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def add_enumerated_multiarray_shapes(spec, feature_name, shapes):
"""
Annotate an input or output multiArray feature in a Neural Network spec to
to accommodate a list of enumerated array shapes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
Th... |
def add_enumerated_image_sizes(spec, feature_name, sizes):
"""
Annotate an input or output image feature in a Neural Network spec to
to accommodate a list of enumerated image sizes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the... |
def update_image_size_range(spec, feature_name, size_range):
"""
Annotate an input or output Image feature in a Neural Network spec to
to accommodate a range of image sizes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the Image f... |
def update_multiarray_shape_range(spec, feature_name, shape_range):
"""
Annotate an input or output MLMultiArray feature in a Neural Network spec
to accommodate a range of shapes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the f... |
def get_allowed_shape_ranges(spec):
"""
For a given model specification, returns a dictionary with a shape range object for each input feature name.
"""
shaper = NeuralNetworkShaper(spec, False)
inputs = _get_input_names(spec)
output = {}
for input in inputs:
output[input] = shaper... |
def can_allow_multiple_input_shapes(spec):
"""
Examines a model specification and determines if it can compute results for more than one output shape.
:param spec: MLModel
The protobuf specification of the model.
:return: Bool
Returns True if the model can allow multiple input shapes, ... |
def isFlexible(self):
"""
Returns true if any one of the channel, height, or width ranges of this shape allow more than one input value.
"""
for key, value in self.arrayShapeRange.items():
if key in _CONSTRAINED_KEYS:
if value.isFlexible:
r... |
def define_macro(out_f, (name, args, body), undefine=False, check=True):
"""Generate a macro definition or undefinition"""
if undefine:
out_f.write(
'#undef {0}\n'
.format(macro_name(name))
)
else:
if args:
arg_list = '({0})'.format(', '.join(args)... |
def filename(out_dir, name, undefine=False):
"""Generate the filename"""
if undefine:
prefix = 'undef_'
else:
prefix = ''
return os.path.join(out_dir, '{0}{1}.hpp'.format(prefix, name.lower())) |
def length_limits(max_length_limit, length_limit_step):
"""Generates the length limits"""
string_len = len(str(max_length_limit))
return [
str(i).zfill(string_len) for i in
xrange(
length_limit_step,
max_length_limit + length_limit_step - 1,
length_limit_s... |
def generate_take(out_f, steps, line_prefix):
"""Generate the take function"""
out_f.write(
'{0}constexpr inline int take(int n_)\n'
'{0}{{\n'
'{0} return {1} 0 {2};\n'
'{0}}}\n'
'\n'.format(
line_prefix,
''.join('n_ >= {0} ? {0} : ('.format(s) fo... |
def generate_make_string(out_f, max_step):
"""Generate the make_string template"""
steps = [2 ** n for n in xrange(int(math.log(max_step, 2)), -1, -1)]
with Namespace(
out_f,
['boost', 'metaparse', 'v{0}'.format(VERSION), 'impl']
) as nsp:
generate_take(out_f, steps, nsp.prefix(... |
def generate_string(out_dir, limits):
"""Generate string.hpp"""
max_limit = max((int(v) for v in limits))
with open(filename(out_dir, 'string'), 'wb') as out_f:
with IncludeGuard(out_f):
out_f.write(
'\n'
'#include <boost/metaparse/v{0}/cpp11/impl/concat.... |
def existing_path(value):
"""Throws when the path does not exist"""
if os.path.exists(value):
return value
else:
raise argparse.ArgumentTypeError("Path {0} not found".format(value)) |
def main():
"""The main function of the script"""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
'--boost_dir',
required=False,
type=existing_path,
help='The path to the include/boost directory of Metaparse'
)
parser.add_argument(
'... |
def begin(self):
"""Generate the beginning part"""
self.out_f.write('\n')
for depth, name in enumerate(self.names):
self.out_f.write(
'{0}namespace {1}\n{0}{{\n'.format(self.prefix(depth), name)
) |
def end(self):
"""Generate the closing part"""
for depth in xrange(len(self.names) - 1, -1, -1):
self.out_f.write('{0}}}\n'.format(self.prefix(depth))) |
def begin(self):
"""Generate the beginning part"""
name = 'BOOST_METAPARSE_V1_CPP11_IMPL_STRING_HPP'
self.out_f.write('#ifndef {0}\n#define {0}\n'.format(name))
write_autogen_info(self.out_f) |
def get_deep_features(audio_data, verbose=True):
'''
Calculates the deep features used by the Sound Classifier.
Internally the Sound Classifier calculates deep features for both model
creation and predictions. If the same data will be used multiple times,
calculating the deep features just once wil... |
def create(dataset, target, feature, max_iterations=10,
custom_layer_sizes=[100, 100], verbose=True,
validation_set='auto', batch_size=64):
'''
Creates a :class:`SoundClassifier` model.
Parameters
----------
dataset : SFrame
Input data. The column named by the 'feature... |
def _load_version(cls, state, version):
"""
A function to load a previously saved SoundClassifier instance.
"""
from ._audio_feature_extractor import _get_feature_extractor
from .._mxnet import _mxnet_utils
state['_feature_extractor'] = _get_feature_extractor(state['feat... |
def classify(self, dataset, verbose=True, batch_size=64):
"""
Return the classification for each examples in the ``dataset``.
The output SFrame contains predicted class labels and its probability.
Parameters
----------
dataset : SFrame | SArray | dict
The aud... |
def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset to use for evaluation, must include a c... |
def export_coreml(self, filename):
"""
Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('./myModel.mlmodel')
"""
import coremltools
from coremltools.proto.FeatureTypes_pb2 import A... |
def predict(self, dataset, output_type='class', verbose=True, batch_size=64):
"""
Return predictions for ``dataset``. Predictions can be generated
as class labels or probabilities.
Parameters
----------
dataset : SFrame | SArray | dict
The audio data to be cl... |
def predict_topk(self, dataset, output_type='probability', k=3, verbose=True, batch_size=64):
"""
Return top-k predictions for the ``dataset``.
Predictions are returned as an SFrame with three columns: `id`,
`class`, and `probability` or `rank` depending on the ``output_type``
pa... |
def _init_data(data, allow_empty, default_name):
"""Convert data into canonical form."""
assert (data is not None) or allow_empty
if data is None:
data = []
if isinstance(data, (np.ndarray, NDArray)):
data = [data]
if isinstance(data, list):
if not allow_empty:
a... |
def provide_data(self):
"""The name and shape of data provided by this iterator"""
return [(k, tuple([self.batch_size] + list(v.shape[1:]))) for k, v in self.data] |
def provide_label(self):
"""The name and shape of label provided by this iterator"""
return [(k, tuple([self.batch_size] + list(v.shape[1:]))) for k, v in self.label] |
def reset ():
""" Clear the module state. This is mainly for testing purposes.
"""
global __generators, __type_to_generators, __generators_for_toolset, __construct_stack
global __overrides, __active_generators
global __viable_generators_cache, __viable_source_types_cache
global __vstg_cached_gen... |
def register (g):
""" Registers new generator instance 'g'.
"""
assert isinstance(g, Generator)
id = g.id()
__generators [id] = g
# A generator can produce several targets of the
# same type. We want unique occurence of that generator
# in .generators.$(t) in that case, otherwise, it w... |
def register_standard (id, source_types, target_types, requirements = []):
""" Creates new instance of the 'generator' class and registers it.
Returns the creates instance.
Rationale: the instance is returned so that it's possible to first register
a generator and then call 'run' method on t... |
def override (overrider_id, overridee_id):
"""Make generator 'overrider-id' be preferred to
'overridee-id'. If, when searching for generators
that could produce a target of certain type,
both those generators are amoung viable generators,
the overridden generator is immediately discarded.
The o... |
def __viable_source_types_real (target_type):
""" Returns a list of source type which can possibly be converted
to 'target_type' by some chain of generator invocation.
More formally, takes all generators for 'target_type' and
returns union of source types for those generators and result
... |
def viable_source_types (target_type):
""" Helper rule, caches the result of '__viable_source_types_real'.
"""
assert isinstance(target_type, basestring)
if target_type not in __viable_source_types_cache:
__vst_cached_types.append(target_type)
__viable_source_types_cache [target_type] = ... |
def viable_source_types_for_generator_real (generator):
""" Returns the list of source types, which, when passed to 'run'
method of 'generator', has some change of being eventually used
(probably after conversion by other generators)
"""
assert isinstance(generator, Generator)
source_typ... |
def viable_source_types_for_generator (generator):
""" Caches the result of 'viable_source_types_for_generator'.
"""
assert isinstance(generator, Generator)
if generator not in __viable_source_types_cache:
__vstg_cached_generators.append(generator)
__viable_source_types_cache[generator] ... |
def try_one_generator_really (project, name, generator, target_type, properties, sources):
""" Returns usage requirements + list of created targets.
"""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or n... |
def try_one_generator (project, name, generator, target_type, properties, sources):
""" Checks if generator invocation can be pruned, because it's guaranteed
to fail. If so, quickly returns empty list. Otherwise, calls
try_one_generator_really.
"""
if __debug__:
from .targets import ... |
def __ensure_type (targets):
""" Ensures all 'targets' have types. If this is not so, exists with
error.
"""
assert is_iterable_typed(targets, virtual_target.VirtualTarget)
for t in targets:
if not t.type ():
get_manager().errors()("target '%s' has no type" % str (t)) |
def find_viable_generators_aux (target_type, prop_set):
""" Returns generators which can be used to construct target of specified type
with specified properties. Uses the following algorithm:
- iterates over requested target_type and all it's bases (in the order returned bt
type.all-bases.... |
def __construct_really (project, name, target_type, prop_set, sources):
""" Attempts to construct target by finding viable generators, running them
and selecting the dependency graph.
"""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
... |
def construct (project, name, target_type, prop_set, sources, top_level=False):
""" Attempts to create target of 'target-type' with 'properties'
from 'sources'. The 'sources' are treated as a collection of
*possible* ingridients -- i.e. it is not required to consume
them all. If 'multiple' i... |
def clone (self, new_id, new_toolset_properties):
""" Returns another generator which differers from $(self) in
- id
- value to <toolset> feature in properties
"""
assert isinstance(new_id, basestring)
assert is_iterable_typed(new_toolset_properties, basestrin... |
def clone_and_change_target_type(self, base, type):
"""Creates another generator that is the same as $(self), except that
if 'base' is in target types of $(self), 'type' will in target types
of the new generator."""
assert isinstance(base, basestring)
assert isinstance(type, base... |
def match_rank (self, ps):
""" Returns true if the generator can be run with the specified
properties.
"""
# See if generator's requirements are satisfied by
# 'properties'. Treat a feature name in requirements
# (i.e. grist-only element), as matching any value of th... |
def run (self, project, name, prop_set, sources):
""" Tries to invoke this generator on the given sources. Returns a
list of generated targets (instances of 'virtual-target').
project: Project for which the targets are generated.
name: Determines the name o... |
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... |
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
... |
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... |
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... |
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... |
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... |
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... |
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... |
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... |
def _read_csv_impl(cls,
url,
delimiter=',',
header=True,
error_bad_lines=False,
comment_char='',
escape_char='\\',
double_quote=True,
qu... |
def read_csv_with_errors(cls,
url,
delimiter=',',
header=True,
comment_char='',
escape_char='\\',
double_quote=True,
... |
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... |
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... |
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... |
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... |
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 ... |
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... |
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... |
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... |
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... |
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... |
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... |
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
--------... |
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... |
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):
... |
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"... |
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
... |
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
----------
... |
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.
... |
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... |
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
... |
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... |
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
... |
def swap_columns(self, column_name_1, column_name_2, inplace=False):
"""
Returns an SFrame with two column positions swapped.
If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies 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... |
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 ... |
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... |
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 | ... |
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``... |
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 ... |
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... |
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... |
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... |
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 ... |
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... |
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... |
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 '... |
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... |
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... |
def add_row_number(self, column_name='id', start=0, inplace=False):
"""
Returns an SFrame with a new column that numbers each row
sequentially. By default the count starts at 0, but this can be changed
to a positive or negative number. The new column will be named with
the given... |
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... |
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... |
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... |
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