INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
|---|---|
Constructs the dependency graph that will be returned by this
generator.
consumed: Already prepared list of consumable targets
If generator requires several source files will contain
exactly len $(self.source_types_) ta... | def construct_result (self, consumed, project, name, prop_set):
""" Constructs the dependency graph that will be returned by this
generator.
consumed: Already prepared list of consumable targets
If generator requires several source files will c... |
Determine the name of the produced target from the
names of the sources. | 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
... |
Constructs targets that are created after consuming 'sources'.
The result will be the list of virtual-target, which the same length
as 'target_types' attribute and with corresponding types.
When 'name' is empty, all source targets must have the same value of
the 'name' a... | def generated_targets (self, sources, prop_set, project, name):
""" Constructs targets that are created after consuming 'sources'.
The result will be the list of virtual-target, which the same length
as 'target_types' attribute and with corresponding types.
When 'name' is em... |
Attempts to convert 'source' to the types that this generator can
handle. The intention is to produce the set of targets can should be
used when generator is run.
only_one: convert 'source' to only one of source types
if there's more that one possibility, re... | def convert_to_consumable_types (self, project, name, prop_set, sources, only_one=False):
""" Attempts to convert 'source' to the types that this generator can
handle. The intention is to produce the set of targets can should be
used when generator is run.
only_one: convert... |
Converts several files to consumable types. | 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, pro... |
Returns the sketch summary for the given set of keys. This is only
applicable for sketch summary created from SArray of sarray or dict type.
For dict SArray, the keys are the keys in dict value.
For array Sarray, the keys are indexes into the array value.
The keys must be passed into or... | 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 SArray of sarray or dict type.
For dict SArray, the keys are the keys in dict value.
For array Sarray, the keys are indexes... |
Convert a Nu-Support Vector Classification (NuSVC) model to the protobuf spec.
Parameters
----------
model: NuSVC
A trained NuSVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (default=None)
Name of the output ... | def convert(model, feature_names, target):
"""Convert a Nu-Support Vector Classification (NuSVC) model to the protobuf spec.
Parameters
----------
model: NuSVC
A trained NuSVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, o... |
Convert a linear regression model to the protobuf spec.
Parameters
----------
model: LinearRegression
A trained linear regression encoder model.
feature_names: [str]
Name of the input columns.
target: str
Name of the output column.
Returns
-------
model_spec: A... | def convert(model, features, target):
"""Convert a linear regression model to the protobuf spec.
Parameters
----------
model: LinearRegression
A trained linear regression encoder model.
feature_names: [str]
Name of the input columns.
target: str
Name of the output colu... |
Fetches all needed information from the top-level DBAPI module,
guessing at the module if it wasn't passed as a parameter. Returns a
dictionary of all the needed variables. This is put in one place to
make sure the error message is clear if the module "guess" is wrong. | 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 a parameter. Returns a
dictionary of all the needed variables. This is put in one place to
make sure the error message is clear if the mod... |
Constructs an SFrame from a CSV file or a path to multiple CSVs, and
returns a pair containing the SFrame and optionally
(if store_errors=True) a dict of filenames to SArrays
indicating for each file, what are the incorrectly parsed lines
encountered.
Parameters
--------... | def _read_csv_impl(cls,
url,
delimiter=',',
header=True,
error_bad_lines=False,
comment_char='',
escape_char='\\',
double_quote=True,
qu... |
Constructs an SFrame from a CSV file or a path to multiple CSVs, and
returns a pair containing the SFrame and a dict of filenames to SArrays
indicating for each file, what are the incorrectly parsed lines
encountered.
Parameters
----------
url : string
Locati... | def read_csv_with_errors(cls,
url,
delimiter=',',
header=True,
comment_char='',
escape_char='\\',
double_quote=True,
... |
Reads a JSON file representing a table into an SFrame.
Parameters
----------
url : string
Location of the CSV file or directory to load. If URL is a directory
or a "glob" pattern, all matching files will be loaded.
orient : string, optional. Either "records" or ... | 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 directory to load. If URL is a directory
or a "glob" p... |
Convert the result of a SQL database query to an SFrame.
Parameters
----------
conn : dbapi2.Connection
A DBAPI2 connection object. Any connection object originating from
the 'connect' method of a DBAPI2-compliant package can be used.
sql_statement : str
T... | def from_sql(cls, conn, sql_statement, params=None, type_inference_rows=100,
dbapi_module=None, column_type_hints=None, cursor_arraysize=128):
"""
Convert the result of a SQL database query to an SFrame.
Parameters
----------
conn : dbapi2.Connection
A DBAPI2 c... |
Convert an SFrame to a single table in a SQL database.
This function does not attempt to create the table or check if a table
named `table_name` exists in the database. It simply assumes that
`table_name` exists in the database and appends to it.
`to_sql` can be thought of as a conveni... | 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 a SQL database.
This function does not attempt to create the table or check if a table
named `table_name` exists i... |
Print the first M rows and N columns of the SFrame in human readable
format.
Parameters
----------
num_rows : int, optional
Number of rows to print.
num_columns : int, optional
Number of columns to print.
max_column_width : int, optional
... | 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 SFrame in human readable
format.
Parameters
----------
num_rows : int, optional
Numb... |
Where other is an SArray of identical length as the current Frame,
this returns a selection of a subset of rows in the current SFrame
where the corresponding row in the selector is non-zero. | 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 current SFrame
where the corresponding row in the selector is non-zero.
"""
if type(other) is SArray:
if ... |
Convert this SFrame to pandas.DataFrame.
This operation will construct a pandas.DataFrame in memory. Care must
be taken when size of the returned object is big.
Returns
-------
out : pandas.DataFrame
The dataframe which contains all rows of SFrame | 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 the returned object is big.
Returns
-------
out : pandas.DataFrame
The dataframe which co... |
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 object is big.
Returns
-------
out : numpy.ndarray
A Numpy Array containing all the values of the SFrame | 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 object is big.
Returns
-------
out : numpy.ndarray
A Numpy Array containing all the valu... |
Transform each row to an :class:`~turicreate.SArray` according to a
specified function. Returns a new SArray of ``dtype`` where each element
in this SArray is transformed by `fn(x)` where `x` is a single row in
the sframe represented as a dictionary. The ``fn`` should return
exactly one... | def apply(self, fn, dtype=None, seed=None):
"""
Transform each row to an :class:`~turicreate.SArray` according to a
specified function. Returns a new SArray of ``dtype`` where each element
in this SArray is transformed by `fn(x)` where `x` is a single row in
the sframe represente... |
Map each row of the SFrame to multiple rows in a new SFrame via a
function.
The output of `fn` must have type List[List[...]]. Each inner list
will be a single row in the new output, and the collection of these
rows within the outer list make up the data for the output SFrame.
... | 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.
The output of `fn` must have type List[List[...]]. Each inner list
will be a single row in the new output, and the collection of... |
Sample a fraction of the current SFrame's rows.
Parameters
----------
fraction : float
Fraction of the rows to fetch. Must be between 0 and 1.
if exact is False (default), the number of rows returned is
approximately the fraction times the number of rows.
... | 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. Must be between 0 and 1.
if exact is False (default), the number of rows returned... |
Randomly split the rows of an SFrame into two SFrames. The first SFrame
contains *M* rows, sampled uniformly (without replacement) from the
original SFrame. *M* is approximately the fraction times the original
number of rows. The second SFrame contains the remaining rows of the
original ... | def random_split(self, fraction, seed=None, exact=False):
"""
Randomly split the rows of an SFrame into two SFrames. The first SFrame
contains *M* rows, sampled uniformly (without replacement) from the
original SFrame. *M* is approximately the fraction times the original
number o... |
Get top k rows according to the given column. Result is according to and
sorted by `column_name` in the given order (default is descending).
When `k` is small, `topk` is more efficient than `sort`.
Parameters
----------
column_name : string
The column to sort on
... | def topk(self, column_name, k=10, reverse=False):
"""
Get top k rows according to the given column. Result is according to and
sorted by `column_name` in the given order (default is descending).
When `k` is small, `topk` is more efficient than `sort`.
Parameters
--------... |
Save the SFrame to a file system for later use.
Parameters
----------
filename : string
The location to save the SFrame. Either a local directory or a
remote URL. If the format is 'binary', a directory will be created
at the location which will contain the sf... | 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 local directory or a
remote URL. If the format is 'binary', a directory will be cr... |
Writes an SFrame to a CSV file.
Parameters
----------
filename : string
The location to save the CSV.
delimiter : string, optional
This describes the delimiter used for writing csv files.
line_terminator: string, optional
The newline charact... | def export_csv(self, filename, delimiter=',', line_terminator='\n',
header=True, quote_level=csv.QUOTE_NONNUMERIC, double_quote=True,
escape_char='\\', quote_char='\"', na_rep='',
file_header='', file_footer='', line_prefix='',
_no_prefix_on_first_value=False, **kwargs):
... |
Writes an SFrame to a JSON file.
Parameters
----------
filename : string
The location to save the JSON file.
orient : string, optional. Either "records" or "lines"
If orient="records" the file is saved as a single JSON array.
If orient="lines", the f... | 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 : string, optional. Either "records" or "lines"... |
Get a reference to the :class:`~turicreate.SArray` that corresponds with
the given column_name. Throws an exception if the column_name is
something other than a string or if the column name is not found.
Parameters
----------
column_name: str
The column name.
... | def select_column(self, column_name):
"""
Get a reference to the :class:`~turicreate.SArray` that corresponds with
the given column_name. Throws an exception if the column_name is
something other than a string or if the column name is not found.
Parameters
----------
... |
Performs an incomplete save of an existing SFrame into a directory.
This saved SFrame may reference SFrames in other locations in the same
filesystem for certain resources.
Parameters
----------
filename : string
The location to save the SFrame. Either a local direct... | def _save_reference(self, filename):
"""
Performs an incomplete save of an existing SFrame into a directory.
This saved SFrame may reference SFrames in other locations in the same
filesystem for certain resources.
Parameters
----------
filename : string
... |
Selects all columns where the name of the column or the type of column
is included in the column_names. An exception is raised if duplicate columns
are selected i.e. sf.select_columns(['a','a']), or non-existent columns
are selected.
Throws an exception for all other input types.
... | def select_columns(self, column_names):
"""
Selects all columns where the name of the column or the type of column
is included in the column_names. An exception is raised if duplicate columns
are selected i.e. sf.select_columns(['a','a']), or non-existent columns
are selected.
... |
Returns an SFrame with a new column. The number of elements in the data
given must match the length of every other column of the SFrame.
If no name is given, a default name is chosen.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame... | 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 length of every other column of the SFrame.
If no name is given, a default name is chosen.
If inplace == False (default) th... |
Returns an SFrame with multiple columns added. The number of
elements in all columns must match the length of every other column of
the SFrame.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this oper... | 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 match the length of every other column of
the SFrame.
If inplace == False (default) this operation does not modify the
... |
Returns an SFrame with a column removed.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
column_name :... | def remove_column(self, column_name, inplace=False):
"""
Returns an SFrame with a column removed.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, ret... |
Returns an SFrame with one or more columns removed.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
co... | def remove_columns(self, column_names, inplace=False):
"""
Returns an SFrame with one or more columns removed.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
... |
Returns an SFrame with columns renamed. ``names`` is expected to be a
dict specifying the old and new names. This changes the names of the
columns given as the keys and replaces them with the names given as the
values.
If inplace == False (default) this operation does not modify the
... | 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 changes the names of the
columns given as the keys and replaces them with the names given as the
values.
If inpla... |
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 column types.
Parameters
----------
other : SFrame
Another SFrame whose rows are appended to the current SFrame.
Returns
... | 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 column types.
Parameters
----------
other : SFrame
Another SFrame whose rows are appended ... |
Perform a group on the key_column_names followed by aggregations on the
columns listed in operations.
The operations parameter is a dictionary that indicates which
aggregation operators to use and which columns to use them on. The
available operators are SUM, MAX, MIN, COUNT, AVG, VAR, ... | def groupby(self, key_column_names, operations, *args):
"""
Perform a group on the key_column_names followed by aggregations on the
columns listed in operations.
The operations parameter is a dictionary that indicates which
aggregation operators to use and which columns to use t... |
Merge two SFrames. Merges the current (left) SFrame with the given
(right) SFrame using a SQL-style equi-join operation by columns.
Parameters
----------
right : SFrame
The SFrame to join.
on : None | str | list | dict, optional
The column name(s) repres... | def join(self, right, on=None, how='inner'):
"""
Merge two SFrames. Merges the current (left) SFrame with the given
(right) SFrame using a SQL-style equi-join operation by columns.
Parameters
----------
right : SFrame
The SFrame to join.
on : None | ... |
Filter an SFrame by values inside an iterable object. Result is an
SFrame that only includes (or excludes) the rows that have a column
with the given ``column_name`` which holds one of the values in the
given ``values`` :class:`~turicreate.SArray`. If ``values`` is not an
SArray, we atte... | def filter_by(self, values, column_name, exclude=False):
"""
Filter an SFrame by values inside an iterable object. Result is an
SFrame that only includes (or excludes) the rows that have a column
with the given ``column_name`` which holds one of the values in the
given ``values``... |
Explore the SFrame in an interactive GUI. Opens a new app window.
Parameters
----------
title : str
The plot title to show for the resulting visualization. Defaults to None.
If the title is None, a default title will be provided.
Returns
-------
... | 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 resulting visualization. Defaults to None.
If the title is None, a default title will be ... |
Pack columns of the current SFrame into one single column. The result
is a new SFrame with the unaffected columns from the original SFrame
plus the newly created column.
The list of columns that are packed is chosen through either the
``column_names`` or ``column_name_prefix`` parameter... | def pack_columns(self, column_names=None, column_name_prefix=None, dtype=list,
fill_na=None, remove_prefix=True, new_column_name=None):
"""
Pack columns of the current SFrame into one single column. The result
is a new SFrame with the unaffected columns from the original SFr... |
Splits a datetime column of SFrame to multiple columns, with each value in a
separate column. Returns a new SFrame with the expanded column replaced with
a list of new columns. The expanded column must be of datetime type.
For more details regarding name generation and
other, refer to :... | 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 value in a
separate column. Returns a new SFrame with the expanded column replaced with
a list of new columns. The expanded co... |
Expand one column of this SFrame to multiple columns with each value in
a separate column. Returns a new SFrame with the unpacked column
replaced with a list of new columns. The column must be of
list/array/dict type.
For more details regarding name generation, missing value handling a... | def unpack(self, column_name=None, column_name_prefix=None, column_types=None,
na_value=None, limit=None):
"""
Expand one column of this SFrame to multiple columns with each value in
a separate column. Returns a new SFrame with the unpacked column
replaced with a list of n... |
Convert a "wide" column of an SFrame to one or two "tall" columns by
stacking all values.
The stack works only for columns of dict, list, or array type. If the
column is dict type, two new columns are created as a result of
stacking: one column holds the key and another column holds th... | 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
stacking all values.
The stack works only for columns of dict, list, or array type. If the
column is dict type, two ... |
Concatenate values from one or two columns into one column, grouping by
all other columns. The resulting column could be of type list, array or
dictionary. If ``column_names`` is a numeric column, the result will be of
array.array type. If ``column_names`` is a non-numeric column, the new colu... | 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 resulting column could be of type list, array or
dictionary. If ``column_names`` is a numeric column, the result will be of
a... |
Sort current SFrame by the given columns, using the given sort order.
Only columns that are type of str, int and float can be sorted.
Parameters
----------
key_column_names : str | list of str | list of (str, bool) pairs
Names of columns to be sorted. The result will be sor... | 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, int and float can be sorted.
Parameters
----------
key_column_names : str | list of str | list of (str, bool) pa... |
Remove missing values from an SFrame. A missing value is either ``None``
or ``NaN``. If ``how`` is 'any', a row will be removed if any of the
columns in the ``columns`` parameter contains at least one missing
value. If ``how`` is 'all', a row will be removed if all of the columns
in th... | 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 will be removed if any of the
columns in the ``columns`` parameter contains at least one missing
value. If ``how`` is '... |
Split rows with missing values from this SFrame. This function has the
same functionality as :py:func:`~turicreate.SFrame.dropna`, but returns a
tuple of two SFrames. The first item is the expected output from
:py:func:`~turicreate.SFrame.dropna`, and the second item contains all the
ro... | def dropna_split(self, columns=None, how='any'):
"""
Split rows with missing values from this SFrame. This function has the
same functionality as :py:func:`~turicreate.SFrame.dropna`, but returns a
tuple of two SFrames. The first item is the expected output from
:py:func:`~turic... |
Fill all missing values with a given value in a given column. If the
``value`` is not the same type as the values in ``column_name``, this method
attempts to convert the value to the original column's type. If this
fails, an error is raised.
Parameters
----------
column_... | 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 ``column_name``, this method
attempts to convert the value to the original column's type. If this
fails, an error is rais... |
Adds the FileDescriptorProto and its types to this pool.
Args:
serialized_file_desc_proto: A bytes string, serialization of the
FileDescriptorProto to add. | def AddSerializedFile(self, serialized_file_desc_proto):
"""Adds the FileDescriptorProto and its types to this pool.
Args:
serialized_file_desc_proto: A bytes string, serialization of the
FileDescriptorProto to add.
"""
# pylint: disable=g-import-not-at-top
from google.protobuf impor... |
Adds a Descriptor to the pool, non-recursively.
If the Descriptor contains nested messages or enums, the caller must
explicitly register them. This method also registers the FileDescriptor
associated with the message.
Args:
desc: A Descriptor. | def AddDescriptor(self, desc):
"""Adds a Descriptor to the pool, non-recursively.
If the Descriptor contains nested messages or enums, the caller must
explicitly register them. This method also registers the FileDescriptor
associated with the message.
Args:
desc: A Descriptor.
"""
if... |
Adds a ServiceDescriptor to the pool.
Args:
service_desc: A ServiceDescriptor. | 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_descri... |
Adds a FieldDescriptor describing an extension to the pool.
Args:
extension: A FieldDescriptor.
Raises:
AssertionError: when another extension with the same number extends the
same message.
TypeError: when the specified extension is not a
descriptor.FieldDescriptor. | def AddExtensionDescriptor(self, extension):
"""Adds a FieldDescriptor describing an extension to the pool.
Args:
extension: A FieldDescriptor.
Raises:
AssertionError: when another extension with the same number extends the
same message.
TypeError: when the specified extension is... |
Adds a FileDescriptor to the pool, non-recursively.
If the FileDescriptor contains messages or enums, the caller must explicitly
register them.
Args:
file_desc: A FileDescriptor. | def AddFileDescriptor(self, file_desc):
"""Adds a FileDescriptor to the pool, non-recursively.
If the FileDescriptor contains messages or enums, the caller must explicitly
register them.
Args:
file_desc: A FileDescriptor.
"""
self._AddFileDescriptor(file_desc)
# TODO(jieluo): This i... |
Adds a FileDescriptor to the pool, non-recursively.
If the FileDescriptor contains messages or enums, the caller must explicitly
register them.
Args:
file_desc: A FileDescriptor. | def _AddFileDescriptor(self, file_desc):
"""Adds a FileDescriptor to the pool, non-recursively.
If the FileDescriptor contains messages or enums, the caller must explicitly
register them.
Args:
file_desc: A FileDescriptor.
"""
if not isinstance(file_desc, descriptor.FileDescriptor):
... |
Gets a FileDescriptor by file name.
Args:
file_name: The path to the file to get a descriptor for.
Returns:
A FileDescriptor for the named file.
Raises:
KeyError: if the file cannot be found in the pool. | 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 FileDescriptor for the named file.
Raises:
KeyError: if the file cannot be found in the pool.
"""
try:
return self._fi... |
Gets the FileDescriptor for the file containing the specified symbol.
Args:
symbol: The name of the symbol to search for.
Returns:
A FileDescriptor that contains the specified symbol.
Raises:
KeyError: if the file cannot be found in the pool. | def FindFileContainingSymbol(self, symbol):
"""Gets the FileDescriptor for the file containing the specified symbol.
Args:
symbol: The name of the symbol to search for.
Returns:
A FileDescriptor that contains the specified symbol.
Raises:
KeyError: if the file cannot be found in the... |
Loads the named descriptor from the pool.
Args:
full_name: The full name of the descriptor to load.
Returns:
The descriptor for the named type.
Raises:
KeyError: if the message cannot be found in the pool. | 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 descriptor for the named type.
Raises:
KeyError: if the message cannot be found in the pool.
"""
full_name = _Norm... |
Loads the named enum descriptor from the pool.
Args:
full_name: The full name of the enum descriptor to load.
Returns:
The enum descriptor for the named type.
Raises:
KeyError: if the enum cannot be found in the pool. | 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:
The enum descriptor for the named type.
Raises:
KeyError: if the enum cannot be found in the pool.
"""
full_nam... |
Loads the named field descriptor from the pool.
Args:
full_name: The full name of the field descriptor to load.
Returns:
The field descriptor for the named field.
Raises:
KeyError: if the field cannot be found in the pool. | 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:
The field descriptor for the named field.
Raises:
KeyError: if the field cannot be found in the pool.
"""
full_na... |
Loads the named service descriptor from the pool.
Args:
full_name: The full name of the service descriptor to load.
Returns:
The service descriptor for the named service.
Raises:
KeyError: if the service cannot be found in the pool. | 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.
Returns:
The service descriptor for the named service.
Raises:
KeyError: if the service cannot be found in the pool.
"""... |
Finds the file in descriptor DB containing the specified symbol.
Args:
symbol: The name of the symbol to search for.
Returns:
A FileDescriptor that contains the specified symbol.
Raises:
KeyError: if the file cannot be found in the descriptor database. | def _FindFileContainingSymbolInDb(self, symbol):
"""Finds the file in descriptor DB containing the specified symbol.
Args:
symbol: The name of the symbol to search for.
Returns:
A FileDescriptor that contains the specified symbol.
Raises:
KeyError: if the file cannot be found in the... |
Creates a FileDescriptor from a proto or returns a cached copy.
This method also has the side effect of loading all the symbols found in
the file into the appropriate dictionaries in the pool.
Args:
file_proto: The proto to convert.
Returns:
A FileDescriptor matching the passed in proto. | def _ConvertFileProtoToFileDescriptor(self, file_proto):
"""Creates a FileDescriptor from a proto or returns a cached copy.
This method also has the side effect of loading all the symbols found in
the file into the appropriate dictionaries in the pool.
Args:
file_proto: The proto to convert.
... |
Adds the proto to the pool in the specified package.
Args:
desc_proto: The descriptor_pb2.DescriptorProto protobuf message.
package: The package the proto should be located in.
file_desc: The file containing this message.
scope: Dict mapping short and full symbols to message and enum types.... | def _ConvertMessageDescriptor(self, desc_proto, package=None, file_desc=None,
scope=None, syntax=None):
"""Adds the proto to the pool in the specified package.
Args:
desc_proto: The descriptor_pb2.DescriptorProto protobuf message.
package: The package the proto shoul... |
Sets all the descriptor's fields's types.
This method also sets the containing types on any extensions.
Args:
package: The current package of desc_proto.
desc_proto: The message descriptor to update.
scope: Enclosing scope of available types. | def _SetAllFieldTypes(self, package, desc_proto, scope):
"""Sets all the descriptor's fields's types.
This method also sets the containing types on any extensions.
Args:
package: The current package of desc_proto.
desc_proto: The message descriptor to update.
scope: Enclosing scope of av... |
Sets the field's type, cpp_type, message_type and enum_type.
Args:
field_proto: Data about the field in proto format.
field_desc: The descriptor to modiy.
package: The package the field's container is in.
scope: Enclosing scope of available types. | 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 the field in proto format.
field_desc: The descriptor to modiy.
package: The package the field's container is in.
scope: Enclos... |
Creates a enum value descriptor object from a enum value proto.
Args:
value_proto: The proto describing the enum value.
index: The index of the enum value.
Returns:
An initialized EnumValueDescriptor object. | def _MakeEnumValueDescriptor(self, value_proto, index):
"""Creates a enum value descriptor object from a enum value proto.
Args:
value_proto: The proto describing the enum value.
index: The index of the enum value.
Returns:
An initialized EnumValueDescriptor object.
"""
return d... |
Make a protobuf ServiceDescriptor given a ServiceDescriptorProto.
Args:
service_proto: The descriptor_pb2.ServiceDescriptorProto protobuf message.
service_index: The index of the service in the File.
scope: Dict mapping short and full symbols to message and enum types.
package: Optional pac... | def _MakeServiceDescriptor(self, service_proto, service_index, scope,
package, file_desc):
"""Make a protobuf ServiceDescriptor given a ServiceDescriptorProto.
Args:
service_proto: The descriptor_pb2.ServiceDescriptorProto protobuf message.
service_index: The index of t... |
Creates a method descriptor from a MethodDescriptorProto.
Args:
method_proto: The proto describing the method.
service_name: The name of the containing service.
package: Optional package name to look up for types.
scope: Scope containing available types.
index: Index of the method in ... | def _MakeMethodDescriptor(self, method_proto, service_name, package, scope,
index):
"""Creates a method descriptor from a MethodDescriptorProto.
Args:
method_proto: The proto describing the method.
service_name: The name of the containing service.
package: Optional... |
Pulls out all the symbols from descriptor protos.
Args:
descriptors: The messages to extract descriptors from.
Yields:
A two element tuple of the type name and descriptor object. | def _ExtractSymbols(self, descriptors):
"""Pulls out all the symbols from descriptor protos.
Args:
descriptors: The messages to extract descriptors from.
Yields:
A two element tuple of the type name and descriptor object.
"""
for desc in descriptors:
yield (_PrefixWithDot(desc.fu... |
Recursively finds dependencies for file protos.
Args:
dependencies: The names of the files being depended on.
Yields:
Each direct and indirect dependency. | def _GetDeps(self, dependencies):
"""Recursively finds dependencies for file protos.
Args:
dependencies: The names of the files being depended on.
Yields:
Each direct and indirect dependency.
"""
for dependency in dependencies:
dep_desc = self.FindFileByName(dependency)
yi... |
Finds a given type name in the current scope.
Args:
package: The package the proto should be located in.
type_name: The name of the type to be found in the scope.
scope: Dict mapping short and full symbols to message and enum types.
Returns:
The descriptor for the requested type. | 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 in.
type_name: The name of the type to be found in the scope.
scope: Dict mapping short and full symbols to message and enum types.
... |
Returns the maximum number in 'elements'. Uses 'ordered' for comparisons,
or '<' is none is provided. | 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]
... |
Returns all of 'elements' for which corresponding element in parallel
list 'rank' is equal to the maximum value in 'rank'. | def select_highest_ranked (elements, ranks):
""" Returns all of 'elements' for which corresponding element in parallel
list 'rank' is equal to the maximum value in 'rank'.
"""
assert is_iterable(elements)
assert is_iterable(ranks)
if not elements:
return []
max_rank = max_elemen... |
Copies the content of the specified message into the current message.
The method clears the current message and then merges the specified
message using MergeFrom.
Args:
other_msg: Message to copy into the current one. | 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 specified
message using MergeFrom.
Args:
other_msg: Message to copy into the current one.
"""
if self is other_msg:
retu... |
Traverse through the tree and append to the tree spec. | def recurse_json(mlkit_tree, xgb_tree_json, tree_id, node_id, feature_map,
force_32bit_float):
"""Traverse through the tree and append to the tree spec.
"""
relative_hit_rate = None
try:
relative_hit_rate = xgb_tree_json['cover']
except KeyError:
pass
# Fill node attri... |
Convert a generic tree model to the protobuf spec.
This currently supports:
* Decision tree regression
Parameters
----------
model: str | Booster
Path on disk where the XGboost JSON representation of the model is or
a handle to the XGboost model.
feature_names : list of stri... | def convert_tree_ensemble(model, feature_names, target, force_32bit_float):
"""Convert a generic tree model to the protobuf spec.
This currently supports:
* Decision tree regression
Parameters
----------
model: str | Booster
Path on disk where the XGboost JSON representation of the m... |
Convert a one-hot-encoder model to the protobuf spec.
Parameters
----------
model: OneHotEncoder
A trained one-hot encoder model.
input_features: str, optional
Name of the input column.
output_features: str, optional
Name of the output column.
Returns
-------
... | def convert(model, input_features, output_features):
"""Convert a one-hot-encoder model to the protobuf spec.
Parameters
----------
model: OneHotEncoder
A trained one-hot encoder model.
input_features: str, optional
Name of the input column.
output_features: str, optional
... |
Given a model that takes an array of dimension input_dimension, returns
the output dimension. | 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(mod... |
Converts a reshape layer from mxnet to coreml.
This doesn't currently handle the deprecated parameters for the reshape layer.
Parameters
----------
net: network
An mxnet network object.
node: layer
Node to convert.
module: module
A module for MXNet
builder: Neura... | def convert_reshape(net, node, module, builder):
"""Converts a reshape layer from mxnet to coreml.
This doesn't currently handle the deprecated parameters for the reshape layer.
Parameters
----------
net: network
An mxnet network object.
node: layer
Node to convert.
modul... |
Convert a scalar multiplication from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_elementwise_mul_scalar(net, node, module, builder):
"""Convert a scalar multiplication from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetwo... |
Convert a dense layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | 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 convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural ... |
Convert a padding layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_padding(net, node, module, builder):
"""Convert a padding layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neu... |
Convert a UpSampling layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_upsample(net, node, module, builder):
"""Convert a UpSampling layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A... |
Convert a softmax layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_softmax(net, node, module, builder):
"""Convert a softmax layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neu... |
Convert highly specific ops | 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(
... |
Convert an embedding layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_embedding(net, node, model, builder):
"""Convert an embedding layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A ne... |
Convert a scalar add layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | 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: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A n... |
Convert a scalar multiply layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_scalar_multiply(net, node, model, builder):
"""Convert a scalar multiply layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
... |
Convert an instance norm layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | def convert_instancenorm(net, node, model, builder):
"""Convert an instance norm layer from mxnet to coreml.
Parameters
----------
net: network
A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
... |
Returns the values stored in the AWS credential environment variables.
Returns the value stored in the AWS_ACCESS_KEY_ID environment variable and
the value stored in the AWS_SECRET_ACCESS_KEY environment variable.
Returns
-------
out : tuple [string]
The first string of the tuple is the val... | def _get_aws_credentials():
"""
Returns the values stored in the AWS credential environment variables.
Returns the value stored in the AWS_ACCESS_KEY_ID environment variable and
the value stored in the AWS_SECRET_ACCESS_KEY environment variable.
Returns
-------
out : tuple [string]
... |
Inject aws credentials into s3 url as s3://[aws_id]:[aws_key]:[bucket/][objectkey]
If s3 url already contains secret key/id pairs, just return as is. | def _try_inject_s3_credentials(url):
"""
Inject aws credentials into s3 url as s3://[aws_id]:[aws_key]:[bucket/][objectkey]
If s3 url already contains secret key/id pairs, just return as is.
"""
assert url.startswith('s3://')
path = url[5:]
# Check if the path already contains credentials
... |
Process user input url string with proper normalization
For all urls:
Expands ~ to $HOME
For S3 urls:
Returns the s3 URL with credentials filled in using turicreate.aws.get_aws_credential().
For example: "s3://mybucket/foo" -> "s3://$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY:mybucket/foo".
... | def _make_internal_url(url):
"""
Process user input url string with proper normalization
For all urls:
Expands ~ to $HOME
For S3 urls:
Returns the s3 URL with credentials filled in using turicreate.aws.get_aws_credential().
For example: "s3://mybucket/foo" -> "s3://$AWS_ACCESS_KEY_ID:$... |
Utility function that returns True if the path provided is a directory that has an SFrame or SGraph in it.
SFrames are written to disk as a directory archive, this function identifies if a given directory is an archive
for an SFrame.
Parameters
----------
path : string
Directory to evaluat... | 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 written to disk as a directory archive, this function identifies if a given directory is an archive
for an SFrame.
Parameters
----------
... |
Returns the contents type for the provided archive path.
Parameters
----------
path : string
Directory to evaluate.
Returns
-------
Returns a string of: sframe, sgraph, raises TypeError for anything else | def get_archive_type(path):
"""
Returns the contents type for the provided archive path.
Parameters
----------
path : string
Directory to evaluate.
Returns
-------
Returns a string of: sframe, sgraph, raises TypeError for anything else
"""
if not is_directory_archive(pa... |
Create an SFrame containing the crossproduct of all provided options.
Parameters
----------
d : dict
Each key is the name of an option, and each value is a list
of the possible values for that option.
Returns
-------
out : SFrame
There will be a column for each key in t... | 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 value is a list
of the possible values for that option.
Returns
-------
out : SFrame
There w... |
Given url where a Turi Create object is persisted, return the Turi
Create object type: 'model', 'graph', 'sframe', or 'sarray' | def get_turicreate_object_type(url):
'''
Given url where a Turi Create object is persisted, return the Turi
Create object type: 'model', 'graph', 'sframe', or 'sarray'
'''
from .._connect import main as _glconnect
ret = _glconnect.get_unity().get_turicreate_object_type(_make_internal_url(url))
... |
Assert the two SFrames are equal.
The default behavior of this function uses the strictest possible
definition of equality, where all columns must be in the same order, with
the same names and have the same data in the same order. Each of these
stipulations can be relaxed individually and in concert w... | 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 are equal.
The default... |
Returns user specified temporary file location.
The temporary location is specified through:
>>> turicreate.config.set_runtime_config('TURI_CACHE_FILE_LOCATIONS', ...) | def _get_temp_file_location():
'''
Returns user specified temporary file location.
The temporary location is specified through:
>>> turicreate.config.set_runtime_config('TURI_CACHE_FILE_LOCATIONS', ...)
'''
from .._connect import main as _glconnect
unity = _glconnect.get_unity()
cache_... |
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 | 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 = ... |
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 | 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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