stored in the model.
Parameters
----------
dataset : SFrame | SArray | turicreate.Image
Query data.
... | 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
data. In general, the query dataset does not need to be the same as
the reference data stored in the model.
Parameters
... |
Construct the similarity graph on the reference dataset, which is
already stored in the model to find the top `k` similar images for each
image in your input dataset.
This is conceptually very similar to running `query` with the reference
set, but this method is optimized for the purpos... | def similarity_graph(self, k=5, radius=None, include_self_edges=False,
output_type='SGraph', verbose=True):
"""
Construct the similarity graph on the reference dataset, which is
already stored in the model to find the top `k` similar images for each
image in your... |
Create a dependency graph from an ast node.
:param node: ast node.
:param call_deps: if true, then the graph will create a cyclic dependence for all
function calls. (i.e for `a.b(c)` a depends on b and b depends on a)
:returns: a tuple of (graph, undefined) | def make_graph(node, call_deps=False):
'''
Create a dependency graph from an ast node.
:param node: ast node.
:param call_deps: if true, then the graph will create a cyclic dependence for all
function calls. (i.e for `a.b(c)` a depends on b and b depends on a)
... |
Save the model in Core ML format.
The exported model calculates the distance between a query image and
each row of the model's stored data. It does not sort and retrieve
the k nearest neighbors of the query image.
See Also
--------
save
Examples
--------... | def export_coreml(self, filename):
"""
Save the model in Core ML format.
The exported model calculates the distance between a query image and
each row of the model's stored data. It does not sort and retrieve
the k nearest neighbors of the query image.
See Also
-... |
Extract a code object from a binary pyc file.
:param binary: a sequence of bytes from a pyc file. | def extract(binary):
'''
Extract a code object from a binary pyc file.
:param binary: a sequence of bytes from a pyc file.
'''
if len(binary) <= 8:
raise Exception("Binary pyc must be greater than 8 bytes (got %i)" % len(binary))
magic = binary[:4]
MAGIC = get_magic()
... |
Compute the size of a varint value. | def _VarintSize(value):
"""Compute the size of a varint value."""
if value <= 0x7f: return 1
if value <= 0x3fff: return 2
if value <= 0x1fffff: return 3
if value <= 0xfffffff: return 4
if value <= 0x7ffffffff: return 5
if value <= 0x3ffffffffff: return 6
if value <= 0x1ffffffffffff: return 7
if value ... |
Compute the size of a signed varint value. | def _SignedVarintSize(value):
"""Compute the size of a signed varint value."""
if value < 0: return 10
if value <= 0x7f: return 1
if value <= 0x3fff: return 2
if value <= 0x1fffff: return 3
if value <= 0xfffffff: return 4
if value <= 0x7ffffffff: return 5
if value <= 0x3ffffffffff: return 6
if value <... |
A sizer which uses the function compute_value_size to compute the size of
each value. Typically compute_value_size is _VarintSize. | def _SimpleSizer(compute_value_size):
"""A sizer which uses the function compute_value_size to compute the size of
each value. Typically compute_value_size is _VarintSize."""
def SpecificSizer(field_number, is_repeated, is_packed):
tag_size = _TagSize(field_number)
if is_packed:
local_VarintSize =... |
Like _SimpleSizer except for a fixed-size field. The input is the size
of one value. | def _FixedSizer(value_size):
"""Like _SimpleSizer except for a fixed-size field. The input is the size
of one value."""
def SpecificSizer(field_number, is_repeated, is_packed):
tag_size = _TagSize(field_number)
if is_packed:
local_VarintSize = _VarintSize
def PackedFieldSize(value):
... |
Returns a sizer for a bytes field. | def BytesSizer(field_number, is_repeated, is_packed):
"""Returns a sizer for a bytes field."""
tag_size = _TagSize(field_number)
local_VarintSize = _VarintSize
local_len = len
assert not is_packed
if is_repeated:
def RepeatedFieldSize(value):
result = tag_size * len(value)
for element in va... |
Returns a sizer for a group field. | def GroupSizer(field_number, is_repeated, is_packed):
"""Returns a sizer for a group field."""
tag_size = _TagSize(field_number) * 2
assert not is_packed
if is_repeated:
def RepeatedFieldSize(value):
result = tag_size * len(value)
for element in value:
result += element.ByteSize()
... |
Returns a sizer for a message field. | def MessageSizer(field_number, is_repeated, is_packed):
"""Returns a sizer for a message field."""
tag_size = _TagSize(field_number)
local_VarintSize = _VarintSize
assert not is_packed
if is_repeated:
def RepeatedFieldSize(value):
result = tag_size * len(value)
for element in value:
l... |
Returns a sizer for extensions of MessageSet.
The message set message looks like this:
message MessageSet {
repeated group Item = 1 {
required int32 type_id = 2;
required string message = 3;
}
} | def MessageSetItemSizer(field_number):
"""Returns a sizer for extensions of MessageSet.
The message set message looks like this:
message MessageSet {
repeated group Item = 1 {
required int32 type_id = 2;
required string message = 3;
}
}
"""
static_size = (_TagSize(1) * 2 + _... |
Returns a sizer for a map field. | def MapSizer(field_descriptor, is_message_map):
"""Returns a sizer for a map field."""
# Can't look at field_descriptor.message_type._concrete_class because it may
# not have been initialized yet.
message_type = field_descriptor.message_type
message_sizer = MessageSizer(field_descriptor.number, False, False)... |
Return an encoder for a basic varint value (does not include tag). | def _VarintEncoder():
"""Return an encoder for a basic varint value (does not include tag)."""
def EncodeVarint(write, value):
bits = value & 0x7f
value >>= 7
while value:
write(six.int2byte(0x80|bits))
bits = value & 0x7f
value >>= 7
return write(six.int2byte(bits))
return Enc... |
Return an encoder for a basic signed varint value (does not include
tag). | def _SignedVarintEncoder():
"""Return an encoder for a basic signed varint value (does not include
tag)."""
def EncodeSignedVarint(write, value):
if value < 0:
value += (1 << 64)
bits = value & 0x7f
value >>= 7
while value:
write(six.int2byte(0x80|bits))
bits = value & 0x7f
... |
Encode the given integer as a varint and return the bytes. This is only
called at startup time so it doesn't need to be fast. | def _VarintBytes(value):
"""Encode the given integer as a varint and return the bytes. This is only
called at startup time so it doesn't need to be fast."""
pieces = []
_EncodeVarint(pieces.append, value)
return b"".join(pieces) |
Return a constructor for an encoder for fields of a particular type.
Args:
wire_type: The field's wire type, for encoding tags.
encode_value: A function which encodes an individual value, e.g.
_EncodeVarint().
compute_value_size: A function which computes the size of an individual
... | def _SimpleEncoder(wire_type, encode_value, compute_value_size):
"""Return a constructor for an encoder for fields of a particular type.
Args:
wire_type: The field's wire type, for encoding tags.
encode_value: A function which encodes an individual value, e.g.
_EncodeVarint().
compute_v... |
Return a constructor for an encoder for a fixed-width field.
Args:
wire_type: The field's wire type, for encoding tags.
format: The format string to pass to struct.pack(). | def _StructPackEncoder(wire_type, format):
"""Return a constructor for an encoder for a fixed-width field.
Args:
wire_type: The field's wire type, for encoding tags.
format: The format string to pass to struct.pack().
"""
value_size = struct.calcsize(format)
def SpecificEncoder(field_number, ... |
Returns an encoder for a boolean field. | def BoolEncoder(field_number, is_repeated, is_packed):
"""Returns an encoder for a boolean field."""
false_byte = b'\x00'
true_byte = b'\x01'
if is_packed:
tag_bytes = TagBytes(field_number, wire_format.WIRETYPE_LENGTH_DELIMITED)
local_EncodeVarint = _EncodeVarint
def EncodePackedField(write, value... |
Returns an encoder for a string field. | def StringEncoder(field_number, is_repeated, is_packed):
"""Returns an encoder for a string field."""
tag = TagBytes(field_number, wire_format.WIRETYPE_LENGTH_DELIMITED)
local_EncodeVarint = _EncodeVarint
local_len = len
assert not is_packed
if is_repeated:
def EncodeRepeatedField(write, value):
... |
Returns an encoder for a group field. | def GroupEncoder(field_number, is_repeated, is_packed):
"""Returns an encoder for a group field."""
start_tag = TagBytes(field_number, wire_format.WIRETYPE_START_GROUP)
end_tag = TagBytes(field_number, wire_format.WIRETYPE_END_GROUP)
assert not is_packed
if is_repeated:
def EncodeRepeatedField(write, val... |
Returns an encoder for a message field. | def MessageEncoder(field_number, is_repeated, is_packed):
"""Returns an encoder for a message field."""
tag = TagBytes(field_number, wire_format.WIRETYPE_LENGTH_DELIMITED)
local_EncodeVarint = _EncodeVarint
assert not is_packed
if is_repeated:
def EncodeRepeatedField(write, value):
for element in v... |
Encoder for extensions of MessageSet.
The message set message looks like this:
message MessageSet {
repeated group Item = 1 {
required int32 type_id = 2;
required string message = 3;
}
} | def MessageSetItemEncoder(field_number):
"""Encoder for extensions of MessageSet.
The message set message looks like this:
message MessageSet {
repeated group Item = 1 {
required int32 type_id = 2;
required string message = 3;
}
}
"""
start_bytes = b"".join([
TagBytes(... |
Encoder for extensions of MessageSet.
Maps always have a wire format like this:
message MapEntry {
key_type key = 1;
value_type value = 2;
}
repeated MapEntry map = N; | def MapEncoder(field_descriptor):
"""Encoder for extensions of MessageSet.
Maps always have a wire format like this:
message MapEntry {
key_type key = 1;
value_type value = 2;
}
repeated MapEntry map = N;
"""
# Can't look at field_descriptor.message_type._concrete_class because it may
... |
Convert a Caffe model to Core ML format.
Parameters
----------
model: str | (str, str) | (str, str, str) | (str, str, dict)
A trained Caffe neural network model which can be represented as:
- Path on disk to a trained Caffe model (.caffemodel)
- A tuple of two paths, where the fir... | def convert(model, image_input_names=[], is_bgr=False,
red_bias=0.0, blue_bias=0.0, green_bias=0.0, gray_bias=0.0,
image_scale=1.0, class_labels=None, predicted_feature_name=None, model_precision=_MLMODEL_FULL_PRECISION):
"""
Convert a Caffe model to Core ML format.
Parameters
-... |
Takes the sklearn SVM model and returns the spec with the protobuf kernel for that model. | def _set_kernel(model, spec):
"""
Takes the sklearn SVM model and returns the spec with the protobuf kernel for that model.
"""
def gamma_value(model):
if(model.gamma == 'auto'):
# auto gamma value is 1/num_features
return 1/float(len(model.support_vectors_[0]))
e... |
Append a single row to an SFrame.
Throws a RuntimeError if one or more column's type is incompatible with
a type appended.
Parameters
----------
data : iterable
An iterable representation of a single row.
segment : int
The segment to write this... | def append(self, data, segment=0):
"""
Append a single row to an SFrame.
Throws a RuntimeError if one or more column's type is incompatible with
a type appended.
Parameters
----------
data : iterable
An iterable representation of a single row.
... |
Append multiple rows to an SFrame.
Throws a RuntimeError if one or more column's type is incompatible with
a type appended.
Parameters
----------
data : iterable[iterable]
A collection of multiple iterables, each representing a single row.
segment : int
... | def append_multiple(self, data, segment=0):
"""
Append multiple rows to an SFrame.
Throws a RuntimeError if one or more column's type is incompatible with
a type appended.
Parameters
----------
data : iterable[iterable]
A collection of multiple iter... |
If <location> is not set, sets it based on the project data. | def update_location(self, ps):
"""If <location> is not set, sets it based on the project data."""
loc = ps.get('location')
if not loc:
loc = os.path.join(self.project().get('location'), self.name())
ps = ps.add_raw(["<location>" + loc])
return ps |
Given the list of source targets explicitly passed to 'stage', returns the
list of targets which must be staged. | def targets_to_stage(self, source_targets, ps):
"""Given the list of source targets explicitly passed to 'stage', returns the
list of targets which must be staged."""
result = []
# Traverse the dependencies, if needed.
if ps.get('install-dependencies') == ['on']:
so... |
Initialize the logging configuration for the turicreate package.
This does not affect the root logging config. | def init_logger():
"""
Initialize the logging configuration for the turicreate package.
This does not affect the root logging config.
"""
import logging as _logging
import logging.config
# Package level logger
_logging.config.dictConfig({
'version': 1,
'disable_existing... |
Returns all the Turi Create configuration variables that can only
be set via environment variables.
- *TURI_FILEIO_WRITER_BUFFER_SIZE*: The file write buffer size.
- *TURI_FILEIO_READER_BUFFER_SIZE*: The file read buffer size.
- *OMP_NUM_THREADS*: The maximum number of threads to use for parallel proce... | def get_environment_config():
"""
Returns all the Turi Create configuration variables that can only
be set via environment variables.
- *TURI_FILEIO_WRITER_BUFFER_SIZE*: The file write buffer size.
- *TURI_FILEIO_READER_BUFFER_SIZE*: The file read buffer size.
- *OMP_NUM_THREADS*: The maximum n... |
Sets the log level.
Lower log levels log more.
if level is 8, nothing is logged. If level is 0, everything is logged. | def set_log_level(level):
"""
Sets the log level.
Lower log levels log more.
if level is 8, nothing is logged. If level is 0, everything is logged.
"""
from .._connect import main as _glconnect
unity = _glconnect.get_unity()
return unity.set_log_level(level) |
Returns all the Turi Create configuration variables that can be set
at runtime. See :py:func:`turicreate.config.set_runtime_config()` to set these
values and for documentation on the effect of each variable.
Returns
-------
Returns a dictionary of {key:value,..}
See Also
--------
set_r... | def get_runtime_config():
"""
Returns all the Turi Create configuration variables that can be set
at runtime. See :py:func:`turicreate.config.set_runtime_config()` to set these
values and for documentation on the effect of each variable.
Returns
-------
Returns a dictionary of {key:value,..... |
Configures system behavior at runtime. These configuration values are also
read from environment variables at program startup if available. See
:py:func:`turicreate.config.get_runtime_config()` to get the current values for
each variable.
Note that defaults may change across versions and the names
... | def set_runtime_config(name, value):
"""
Configures system behavior at runtime. These configuration values are also
read from environment variables at program startup if available. See
:py:func:`turicreate.config.get_runtime_config()` to get the current values for
each variable.
Note that defau... |
Load SGraph from text file or previously saved SGraph binary.
Parameters
----------
filename : string
Location of the file. Can be a local path or a remote URL.
format : {'binary', 'snap', 'csv', 'tsv'}, optional
Format to of the file to load.
- 'binary': native graph format o... | def load_sgraph(filename, format='binary', delimiter='auto'):
"""
Load SGraph from text file or previously saved SGraph binary.
Parameters
----------
filename : string
Location of the file. Can be a local path or a remote URL.
format : {'binary', 'snap', 'csv', 'tsv'}, optional
... |
Convert a list of vertices into dataframe. | def _vertex_list_to_dataframe(ls, id_column_name):
"""
Convert a list of vertices into dataframe.
"""
assert HAS_PANDAS, 'Cannot use dataframe because Pandas is not available or version is too low.'
cols = reduce(set.union, (set(v.attr.keys()) for v in ls))
df = pd.DataFrame({id_column_name: [v.... |
Convert a list of vertices into an SFrame. | def _vertex_list_to_sframe(ls, id_column_name):
"""
Convert a list of vertices into an SFrame.
"""
sf = SFrame()
if type(ls) == list:
cols = reduce(set.union, (set(v.attr.keys()) for v in ls))
sf[id_column_name] = [v.vid for v in ls]
for c in cols:
sf[c] = [v.att... |
Convert a list of edges into dataframe. | def _edge_list_to_dataframe(ls, src_column_name, dst_column_name):
"""
Convert a list of edges into dataframe.
"""
assert HAS_PANDAS, 'Cannot use dataframe because Pandas is not available or version is too low.'
cols = reduce(set.union, (set(e.attr.keys()) for e in ls))
df = pd.DataFrame({
... |
Convert a list of edges into an SFrame. | def _edge_list_to_sframe(ls, src_column_name, dst_column_name):
"""
Convert a list of edges into an SFrame.
"""
sf = SFrame()
if type(ls) == list:
cols = reduce(set.union, (set(v.attr.keys()) for v in ls))
sf[src_column_name] = [e.src_vid for e in ls]
sf[dst_column_name] = [... |
Convert dataframe into list of vertices, assuming that vertex ids are stored in _VID_COLUMN. | def _dataframe_to_vertex_list(df):
"""
Convert dataframe into list of vertices, assuming that vertex ids are stored in _VID_COLUMN.
"""
cols = df.columns
if len(cols):
assert _VID_COLUMN in cols, "Vertex DataFrame must contain column %s" % _VID_COLUMN
df = df[cols].T
ret = [V... |
Convert dataframe into list of edges, assuming that source and target ids are stored in _SRC_VID_COLUMN, and _DST_VID_COLUMN respectively. | def _dataframe_to_edge_list(df):
"""
Convert dataframe into list of edges, assuming that source and target ids are stored in _SRC_VID_COLUMN, and _DST_VID_COLUMN respectively.
"""
cols = df.columns
if len(cols):
assert _SRC_VID_COLUMN in cols, "Vertex DataFrame must contain column %s" % _SRC... |
Convert data into a vertex data sframe. Using vid_field to identify the id
column. The returned sframe will have id column name '__id'. | def _vertex_data_to_sframe(data, vid_field):
"""
Convert data into a vertex data sframe. Using vid_field to identify the id
column. The returned sframe will have id column name '__id'.
"""
if isinstance(data, SFrame):
# '__id' already in the sframe, and it is ok to not specify vid_field
... |
Convert data into an edge data sframe. Using src_field and dst_field to
identify the source and target id column. The returned sframe will have id
column name '__src_id', '__dst_id' | def _edge_data_to_sframe(data, src_field, dst_field):
"""
Convert data into an edge data sframe. Using src_field and dst_field to
identify the source and target id column. The returned sframe will have id
column name '__src_id', '__dst_id'
"""
if isinstance(data, SFrame):
# '__src_vid' a... |
get_vertices(self, ids=list(), fields={}, format='sframe')
Return a collection of vertices and their attributes.
Parameters
----------
ids : list [int | float | str] or SArray
List of vertex IDs to retrieve. Only vertices in this list will be
returned. Also acce... | def get_vertices(self, ids=[], fields={}, format='sframe'):
"""
get_vertices(self, ids=list(), fields={}, format='sframe')
Return a collection of vertices and their attributes.
Parameters
----------
ids : list [int | float | str] or SArray
List of vertex IDs... |
get_edges(self, src_ids=list(), dst_ids=list(), fields={}, format='sframe')
Return a collection of edges and their attributes. This function is used
to find edges by vertex IDs, filter on edge attributes, or list in-out
neighbors of vertex sets.
Parameters
----------
src... | def get_edges(self, src_ids=[], dst_ids=[], fields={}, format='sframe'):
"""
get_edges(self, src_ids=list(), dst_ids=list(), fields={}, format='sframe')
Return a collection of edges and their attributes. This function is used
to find edges by vertex IDs, filter on edge attributes, or lis... |
Add vertices to the SGraph. Vertices should be input as a list of
:class:`~turicreate.Vertex` objects, an :class:`~turicreate.SFrame`, or a
pandas DataFrame. If vertices are specified by SFrame or DataFrame,
``vid_field`` specifies which column contains the vertex ID. Remaining
columns a... | def add_vertices(self, vertices, vid_field=None):
"""
Add vertices to the SGraph. Vertices should be input as a list of
:class:`~turicreate.Vertex` objects, an :class:`~turicreate.SFrame`, or a
pandas DataFrame. If vertices are specified by SFrame or DataFrame,
``vid_field`` spec... |
Add edges to the SGraph. Edges should be input as a list of
:class:`~turicreate.Edge` objects, an :class:`~turicreate.SFrame`, or a
Pandas DataFrame. If the new edges are in an SFrame or DataFrame, then
``src_field`` and ``dst_field`` are required to specify the columns that
contain the ... | def add_edges(self, edges, src_field=None, dst_field=None):
"""
Add edges to the SGraph. Edges should be input as a list of
:class:`~turicreate.Edge` objects, an :class:`~turicreate.SFrame`, or a
Pandas DataFrame. If the new edges are in an SFrame or DataFrame, then
``src_field``... |
Return a new SGraph with only the selected fields. Other fields are
discarded, while fields that do not exist in the SGraph are ignored.
Parameters
----------
fields : string | list [string]
A single field name or a list of field names to select.
Returns
---... | def select_fields(self, fields):
"""
Return a new SGraph with only the selected fields. Other fields are
discarded, while fields that do not exist in the SGraph are ignored.
Parameters
----------
fields : string | list [string]
A single field name or a list o... |
Apply a transform function to each edge and its associated source and
target vertices in parallel. Each edge is visited once and in parallel.
Modification to vertex data is protected by lock. The effect on the
returned SGraph is equivalent to the following pseudocode:
>>> PARALLEL FOR (... | def triple_apply(self, triple_apply_fn, mutated_fields, input_fields=None):
'''
Apply a transform function to each edge and its associated source and
target vertices in parallel. Each edge is visited once and in parallel.
Modification to vertex data is protected by lock. The effect on th... |
Save the SGraph to disk. If the graph is saved in binary format, the
graph can be re-loaded using the :py:func:`load_sgraph` method.
Alternatively, the SGraph can be saved in JSON format for a
human-readable and portable representation.
Parameters
----------
filename : s... | def save(self, filename, format='auto'):
"""
Save the SGraph to disk. If the graph is saved in binary format, the
graph can be re-loaded using the :py:func:`load_sgraph` method.
Alternatively, the SGraph can be saved in JSON format for a
human-readable and portable representation... |
Retrieve the graph neighborhood around a set of vertices, ignoring edge
directions. Note that setting radius greater than two often results in a
time-consuming query for a very large subgraph.
Parameters
----------
ids : list [int | float | str]
List of target vertex... | def get_neighborhood(self, ids, radius=1, full_subgraph=True):
"""
Retrieve the graph neighborhood around a set of vertices, ignoring edge
directions. Note that setting radius greater than two often results in a
time-consuming query for a very large subgraph.
Parameters
... |
Create a (binary or multi-class) classifier model of type
:class:`~turicreate.boosted_trees_classifier.BoostedTreesClassifier` using
gradient boosted trees (sometimes known as GBMs).
Parameters
----------
dataset : SFrame
A training dataset containing feature columns and a target column.
... | def create(dataset, target,
features=None, max_iterations=10,
validation_set='auto',
class_weights = None,
max_depth=6, step_size=0.3,
min_loss_reduction=0.0, min_child_weight=0.1,
row_subsample=1.0, column_subsample=1.0,
verbose=True,
... |
Return a classification, for each example in the ``dataset``, using the
trained boosted trees model. The output SFrame contains predictions
as class labels (0 or 1) and probabilities associated with the the example.
Parameters
----------
dataset : SFrame
Dataset of n... | def classify(self, dataset, missing_value_action='auto'):
"""
Return a classification, for each example in the ``dataset``, using the
trained boosted trees model. The output SFrame contains predictions
as class labels (0 or 1) and probabilities associated with the the example.
P... |
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel") | def export_coreml(self, filename):
"""
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel")
"""
from ... |
Return the value for the queried field.
Get the value of a given field. The list of all queryable fields is
documented in the beginning of the model class.
>>> out = m._get('graph')
Parameters
----------
field : string
Name of the field to be retrieved.
... | def _get(self, field):
"""
Return the value for the queried field.
Get the value of a given field. The list of all queryable fields is
documented in the beginning of the model class.
>>> out = m._get('graph')
Parameters
----------
field : string
... |
Return a dictionary for the class fields description.
Fields should NOT be wrapped by _precomputed_field, if necessary | def _describe_fields(cls):
"""
Return a dictionary for the class fields description.
Fields should NOT be wrapped by _precomputed_field, if necessary
"""
dispatch_table = {
'ShortestPathModel': 'sssp',
'GraphColoringModel': 'graph_coloring',
'P... |
Returns a structured description of the model, including (where relevant)
the schema of the training data, description of the training data,
training statistics, and model hyperparameters.
Returns
-------
sections : list (of list of tuples)
A list of summary sections... | def _get_summary_struct(self):
"""
Returns a structured description of the model, including (where relevant)
the schema of the training data, description of the training data,
training statistics, and model hyperparameters.
Returns
-------
sections : list (of lis... |
Check if the input is of expected type.
Parameters
----------
arg : Input argument.
expected_type : A type OR a list of types that the argument is expected
to be.
arg_name : The name of the variable in the function being used. No
name is a... | def _raise_error_if_not_of_type(arg, expected_type, arg_name=None):
"""
Check if the input is of expected type.
Parameters
----------
arg : Input argument.
expected_type : A type OR a list of types that the argument is expected
to be.
arg_name : The n... |
Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range [-1.0, +1.0],
although this is not required.... | def waveform_to_examples(data, sample_rate):
"""Converts audio waveform into an array of examples for VGGish.
Args:
data: np.array of either one dimension (mono) or two dimensions
(multi-channel, with the outer dimension representing channels).
Each sample is generally expected to lie in the range ... |
Convenience wrapper around waveform_to_examples() for a common WAV format.
Args:
wav_file: String path to a file, or a file-like object. The file
is assumed to contain WAV audio data with signed 16-bit PCM samples.
Returns:
See waveform_to_examples. | def wavfile_to_examples(wav_file):
"""Convenience wrapper around waveform_to_examples() for a common WAV format.
Args:
wav_file: String path to a file, or a file-like object. The file
is assumed to contain WAV audio data with signed 16-bit PCM samples.
Returns:
See waveform_to_examples.
"""
from... |
Expand the given build request by combining all property_sets which don't
specify conflicting non-free features. | def expand_no_defaults (property_sets):
""" Expand the given build request by combining all property_sets which don't
specify conflicting non-free features.
"""
assert is_iterable_typed(property_sets, property_set.PropertySet)
# First make all features and subfeatures explicit
expanded_prope... |
Return the cross-product of all elements of property_sets, less any
that would contain conflicting values for single-valued features. | def __x_product (property_sets):
""" Return the cross-product of all elements of property_sets, less any
that would contain conflicting values for single-valued features.
"""
assert is_iterable_typed(property_sets, property_set.PropertySet)
x_product_seen = set()
return __x_product_aux (prop... |
Returns non-conflicting combinations of property sets.
property_sets is a list of PropertySet instances. seen_features is a set of Property
instances.
Returns a tuple of:
- list of lists of Property instances, such that within each list, no two Property instance
have the same feature, and no Prope... | def __x_product_aux (property_sets, seen_features):
"""Returns non-conflicting combinations of property sets.
property_sets is a list of PropertySet instances. seen_features is a set of Property
instances.
Returns a tuple of:
- list of lists of Property instances, such that within each list, no tw... |
Returns true if 'v' is either implicit value, or
the part before the first '-' symbol is implicit value. | def looks_like_implicit_value(v):
"""Returns true if 'v' is either implicit value, or
the part before the first '-' symbol is implicit value."""
assert isinstance(v, basestring)
if feature.is_implicit_value(v):
return 1
else:
split = v.split("-")
if feature.is_implicit_value(... |
Takes the command line tokens (such as taken from ARGV rule)
and constructs build request from it. Returns a list of two
lists. First is the set of targets specified in the command line,
and second is the set of requested build properties. | def from_command_line(command_line):
"""Takes the command line tokens (such as taken from ARGV rule)
and constructs build request from it. Returns a list of two
lists. First is the set of targets specified in the command line,
and second is the set of requested build properties."""
assert is_iterabl... |
Format a human-readable error message from a regex | def regex_to_error_msg(regex):
"""Format a human-readable error message from a regex"""
return re.sub('([^\\\\])[()]', '\\1', regex) \
.replace('[ \t]*$', '') \
.replace('^', '') \
.replace('$', '') \
.replace('[ \t]*', ' ') \
.replace('[ \t]+', ' ') \
.replace('[... |
Generate random characters | def random_chars(number):
"""Generate random characters"""
char_map = {
k: v for k, v in chars.CHARS.iteritems()
if not format_character(k).startswith('\\x')
}
char_num = sum(char_map.values())
return (
format_character(nth_char(char_map, random.randint(0, char_num - 1)))
... |
Enumerate the templates found in path | def templates_in(path):
"""Enumerate the templates found in path"""
ext = '.cpp'
return (
Template(f[0:-len(ext)], load_file(os.path.join(path, f)))
for f in os.listdir(path) if f.endswith(ext)
) |
Returns the nth character of a character->occurrence map | def nth_char(char_map, index):
"""Returns the nth character of a character->occurrence map"""
for char in char_map:
if index < char_map[char]:
return char
index = index - char_map[char]
return None |
Returns the C-formatting of the character | def format_character(char):
"""Returns the C-formatting of the character"""
if \
char in string.ascii_letters \
or char in string.digits \
or char in [
'_', '.', ':', ';', ' ', '!', '?', '+', '-', '/', '=', '<',
'>', '$', '(', ')', '@', '~', '`', '|', '#',... |
Create the file with the given content | def write_file(filename, content):
"""Create the file with the given content"""
print 'Generating {0}'.format(filename)
with open(filename, 'wb') as out_f:
out_f.write(content) |
Determine the output filename | def out_filename(template, n_val, mode):
"""Determine the output filename"""
return '{0}_{1}_{2}.cpp'.format(template.name, n_val, mode.identifier) |
The main function of the script | def main():
"""The main function of the script"""
desc = 'Generate files to benchmark'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument(
'--src',
dest='src_dir',
default='src',
help='The directory containing the templates'
)
parser.add_argume... |
Convert a BOOST_METAPARSE_STRING mode document into one with
this mode | def convert_from(self, base):
"""Convert a BOOST_METAPARSE_STRING mode document into one with
this mode"""
if self.identifier == 'bmp':
return base
elif self.identifier == 'man':
result = []
prefix = 'BOOST_METAPARSE_STRING("'
while True:
... |
Instantiates the template | def instantiate(self, value_of_n):
"""Instantiates the template"""
template = Cheetah.Template.Template(
self.content,
searchList={'n': value_of_n}
)
template.random_string = random_string
return str(template) |
Returns the range for N | def range(self):
"""Returns the range for N"""
match = self._match(in_comment(
'n[ \t]+in[ \t]*\\[([0-9]+)\\.\\.([0-9]+)\\),[ \t]+'
'step[ \t]+([0-9]+)'
))
return range(
int(match.group(1)),
int(match.group(2)),
int(match.group(... |
Add a protobuf spec or :py:class:`models.MLModel` instance to the pipeline.
All input features of this model must either match the input_features
of the pipeline, or match the outputs of a previous model.
Parameters
----------
spec: [MLModel, Model_pb2]
A protobuf s... | def add_model(self, spec):
"""
Add a protobuf spec or :py:class:`models.MLModel` instance to the pipeline.
All input features of this model must either match the input_features
of the pipeline, or match the outputs of a previous model.
Parameters
----------
spec... |
Gets the version from google/protobuf/__init__.py
Do not import google.protobuf.__init__ directly, because an installed
protobuf library may be loaded instead. | def GetVersion():
"""Gets the version from google/protobuf/__init__.py
Do not import google.protobuf.__init__ directly, because an installed
protobuf library may be loaded instead."""
with open(os.path.join('google', 'protobuf', '__init__.py')) as version_file:
exec(version_file.read(), globals())
ret... |
Invokes the Protocol Compiler to generate a _pb2.py from the given
.proto file. Does nothing if the output already exists and is newer than
the input. | def generate_proto(source, require = True):
"""Invokes the Protocol Compiler to generate a _pb2.py from the given
.proto file. Does nothing if the output already exists and is newer than
the input."""
if not require and not os.path.exists(source):
return
output = source.replace(".proto", "_pb2.py").rep... |
Generate a new column name that is guaranteed not to conflict with an
existing set of column names.
Parameters
----------
base_name : str
The base of the new column name. Usually this does not conflict with
the existing column names, in which case this function simply returns
`b... | def _robust_column_name(base_name, column_names):
"""
Generate a new column name that is guaranteed not to conflict with an
existing set of column names.
Parameters
----------
base_name : str
The base of the new column name. Usually this does not conflict with
the existing colum... |
Validate a row label column.
Parameters
----------
label : str
Name of the row label column.
column_type_map : dict[str, type]
Dictionary mapping the name of each column in an SFrame to the type of
the values in the column. | def _validate_row_label(label, column_type_map):
"""
Validate a row label column.
Parameters
----------
label : str
Name of the row label column.
column_type_map : dict[str, type]
Dictionary mapping the name of each column in an SFrame to the type of
the values in the c... |
Utility function for selecting columns of only valid feature types.
Parameters
----------
dataset: SFrame
The input SFrame containing columns of potential features.
features: list[str]
List of feature column names. If None, the candidate feature set is
taken to be all the colu... | def _select_valid_features(dataset, features, valid_feature_types,
target_column=None):
"""
Utility function for selecting columns of only valid feature types.
Parameters
----------
dataset: SFrame
The input SFrame containing columns of potential features.
fe... |
Returns true if all of the elements in the list are equal. | def _check_elements_equal(lst):
"""
Returns true if all of the elements in the list are equal.
"""
assert isinstance(lst, list), "Input value must be a list."
return not lst or lst.count(lst[0]) == len(lst) |
For a list-typed SArray, check whether the first elements are lists that
- contain only the provided types
- all have the same lengths (optionally)
Parameters
----------
sa : SArray
An SArray containing lists.
allowed_types : list
A list of types that are allowed in each list.
... | def _validate_lists(sa, allowed_types=[str], require_same_type=True,
require_equal_length=False, num_to_check=10):
"""
For a list-typed SArray, check whether the first elements are lists that
- contain only the provided types
- all have the same lengths (optionally)
Parameters
... |
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