id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1 value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
29,000 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | can_allow_multiple_input_shapes | 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, False otherwise.
"""
# First, check that the model actually has a neural network in it
try:
layers = _get_nn_layers(spec)
except:
raise Exception('Unable to verify that this model contains a neural network.')
try:
shaper = NeuralNetworkShaper(spec, False)
except:
raise Exception('Unable to compute shapes for this neural network.')
inputs = _get_input_names(spec)
for name in inputs:
shape_dict = shaper.shape(name)
shape = NeuralNetworkMultiArrayShapeRange(shape_dict)
if (shape.isFlexible()):
return True
return False | python | 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, False otherwise.
"""
# First, check that the model actually has a neural network in it
try:
layers = _get_nn_layers(spec)
except:
raise Exception('Unable to verify that this model contains a neural network.')
try:
shaper = NeuralNetworkShaper(spec, False)
except:
raise Exception('Unable to compute shapes for this neural network.')
inputs = _get_input_names(spec)
for name in inputs:
shape_dict = shaper.shape(name)
shape = NeuralNetworkMultiArrayShapeRange(shape_dict)
if (shape.isFlexible()):
return True
return False | [
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:param spec: MLModel
The protobuf specification of the model.
:return: Bool
Returns True if the model can allow multiple input shapes, False otherwise. | [
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29,001 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | NeuralNetworkMultiArrayShapeRange.isFlexible | 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:
return True
return False | python | 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:
return True
return False | [
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29,002 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | define_macro | 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))
else:
arg_list = ''
if check:
out_f.write(
'#ifdef {0}\n'
'# error {0} already defined.\n'
'#endif\n'
.format(macro_name(name))
)
out_f.write(
'#define {0}{1} {2}\n'.format(macro_name(name), arg_list, body)
) | python | 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))
else:
arg_list = ''
if check:
out_f.write(
'#ifdef {0}\n'
'# error {0} already defined.\n'
'#endif\n'
.format(macro_name(name))
)
out_f.write(
'#define {0}{1} {2}\n'.format(macro_name(name), arg_list, body)
) | [
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29,003 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | filename | 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())) | python | 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())) | [
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29,004 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | length_limits | 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_step
)
] | python | 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_step
)
] | [
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29,005 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | generate_take | 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) for s in steps),
')' * len(steps)
)
) | python | 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) for s in steps),
')' * len(steps)
)
) | [
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29,006 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | generate_make_string | 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())
out_f.write(
'{0}template <int LenNow, int LenRemaining, char... Cs>\n'
'{0}struct make_string;\n'
'\n'
'{0}template <char... Cs>'
' struct make_string<0, 0, Cs...> : string<> {{}};\n'
.format(nsp.prefix())
)
disable_sun = False
for i in reversed(steps):
if i > 64 and not disable_sun:
out_f.write('#ifndef __SUNPRO_CC\n')
disable_sun = True
out_f.write(
'{0}template <int LenRemaining,{1}char... Cs>'
' struct make_string<{2},LenRemaining,{3}Cs...> :'
' concat<string<{4}>,'
' typename make_string<take(LenRemaining),'
'LenRemaining-take(LenRemaining),Cs...>::type> {{}};\n'
.format(
nsp.prefix(),
''.join('char {0},'.format(n) for n in unique_names(i)),
i,
''.join('{0},'.format(n) for n in unique_names(i)),
','.join(unique_names(i))
)
)
if disable_sun:
out_f.write('#endif\n') | python | 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())
out_f.write(
'{0}template <int LenNow, int LenRemaining, char... Cs>\n'
'{0}struct make_string;\n'
'\n'
'{0}template <char... Cs>'
' struct make_string<0, 0, Cs...> : string<> {{}};\n'
.format(nsp.prefix())
)
disable_sun = False
for i in reversed(steps):
if i > 64 and not disable_sun:
out_f.write('#ifndef __SUNPRO_CC\n')
disable_sun = True
out_f.write(
'{0}template <int LenRemaining,{1}char... Cs>'
' struct make_string<{2},LenRemaining,{3}Cs...> :'
' concat<string<{4}>,'
' typename make_string<take(LenRemaining),'
'LenRemaining-take(LenRemaining),Cs...>::type> {{}};\n'
.format(
nsp.prefix(),
''.join('char {0},'.format(n) for n in unique_names(i)),
i,
''.join('{0},'.format(n) for n in unique_names(i)),
','.join(unique_names(i))
)
)
if disable_sun:
out_f.write('#endif\n') | [
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py#L160-L199 |
29,007 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | generate_string | 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.hpp>\n'
'#include <boost/preprocessor/cat.hpp>\n'
.format(VERSION)
)
generate_make_string(out_f, 512)
out_f.write(
'\n'
'#ifndef BOOST_METAPARSE_LIMIT_STRING_SIZE\n'
'# error BOOST_METAPARSE_LIMIT_STRING_SIZE not defined\n'
'#endif\n'
'\n'
'#if BOOST_METAPARSE_LIMIT_STRING_SIZE > {0}\n'
'# error BOOST_METAPARSE_LIMIT_STRING_SIZE is greater than'
' {0}. To increase the limit run tools/string_headers.py of'
' Boost.Metaparse against your Boost headers.\n'
'#endif\n'
'\n'
.format(max_limit)
)
define_macro(out_f, (
'STRING',
['s'],
'{0}::make_string< '
'{0}::take(sizeof(s)-1), sizeof(s)-1-{0}::take(sizeof(s)-1),'
'BOOST_PP_CAT({1}, BOOST_METAPARSE_LIMIT_STRING_SIZE)(s)'
'>::type'
.format(
'::boost::metaparse::v{0}::impl'.format(VERSION),
macro_name('I')
)
))
out_f.write('\n')
for limit in xrange(0, max_limit + 1):
out_f.write(
'#define {0} {1}\n'
.format(
macro_name('I{0}'.format(limit)),
macro_name('INDEX_STR{0}'.format(
min(int(l) for l in limits if int(l) >= limit)
))
)
)
out_f.write('\n')
prev_macro = None
prev_limit = 0
for length_limit in (int(l) for l in limits):
this_macro = macro_name('INDEX_STR{0}'.format(length_limit))
out_f.write(
'#define {0}(s) {1}{2}\n'
.format(
this_macro,
'{0}(s),'.format(prev_macro) if prev_macro else '',
','.join(
'{0}((s), {1})'
.format(macro_name('STRING_AT'), i)
for i in xrange(prev_limit, length_limit)
)
)
)
prev_macro = this_macro
prev_limit = length_limit | python | 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.hpp>\n'
'#include <boost/preprocessor/cat.hpp>\n'
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out_f.write('\n')
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29,008 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | existing_path | 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)) | python | 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)) | [
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29,009 | apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | Namespace.end | 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))) | python | 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))) | [
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29,010 | apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | get_deep_features | 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 will result in a significant speed
up.
Parameters
----------
audio_data : SArray
Audio data is represented as dicts with key 'data' and 'sample_rate',
see `turicreate.load_audio(...)`.
Examples
--------
>>> my_audio_data['deep_features'] = get_deep_features(my_audio_data['audio'])
>>> train, test = my_audio_data.random_split(.8)
>>> model = tc.sound_classifier.create(train, 'label', 'deep_features')
>>> predictions = model.predict(test)
'''
from ._audio_feature_extractor import _get_feature_extractor
if not _is_audio_data_sarray(audio_data):
raise TypeError("Input must be audio data")
feature_extractor_name = 'VGGish'
feature_extractor = _get_feature_extractor(feature_extractor_name)
return feature_extractor.get_deep_features(audio_data, verbose=verbose) | python | 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 will result in a significant speed
up.
Parameters
----------
audio_data : SArray
Audio data is represented as dicts with key 'data' and 'sample_rate',
see `turicreate.load_audio(...)`.
Examples
--------
>>> my_audio_data['deep_features'] = get_deep_features(my_audio_data['audio'])
>>> train, test = my_audio_data.random_split(.8)
>>> model = tc.sound_classifier.create(train, 'label', 'deep_features')
>>> predictions = model.predict(test)
'''
from ._audio_feature_extractor import _get_feature_extractor
if not _is_audio_data_sarray(audio_data):
raise TypeError("Input must be audio data")
feature_extractor_name = 'VGGish'
feature_extractor = _get_feature_extractor(feature_extractor_name)
return feature_extractor.get_deep_features(audio_data, verbose=verbose) | [
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Parameters
----------
audio_data : SArray
Audio data is represented as dicts with key 'data' and 'sample_rate',
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Examples
--------
>>> my_audio_data['deep_features'] = get_deep_features(my_audio_data['audio'])
>>> train, test = my_audio_data.random_split(.8)
>>> model = tc.sound_classifier.create(train, 'label', 'deep_features')
>>> predictions = model.predict(test) | [
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29,011 | apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | SoundClassifier._load_version | def _load_version(cls, state, version):
"""
A function to load a previously saved SoundClassifier instance.
"""
from ._audio_feature_extractor import _get_feature_extractor
from .._mxnet import _mxnet_utils
state['_feature_extractor'] = _get_feature_extractor(state['feature_extractor_name'])
# Load the custom nerual network
num_classes = state['num_classes']
num_inputs = state['_feature_extractor'].output_length
if 'custom_layer_sizes' in state:
# These are deserialized as floats
custom_layer_sizes = list(map(int, state['custom_layer_sizes']))
else:
# Default value, was not part of state for only Turi Create 5.4
custom_layer_sizes = [100, 100]
state['custom_layer_sizes'] = custom_layer_sizes
net = SoundClassifier._build_custom_neural_network(num_inputs, num_classes, custom_layer_sizes)
net_params = net.collect_params()
ctx = _mxnet_utils.get_mxnet_context()
_mxnet_utils.load_net_params_from_state(net_params, state['_custom_classifier'], ctx=ctx)
state['_custom_classifier'] = net
return SoundClassifier(state) | python | def _load_version(cls, state, version):
"""
A function to load a previously saved SoundClassifier instance.
"""
from ._audio_feature_extractor import _get_feature_extractor
from .._mxnet import _mxnet_utils
state['_feature_extractor'] = _get_feature_extractor(state['feature_extractor_name'])
# Load the custom nerual network
num_classes = state['num_classes']
num_inputs = state['_feature_extractor'].output_length
if 'custom_layer_sizes' in state:
# These are deserialized as floats
custom_layer_sizes = list(map(int, state['custom_layer_sizes']))
else:
# Default value, was not part of state for only Turi Create 5.4
custom_layer_sizes = [100, 100]
state['custom_layer_sizes'] = custom_layer_sizes
net = SoundClassifier._build_custom_neural_network(num_inputs, num_classes, custom_layer_sizes)
net_params = net.collect_params()
ctx = _mxnet_utils.get_mxnet_context()
_mxnet_utils.load_net_params_from_state(net_params, state['_custom_classifier'], ctx=ctx)
state['_custom_classifier'] = net
return SoundClassifier(state) | [
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29,012 | apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | SoundClassifier.classify | 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 audio data to be classified.
If dataset is an SFrame, it must have a column with the same name as
the feature used for model training, but does not require a target
column. Additional columns are ignored.
verbose : bool, optional
If True, prints progress updates and model details.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve performance.
Returns
-------
out : SFrame
An SFrame with model predictions, both class labels and probabilities.
See Also
----------
create, evaluate, predict
Examples
----------
>>> classes = model.classify(data)
"""
prob_vector = self.predict(dataset, output_type='probability_vector',
verbose=verbose, batch_size=batch_size)
id_to_label = self._id_to_class_label
return _tc.SFrame({
'class': prob_vector.apply(lambda v: id_to_label[_np.argmax(v)]),
'probability': prob_vector.apply(_np.max)
}) | python | 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 audio data to be classified.
If dataset is an SFrame, it must have a column with the same name as
the feature used for model training, but does not require a target
column. Additional columns are ignored.
verbose : bool, optional
If True, prints progress updates and model details.
batch_size : int, optional
If you are getting memory errors, try decreasing this value. If you
have a powerful computer, increasing this value may improve performance.
Returns
-------
out : SFrame
An SFrame with model predictions, both class labels and probabilities.
See Also
----------
create, evaluate, predict
Examples
----------
>>> classes = model.classify(data)
"""
prob_vector = self.predict(dataset, output_type='probability_vector',
verbose=verbose, batch_size=batch_size)
id_to_label = self._id_to_class_label
return _tc.SFrame({
'class': prob_vector.apply(lambda v: id_to_label[_np.argmax(v)]),
'probability': prob_vector.apply(_np.max)
}) | [
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create, evaluate, predict
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29,013 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mx_sframe_iter.py | _init_data | 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)):
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if not allow_empty:
assert(len(data) > 0)
if len(data) == 1:
data = OrderedDict([(default_name, data[0])])
else:
data = OrderedDict([('_%d_%s' % (i, default_name), d) for i, d in enumerate(data)])
if not isinstance(data, dict):
raise TypeError("Input must be NDArray, numpy.ndarray, " + \
"a list of them or dict with them as values")
for k, v in data.items():
if isinstance(v, NDArray):
data[k] = v.asnumpy()
for k, v in data.items():
if not isinstance(v, np.ndarray):
raise TypeError(("Invalid type '%s' for %s, " % (type(v), k)) + \
"should be NDArray or numpy.ndarray")
return list(data.items()) | python | def _init_data(data, allow_empty, default_name):
"""Convert data into canonical form."""
assert (data is not None) or allow_empty
if data is None:
data = []
if isinstance(data, (np.ndarray, NDArray)):
data = [data]
if isinstance(data, list):
if not allow_empty:
assert(len(data) > 0)
if len(data) == 1:
data = OrderedDict([(default_name, data[0])])
else:
data = OrderedDict([('_%d_%s' % (i, default_name), d) for i, d in enumerate(data)])
if not isinstance(data, dict):
raise TypeError("Input must be NDArray, numpy.ndarray, " + \
"a list of them or dict with them as values")
for k, v in data.items():
if isinstance(v, NDArray):
data[k] = v.asnumpy()
for k, v in data.items():
if not isinstance(v, np.ndarray):
raise TypeError(("Invalid type '%s' for %s, " % (type(v), k)) + \
"should be NDArray or numpy.ndarray")
return list(data.items()) | [
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29,014 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mx_sframe_iter.py | SFrameIter.provide_data | 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] | python | def provide_data(self):
"""The name and shape of data provided by this iterator"""
return [(k, tuple([self.batch_size] + list(v.shape[1:]))) for k, v in self.data] | [
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29,015 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mx_sframe_iter.py | SFrameIter.provide_label | 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] | python | def provide_label(self):
"""The name and shape of label provided by this iterator"""
return [(k, tuple([self.batch_size] + list(v.shape[1:]))) for k, v in self.label] | [
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29,016 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | register | def register (g):
""" Registers new generator instance 'g'.
"""
assert isinstance(g, Generator)
id = g.id()
__generators [id] = g
# A generator can produce several targets of the
# same type. We want unique occurence of that generator
# in .generators.$(t) in that case, otherwise, it will
# be tried twice and we'll get false ambiguity.
for t in sequence.unique(g.target_types()):
__type_to_generators.setdefault(t, []).append(g)
# Update the set of generators for toolset
# TODO: should we check that generator with this id
# is not already registered. For example, the fop.jam
# module intentionally declared two generators with the
# same id, so such check will break it.
# Some generators have multiple periods in their name, so the
# normal $(id:S=) won't generate the right toolset name.
# e.g. if id = gcc.compile.c++, then
# .generators-for-toolset.$(id:S=) will append to
# .generators-for-toolset.gcc.compile, which is a separate
# value from .generators-for-toolset.gcc. Correcting this
# makes generator inheritance work properly.
# See also inherit-generators in module toolset
base = id.split ('.', 100) [0]
__generators_for_toolset.setdefault(base, []).append(g)
# After adding a new generator that can construct new target types, we need
# to clear the related cached viable source target type information for
# constructing a specific target type or using a specific generator. Cached
# viable source target type lists affected by this are those containing any
# of the target types constructed by the new generator or any of their base
# target types.
#
# A more advanced alternative to clearing that cached viable source target
# type information would be to expand it with additional source types or
# even better - mark it as needing to be expanded on next use.
#
# For now we just clear all the cached viable source target type information
# that does not simply state 'all types' and may implement a more detailed
# algorithm later on if it becomes needed.
invalidate_extendable_viable_source_target_type_cache() | python | def register (g):
""" Registers new generator instance 'g'.
"""
assert isinstance(g, Generator)
id = g.id()
__generators [id] = g
# A generator can produce several targets of the
# same type. We want unique occurence of that generator
# in .generators.$(t) in that case, otherwise, it will
# be tried twice and we'll get false ambiguity.
for t in sequence.unique(g.target_types()):
__type_to_generators.setdefault(t, []).append(g)
# Update the set of generators for toolset
# TODO: should we check that generator with this id
# is not already registered. For example, the fop.jam
# module intentionally declared two generators with the
# same id, so such check will break it.
# Some generators have multiple periods in their name, so the
# normal $(id:S=) won't generate the right toolset name.
# e.g. if id = gcc.compile.c++, then
# .generators-for-toolset.$(id:S=) will append to
# .generators-for-toolset.gcc.compile, which is a separate
# value from .generators-for-toolset.gcc. Correcting this
# makes generator inheritance work properly.
# See also inherit-generators in module toolset
base = id.split ('.', 100) [0]
__generators_for_toolset.setdefault(base, []).append(g)
# After adding a new generator that can construct new target types, we need
# to clear the related cached viable source target type information for
# constructing a specific target type or using a specific generator. Cached
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#
# For now we just clear all the cached viable source target type information
# that does not simply state 'all types' and may implement a more detailed
# algorithm later on if it becomes needed.
invalidate_extendable_viable_source_target_type_cache() | [
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29,017 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | override | 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 overridden generators are discarded immediately
after computing the list of viable generators, before
running any of them."""
assert isinstance(overrider_id, basestring)
assert isinstance(overridee_id, basestring)
__overrides.setdefault(overrider_id, []).append(overridee_id) | python | 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 overridden generators are discarded immediately
after computing the list of viable generators, before
running any of them."""
assert isinstance(overrider_id, basestring)
assert isinstance(overridee_id, basestring)
__overrides.setdefault(overrider_id, []).append(overridee_id) | [
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29,018 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | __viable_source_types_real | 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
of calling itself recusrively on source types.
"""
assert isinstance(target_type, basestring)
generators = []
# 't0' is the initial list of target types we need to process to get a list
# of their viable source target types. New target types will not be added to
# this list.
t0 = type.all_bases (target_type)
# 't' is the list of target types which have not yet been processed to get a
# list of their viable source target types. This list will get expanded as
# we locate more target types to process.
t = t0
result = []
while t:
# Find all generators for current type.
# Unlike 'find_viable_generators' we don't care about prop_set.
generators = __type_to_generators.get (t [0], [])
t = t[1:]
for g in generators:
if not g.source_types():
# Empty source types -- everything can be accepted
result = "*"
# This will terminate outer loop.
t = None
break
for source_type in g.source_types ():
if not source_type in result:
# If generator accepts 'source_type' it
# will happily accept any type derived from it
all = type.all_derived (source_type)
for n in all:
if not n in result:
# Here there is no point in adding target types to
# the list of types to process in case they are or
# have already been on that list. We optimize this
# check by realizing that we only need to avoid the
# original target type's base types. Other target
# types that are or have been on the list of target
# types to process have been added to the 'result'
# list as well and have thus already been eliminated
# by the previous if.
if not n in t0:
t.append (n)
result.append (n)
return result | python | 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
of calling itself recusrively on source types.
"""
assert isinstance(target_type, basestring)
generators = []
# 't0' is the initial list of target types we need to process to get a list
# of their viable source target types. New target types will not be added to
# this list.
t0 = type.all_bases (target_type)
# 't' is the list of target types which have not yet been processed to get a
# list of their viable source target types. This list will get expanded as
# we locate more target types to process.
t = t0
result = []
while t:
# Find all generators for current type.
# Unlike 'find_viable_generators' we don't care about prop_set.
generators = __type_to_generators.get (t [0], [])
t = t[1:]
for g in generators:
if not g.source_types():
# Empty source types -- everything can be accepted
result = "*"
# This will terminate outer loop.
t = None
break
for source_type in g.source_types ():
if not source_type in result:
# If generator accepts 'source_type' it
# will happily accept any type derived from it
all = type.all_derived (source_type)
for n in all:
if not n in result:
# Here there is no point in adding target types to
# the list of types to process in case they are or
# have already been on that list. We optimize this
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# types to process have been added to the 'result'
# list as well and have thus already been eliminated
# by the previous if.
if not n in t0:
t.append (n)
result.append (n)
return result | [
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29,019 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | viable_source_types_for_generator | def viable_source_types_for_generator (generator):
""" Caches the result of 'viable_source_types_for_generator'.
"""
assert isinstance(generator, Generator)
if generator not in __viable_source_types_cache:
__vstg_cached_generators.append(generator)
__viable_source_types_cache[generator] = viable_source_types_for_generator_real (generator)
return __viable_source_types_cache[generator] | python | def viable_source_types_for_generator (generator):
""" Caches the result of 'viable_source_types_for_generator'.
"""
assert isinstance(generator, Generator)
if generator not in __viable_source_types_cache:
__vstg_cached_generators.append(generator)
__viable_source_types_cache[generator] = viable_source_types_for_generator_real (generator)
return __viable_source_types_cache[generator] | [
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29,020 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | try_one_generator_really | def try_one_generator_really (project, name, generator, target_type, properties, sources):
""" Returns usage requirements + list of created targets.
"""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(generator, Generator)
assert isinstance(target_type, basestring)
assert isinstance(properties, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
targets = generator.run (project, name, properties, sources)
usage_requirements = []
success = False
dout("returned " + str(targets))
if targets:
success = True;
if isinstance (targets[0], property_set.PropertySet):
usage_requirements = targets [0]
targets = targets [1]
else:
usage_requirements = property_set.empty ()
dout( " generator" + generator.id() + " spawned ")
# generators.dout [ indent ] " " $(targets) ;
# if $(usage-requirements)
# {
# generators.dout [ indent ] " with usage requirements:" $(x) ;
# }
if success:
return (usage_requirements, targets)
else:
return None | python | def try_one_generator_really (project, name, generator, target_type, properties, sources):
""" Returns usage requirements + list of created targets.
"""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(generator, Generator)
assert isinstance(target_type, basestring)
assert isinstance(properties, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
targets = generator.run (project, name, properties, sources)
usage_requirements = []
success = False
dout("returned " + str(targets))
if targets:
success = True;
if isinstance (targets[0], property_set.PropertySet):
usage_requirements = targets [0]
targets = targets [1]
else:
usage_requirements = property_set.empty ()
dout( " generator" + generator.id() + " spawned ")
# generators.dout [ indent ] " " $(targets) ;
# if $(usage-requirements)
# {
# generators.dout [ indent ] " with usage requirements:" $(x) ;
# }
if success:
return (usage_requirements, targets)
else:
return None | [
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29,021 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | try_one_generator | 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 ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(generator, Generator)
assert isinstance(target_type, basestring)
assert isinstance(properties, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
source_types = []
for s in sources:
source_types.append (s.type ())
viable_source_types = viable_source_types_for_generator (generator)
if source_types and viable_source_types != ['*'] and\
not set_.intersection (source_types, viable_source_types):
if project.manager ().logger ().on ():
id = generator.id ()
project.manager ().logger ().log (__name__, "generator '%s' pruned" % id)
project.manager ().logger ().log (__name__, "source_types" '%s' % source_types)
project.manager ().logger ().log (__name__, "viable_source_types '%s'" % viable_source_types)
return []
else:
return try_one_generator_really (project, name, generator, target_type, properties, sources) | python | 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 ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(generator, Generator)
assert isinstance(target_type, basestring)
assert isinstance(properties, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
source_types = []
for s in sources:
source_types.append (s.type ())
viable_source_types = viable_source_types_for_generator (generator)
if source_types and viable_source_types != ['*'] and\
not set_.intersection (source_types, viable_source_types):
if project.manager ().logger ().on ():
id = generator.id ()
project.manager ().logger ().log (__name__, "generator '%s' pruned" % id)
project.manager ().logger ().log (__name__, "source_types" '%s' % source_types)
project.manager ().logger ().log (__name__, "viable_source_types '%s'" % viable_source_types)
return []
else:
return try_one_generator_really (project, name, generator, target_type, properties, sources) | [
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29,022 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | __ensure_type | 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)) | python | def __ensure_type (targets):
""" Ensures all 'targets' have types. If this is not so, exists with
error.
"""
assert is_iterable_typed(targets, virtual_target.VirtualTarget)
for t in targets:
if not t.type ():
get_manager().errors()("target '%s' has no type" % str (t)) | [
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29,023 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | __construct_really | 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)
assert isinstance(name, basestring) or name is None
assert isinstance(target_type, basestring)
assert isinstance(prop_set, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
viable_generators = find_viable_generators (target_type, prop_set)
result = []
dout(" *** %d viable generators" % len (viable_generators))
generators_that_succeeded = []
for g in viable_generators:
__active_generators.append(g)
r = try_one_generator (project, name, g, target_type, prop_set, sources)
del __active_generators[-1]
if r:
generators_that_succeeded.append(g)
if result:
output = cStringIO.StringIO()
print >>output, "ambiguity found when searching for best transformation"
print >>output, "Trying to produce type '%s' from: " % (target_type)
for s in sources:
print >>output, " - " + s.str()
print >>output, "Generators that succeeded:"
for g in generators_that_succeeded:
print >>output, " - " + g.id()
print >>output, "First generator produced: "
for t in result[1:]:
print >>output, " - " + str(t)
print >>output, "Second generator produced:"
for t in r[1:]:
print >>output, " - " + str(t)
get_manager().errors()(output.getvalue())
else:
result = r;
return result; | python | 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)
assert isinstance(name, basestring) or name is None
assert isinstance(target_type, basestring)
assert isinstance(prop_set, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
viable_generators = find_viable_generators (target_type, prop_set)
result = []
dout(" *** %d viable generators" % len (viable_generators))
generators_that_succeeded = []
for g in viable_generators:
__active_generators.append(g)
r = try_one_generator (project, name, g, target_type, prop_set, sources)
del __active_generators[-1]
if r:
generators_that_succeeded.append(g)
if result:
output = cStringIO.StringIO()
print >>output, "ambiguity found when searching for best transformation"
print >>output, "Trying to produce type '%s' from: " % (target_type)
for s in sources:
print >>output, " - " + s.str()
print >>output, "Generators that succeeded:"
for g in generators_that_succeeded:
print >>output, " - " + g.id()
print >>output, "First generator produced: "
for t in result[1:]:
print >>output, " - " + str(t)
print >>output, "Second generator produced:"
for t in r[1:]:
print >>output, " - " + str(t)
get_manager().errors()(output.getvalue())
else:
result = r;
return result; | [
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29,024 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | Generator.match_rank | def match_rank (self, ps):
""" Returns true if the generator can be run with the specified
properties.
"""
# See if generator's requirements are satisfied by
# 'properties'. Treat a feature name in requirements
# (i.e. grist-only element), as matching any value of the
# feature.
assert isinstance(ps, property_set.PropertySet)
all_requirements = self.requirements ()
property_requirements = []
feature_requirements = []
# This uses strings because genenator requirements allow
# the '<feature>' syntax without value and regular validation
# is not happy about that.
for r in all_requirements:
if get_value (r):
property_requirements.append (r)
else:
feature_requirements.append (r)
return all(ps.get(get_grist(s)) == [get_value(s)] for s in property_requirements) \
and all(ps.get(get_grist(s)) for s in feature_requirements) | python | def match_rank (self, ps):
""" Returns true if the generator can be run with the specified
properties.
"""
# See if generator's requirements are satisfied by
# 'properties'. Treat a feature name in requirements
# (i.e. grist-only element), as matching any value of the
# feature.
assert isinstance(ps, property_set.PropertySet)
all_requirements = self.requirements ()
property_requirements = []
feature_requirements = []
# This uses strings because genenator requirements allow
# the '<feature>' syntax without value and regular validation
# is not happy about that.
for r in all_requirements:
if get_value (r):
property_requirements.append (r)
else:
feature_requirements.append (r)
return all(ps.get(get_grist(s)) == [get_value(s)] for s in property_requirements) \
and all(ps.get(get_grist(s)) for s in feature_requirements) | [
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29,025 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | Generator.determine_output_name | def determine_output_name(self, sources):
"""Determine the name of the produced target from the
names of the sources."""
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
# The simple case if when a name
# of source has single dot. Then, we take the part before
# dot. Several dots can be caused by:
# - Using source file like a.host.cpp
# - A type which suffix has a dot. Say, we can
# type 'host_cpp' with extension 'host.cpp'.
# In the first case, we want to take the part till the last
# dot. In the second case -- no sure, but for now take
# the part till the last dot too.
name = os.path.splitext(sources[0].name())[0]
for s in sources[1:]:
n2 = os.path.splitext(s.name())
if n2 != name:
get_manager().errors()(
"%s: source targets have different names: cannot determine target name"
% (self.id_))
# Names of sources might include directory. We should strip it.
return self.determine_target_name(sources[0].name()) | python | def determine_output_name(self, sources):
"""Determine the name of the produced target from the
names of the sources."""
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
# The simple case if when a name
# of source has single dot. Then, we take the part before
# dot. Several dots can be caused by:
# - Using source file like a.host.cpp
# - A type which suffix has a dot. Say, we can
# type 'host_cpp' with extension 'host.cpp'.
# In the first case, we want to take the part till the last
# dot. In the second case -- no sure, but for now take
# the part till the last dot too.
name = os.path.splitext(sources[0].name())[0]
for s in sources[1:]:
n2 = os.path.splitext(s.name())
if n2 != name:
get_manager().errors()(
"%s: source targets have different names: cannot determine target name"
% (self.id_))
# Names of sources might include directory. We should strip it.
return self.determine_target_name(sources[0].name()) | [
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29,026 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | Generator.generated_targets | 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 empty, all source targets must have the same value of
the 'name' attribute, which will be used instead of the 'name' argument.
The value of 'name' attribute for each generated target will be equal to
the 'name' parameter if there's no name pattern for this type. Otherwise,
the '%' symbol in the name pattern will be replaced with the 'name' parameter
to obtain the 'name' attribute.
For example, if targets types are T1 and T2(with name pattern "%_x"), suffixes
for T1 and T2 are .t1 and t2, and source if foo.z, then created files would
be "foo.t1" and "foo_x.t2". The 'name' attribute actually determined the
basename of a file.
Note that this pattern mechanism has nothing to do with implicit patterns
in make. It's a way to produce target which name is different for name of
source.
"""
if __debug__:
from .targets import ProjectTarget
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
assert isinstance(prop_set, property_set.PropertySet)
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
if not name:
name = self.determine_output_name(sources)
# Assign an action for each target
action = self.action_class()
a = action(project.manager(), sources, self.id_, prop_set)
# Create generated target for each target type.
targets = []
pre = self.name_prefix_
post = self.name_postfix_
for t in self.target_types_:
basename = os.path.basename(name)
generated_name = pre[0] + basename + post[0]
generated_name = os.path.join(os.path.dirname(name), generated_name)
pre = pre[1:]
post = post[1:]
targets.append(virtual_target.FileTarget(generated_name, t, project, a))
return [ project.manager().virtual_targets().register(t) for t in targets ] | python | 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 empty, all source targets must have the same value of
the 'name' attribute, which will be used instead of the 'name' argument.
The value of 'name' attribute for each generated target will be equal to
the 'name' parameter if there's no name pattern for this type. Otherwise,
the '%' symbol in the name pattern will be replaced with the 'name' parameter
to obtain the 'name' attribute.
For example, if targets types are T1 and T2(with name pattern "%_x"), suffixes
for T1 and T2 are .t1 and t2, and source if foo.z, then created files would
be "foo.t1" and "foo_x.t2". The 'name' attribute actually determined the
basename of a file.
Note that this pattern mechanism has nothing to do with implicit patterns
in make. It's a way to produce target which name is different for name of
source.
"""
if __debug__:
from .targets import ProjectTarget
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
assert isinstance(prop_set, property_set.PropertySet)
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
if not name:
name = self.determine_output_name(sources)
# Assign an action for each target
action = self.action_class()
a = action(project.manager(), sources, self.id_, prop_set)
# Create generated target for each target type.
targets = []
pre = self.name_prefix_
post = self.name_postfix_
for t in self.target_types_:
basename = os.path.basename(name)
generated_name = pre[0] + basename + post[0]
generated_name = os.path.join(os.path.dirname(name), generated_name)
pre = pre[1:]
post = post[1:]
targets.append(virtual_target.FileTarget(generated_name, t, project, a))
return [ project.manager().virtual_targets().register(t) for t in targets ] | [
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the 'name' attribute, which will be used instead of the 'name' argument.
The value of 'name' attribute for each generated target will be equal to
the 'name' parameter if there's no name pattern for this type. Otherwise,
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For example, if targets types are T1 and T2(with name pattern "%_x"), suffixes
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29,027 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/generators.py | Generator.convert_multiple_sources_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, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
if not self.source_types_:
return list(sources)
acceptable_types = set()
for t in self.source_types_:
acceptable_types.update(type.all_derived(t))
result = []
for source in sources:
if source.type() not in acceptable_types:
transformed = construct_types(
project, None,self.source_types_, prop_set, [source])
# construct_types returns [prop_set, [targets]]
for t in transformed[1]:
if t.type() in self.source_types_:
result.append(t)
if not transformed:
project.manager().logger().log(__name__, " failed to convert ", source)
else:
result.append(source)
result = sequence.unique(result, stable=True)
return result | python | def convert_multiple_sources_to_consumable_types (self, project, prop_set, sources):
""" Converts several files to consumable types.
"""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(prop_set, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
if not self.source_types_:
return list(sources)
acceptable_types = set()
for t in self.source_types_:
acceptable_types.update(type.all_derived(t))
result = []
for source in sources:
if source.type() not in acceptable_types:
transformed = construct_types(
project, None,self.source_types_, prop_set, [source])
# construct_types returns [prop_set, [targets]]
for t in transformed[1]:
if t.type() in self.source_types_:
result.append(t)
if not transformed:
project.manager().logger().log(__name__, " failed to convert ", source)
else:
result.append(source)
result = sequence.unique(result, stable=True)
return result | [
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/deps/src/boost_1_68_0/tools/build/src/build/generators.py#L588-L619 |
29,028 | apple/turicreate | src/unity/python/turicreate/data_structures/sketch.py | Sketch.element_sub_sketch | 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 into the array value.
The keys must be passed into original summary() call in order to
be able to be retrieved later
Parameters
-----------
keys : list of str | str | list of int | int
The list of dictionary keys or array index to get sub sketch from.
if not given, then retrieve all sub sketches that are available
Returns
-------
A dictionary that maps from the key(index) to the actual sketch summary
for that key(index)
Examples
--------
>>> sa = turicreate.SArray([{'a':1, 'b':2}, {'a':4, 'd':1}])
>>> s = sa.summary(sub_sketch_keys=['a','b'])
>>> s.element_sub_sketch(['a'])
{'a':
+--------------------+-------+----------+
| item | value | is exact |
+--------------------+-------+----------+
| Length | 2 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 5.0 | Yes |
| Variance | 2.25 | Yes |
| Standard Deviation | 1.5 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 2 | No |
+--------------------+-------+----------+
Most frequent items:
+-------+-----+-----+
| value | 1.0 | 4.0 |
+-------+-----+-----+
| count | 1 | 1 |
+-------+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+}
"""
single_val = False
if keys is None:
keys = []
else:
if not isinstance(keys, list):
single_val = True
keys = [keys]
value_types = set([type(i) for i in keys])
if (len(value_types) > 1):
raise ValueError("All keys should have the same type.")
with cython_context():
ret_sketches = self.__proxy__.element_sub_sketch(keys)
ret = {}
# check return key matches input key
for key in keys:
if key not in ret_sketches:
raise KeyError("Cannot retrieve element sub sketch for key '" + str(key) + "'. Element sub sketch can only be retrieved when the summary object was created using the 'sub_sketch_keys' option.")
for key in ret_sketches:
ret[key] = Sketch(_proxy = ret_sketches[key])
if single_val:
return ret[keys[0]]
else:
return ret | python | 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 into the array value.
The keys must be passed into original summary() call in order to
be able to be retrieved later
Parameters
-----------
keys : list of str | str | list of int | int
The list of dictionary keys or array index to get sub sketch from.
if not given, then retrieve all sub sketches that are available
Returns
-------
A dictionary that maps from the key(index) to the actual sketch summary
for that key(index)
Examples
--------
>>> sa = turicreate.SArray([{'a':1, 'b':2}, {'a':4, 'd':1}])
>>> s = sa.summary(sub_sketch_keys=['a','b'])
>>> s.element_sub_sketch(['a'])
{'a':
+--------------------+-------+----------+
| item | value | is exact |
+--------------------+-------+----------+
| Length | 2 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 5.0 | Yes |
| Variance | 2.25 | Yes |
| Standard Deviation | 1.5 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 2 | No |
+--------------------+-------+----------+
Most frequent items:
+-------+-----+-----+
| value | 1.0 | 4.0 |
+-------+-----+-----+
| count | 1 | 1 |
+-------+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+}
"""
single_val = False
if keys is None:
keys = []
else:
if not isinstance(keys, list):
single_val = True
keys = [keys]
value_types = set([type(i) for i in keys])
if (len(value_types) > 1):
raise ValueError("All keys should have the same type.")
with cython_context():
ret_sketches = self.__proxy__.element_sub_sketch(keys)
ret = {}
# check return key matches input key
for key in keys:
if key not in ret_sketches:
raise KeyError("Cannot retrieve element sub sketch for key '" + str(key) + "'. Element sub sketch can only be retrieved when the summary object was created using the 'sub_sketch_keys' option.")
for key in ret_sketches:
ret[key] = Sketch(_proxy = ret_sketches[key])
if single_val:
return ret[keys[0]]
else:
return ret | [
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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 original summary() call in order to
be able to be retrieved later
Parameters
-----------
keys : list of str | str | list of int | int
The list of dictionary keys or array index to get sub sketch from.
if not given, then retrieve all sub sketches that are available
Returns
-------
A dictionary that maps from the key(index) to the actual sketch summary
for that key(index)
Examples
--------
>>> sa = turicreate.SArray([{'a':1, 'b':2}, {'a':4, 'd':1}])
>>> s = sa.summary(sub_sketch_keys=['a','b'])
>>> s.element_sub_sketch(['a'])
{'a':
+--------------------+-------+----------+
| item | value | is exact |
+--------------------+-------+----------+
| Length | 2 | Yes |
| Min | 1.0 | Yes |
| Max | 4.0 | Yes |
| Mean | 2.5 | Yes |
| Sum | 5.0 | Yes |
| Variance | 2.25 | Yes |
| Standard Deviation | 1.5 | Yes |
| # Missing Values | 0 | Yes |
| # unique values | 2 | No |
+--------------------+-------+----------+
Most frequent items:
+-------+-----+-----+
| value | 1.0 | 4.0 |
+-------+-----+-----+
| count | 1 | 1 |
+-------+-----+-----+
Quantiles:
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 0% | 1% | 5% | 25% | 50% | 75% | 95% | 99% | 100% |
+-----+-----+-----+-----+-----+-----+-----+-----+------+
| 1.0 | 1.0 | 1.0 | 1.0 | 4.0 | 4.0 | 4.0 | 4.0 | 4.0 |
+-----+-----+-----+-----+-----+-----+-----+-----+------+} | [
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29,029 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | _get_global_dbapi_info | 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 module "guess" is wrong.
"""
module_given_msg = "The DBAPI2 module given ({0}) is missing the global\n"+\
"variable '{1}'. Please make sure you are supplying a module that\n"+\
"conforms to the DBAPI 2.0 standard (PEP 0249)."
module_not_given_msg = "Hello! I gave my best effort to find the\n"+\
"top-level module that the connection object you gave me came from.\n"+\
"I found '{0}' which doesn't have the global variable '{1}'.\n"+\
"To avoid this confusion, you can pass the module as a parameter using\n"+\
"the 'dbapi_module' argument to either from_sql or to_sql."
if dbapi_module is None:
dbapi_module = _get_module_from_object(conn)
module_given = False
else:
module_given = True
module_name = dbapi_module.__name__ if hasattr(dbapi_module, '__name__') else None
needed_vars = ['apilevel','paramstyle','Error','DATETIME','NUMBER','ROWID']
ret_dict = {}
ret_dict['module_name'] = module_name
for i in needed_vars:
tmp = None
try:
tmp = eval("dbapi_module."+i)
except AttributeError as e:
# Some DBs don't actually care about types, so they won't define
# the types. These are the ACTUALLY needed variables though
if i not in ['apilevel','paramstyle','Error']:
pass
elif module_given:
raise AttributeError(module_given_msg.format(module_name, i))
else:
raise AttributeError(module_not_given_msg.format(module_name, i))
ret_dict[i] = tmp
try:
if ret_dict['apilevel'][0:3] != "2.0":
raise NotImplementedError("Unsupported API version " +\
str(ret_dict['apilevel']) + ". Only DBAPI 2.0 is supported.")
except TypeError as e:
e.message = "Module's 'apilevel' value is invalid."
raise e
acceptable_paramstyles = ['qmark','numeric','named','format','pyformat']
try:
if ret_dict['paramstyle'] not in acceptable_paramstyles:
raise TypeError("Module's 'paramstyle' value is invalid.")
except TypeError as e:
raise TypeError("Module's 'paramstyle' value is invalid.")
return ret_dict | python | 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 module "guess" is wrong.
"""
module_given_msg = "The DBAPI2 module given ({0}) is missing the global\n"+\
"variable '{1}'. Please make sure you are supplying a module that\n"+\
"conforms to the DBAPI 2.0 standard (PEP 0249)."
module_not_given_msg = "Hello! I gave my best effort to find the\n"+\
"top-level module that the connection object you gave me came from.\n"+\
"I found '{0}' which doesn't have the global variable '{1}'.\n"+\
"To avoid this confusion, you can pass the module as a parameter using\n"+\
"the 'dbapi_module' argument to either from_sql or to_sql."
if dbapi_module is None:
dbapi_module = _get_module_from_object(conn)
module_given = False
else:
module_given = True
module_name = dbapi_module.__name__ if hasattr(dbapi_module, '__name__') else None
needed_vars = ['apilevel','paramstyle','Error','DATETIME','NUMBER','ROWID']
ret_dict = {}
ret_dict['module_name'] = module_name
for i in needed_vars:
tmp = None
try:
tmp = eval("dbapi_module."+i)
except AttributeError as e:
# Some DBs don't actually care about types, so they won't define
# the types. These are the ACTUALLY needed variables though
if i not in ['apilevel','paramstyle','Error']:
pass
elif module_given:
raise AttributeError(module_given_msg.format(module_name, i))
else:
raise AttributeError(module_not_given_msg.format(module_name, i))
ret_dict[i] = tmp
try:
if ret_dict['apilevel'][0:3] != "2.0":
raise NotImplementedError("Unsupported API version " +\
str(ret_dict['apilevel']) + ". Only DBAPI 2.0 is supported.")
except TypeError as e:
e.message = "Module's 'apilevel' value is invalid."
raise e
acceptable_paramstyles = ['qmark','numeric','named','format','pyformat']
try:
if ret_dict['paramstyle'] not in acceptable_paramstyles:
raise TypeError("Module's 'paramstyle' value is invalid.")
except TypeError as e:
raise TypeError("Module's 'paramstyle' value is invalid.")
return ret_dict | [
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guessing at the module if it wasn't passed as a parameter. Returns a
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make sure the error message is clear if the module "guess" is wrong. | [
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29,030 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.read_csv_with_errors | def read_csv_with_errors(cls,
url,
delimiter=',',
header=True,
comment_char='',
escape_char='\\',
double_quote=True,
quote_char='\"',
skip_initial_space=True,
column_type_hints=None,
na_values=["NA"],
line_terminator='\n',
usecols = [],
nrows=None,
skiprows=0,
verbose=True,
nrows_to_infer=100,
true_values=[],
false_values=[],
_only_raw_string_substitutions=False,
**kwargs):
"""
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
Location of the CSV file or directory to load. If URL is a directory
or a "glob" pattern, all matching files will be loaded.
delimiter : string, optional
This describes the delimiter used for parsing csv files.
header : bool, optional
If true, uses the first row as the column names. Otherwise use the
default column names: 'X1, X2, ...'.
comment_char : string, optional
The character which denotes that the
remainder of the line is a comment.
escape_char : string, optional
Character which begins a C escape sequence. Defaults to backslash(\\)
Set to None to disable.
double_quote : bool, optional
If True, two consecutive quotes in a string are parsed to a single
quote.
quote_char : string, optional
Character sequence that indicates a quote.
skip_initial_space : bool, optional
Ignore extra spaces at the start of a field
column_type_hints : None, type, list[type], dict[string, type], optional
This provides type hints for each column. By default, this method
attempts to detect the type of each column automatically.
Supported types are int, float, str, list, dict, and array.array.
* If a single type is provided, the type will be
applied to all columns. For instance, column_type_hints=float
will force all columns to be parsed as float.
* If a list of types is provided, the types applies
to each column in order, e.g.[int, float, str]
will parse the first column as int, second as float and third as
string.
* If a dictionary of column name to type is provided,
each type value in the dictionary is applied to the key it
belongs to.
For instance {'user':int} will hint that the column called "user"
should be parsed as an integer, and the rest will be type inferred.
na_values : str | list of str, optional
A string or list of strings to be interpreted as missing values.
true_values : str | list of str, optional
A string or list of strings to be interpreted as 1
false_values : str | list of str, optional
A string or list of strings to be interpreted as 0
line_terminator : str, optional
A string to be interpreted as the line terminator. Defaults to "\\n"
which will also correctly match Mac, Linux and Windows line endings
("\\r", "\\n" and "\\r\\n" respectively)
usecols : list of str, optional
A subset of column names to output. If unspecified (default),
all columns will be read. This can provide performance gains if the
number of columns are large. If the input file has no headers,
usecols=['X1','X3'] will read columns 1 and 3.
nrows : int, optional
If set, only this many rows will be read from the file.
skiprows : int, optional
If set, this number of rows at the start of the file are skipped.
verbose : bool, optional
If True, print the progress.
Returns
-------
out : tuple
The first element is the SFrame with good data. The second element
is a dictionary of filenames to SArrays indicating for each file,
what are the incorrectly parsed lines encountered.
See Also
--------
read_csv, SFrame
Examples
--------
>>> bad_url = 'https://static.turi.com/datasets/bad_csv_example.csv'
>>> (sf, bad_lines) = turicreate.SFrame.read_csv_with_errors(bad_url)
>>> sf
+---------+----------+--------+
| user_id | movie_id | rating |
+---------+----------+--------+
| 25904 | 1663 | 3 |
| 25907 | 1663 | 3 |
| 25923 | 1663 | 3 |
| 25924 | 1663 | 3 |
| 25928 | 1663 | 2 |
| ... | ... | ... |
+---------+----------+--------+
[98 rows x 3 columns]
>>> bad_lines
{'https://static.turi.com/datasets/bad_csv_example.csv': dtype: str
Rows: 1
['x,y,z,a,b,c']}
"""
return cls._read_csv_impl(url,
delimiter=delimiter,
header=header,
error_bad_lines=False, # we are storing errors,
# thus we must not fail
# on bad lines
comment_char=comment_char,
escape_char=escape_char,
double_quote=double_quote,
quote_char=quote_char,
skip_initial_space=skip_initial_space,
column_type_hints=column_type_hints,
na_values=na_values,
line_terminator=line_terminator,
usecols=usecols,
nrows=nrows,
verbose=verbose,
skiprows=skiprows,
store_errors=True,
nrows_to_infer=nrows_to_infer,
true_values=true_values,
false_values=false_values,
_only_raw_string_substitutions=_only_raw_string_substitutions,
**kwargs) | python | def read_csv_with_errors(cls,
url,
delimiter=',',
header=True,
comment_char='',
escape_char='\\',
double_quote=True,
quote_char='\"',
skip_initial_space=True,
column_type_hints=None,
na_values=["NA"],
line_terminator='\n',
usecols = [],
nrows=None,
skiprows=0,
verbose=True,
nrows_to_infer=100,
true_values=[],
false_values=[],
_only_raw_string_substitutions=False,
**kwargs):
"""
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
Location of the CSV file or directory to load. If URL is a directory
or a "glob" pattern, all matching files will be loaded.
delimiter : string, optional
This describes the delimiter used for parsing csv files.
header : bool, optional
If true, uses the first row as the column names. Otherwise use the
default column names: 'X1, X2, ...'.
comment_char : string, optional
The character which denotes that the
remainder of the line is a comment.
escape_char : string, optional
Character which begins a C escape sequence. Defaults to backslash(\\)
Set to None to disable.
double_quote : bool, optional
If True, two consecutive quotes in a string are parsed to a single
quote.
quote_char : string, optional
Character sequence that indicates a quote.
skip_initial_space : bool, optional
Ignore extra spaces at the start of a field
column_type_hints : None, type, list[type], dict[string, type], optional
This provides type hints for each column. By default, this method
attempts to detect the type of each column automatically.
Supported types are int, float, str, list, dict, and array.array.
* If a single type is provided, the type will be
applied to all columns. For instance, column_type_hints=float
will force all columns to be parsed as float.
* If a list of types is provided, the types applies
to each column in order, e.g.[int, float, str]
will parse the first column as int, second as float and third as
string.
* If a dictionary of column name to type is provided,
each type value in the dictionary is applied to the key it
belongs to.
For instance {'user':int} will hint that the column called "user"
should be parsed as an integer, and the rest will be type inferred.
na_values : str | list of str, optional
A string or list of strings to be interpreted as missing values.
true_values : str | list of str, optional
A string or list of strings to be interpreted as 1
false_values : str | list of str, optional
A string or list of strings to be interpreted as 0
line_terminator : str, optional
A string to be interpreted as the line terminator. Defaults to "\\n"
which will also correctly match Mac, Linux and Windows line endings
("\\r", "\\n" and "\\r\\n" respectively)
usecols : list of str, optional
A subset of column names to output. If unspecified (default),
all columns will be read. This can provide performance gains if the
number of columns are large. If the input file has no headers,
usecols=['X1','X3'] will read columns 1 and 3.
nrows : int, optional
If set, only this many rows will be read from the file.
skiprows : int, optional
If set, this number of rows at the start of the file are skipped.
verbose : bool, optional
If True, print the progress.
Returns
-------
out : tuple
The first element is the SFrame with good data. The second element
is a dictionary of filenames to SArrays indicating for each file,
what are the incorrectly parsed lines encountered.
See Also
--------
read_csv, SFrame
Examples
--------
>>> bad_url = 'https://static.turi.com/datasets/bad_csv_example.csv'
>>> (sf, bad_lines) = turicreate.SFrame.read_csv_with_errors(bad_url)
>>> sf
+---------+----------+--------+
| user_id | movie_id | rating |
+---------+----------+--------+
| 25904 | 1663 | 3 |
| 25907 | 1663 | 3 |
| 25923 | 1663 | 3 |
| 25924 | 1663 | 3 |
| 25928 | 1663 | 2 |
| ... | ... | ... |
+---------+----------+--------+
[98 rows x 3 columns]
>>> bad_lines
{'https://static.turi.com/datasets/bad_csv_example.csv': dtype: str
Rows: 1
['x,y,z,a,b,c']}
"""
return cls._read_csv_impl(url,
delimiter=delimiter,
header=header,
error_bad_lines=False, # we are storing errors,
# thus we must not fail
# on bad lines
comment_char=comment_char,
escape_char=escape_char,
double_quote=double_quote,
quote_char=quote_char,
skip_initial_space=skip_initial_space,
column_type_hints=column_type_hints,
na_values=na_values,
line_terminator=line_terminator,
usecols=usecols,
nrows=nrows,
verbose=verbose,
skiprows=skiprows,
store_errors=True,
nrows_to_infer=nrows_to_infer,
true_values=true_values,
false_values=false_values,
_only_raw_string_substitutions=_only_raw_string_substitutions,
**kwargs) | [
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encountered.
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.
delimiter : string, optional
This describes the delimiter used for parsing csv files.
header : bool, optional
If true, uses the first row as the column names. Otherwise use the
default column names: 'X1, X2, ...'.
comment_char : string, optional
The character which denotes that the
remainder of the line is a comment.
escape_char : string, optional
Character which begins a C escape sequence. Defaults to backslash(\\)
Set to None to disable.
double_quote : bool, optional
If True, two consecutive quotes in a string are parsed to a single
quote.
quote_char : string, optional
Character sequence that indicates a quote.
skip_initial_space : bool, optional
Ignore extra spaces at the start of a field
column_type_hints : None, type, list[type], dict[string, type], optional
This provides type hints for each column. By default, this method
attempts to detect the type of each column automatically.
Supported types are int, float, str, list, dict, and array.array.
* If a single type is provided, the type will be
applied to all columns. For instance, column_type_hints=float
will force all columns to be parsed as float.
* If a list of types is provided, the types applies
to each column in order, e.g.[int, float, str]
will parse the first column as int, second as float and third as
string.
* If a dictionary of column name to type is provided,
each type value in the dictionary is applied to the key it
belongs to.
For instance {'user':int} will hint that the column called "user"
should be parsed as an integer, and the rest will be type inferred.
na_values : str | list of str, optional
A string or list of strings to be interpreted as missing values.
true_values : str | list of str, optional
A string or list of strings to be interpreted as 1
false_values : str | list of str, optional
A string or list of strings to be interpreted as 0
line_terminator : str, optional
A string to be interpreted as the line terminator. Defaults to "\\n"
which will also correctly match Mac, Linux and Windows line endings
("\\r", "\\n" and "\\r\\n" respectively)
usecols : list of str, optional
A subset of column names to output. If unspecified (default),
all columns will be read. This can provide performance gains if the
number of columns are large. If the input file has no headers,
usecols=['X1','X3'] will read columns 1 and 3.
nrows : int, optional
If set, only this many rows will be read from the file.
skiprows : int, optional
If set, this number of rows at the start of the file are skipped.
verbose : bool, optional
If True, print the progress.
Returns
-------
out : tuple
The first element is the SFrame with good data. The second element
is a dictionary of filenames to SArrays indicating for each file,
what are the incorrectly parsed lines encountered.
See Also
--------
read_csv, SFrame
Examples
--------
>>> bad_url = 'https://static.turi.com/datasets/bad_csv_example.csv'
>>> (sf, bad_lines) = turicreate.SFrame.read_csv_with_errors(bad_url)
>>> sf
+---------+----------+--------+
| user_id | movie_id | rating |
+---------+----------+--------+
| 25904 | 1663 | 3 |
| 25907 | 1663 | 3 |
| 25923 | 1663 | 3 |
| 25924 | 1663 | 3 |
| 25928 | 1663 | 2 |
| ... | ... | ... |
+---------+----------+--------+
[98 rows x 3 columns]
>>> bad_lines
{'https://static.turi.com/datasets/bad_csv_example.csv': dtype: str
Rows: 1
['x,y,z,a,b,c']} | [
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29,031 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.read_json | 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" pattern, all matching files will be loaded.
orient : string, optional. Either "records" or "lines"
If orient="records" the file is expected to contain a single JSON
array, where each array element is a dictionary. If orient="lines",
the file is expected to contain a JSON element per line.
Examples
--------
The orient parameter describes the expected input format of the JSON
file.
If orient="records", the JSON file is expected to contain a single
JSON Array where each array element is a dictionary describing the row.
For instance:
>>> !cat input.json
[{'a':1,'b':1}, {'a':2,'b':2}, {'a':3,'b':3}]
>>> SFrame.read_json('input.json', orient='records')
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
If orient="lines", the JSON file is expected to contain a JSON element
per line. If each line contains a dictionary, it is automatically
unpacked.
>>> !cat input.json
{'a':1,'b':1}
{'a':2,'b':2}
{'a':3,'b':3}
>>> g = SFrame.read_json('input.json', orient='lines')
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
If the lines are not dictionaries, the original format is maintained.
>>> !cat input.json
['a','b','c']
['d','e','f']
['g','h','i']
[1,2,3]
>>> g = SFrame.read_json('input.json', orient='lines')
Columns:
X1 list
Rows: 3
Data:
+-----------+
| X1 |
+-----------+
| [a, b, c] |
| [d, e, f] |
| [g, h, i] |
+-----------+
[3 rows x 1 columns]
"""
if orient == "records":
g = SArray.read_json(url)
if len(g) == 0:
return SFrame()
g = SFrame({'X1':g})
return g.unpack('X1','')
elif orient == "lines":
g = cls.read_csv(url, header=False,na_values=['null'],true_values=['true'],false_values=['false'],
_only_raw_string_substitutions=True)
if g.num_rows() == 0:
return SFrame()
if g.num_columns() != 1:
raise RuntimeError("Input JSON not of expected format")
if g['X1'].dtype == dict:
return g.unpack('X1','')
else:
return g
else:
raise ValueError("Invalid value for orient parameter (" + str(orient) + ")") | python | 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" pattern, all matching files will be loaded.
orient : string, optional. Either "records" or "lines"
If orient="records" the file is expected to contain a single JSON
array, where each array element is a dictionary. If orient="lines",
the file is expected to contain a JSON element per line.
Examples
--------
The orient parameter describes the expected input format of the JSON
file.
If orient="records", the JSON file is expected to contain a single
JSON Array where each array element is a dictionary describing the row.
For instance:
>>> !cat input.json
[{'a':1,'b':1}, {'a':2,'b':2}, {'a':3,'b':3}]
>>> SFrame.read_json('input.json', orient='records')
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
If orient="lines", the JSON file is expected to contain a JSON element
per line. If each line contains a dictionary, it is automatically
unpacked.
>>> !cat input.json
{'a':1,'b':1}
{'a':2,'b':2}
{'a':3,'b':3}
>>> g = SFrame.read_json('input.json', orient='lines')
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
If the lines are not dictionaries, the original format is maintained.
>>> !cat input.json
['a','b','c']
['d','e','f']
['g','h','i']
[1,2,3]
>>> g = SFrame.read_json('input.json', orient='lines')
Columns:
X1 list
Rows: 3
Data:
+-----------+
| X1 |
+-----------+
| [a, b, c] |
| [d, e, f] |
| [g, h, i] |
+-----------+
[3 rows x 1 columns]
"""
if orient == "records":
g = SArray.read_json(url)
if len(g) == 0:
return SFrame()
g = SFrame({'X1':g})
return g.unpack('X1','')
elif orient == "lines":
g = cls.read_csv(url, header=False,na_values=['null'],true_values=['true'],false_values=['false'],
_only_raw_string_substitutions=True)
if g.num_rows() == 0:
return SFrame()
if g.num_columns() != 1:
raise RuntimeError("Input JSON not of expected format")
if g['X1'].dtype == dict:
return g.unpack('X1','')
else:
return g
else:
raise ValueError("Invalid value for orient parameter (" + str(orient) + ")") | [
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Parameters
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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 "lines"
If orient="records" the file is expected to contain a single JSON
array, where each array element is a dictionary. If orient="lines",
the file is expected to contain a JSON element per line.
Examples
--------
The orient parameter describes the expected input format of the JSON
file.
If orient="records", the JSON file is expected to contain a single
JSON Array where each array element is a dictionary describing the row.
For instance:
>>> !cat input.json
[{'a':1,'b':1}, {'a':2,'b':2}, {'a':3,'b':3}]
>>> SFrame.read_json('input.json', orient='records')
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
If orient="lines", the JSON file is expected to contain a JSON element
per line. If each line contains a dictionary, it is automatically
unpacked.
>>> !cat input.json
{'a':1,'b':1}
{'a':2,'b':2}
{'a':3,'b':3}
>>> g = SFrame.read_json('input.json', orient='lines')
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
If the lines are not dictionaries, the original format is maintained.
>>> !cat input.json
['a','b','c']
['d','e','f']
['g','h','i']
[1,2,3]
>>> g = SFrame.read_json('input.json', orient='lines')
Columns:
X1 list
Rows: 3
Data:
+-----------+
| X1 |
+-----------+
| [a, b, c] |
| [d, e, f] |
| [g, h, i] |
+-----------+
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29,032 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.to_sql | 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 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 convenience wrapper around
parameterized SQL insert statements.
Parameters
----------
conn : dbapi2.Connection
A DBAPI2 connection object. Any connection object originating from
the 'connect' method of a DBAPI2-compliant package can be used.
table_name : str
The name of the table to append the data in this SFrame.
dbapi_module : module | package, optional
The top-level DBAPI2 module/package that constructed the given
connection object. By default, a best guess of which module the
connection came from is made. In the event that this guess is wrong,
this will need to be specified.
use_python_type_specifiers : bool, optional
If the DBAPI2 module's parameter marker style is 'format' or
'pyformat', attempt to use accurate type specifiers for each value
('s' for string, 'd' for integer, etc.). Many DBAPI2 modules simply
use 's' for all types if they use these parameter markers, so this is
False by default.
use_exact_column_names : bool, optional
Specify the column names of the SFrame when inserting its contents
into the DB. If the specified table does not have the exact same
column names as the SFrame, inserting the data will fail. If False,
the columns in the SFrame are inserted in order without care of the
schema of the DB table. True by default.
"""
mod_info = _get_global_dbapi_info(dbapi_module, conn)
c = conn.cursor()
col_info = list(zip(self.column_names(), self.column_types()))
if not use_python_type_specifiers:
_pytype_to_printf = lambda x: 's'
# DBAPI2 standard allows for five different ways to specify parameters
sql_param = {
'qmark' : lambda name,col_num,col_type: '?',
'numeric' : lambda name,col_num,col_type:':'+str(col_num+1),
'named' : lambda name,col_num,col_type:':'+str(name),
'format' : lambda name,col_num,col_type:'%'+_pytype_to_printf(col_type),
'pyformat': lambda name,col_num,col_type:'%('+str(name)+')'+_pytype_to_printf(col_type),
}
get_sql_param = sql_param[mod_info['paramstyle']]
# form insert string
ins_str = "INSERT INTO " + str(table_name)
value_str = " VALUES ("
col_str = " ("
count = 0
for i in col_info:
col_str += i[0]
value_str += get_sql_param(i[0],count,i[1])
if count < len(col_info)-1:
col_str += ","
value_str += ","
count += 1
col_str += ")"
value_str += ")"
if use_exact_column_names:
ins_str += col_str
ins_str += value_str
# Some formats require values in an iterable, some a dictionary
if (mod_info['paramstyle'] == 'named' or\
mod_info['paramstyle'] == 'pyformat'):
prepare_sf_row = lambda x:x
else:
col_names = self.column_names()
prepare_sf_row = lambda x: [x[i] for i in col_names]
for i in self:
try:
c.execute(ins_str, prepare_sf_row(i))
except mod_info['Error'] as e:
if hasattr(conn, 'rollback'):
conn.rollback()
raise e
conn.commit()
c.close() | python | 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 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 convenience wrapper around
parameterized SQL insert statements.
Parameters
----------
conn : dbapi2.Connection
A DBAPI2 connection object. Any connection object originating from
the 'connect' method of a DBAPI2-compliant package can be used.
table_name : str
The name of the table to append the data in this SFrame.
dbapi_module : module | package, optional
The top-level DBAPI2 module/package that constructed the given
connection object. By default, a best guess of which module the
connection came from is made. In the event that this guess is wrong,
this will need to be specified.
use_python_type_specifiers : bool, optional
If the DBAPI2 module's parameter marker style is 'format' or
'pyformat', attempt to use accurate type specifiers for each value
('s' for string, 'd' for integer, etc.). Many DBAPI2 modules simply
use 's' for all types if they use these parameter markers, so this is
False by default.
use_exact_column_names : bool, optional
Specify the column names of the SFrame when inserting its contents
into the DB. If the specified table does not have the exact same
column names as the SFrame, inserting the data will fail. If False,
the columns in the SFrame are inserted in order without care of the
schema of the DB table. True by default.
"""
mod_info = _get_global_dbapi_info(dbapi_module, conn)
c = conn.cursor()
col_info = list(zip(self.column_names(), self.column_types()))
if not use_python_type_specifiers:
_pytype_to_printf = lambda x: 's'
# DBAPI2 standard allows for five different ways to specify parameters
sql_param = {
'qmark' : lambda name,col_num,col_type: '?',
'numeric' : lambda name,col_num,col_type:':'+str(col_num+1),
'named' : lambda name,col_num,col_type:':'+str(name),
'format' : lambda name,col_num,col_type:'%'+_pytype_to_printf(col_type),
'pyformat': lambda name,col_num,col_type:'%('+str(name)+')'+_pytype_to_printf(col_type),
}
get_sql_param = sql_param[mod_info['paramstyle']]
# form insert string
ins_str = "INSERT INTO " + str(table_name)
value_str = " VALUES ("
col_str = " ("
count = 0
for i in col_info:
col_str += i[0]
value_str += get_sql_param(i[0],count,i[1])
if count < len(col_info)-1:
col_str += ","
value_str += ","
count += 1
col_str += ")"
value_str += ")"
if use_exact_column_names:
ins_str += col_str
ins_str += value_str
# Some formats require values in an iterable, some a dictionary
if (mod_info['paramstyle'] == 'named' or\
mod_info['paramstyle'] == 'pyformat'):
prepare_sf_row = lambda x:x
else:
col_names = self.column_names()
prepare_sf_row = lambda x: [x[i] for i in col_names]
for i in self:
try:
c.execute(ins_str, prepare_sf_row(i))
except mod_info['Error'] as e:
if hasattr(conn, 'rollback'):
conn.rollback()
raise e
conn.commit()
c.close() | [
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`table_name` exists in the database and appends to it.
`to_sql` can be thought of as a convenience wrapper around
parameterized SQL insert statements.
Parameters
----------
conn : dbapi2.Connection
A DBAPI2 connection object. Any connection object originating from
the 'connect' method of a DBAPI2-compliant package can be used.
table_name : str
The name of the table to append the data in this SFrame.
dbapi_module : module | package, optional
The top-level DBAPI2 module/package that constructed the given
connection object. By default, a best guess of which module the
connection came from is made. In the event that this guess is wrong,
this will need to be specified.
use_python_type_specifiers : bool, optional
If the DBAPI2 module's parameter marker style is 'format' or
'pyformat', attempt to use accurate type specifiers for each value
('s' for string, 'd' for integer, etc.). Many DBAPI2 modules simply
use 's' for all types if they use these parameter markers, so this is
False by default.
use_exact_column_names : bool, optional
Specify the column names of the SFrame when inserting its contents
into the DB. If the specified table does not have the exact same
column names as the SFrame, inserting the data will fail. If False,
the columns in the SFrame are inserted in order without care of the
schema of the DB table. True by default. | [
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29,033 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.print_rows | 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
Number of rows to print.
num_columns : int, optional
Number of columns to print.
max_column_width : int, optional
Maximum width of a column. Columns use fewer characters if possible.
max_row_width : int, optional
Maximum width of a printed row. Columns beyond this width wrap to a
new line. `max_row_width` is automatically reset to be the
larger of itself and `max_column_width`.
output_file: file, optional
The stream or file that receives the output. By default the output
goes to sys.stdout, but it can also be redirected to a file or a
string (using an object of type StringIO).
See Also
--------
head, tail
"""
if output_file is None:
output_file = sys.stdout
max_row_width = max(max_row_width, max_column_width + 1)
printed_sf = self._imagecols_to_stringcols(num_rows)
row_of_tables = printed_sf.__get_pretty_tables__(wrap_text=False,
max_rows_to_display=num_rows,
max_columns=num_columns,
max_column_width=max_column_width,
max_row_width=max_row_width)
footer = "[%d rows x %d columns]\n" % self.shape
print('\n'.join([str(tb) for tb in row_of_tables]) + "\n" + footer, file=output_file) | python | 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
Number of rows to print.
num_columns : int, optional
Number of columns to print.
max_column_width : int, optional
Maximum width of a column. Columns use fewer characters if possible.
max_row_width : int, optional
Maximum width of a printed row. Columns beyond this width wrap to a
new line. `max_row_width` is automatically reset to be the
larger of itself and `max_column_width`.
output_file: file, optional
The stream or file that receives the output. By default the output
goes to sys.stdout, but it can also be redirected to a file or a
string (using an object of type StringIO).
See Also
--------
head, tail
"""
if output_file is None:
output_file = sys.stdout
max_row_width = max(max_row_width, max_column_width + 1)
printed_sf = self._imagecols_to_stringcols(num_rows)
row_of_tables = printed_sf.__get_pretty_tables__(wrap_text=False,
max_rows_to_display=num_rows,
max_columns=num_columns,
max_column_width=max_column_width,
max_row_width=max_row_width)
footer = "[%d rows x %d columns]\n" % self.shape
print('\n'.join([str(tb) for tb in row_of_tables]) + "\n" + footer, file=output_file) | [
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num_rows : int, optional
Number of rows to print.
num_columns : int, optional
Number of columns to print.
max_column_width : int, optional
Maximum width of a column. Columns use fewer characters if possible.
max_row_width : int, optional
Maximum width of a printed row. Columns beyond this width wrap to a
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output_file: file, optional
The stream or file that receives the output. By default the output
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See Also
--------
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29,034 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame._row_selector | 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 self.__has_size__() and other.__has_size__() and len(other) != len(self):
raise IndexError("Cannot perform logical indexing on arrays of different length.")
with cython_context():
return SFrame(_proxy=self.__proxy__.logical_filter(other.__proxy__)) | python | 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 self.__has_size__() and other.__has_size__() and len(other) != len(self):
raise IndexError("Cannot perform logical indexing on arrays of different length.")
with cython_context():
return SFrame(_proxy=self.__proxy__.logical_filter(other.__proxy__)) | [
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29,035 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.to_dataframe | 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 contains all rows of SFrame
"""
assert HAS_PANDAS, 'pandas is not installed.'
df = pandas.DataFrame()
for i in range(self.num_columns()):
column_name = self.column_names()[i]
df[column_name] = list(self[column_name])
if len(df[column_name]) == 0:
df[column_name] = df[column_name].astype(self.column_types()[i])
return df | python | 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 contains all rows of SFrame
"""
assert HAS_PANDAS, 'pandas is not installed.'
df = pandas.DataFrame()
for i in range(self.num_columns()):
column_name = self.column_names()[i]
df[column_name] = list(self[column_name])
if len(df[column_name]) == 0:
df[column_name] = df[column_name].astype(self.column_types()[i])
return df | [
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Returns
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29,036 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.to_numpy | 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 values of the SFrame
"""
assert HAS_NUMPY, 'numpy is not installed.'
import numpy
return numpy.transpose(numpy.asarray([self[x] for x in self.column_names()])) | python | 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 values of the SFrame
"""
assert HAS_NUMPY, 'numpy is not installed.'
import numpy
return numpy.transpose(numpy.asarray([self[x] for x in self.column_names()])) | [
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29,037 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.flat_map | 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 these
rows within the outer list make up the data for the output SFrame.
All rows must have the same length and the same order of types to
make sure the result columns are homogeneously typed. For example, if
the first element emitted into in the outer list by `fn` is
[43, 2.3, 'string'], then all other elements emitted into the outer
list must be a list with three elements, where the first is an int,
second is a float, and third is a string. If column_types is not
specified, the first 10 rows of the SFrame are used to determine the
column types of the returned sframe.
Parameters
----------
column_names : list[str]
The column names for the returned SFrame.
fn : function
The function that maps each of the sframe row into multiple rows,
returning List[List[...]]. All outputted rows must have the same
length and order of types.
column_types : list[type], optional
The column types of the output SFrame. Default value will be
automatically inferred by running `fn` on the first 10 rows of the
input. If the types cannot be inferred from the first 10 rows, an
error is raised.
seed : int, optional
Used as the seed if a random number generator is included in `fn`.
Returns
-------
out : SFrame
A new SFrame containing the results of the flat_map of the
original SFrame.
Examples
---------
Repeat each row according to the value in the 'number' column.
>>> sf = turicreate.SFrame({'letter': ['a', 'b', 'c'],
... 'number': [1, 2, 3]})
>>> sf.flat_map(['number', 'letter'],
... lambda x: [list(x.itervalues()) for i in range(0, x['number'])])
+--------+--------+
| number | letter |
+--------+--------+
| 1 | a |
| 2 | b |
| 2 | b |
| 3 | c |
| 3 | c |
| 3 | c |
+--------+--------+
[6 rows x 2 columns]
"""
assert callable(fn), "Input must be callable"
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
# determine the column_types
if column_types == 'auto':
types = set()
sample = self[0:10]
results = [fn(row) for row in sample]
for rows in results:
if type(rows) is not list:
raise TypeError("Output type of the lambda function must be a list of lists")
# note: this skips empty lists
for row in rows:
if type(row) is not list:
raise TypeError("Output type of the lambda function must be a list of lists")
types.add(tuple([type(v) for v in row]))
if len(types) == 0:
raise TypeError(
"Could not infer output column types from the first ten rows " +\
"of the SFrame. Please use the 'column_types' parameter to " +\
"set the types.")
if len(types) > 1:
raise TypeError("Mapped rows must have the same length and types")
column_types = list(types.pop())
assert type(column_types) is list, "'column_types' must be a list."
assert len(column_types) == len(column_names), "Number of output columns must match the size of column names"
with cython_context():
return SFrame(_proxy=self.__proxy__.flat_map(fn, column_names, column_types, seed)) | python | 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 these
rows within the outer list make up the data for the output SFrame.
All rows must have the same length and the same order of types to
make sure the result columns are homogeneously typed. For example, if
the first element emitted into in the outer list by `fn` is
[43, 2.3, 'string'], then all other elements emitted into the outer
list must be a list with three elements, where the first is an int,
second is a float, and third is a string. If column_types is not
specified, the first 10 rows of the SFrame are used to determine the
column types of the returned sframe.
Parameters
----------
column_names : list[str]
The column names for the returned SFrame.
fn : function
The function that maps each of the sframe row into multiple rows,
returning List[List[...]]. All outputted rows must have the same
length and order of types.
column_types : list[type], optional
The column types of the output SFrame. Default value will be
automatically inferred by running `fn` on the first 10 rows of the
input. If the types cannot be inferred from the first 10 rows, an
error is raised.
seed : int, optional
Used as the seed if a random number generator is included in `fn`.
Returns
-------
out : SFrame
A new SFrame containing the results of the flat_map of the
original SFrame.
Examples
---------
Repeat each row according to the value in the 'number' column.
>>> sf = turicreate.SFrame({'letter': ['a', 'b', 'c'],
... 'number': [1, 2, 3]})
>>> sf.flat_map(['number', 'letter'],
... lambda x: [list(x.itervalues()) for i in range(0, x['number'])])
+--------+--------+
| number | letter |
+--------+--------+
| 1 | a |
| 2 | b |
| 2 | b |
| 3 | c |
| 3 | c |
| 3 | c |
+--------+--------+
[6 rows x 2 columns]
"""
assert callable(fn), "Input must be callable"
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
# determine the column_types
if column_types == 'auto':
types = set()
sample = self[0:10]
results = [fn(row) for row in sample]
for rows in results:
if type(rows) is not list:
raise TypeError("Output type of the lambda function must be a list of lists")
# note: this skips empty lists
for row in rows:
if type(row) is not list:
raise TypeError("Output type of the lambda function must be a list of lists")
types.add(tuple([type(v) for v in row]))
if len(types) == 0:
raise TypeError(
"Could not infer output column types from the first ten rows " +\
"of the SFrame. Please use the 'column_types' parameter to " +\
"set the types.")
if len(types) > 1:
raise TypeError("Mapped rows must have the same length and types")
column_types = list(types.pop())
assert type(column_types) is list, "'column_types' must be a list."
assert len(column_types) == len(column_names), "Number of output columns must match the size of column names"
with cython_context():
return SFrame(_proxy=self.__proxy__.flat_map(fn, column_names, column_types, seed)) | [
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column types of the returned sframe.
Parameters
----------
column_names : list[str]
The column names for the returned SFrame.
fn : function
The function that maps each of the sframe row into multiple rows,
returning List[List[...]]. All outputted rows must have the same
length and order of types.
column_types : list[type], optional
The column types of the output SFrame. Default value will be
automatically inferred by running `fn` on the first 10 rows of the
input. If the types cannot be inferred from the first 10 rows, an
error is raised.
seed : int, optional
Used as the seed if a random number generator is included in `fn`.
Returns
-------
out : SFrame
A new SFrame containing the results of the flat_map of the
original SFrame.
Examples
---------
Repeat each row according to the value in the 'number' column.
>>> sf = turicreate.SFrame({'letter': ['a', 'b', 'c'],
... 'number': [1, 2, 3]})
>>> sf.flat_map(['number', 'letter'],
... lambda x: [list(x.itervalues()) for i in range(0, x['number'])])
+--------+--------+
| number | letter |
+--------+--------+
| 1 | a |
| 2 | b |
| 2 | b |
| 3 | c |
| 3 | c |
| 3 | c |
+--------+--------+
[6 rows x 2 columns] | [
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29,038 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.sample | 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 is
approximately the fraction times the number of rows.
seed : int, optional
Seed for the random number generator used to sample.
exact: bool, optional
Defaults to False. If exact=True, an exact fraction is returned,
but at a performance penalty.
Returns
-------
out : SFrame
A new SFrame containing sampled rows of the current SFrame.
Examples
--------
Suppose we have an SFrame with 6,145 rows.
>>> import random
>>> sf = SFrame({'id': range(0, 6145)})
Retrieve about 30% of the SFrame rows with repeatable results by
setting the random seed.
>>> len(sf.sample(.3, seed=5))
1783
"""
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
if (fraction > 1 or fraction < 0):
raise ValueError('Invalid sampling rate: ' + str(fraction))
if (self.num_rows() == 0 or self.num_columns() == 0):
return self
else:
with cython_context():
return SFrame(_proxy=self.__proxy__.sample(fraction, seed, exact)) | python | 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 is
approximately the fraction times the number of rows.
seed : int, optional
Seed for the random number generator used to sample.
exact: bool, optional
Defaults to False. If exact=True, an exact fraction is returned,
but at a performance penalty.
Returns
-------
out : SFrame
A new SFrame containing sampled rows of the current SFrame.
Examples
--------
Suppose we have an SFrame with 6,145 rows.
>>> import random
>>> sf = SFrame({'id': range(0, 6145)})
Retrieve about 30% of the SFrame rows with repeatable results by
setting the random seed.
>>> len(sf.sample(.3, seed=5))
1783
"""
if seed is None:
seed = abs(hash("%0.20f" % time.time())) % (2 ** 31)
if (fraction > 1 or fraction < 0):
raise ValueError('Invalid sampling rate: ' + str(fraction))
if (self.num_rows() == 0 or self.num_columns() == 0):
return self
else:
with cython_context():
return SFrame(_proxy=self.__proxy__.sample(fraction, seed, exact)) | [
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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.
seed : int, optional
Seed for the random number generator used to sample.
exact: bool, optional
Defaults to False. If exact=True, an exact fraction is returned,
but at a performance penalty.
Returns
-------
out : SFrame
A new SFrame containing sampled rows of the current SFrame.
Examples
--------
Suppose we have an SFrame with 6,145 rows.
>>> import random
>>> sf = SFrame({'id': range(0, 6145)})
Retrieve about 30% of the SFrame rows with repeatable results by
setting the random seed.
>>> len(sf.sample(.3, seed=5))
1783 | [
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29,039 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.save | 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 created
at the location which will contain the sframe.
format : {'binary', 'csv', 'json'}, optional
Format in which to save the SFrame. Binary saved SFrames can be
loaded much faster and without any format conversion losses. If not
given, will try to infer the format from filename given. If file
name ends with 'csv' or '.csv.gz', then save as 'csv' format,
otherwise save as 'binary' format.
See export_csv for more csv saving options.
See Also
--------
load_sframe, SFrame
Examples
--------
>>> # Save the sframe into binary format
>>> sf.save('data/training_data_sframe')
>>> # Save the sframe into csv format
>>> sf.save('data/training_data.csv', format='csv')
"""
if format is None:
if filename.endswith(('.csv', '.csv.gz')):
format = 'csv'
elif filename.endswith(('.json')):
format = 'json'
else:
format = 'binary'
else:
if format is 'csv':
if not filename.endswith(('.csv', '.csv.gz')):
filename = filename + '.csv'
elif format is not 'binary' and format is not 'json':
raise ValueError("Invalid format: {}. Supported formats are 'csv' and 'binary' and 'json'".format(format))
## Save the SFrame
url = _make_internal_url(filename)
with cython_context():
if format is 'binary':
self.__proxy__.save(url)
elif format is 'csv':
assert filename.endswith(('.csv', '.csv.gz'))
self.__proxy__.save_as_csv(url, {})
elif format is 'json':
self.export_json(url)
else:
raise ValueError("Unsupported format: {}".format(format)) | python | 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 created
at the location which will contain the sframe.
format : {'binary', 'csv', 'json'}, optional
Format in which to save the SFrame. Binary saved SFrames can be
loaded much faster and without any format conversion losses. If not
given, will try to infer the format from filename given. If file
name ends with 'csv' or '.csv.gz', then save as 'csv' format,
otherwise save as 'binary' format.
See export_csv for more csv saving options.
See Also
--------
load_sframe, SFrame
Examples
--------
>>> # Save the sframe into binary format
>>> sf.save('data/training_data_sframe')
>>> # Save the sframe into csv format
>>> sf.save('data/training_data.csv', format='csv')
"""
if format is None:
if filename.endswith(('.csv', '.csv.gz')):
format = 'csv'
elif filename.endswith(('.json')):
format = 'json'
else:
format = 'binary'
else:
if format is 'csv':
if not filename.endswith(('.csv', '.csv.gz')):
filename = filename + '.csv'
elif format is not 'binary' and format is not 'json':
raise ValueError("Invalid format: {}. Supported formats are 'csv' and 'binary' and 'json'".format(format))
## Save the SFrame
url = _make_internal_url(filename)
with cython_context():
if format is 'binary':
self.__proxy__.save(url)
elif format is 'csv':
assert filename.endswith(('.csv', '.csv.gz'))
self.__proxy__.save_as_csv(url, {})
elif format is 'json':
self.export_json(url)
else:
raise ValueError("Unsupported format: {}".format(format)) | [
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The location to save the SFrame. Either a local directory or a
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at the location which will contain the sframe.
format : {'binary', 'csv', 'json'}, optional
Format in which to save the SFrame. Binary saved SFrames can be
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name ends with 'csv' or '.csv.gz', then save as 'csv' format,
otherwise save as 'binary' format.
See export_csv for more csv saving options.
See Also
--------
load_sframe, SFrame
Examples
--------
>>> # Save the sframe into binary format
>>> sf.save('data/training_data_sframe')
>>> # Save the sframe into csv format
>>> sf.save('data/training_data.csv', format='csv') | [
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29,040 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.export_csv | 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='',
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"""
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 character
header : bool, optional
If true, the column names are emitted as a header.
quote_level: csv.QUOTE_ALL | csv.QUOTE_NONE | csv.QUOTE_NONNUMERIC, optional
The quoting level. If csv.QUOTE_ALL, every field is quoted.
if csv.quote_NONE, no field is quoted. If csv.QUOTE_NONNUMERIC, only
non-numeric fileds are quoted. csv.QUOTE_MINIMAL is interpreted as
csv.QUOTE_NONNUMERIC.
double_quote : bool, optional
If True, quotes are escaped as two consecutive quotes
escape_char : string, optional
Character which begins a C escape sequence
quote_char: string, optional
Character used to quote fields
na_rep: string, optional
The value used to denote a missing value.
file_header: string, optional
A string printed to the start of the file
file_footer: string, optional
A string printed to the end of the file
line_prefix: string, optional
A string printed at the start of each value line
"""
# Pandas argument compatibility
if "sep" in kwargs:
delimiter = kwargs['sep']
del kwargs['sep']
if "quotechar" in kwargs:
quote_char = kwargs['quotechar']
del kwargs['quotechar']
if "doublequote" in kwargs:
double_quote = kwargs['doublequote']
del kwargs['doublequote']
if "lineterminator" in kwargs:
line_terminator = kwargs['lineterminator']
del kwargs['lineterminator']
if len(kwargs) > 0:
raise TypeError("Unexpected keyword arguments " + str(list(kwargs.keys())))
write_csv_options = {}
write_csv_options['delimiter'] = delimiter
write_csv_options['escape_char'] = escape_char
write_csv_options['double_quote'] = double_quote
write_csv_options['quote_char'] = quote_char
if quote_level == csv.QUOTE_MINIMAL:
write_csv_options['quote_level'] = 0
elif quote_level == csv.QUOTE_ALL:
write_csv_options['quote_level'] = 1
elif quote_level == csv.QUOTE_NONNUMERIC:
write_csv_options['quote_level'] = 2
elif quote_level == csv.QUOTE_NONE:
write_csv_options['quote_level'] = 3
write_csv_options['header'] = header
write_csv_options['line_terminator'] = line_terminator
write_csv_options['na_value'] = na_rep
write_csv_options['file_header'] = file_header
write_csv_options['file_footer'] = file_footer
write_csv_options['line_prefix'] = line_prefix
# undocumented option. Disables line prefix on the first value line
write_csv_options['_no_prefix_on_first_value'] = _no_prefix_on_first_value
url = _make_internal_url(filename)
self.__proxy__.save_as_csv(url, write_csv_options) | python | 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 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 character
header : bool, optional
If true, the column names are emitted as a header.
quote_level: csv.QUOTE_ALL | csv.QUOTE_NONE | csv.QUOTE_NONNUMERIC, optional
The quoting level. If csv.QUOTE_ALL, every field is quoted.
if csv.quote_NONE, no field is quoted. If csv.QUOTE_NONNUMERIC, only
non-numeric fileds are quoted. csv.QUOTE_MINIMAL is interpreted as
csv.QUOTE_NONNUMERIC.
double_quote : bool, optional
If True, quotes are escaped as two consecutive quotes
escape_char : string, optional
Character which begins a C escape sequence
quote_char: string, optional
Character used to quote fields
na_rep: string, optional
The value used to denote a missing value.
file_header: string, optional
A string printed to the start of the file
file_footer: string, optional
A string printed to the end of the file
line_prefix: string, optional
A string printed at the start of each value line
"""
# Pandas argument compatibility
if "sep" in kwargs:
delimiter = kwargs['sep']
del kwargs['sep']
if "quotechar" in kwargs:
quote_char = kwargs['quotechar']
del kwargs['quotechar']
if "doublequote" in kwargs:
double_quote = kwargs['doublequote']
del kwargs['doublequote']
if "lineterminator" in kwargs:
line_terminator = kwargs['lineterminator']
del kwargs['lineterminator']
if len(kwargs) > 0:
raise TypeError("Unexpected keyword arguments " + str(list(kwargs.keys())))
write_csv_options = {}
write_csv_options['delimiter'] = delimiter
write_csv_options['escape_char'] = escape_char
write_csv_options['double_quote'] = double_quote
write_csv_options['quote_char'] = quote_char
if quote_level == csv.QUOTE_MINIMAL:
write_csv_options['quote_level'] = 0
elif quote_level == csv.QUOTE_ALL:
write_csv_options['quote_level'] = 1
elif quote_level == csv.QUOTE_NONNUMERIC:
write_csv_options['quote_level'] = 2
elif quote_level == csv.QUOTE_NONE:
write_csv_options['quote_level'] = 3
write_csv_options['header'] = header
write_csv_options['line_terminator'] = line_terminator
write_csv_options['na_value'] = na_rep
write_csv_options['file_header'] = file_header
write_csv_options['file_footer'] = file_footer
write_csv_options['line_prefix'] = line_prefix
# undocumented option. Disables line prefix on the first value line
write_csv_options['_no_prefix_on_first_value'] = _no_prefix_on_first_value
url = _make_internal_url(filename)
self.__proxy__.save_as_csv(url, write_csv_options) | [
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Parameters
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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 character
header : bool, optional
If true, the column names are emitted as a header.
quote_level: csv.QUOTE_ALL | csv.QUOTE_NONE | csv.QUOTE_NONNUMERIC, optional
The quoting level. If csv.QUOTE_ALL, every field is quoted.
if csv.quote_NONE, no field is quoted. If csv.QUOTE_NONNUMERIC, only
non-numeric fileds are quoted. csv.QUOTE_MINIMAL is interpreted as
csv.QUOTE_NONNUMERIC.
double_quote : bool, optional
If True, quotes are escaped as two consecutive quotes
escape_char : string, optional
Character which begins a C escape sequence
quote_char: string, optional
Character used to quote fields
na_rep: string, optional
The value used to denote a missing value.
file_header: string, optional
A string printed to the start of the file
file_footer: string, optional
A string printed to the end of the file
line_prefix: string, optional
A string printed at the start of each value line | [
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29,041 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.export_json | 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"
If orient="records" the file is saved as a single JSON array.
If orient="lines", the file is saves as a JSON value per line.
Examples
--------
The orient parameter describes the expected input format of the JSON
file.
If orient="records", the output will be a single JSON Array where
each array element is a dictionary describing the row.
>>> g
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
>>> g.export('output.json', orient='records')
>>> !cat output.json
[
{'a':1,'b':1},
{'a':2,'b':2},
{'a':3,'b':3},
]
If orient="rows", each row will be emitted as a JSON dictionary to
each file line.
>>> g
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
>>> g.export('output.json', orient='rows')
>>> !cat output.json
{'a':1,'b':1}
{'a':2,'b':2}
{'a':3,'b':3}
"""
if orient == "records":
self.pack_columns(dtype=dict).export_csv(
filename, file_header='[', file_footer=']',
header=False, double_quote=False,
quote_level=csv.QUOTE_NONE,
line_prefix=',',
_no_prefix_on_first_value=True)
elif orient == "lines":
self.pack_columns(dtype=dict).export_csv(
filename, header=False, double_quote=False, quote_level=csv.QUOTE_NONE)
else:
raise ValueError("Invalid value for orient parameter (" + str(orient) + ")") | python | 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"
If orient="records" the file is saved as a single JSON array.
If orient="lines", the file is saves as a JSON value per line.
Examples
--------
The orient parameter describes the expected input format of the JSON
file.
If orient="records", the output will be a single JSON Array where
each array element is a dictionary describing the row.
>>> g
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
>>> g.export('output.json', orient='records')
>>> !cat output.json
[
{'a':1,'b':1},
{'a':2,'b':2},
{'a':3,'b':3},
]
If orient="rows", each row will be emitted as a JSON dictionary to
each file line.
>>> g
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
>>> g.export('output.json', orient='rows')
>>> !cat output.json
{'a':1,'b':1}
{'a':2,'b':2}
{'a':3,'b':3}
"""
if orient == "records":
self.pack_columns(dtype=dict).export_csv(
filename, file_header='[', file_footer=']',
header=False, double_quote=False,
quote_level=csv.QUOTE_NONE,
line_prefix=',',
_no_prefix_on_first_value=True)
elif orient == "lines":
self.pack_columns(dtype=dict).export_csv(
filename, header=False, double_quote=False, quote_level=csv.QUOTE_NONE)
else:
raise ValueError("Invalid value for orient parameter (" + str(orient) + ")") | [
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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 file is saves as a JSON value per line.
Examples
--------
The orient parameter describes the expected input format of the JSON
file.
If orient="records", the output will be a single JSON Array where
each array element is a dictionary describing the row.
>>> g
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
>>> g.export('output.json', orient='records')
>>> !cat output.json
[
{'a':1,'b':1},
{'a':2,'b':2},
{'a':3,'b':3},
]
If orient="rows", each row will be emitted as a JSON dictionary to
each file line.
>>> g
Columns:
a int
b int
Rows: 3
Data:
+---+---+
| a | b |
+---+---+
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
+---+---+
>>> g.export('output.json', orient='rows')
>>> !cat output.json
{'a':1,'b':1}
{'a':2,'b':2}
{'a':3,'b':3} | [
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29,042 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame._save_reference | 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
The location to save the SFrame. Either a local directory or a
remote URL.
See Also
--------
load_sframe, SFrame
Examples
--------
>>> # Save the sframe into binary format
>>> sf.save_reference('data/training_data_sframe')
"""
## Save the SFrame
url = _make_internal_url(filename)
with cython_context():
self.__proxy__.save_reference(url) | python | 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
The location to save the SFrame. Either a local directory or a
remote URL.
See Also
--------
load_sframe, SFrame
Examples
--------
>>> # Save the sframe into binary format
>>> sf.save_reference('data/training_data_sframe')
"""
## Save the SFrame
url = _make_internal_url(filename)
with cython_context():
self.__proxy__.save_reference(url) | [
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Parameters
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filename : string
The location to save the SFrame. Either a local directory or a
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See Also
--------
load_sframe, SFrame
Examples
--------
>>> # Save the sframe into binary format
>>> sf.save_reference('data/training_data_sframe') | [
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29,043 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.add_column | 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) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
data : SArray
The 'column' of data to add.
column_name : string, optional
The name of the column. If no name is given, a default name is
chosen.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
add_columns
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sa = turicreate.SArray(['cat', 'dog', 'fossa'])
>>> # This line is equivalent to `sf['species'] = sa`
>>> res = sf.add_column(sa, 'species')
>>> res
+----+-----+---------+
| id | val | species |
+----+-----+---------+
| 1 | A | cat |
| 2 | B | dog |
| 3 | C | fossa |
+----+-----+---------+
[3 rows x 3 columns]
"""
# Check type for pandas dataframe or SArray?
if not isinstance(data, SArray):
if isinstance(data, _Iterable):
data = SArray(data)
else:
if self.num_columns() == 0:
data = SArray([data])
else:
data = SArray.from_const(data, self.num_rows())
if not isinstance(column_name, str):
raise TypeError("Invalid column name: must be str")
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.add_column(data.__proxy__, column_name)
ret._cache = None
return ret | python | 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) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
data : SArray
The 'column' of data to add.
column_name : string, optional
The name of the column. If no name is given, a default name is
chosen.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
add_columns
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sa = turicreate.SArray(['cat', 'dog', 'fossa'])
>>> # This line is equivalent to `sf['species'] = sa`
>>> res = sf.add_column(sa, 'species')
>>> res
+----+-----+---------+
| id | val | species |
+----+-----+---------+
| 1 | A | cat |
| 2 | B | dog |
| 3 | C | fossa |
+----+-----+---------+
[3 rows x 3 columns]
"""
# Check type for pandas dataframe or SArray?
if not isinstance(data, SArray):
if isinstance(data, _Iterable):
data = SArray(data)
else:
if self.num_columns() == 0:
data = SArray([data])
else:
data = SArray.from_const(data, self.num_rows())
if not isinstance(column_name, str):
raise TypeError("Invalid column name: must be str")
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.add_column(data.__proxy__, column_name)
ret._cache = None
return ret | [
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given must match the length of every other column of the SFrame.
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If inplace == False (default) this operation does not modify the
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
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Parameters
----------
data : SArray
The 'column' of data to add.
column_name : string, optional
The name of the column. If no name is given, a default name is
chosen.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
add_columns
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sa = turicreate.SArray(['cat', 'dog', 'fossa'])
>>> # This line is equivalent to `sf['species'] = sa`
>>> res = sf.add_column(sa, 'species')
>>> res
+----+-----+---------+
| id | val | species |
+----+-----+---------+
| 1 | A | cat |
| 2 | B | dog |
| 3 | C | fossa |
+----+-----+---------+
[3 rows x 3 columns] | [
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29,044 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.add_columns | 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
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
data : list[SArray] or SFrame
The columns to add.
column_names: list of string, optional
A list of column names. All names must be specified. ``column_names`` is
ignored if data is an SFrame.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
add_column
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sf2 = turicreate.SFrame({'species': ['cat', 'dog', 'fossa'],
... 'age': [3, 5, 9]})
>>> res = sf.add_columns(sf2)
>>> res
+----+-----+-----+---------+
| id | val | age | species |
+----+-----+-----+---------+
| 1 | A | 3 | cat |
| 2 | B | 5 | dog |
| 3 | C | 9 | fossa |
+----+-----+-----+---------+
[3 rows x 4 columns]
"""
datalist = data
if isinstance(data, SFrame):
other = data
datalist = [other.select_column(name) for name in other.column_names()]
column_names = other.column_names()
my_columns = set(self.column_names())
for name in column_names:
if name in my_columns:
raise ValueError("Column '" + name + "' already exists in current SFrame")
else:
if not _is_non_string_iterable(datalist):
raise TypeError("datalist must be an iterable")
if not _is_non_string_iterable(column_names):
raise TypeError("column_names must be an iterable")
if not all([isinstance(x, SArray) for x in datalist]):
raise TypeError("Must give column as SArray")
if not all([isinstance(x, str) for x in column_names]):
raise TypeError("Invalid column name in list : must all be str")
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.add_columns([x.__proxy__ for x in datalist], column_names)
ret._cache = None
return ret | python | 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
current SFrame, returning a new SFrame.
If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
data : list[SArray] or SFrame
The columns to add.
column_names: list of string, optional
A list of column names. All names must be specified. ``column_names`` is
ignored if data is an SFrame.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
add_column
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sf2 = turicreate.SFrame({'species': ['cat', 'dog', 'fossa'],
... 'age': [3, 5, 9]})
>>> res = sf.add_columns(sf2)
>>> res
+----+-----+-----+---------+
| id | val | age | species |
+----+-----+-----+---------+
| 1 | A | 3 | cat |
| 2 | B | 5 | dog |
| 3 | C | 9 | fossa |
+----+-----+-----+---------+
[3 rows x 4 columns]
"""
datalist = data
if isinstance(data, SFrame):
other = data
datalist = [other.select_column(name) for name in other.column_names()]
column_names = other.column_names()
my_columns = set(self.column_names())
for name in column_names:
if name in my_columns:
raise ValueError("Column '" + name + "' already exists in current SFrame")
else:
if not _is_non_string_iterable(datalist):
raise TypeError("datalist must be an iterable")
if not _is_non_string_iterable(column_names):
raise TypeError("column_names must be an iterable")
if not all([isinstance(x, SArray) for x in datalist]):
raise TypeError("Must give column as SArray")
if not all([isinstance(x, str) for x in column_names]):
raise TypeError("Invalid column name in list : must all be str")
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.add_columns([x.__proxy__ for x in datalist], column_names)
ret._cache = None
return ret | [
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the SFrame.
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
----------
data : list[SArray] or SFrame
The columns to add.
column_names: list of string, optional
A list of column names. All names must be specified. ``column_names`` is
ignored if data is an SFrame.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
add_column
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sf2 = turicreate.SFrame({'species': ['cat', 'dog', 'fossa'],
... 'age': [3, 5, 9]})
>>> res = sf.add_columns(sf2)
>>> res
+----+-----+-----+---------+
| id | val | age | species |
+----+-----+-----+---------+
| 1 | A | 3 | cat |
| 2 | B | 5 | dog |
| 3 | C | 9 | fossa |
+----+-----+-----+---------+
[3 rows x 4 columns] | [
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29,045 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.remove_column | 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, returning self.
Parameters
----------
column_name : string
The name of the column to remove.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with given column removed.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> # This is equivalent to `del sf['val']`
>>> res = sf.remove_column('val')
>>> res
+----+
| id |
+----+
| 1 |
| 2 |
| 3 |
+----+
[3 rows x 1 columns]
"""
column_name = str(column_name)
if column_name not in self.column_names():
raise KeyError('Cannot find column %s' % column_name)
colid = self.column_names().index(column_name)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.remove_column(colid)
ret._cache = None
return ret | python | 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, returning self.
Parameters
----------
column_name : string
The name of the column to remove.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with given column removed.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> # This is equivalent to `del sf['val']`
>>> res = sf.remove_column('val')
>>> res
+----+
| id |
+----+
| 1 |
| 2 |
| 3 |
+----+
[3 rows x 1 columns]
"""
column_name = str(column_name)
if column_name not in self.column_names():
raise KeyError('Cannot find column %s' % column_name)
colid = self.column_names().index(column_name)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.remove_column(colid)
ret._cache = None
return ret | [
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inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
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Examples
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>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> # This is equivalent to `del sf['val']`
>>> res = sf.remove_column('val')
>>> res
+----+
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| 1 |
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| 3 |
+----+
[3 rows x 1 columns] | [
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29,046 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.remove_columns | 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
SFrame, returning self.
Parameters
----------
column_names : list or iterable
A list or iterable of column names.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with given columns removed.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val1': ['A', 'B', 'C'], 'val2' : [10, 11, 12]})
>>> res = sf.remove_columns(['val1', 'val2'])
>>> res
+----+
| id |
+----+
| 1 |
| 2 |
| 3 |
+----+
[3 rows x 1 columns]
"""
column_names = list(column_names)
existing_columns = dict((k, i) for i, k in enumerate(self.column_names()))
for name in column_names:
if name not in existing_columns:
raise KeyError('Cannot find column %s' % name)
# Delete it going backwards so we don't invalidate indices
deletion_indices = sorted(existing_columns[name] for name in column_names)
if inplace:
ret = self
else:
ret = self.copy()
for colid in reversed(deletion_indices):
with cython_context():
ret.__proxy__.remove_column(colid)
ret._cache = None
return ret | python | 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
SFrame, returning self.
Parameters
----------
column_names : list or iterable
A list or iterable of column names.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with given columns removed.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val1': ['A', 'B', 'C'], 'val2' : [10, 11, 12]})
>>> res = sf.remove_columns(['val1', 'val2'])
>>> res
+----+
| id |
+----+
| 1 |
| 2 |
| 3 |
+----+
[3 rows x 1 columns]
"""
column_names = list(column_names)
existing_columns = dict((k, i) for i, k in enumerate(self.column_names()))
for name in column_names:
if name not in existing_columns:
raise KeyError('Cannot find column %s' % name)
# Delete it going backwards so we don't invalidate indices
deletion_indices = sorted(existing_columns[name] for name in column_names)
if inplace:
ret = self
else:
ret = self.copy()
for colid in reversed(deletion_indices):
with cython_context():
ret.__proxy__.remove_column(colid)
ret._cache = None
return ret | [
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Parameters
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column_names : list or iterable
A list or iterable of column names.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with given columns removed.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val1': ['A', 'B', 'C'], 'val2' : [10, 11, 12]})
>>> res = sf.remove_columns(['val1', 'val2'])
>>> res
+----+
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+----+
| 1 |
| 2 |
| 3 |
+----+
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/data_structures/sframe.py#L3349-L3406 |
29,047 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.swap_columns | 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 current
SFrame, returning self.
Parameters
----------
column_name_1 : string
Name of column to swap
column_name_2 : string
Name of other column to swap
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with swapped columns.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> res = sf.swap_columns('id', 'val')
>>> res
+-----+-----+
| val | id |
+-----+-----+
| A | 1 |
| B | 2 |
| C | 3 |
+----+-----+
[3 rows x 2 columns]
"""
colnames = self.column_names()
colid_1 = colnames.index(column_name_1)
colid_2 = colnames.index(column_name_2)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.swap_columns(colid_1, colid_2)
ret._cache = None
return ret | python | 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 current
SFrame, returning self.
Parameters
----------
column_name_1 : string
Name of column to swap
column_name_2 : string
Name of other column to swap
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with swapped columns.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> res = sf.swap_columns('id', 'val')
>>> res
+-----+-----+
| val | id |
+-----+-----+
| A | 1 |
| B | 2 |
| C | 3 |
+----+-----+
[3 rows x 2 columns]
"""
colnames = self.column_names()
colid_1 = colnames.index(column_name_1)
colid_2 = colnames.index(column_name_2)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
ret.__proxy__.swap_columns(colid_1, colid_2)
ret._cache = None
return ret | [
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Parameters
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Name of column to swap
column_name_2 : string
Name of other column to swap
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The SFrame with swapped columns.
Examples
--------
>>> sf = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> res = sf.swap_columns('id', 'val')
>>> res
+-----+-----+
| val | id |
+-----+-----+
| A | 1 |
| B | 2 |
| C | 3 |
+----+-----+
[3 rows x 2 columns] | [
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29,048 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.rename | 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 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
----------
names : dict [string, string]
Dictionary of [old_name, new_name]
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
column_names
Examples
--------
>>> sf = SFrame({'X1': ['Alice','Bob'],
... 'X2': ['123 Fake Street','456 Fake Street']})
>>> res = sf.rename({'X1': 'name', 'X2':'address'})
>>> res
+-------+-----------------+
| name | address |
+-------+-----------------+
| Alice | 123 Fake Street |
| Bob | 456 Fake Street |
+-------+-----------------+
[2 rows x 2 columns]
"""
if (type(names) is not dict):
raise TypeError('names must be a dictionary: oldname -> newname')
all_columns = set(self.column_names())
for k in names:
if not k in all_columns:
raise ValueError('Cannot find column %s in the SFrame' % k)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
for k in names:
colid = ret.column_names().index(k)
ret.__proxy__.set_column_name(colid, names[k])
ret._cache = None
return ret | python | 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 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
----------
names : dict [string, string]
Dictionary of [old_name, new_name]
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
column_names
Examples
--------
>>> sf = SFrame({'X1': ['Alice','Bob'],
... 'X2': ['123 Fake Street','456 Fake Street']})
>>> res = sf.rename({'X1': 'name', 'X2':'address'})
>>> res
+-------+-----------------+
| name | address |
+-------+-----------------+
| Alice | 123 Fake Street |
| Bob | 456 Fake Street |
+-------+-----------------+
[2 rows x 2 columns]
"""
if (type(names) is not dict):
raise TypeError('names must be a dictionary: oldname -> newname')
all_columns = set(self.column_names())
for k in names:
if not k in all_columns:
raise ValueError('Cannot find column %s in the SFrame' % k)
if inplace:
ret = self
else:
ret = self.copy()
with cython_context():
for k in names:
colid = ret.column_names().index(k)
ret.__proxy__.set_column_name(colid, names[k])
ret._cache = None
return ret | [
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values.
If inplace == False (default) this operation does not modify the
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If inplace == True, this operation modifies the current
SFrame, returning self.
Parameters
----------
names : dict [string, string]
Dictionary of [old_name, new_name]
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The current SFrame.
See Also
--------
column_names
Examples
--------
>>> sf = SFrame({'X1': ['Alice','Bob'],
... 'X2': ['123 Fake Street','456 Fake Street']})
>>> res = sf.rename({'X1': 'name', 'X2':'address'})
>>> res
+-------+-----------------+
| name | address |
+-------+-----------------+
| Alice | 123 Fake Street |
| Bob | 456 Fake Street |
+-------+-----------------+
[2 rows x 2 columns] | [
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29,049 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.append | 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 to the current SFrame.
Returns
-------
out : SFrame
The result SFrame from the append operation.
Examples
--------
>>> sf = turicreate.SFrame({'id': [4, 6, 8], 'val': ['D', 'F', 'H']})
>>> sf2 = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sf = sf.append(sf2)
>>> sf
+----+-----+
| id | val |
+----+-----+
| 4 | D |
| 6 | F |
| 8 | H |
| 1 | A |
| 2 | B |
| 3 | C |
+----+-----+
[6 rows x 2 columns]
"""
if type(other) is not SFrame:
raise RuntimeError("SFrame append can only work with SFrame")
left_empty = len(self.column_names()) == 0
right_empty = len(other.column_names()) == 0
if (left_empty and right_empty):
return SFrame()
if (left_empty or right_empty):
non_empty_sframe = self if right_empty else other
return non_empty_sframe.__copy__()
my_column_names = self.column_names()
my_column_types = self.column_types()
other_column_names = other.column_names()
if (len(my_column_names) != len(other_column_names)):
raise RuntimeError("Two SFrames have to have the same number of columns")
# check if the order of column name is the same
column_name_order_match = True
for i in range(len(my_column_names)):
if other_column_names[i] != my_column_names[i]:
column_name_order_match = False
break
processed_other_frame = other
if not column_name_order_match:
# we allow name order of two sframes to be different, so we create a new sframe from
# "other" sframe to make it has exactly the same shape
processed_other_frame = SFrame()
for i in range(len(my_column_names)):
col_name = my_column_names[i]
if(col_name not in other_column_names):
raise RuntimeError("Column " + my_column_names[i] + " does not exist in second SFrame")
other_column = other.select_column(col_name)
processed_other_frame.add_column(other_column, col_name, inplace=True)
# check column type
if my_column_types[i] != other_column.dtype:
raise RuntimeError("Column " + my_column_names[i] + " type is not the same in two SFrames, one is " + str(my_column_types[i]) + ", the other is " + str(other_column.dtype))
with cython_context():
return SFrame(_proxy=self.__proxy__.append(processed_other_frame.__proxy__)) | python | 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 to the current SFrame.
Returns
-------
out : SFrame
The result SFrame from the append operation.
Examples
--------
>>> sf = turicreate.SFrame({'id': [4, 6, 8], 'val': ['D', 'F', 'H']})
>>> sf2 = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sf = sf.append(sf2)
>>> sf
+----+-----+
| id | val |
+----+-----+
| 4 | D |
| 6 | F |
| 8 | H |
| 1 | A |
| 2 | B |
| 3 | C |
+----+-----+
[6 rows x 2 columns]
"""
if type(other) is not SFrame:
raise RuntimeError("SFrame append can only work with SFrame")
left_empty = len(self.column_names()) == 0
right_empty = len(other.column_names()) == 0
if (left_empty and right_empty):
return SFrame()
if (left_empty or right_empty):
non_empty_sframe = self if right_empty else other
return non_empty_sframe.__copy__()
my_column_names = self.column_names()
my_column_types = self.column_types()
other_column_names = other.column_names()
if (len(my_column_names) != len(other_column_names)):
raise RuntimeError("Two SFrames have to have the same number of columns")
# check if the order of column name is the same
column_name_order_match = True
for i in range(len(my_column_names)):
if other_column_names[i] != my_column_names[i]:
column_name_order_match = False
break
processed_other_frame = other
if not column_name_order_match:
# we allow name order of two sframes to be different, so we create a new sframe from
# "other" sframe to make it has exactly the same shape
processed_other_frame = SFrame()
for i in range(len(my_column_names)):
col_name = my_column_names[i]
if(col_name not in other_column_names):
raise RuntimeError("Column " + my_column_names[i] + " does not exist in second SFrame")
other_column = other.select_column(col_name)
processed_other_frame.add_column(other_column, col_name, inplace=True)
# check column type
if my_column_types[i] != other_column.dtype:
raise RuntimeError("Column " + my_column_names[i] + " type is not the same in two SFrames, one is " + str(my_column_types[i]) + ", the other is " + str(other_column.dtype))
with cython_context():
return SFrame(_proxy=self.__proxy__.append(processed_other_frame.__proxy__)) | [
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Parameters
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other : SFrame
Another SFrame whose rows are appended to the current SFrame.
Returns
-------
out : SFrame
The result SFrame from the append operation.
Examples
--------
>>> sf = turicreate.SFrame({'id': [4, 6, 8], 'val': ['D', 'F', 'H']})
>>> sf2 = turicreate.SFrame({'id': [1, 2, 3], 'val': ['A', 'B', 'C']})
>>> sf = sf.append(sf2)
>>> sf
+----+-----+
| id | val |
+----+-----+
| 4 | D |
| 6 | F |
| 8 | H |
| 1 | A |
| 2 | B |
| 3 | C |
+----+-----+
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29,050 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.explore | 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 provided.
Returns
-------
None
Examples
--------
Suppose 'sf' is an SFrame, we can view it using:
>>> sf.explore()
To override the default plot title and axis labels:
>>> sf.explore(title="My Plot Title")
"""
import sys
import os
if sys.platform != 'darwin' and sys.platform != 'linux2' and sys.platform != 'linux':
raise NotImplementedError('Visualization is currently supported only on macOS and Linux.')
path_to_client = _get_client_app_path()
if title is None:
title = ""
self.__proxy__.explore(path_to_client, title) | python | 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 provided.
Returns
-------
None
Examples
--------
Suppose 'sf' is an SFrame, we can view it using:
>>> sf.explore()
To override the default plot title and axis labels:
>>> sf.explore(title="My Plot Title")
"""
import sys
import os
if sys.platform != 'darwin' and sys.platform != 'linux2' and sys.platform != 'linux':
raise NotImplementedError('Visualization is currently supported only on macOS and Linux.')
path_to_client = _get_client_app_path()
if title is None:
title = ""
self.__proxy__.explore(path_to_client, title) | [
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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
-------
None
Examples
--------
Suppose 'sf' is an SFrame, we can view it using:
>>> sf.explore()
To override the default plot title and axis labels:
>>> sf.explore(title="My Plot Title") | [
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29,051 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.split_datetime | 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 column must be of datetime type.
For more details regarding name generation and
other, refer to :py:func:`turicreate.SArray.split_datetime()`
Parameters
----------
column_name : str
Name of the unpacked column.
column_name_prefix : str, optional
If provided, expanded column names would start with the given prefix.
If not provided, the default value is the name of the expanded column.
limit: list[str], optional
Limits the set of datetime elements to expand.
Possible values are 'year','month','day','hour','minute','second',
'weekday', 'isoweekday', 'tmweekday', and 'us'.
If not provided, only ['year','month','day','hour','minute','second']
are expanded.
timezone : bool, optional
A boolean parameter that determines whether to show the timezone
column or not. Defaults to False.
Returns
-------
out : SFrame
A new SFrame that contains rest of columns from original SFrame with
the given column replaced with a collection of expanded columns.
Examples
---------
>>> sf
Columns:
id int
submission datetime
Rows: 2
Data:
+----+-------------------------------------------------+
| id | submission |
+----+-------------------------------------------------+
| 1 | datetime(2011, 1, 21, 7, 17, 21, tzinfo=GMT(+1))|
| 2 | datetime(2011, 1, 21, 5, 43, 21, tzinfo=GMT(+1))|
+----+-------------------------------------------------+
>>> sf.split_datetime('submission',limit=['hour','minute'])
Columns:
id int
submission.hour int
submission.minute int
Rows: 2
Data:
+----+-----------------+-------------------+
| id | submission.hour | submission.minute |
+----+-----------------+-------------------+
| 1 | 7 | 17 |
| 2 | 5 | 43 |
+----+-----------------+-------------------+
"""
if column_name not in self.column_names():
raise KeyError("column '" + column_name + "' does not exist in current SFrame")
if column_name_prefix is None:
column_name_prefix = column_name
new_sf = self[column_name].split_datetime(column_name_prefix, limit, timezone)
# construct return SFrame, check if there is conflict
rest_columns = [name for name in self.column_names() if name != column_name]
new_names = new_sf.column_names()
while set(new_names).intersection(rest_columns):
new_names = [name + ".1" for name in new_names]
new_sf.rename(dict(list(zip(new_sf.column_names(), new_names))), inplace=True)
ret_sf = self.select_columns(rest_columns)
ret_sf.add_columns(new_sf, inplace=True)
return ret_sf | python | 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 column must be of datetime type.
For more details regarding name generation and
other, refer to :py:func:`turicreate.SArray.split_datetime()`
Parameters
----------
column_name : str
Name of the unpacked column.
column_name_prefix : str, optional
If provided, expanded column names would start with the given prefix.
If not provided, the default value is the name of the expanded column.
limit: list[str], optional
Limits the set of datetime elements to expand.
Possible values are 'year','month','day','hour','minute','second',
'weekday', 'isoweekday', 'tmweekday', and 'us'.
If not provided, only ['year','month','day','hour','minute','second']
are expanded.
timezone : bool, optional
A boolean parameter that determines whether to show the timezone
column or not. Defaults to False.
Returns
-------
out : SFrame
A new SFrame that contains rest of columns from original SFrame with
the given column replaced with a collection of expanded columns.
Examples
---------
>>> sf
Columns:
id int
submission datetime
Rows: 2
Data:
+----+-------------------------------------------------+
| id | submission |
+----+-------------------------------------------------+
| 1 | datetime(2011, 1, 21, 7, 17, 21, tzinfo=GMT(+1))|
| 2 | datetime(2011, 1, 21, 5, 43, 21, tzinfo=GMT(+1))|
+----+-------------------------------------------------+
>>> sf.split_datetime('submission',limit=['hour','minute'])
Columns:
id int
submission.hour int
submission.minute int
Rows: 2
Data:
+----+-----------------+-------------------+
| id | submission.hour | submission.minute |
+----+-----------------+-------------------+
| 1 | 7 | 17 |
| 2 | 5 | 43 |
+----+-----------------+-------------------+
"""
if column_name not in self.column_names():
raise KeyError("column '" + column_name + "' does not exist in current SFrame")
if column_name_prefix is None:
column_name_prefix = column_name
new_sf = self[column_name].split_datetime(column_name_prefix, limit, timezone)
# construct return SFrame, check if there is conflict
rest_columns = [name for name in self.column_names() if name != column_name]
new_names = new_sf.column_names()
while set(new_names).intersection(rest_columns):
new_names = [name + ".1" for name in new_names]
new_sf.rename(dict(list(zip(new_sf.column_names(), new_names))), inplace=True)
ret_sf = self.select_columns(rest_columns)
ret_sf.add_columns(new_sf, inplace=True)
return ret_sf | [
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a list of new columns. The expanded column must be of datetime type.
For more details regarding name generation and
other, refer to :py:func:`turicreate.SArray.split_datetime()`
Parameters
----------
column_name : str
Name of the unpacked column.
column_name_prefix : str, optional
If provided, expanded column names would start with the given prefix.
If not provided, the default value is the name of the expanded column.
limit: list[str], optional
Limits the set of datetime elements to expand.
Possible values are 'year','month','day','hour','minute','second',
'weekday', 'isoweekday', 'tmweekday', and 'us'.
If not provided, only ['year','month','day','hour','minute','second']
are expanded.
timezone : bool, optional
A boolean parameter that determines whether to show the timezone
column or not. Defaults to False.
Returns
-------
out : SFrame
A new SFrame that contains rest of columns from original SFrame with
the given column replaced with a collection of expanded columns.
Examples
---------
>>> sf
Columns:
id int
submission datetime
Rows: 2
Data:
+----+-------------------------------------------------+
| id | submission |
+----+-------------------------------------------------+
| 1 | datetime(2011, 1, 21, 7, 17, 21, tzinfo=GMT(+1))|
| 2 | datetime(2011, 1, 21, 5, 43, 21, tzinfo=GMT(+1))|
+----+-------------------------------------------------+
>>> sf.split_datetime('submission',limit=['hour','minute'])
Columns:
id int
submission.hour int
submission.minute int
Rows: 2
Data:
+----+-----------------+-------------------+
| id | submission.hour | submission.minute |
+----+-----------------+-------------------+
| 1 | 7 | 17 |
| 2 | 5 | 43 |
+----+-----------------+-------------------+ | [
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29,052 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.stack | 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 new columns are created as a result of
stacking: one column holds the key and another column holds the value.
The rest of the columns are repeated for each key/value pair.
If the column is array or list type, one new column is created as a
result of stacking. With each row holds one element of the array or list
value, and the rest columns from the same original row repeated.
The returned SFrame includes the newly created column(s) and all
columns other than the one that is stacked.
Parameters
--------------
column_name : str
The column to stack. This column must be of dict/list/array type
new_column_name : str | list of str, optional
The new column name(s). If original column is list/array type,
new_column_name must a string. If original column is dict type,
new_column_name must be a list of two strings. If not given, column
names are generated automatically.
drop_na : boolean, optional
If True, missing values and empty list/array/dict are all dropped
from the resulting column(s). If False, missing values are
maintained in stacked column(s).
new_column_type : type | list of types, optional
The new column types. If original column is a list/array type
new_column_type must be a single type, or a list of one type. If
original column is of dict type, new_column_type must be a list of
two types. If not provided, the types are automatically inferred
from the first 100 values of the SFrame.
Returns
-------
out : SFrame
A new SFrame that contains newly stacked column(s) plus columns in
original SFrame other than the stacked column.
See Also
--------
unstack
Examples
---------
Suppose 'sf' is an SFrame that contains a column of dict type:
>>> sf = turicreate.SFrame({'topic':[1,2,3,4],
... 'words': [{'a':3, 'cat':2},
... {'a':1, 'the':2},
... {'the':1, 'dog':3},
... {}]
... })
+-------+----------------------+
| topic | words |
+-------+----------------------+
| 1 | {'a': 3, 'cat': 2} |
| 2 | {'a': 1, 'the': 2} |
| 3 | {'the': 1, 'dog': 3} |
| 4 | {} |
+-------+----------------------+
[4 rows x 2 columns]
Stack would stack all keys in one column and all values in another
column:
>>> sf.stack('words', new_column_name=['word', 'count'])
+-------+------+-------+
| topic | word | count |
+-------+------+-------+
| 1 | a | 3 |
| 1 | cat | 2 |
| 2 | a | 1 |
| 2 | the | 2 |
| 3 | the | 1 |
| 3 | dog | 3 |
| 4 | None | None |
+-------+------+-------+
[7 rows x 3 columns]
Observe that since topic 4 had no words, an empty row is inserted.
To drop that row, set drop_na=True in the parameters to stack.
Suppose 'sf' is an SFrame that contains a user and his/her friends,
where 'friends' columns is an array type. Stack on 'friends' column
would create a user/friend list for each user/friend pair:
>>> sf = turicreate.SFrame({'topic':[1,2,3],
... 'friends':[[2,3,4], [5,6],
... [4,5,10,None]]
... })
>>> sf
+-------+------------------+
| topic | friends |
+-------+------------------+
| 1 | [2, 3, 4] |
| 2 | [5, 6] |
| 3 | [4, 5, 10, None] |
+----- -+------------------+
[3 rows x 2 columns]
>>> sf.stack('friends', new_column_name='friend')
+-------+--------+
| topic | friend |
+-------+--------+
| 1 | 2 |
| 1 | 3 |
| 1 | 4 |
| 2 | 5 |
| 2 | 6 |
| 3 | 4 |
| 3 | 5 |
| 3 | 10 |
| 3 | None |
+-------+--------+
[9 rows x 2 columns]
"""
# validate column_name
column_name = str(column_name)
if column_name not in self.column_names():
raise ValueError("Cannot find column '" + str(column_name) + "' in the SFrame.")
stack_column_type = self[column_name].dtype
if (stack_column_type not in [dict, array.array, list]):
raise TypeError("Stack is only supported for column of dict/list/array type.")
# user defined types. do some checking
if new_column_type is not None:
# if new_column_type is a single type, just make it a list of one type
if type(new_column_type) is type:
new_column_type = [new_column_type]
if (stack_column_type in [list, array.array]) and len(new_column_type) != 1:
raise ValueError("Expecting a single column type to unpack list or array columns")
if (stack_column_type in [dict]) and len(new_column_type) != 2:
raise ValueError("Expecting two column types to unpack a dict column")
if (new_column_name is not None):
if stack_column_type == dict:
if (type(new_column_name) is not list):
raise TypeError("new_column_name has to be a list to stack dict type")
elif (len(new_column_name) != 2):
raise TypeError("new_column_name must have length of two")
else:
if (type(new_column_name) != str):
raise TypeError("new_column_name has to be a str")
new_column_name = [new_column_name]
# check if the new column name conflicts with existing ones
for name in new_column_name:
if (name in self.column_names()) and (name != column_name):
raise ValueError("Column with name '" + name + "' already exists, pick a new column name")
else:
if stack_column_type == dict:
new_column_name = ["",""]
else:
new_column_name = [""]
# infer column types
head_row = SArray(self[column_name].head(100)).dropna()
if (len(head_row) == 0):
raise ValueError("Cannot infer column type because there is not enough rows to infer value")
if new_column_type is None:
# we have to perform type inference
if stack_column_type == dict:
# infer key/value type
keys = []; values = []
for row in head_row:
for val in row:
keys.append(val)
if val is not None: values.append(row[val])
new_column_type = [
infer_type_of_list(keys),
infer_type_of_list(values)
]
else:
values = [v for v in itertools.chain.from_iterable(head_row)]
new_column_type = [infer_type_of_list(values)]
with cython_context():
return SFrame(_proxy=self.__proxy__.stack(column_name,
new_column_name,
new_column_type, drop_na)) | python | 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 new columns are created as a result of
stacking: one column holds the key and another column holds the value.
The rest of the columns are repeated for each key/value pair.
If the column is array or list type, one new column is created as a
result of stacking. With each row holds one element of the array or list
value, and the rest columns from the same original row repeated.
The returned SFrame includes the newly created column(s) and all
columns other than the one that is stacked.
Parameters
--------------
column_name : str
The column to stack. This column must be of dict/list/array type
new_column_name : str | list of str, optional
The new column name(s). If original column is list/array type,
new_column_name must a string. If original column is dict type,
new_column_name must be a list of two strings. If not given, column
names are generated automatically.
drop_na : boolean, optional
If True, missing values and empty list/array/dict are all dropped
from the resulting column(s). If False, missing values are
maintained in stacked column(s).
new_column_type : type | list of types, optional
The new column types. If original column is a list/array type
new_column_type must be a single type, or a list of one type. If
original column is of dict type, new_column_type must be a list of
two types. If not provided, the types are automatically inferred
from the first 100 values of the SFrame.
Returns
-------
out : SFrame
A new SFrame that contains newly stacked column(s) plus columns in
original SFrame other than the stacked column.
See Also
--------
unstack
Examples
---------
Suppose 'sf' is an SFrame that contains a column of dict type:
>>> sf = turicreate.SFrame({'topic':[1,2,3,4],
... 'words': [{'a':3, 'cat':2},
... {'a':1, 'the':2},
... {'the':1, 'dog':3},
... {}]
... })
+-------+----------------------+
| topic | words |
+-------+----------------------+
| 1 | {'a': 3, 'cat': 2} |
| 2 | {'a': 1, 'the': 2} |
| 3 | {'the': 1, 'dog': 3} |
| 4 | {} |
+-------+----------------------+
[4 rows x 2 columns]
Stack would stack all keys in one column and all values in another
column:
>>> sf.stack('words', new_column_name=['word', 'count'])
+-------+------+-------+
| topic | word | count |
+-------+------+-------+
| 1 | a | 3 |
| 1 | cat | 2 |
| 2 | a | 1 |
| 2 | the | 2 |
| 3 | the | 1 |
| 3 | dog | 3 |
| 4 | None | None |
+-------+------+-------+
[7 rows x 3 columns]
Observe that since topic 4 had no words, an empty row is inserted.
To drop that row, set drop_na=True in the parameters to stack.
Suppose 'sf' is an SFrame that contains a user and his/her friends,
where 'friends' columns is an array type. Stack on 'friends' column
would create a user/friend list for each user/friend pair:
>>> sf = turicreate.SFrame({'topic':[1,2,3],
... 'friends':[[2,3,4], [5,6],
... [4,5,10,None]]
... })
>>> sf
+-------+------------------+
| topic | friends |
+-------+------------------+
| 1 | [2, 3, 4] |
| 2 | [5, 6] |
| 3 | [4, 5, 10, None] |
+----- -+------------------+
[3 rows x 2 columns]
>>> sf.stack('friends', new_column_name='friend')
+-------+--------+
| topic | friend |
+-------+--------+
| 1 | 2 |
| 1 | 3 |
| 1 | 4 |
| 2 | 5 |
| 2 | 6 |
| 3 | 4 |
| 3 | 5 |
| 3 | 10 |
| 3 | None |
+-------+--------+
[9 rows x 2 columns]
"""
# validate column_name
column_name = str(column_name)
if column_name not in self.column_names():
raise ValueError("Cannot find column '" + str(column_name) + "' in the SFrame.")
stack_column_type = self[column_name].dtype
if (stack_column_type not in [dict, array.array, list]):
raise TypeError("Stack is only supported for column of dict/list/array type.")
# user defined types. do some checking
if new_column_type is not None:
# if new_column_type is a single type, just make it a list of one type
if type(new_column_type) is type:
new_column_type = [new_column_type]
if (stack_column_type in [list, array.array]) and len(new_column_type) != 1:
raise ValueError("Expecting a single column type to unpack list or array columns")
if (stack_column_type in [dict]) and len(new_column_type) != 2:
raise ValueError("Expecting two column types to unpack a dict column")
if (new_column_name is not None):
if stack_column_type == dict:
if (type(new_column_name) is not list):
raise TypeError("new_column_name has to be a list to stack dict type")
elif (len(new_column_name) != 2):
raise TypeError("new_column_name must have length of two")
else:
if (type(new_column_name) != str):
raise TypeError("new_column_name has to be a str")
new_column_name = [new_column_name]
# check if the new column name conflicts with existing ones
for name in new_column_name:
if (name in self.column_names()) and (name != column_name):
raise ValueError("Column with name '" + name + "' already exists, pick a new column name")
else:
if stack_column_type == dict:
new_column_name = ["",""]
else:
new_column_name = [""]
# infer column types
head_row = SArray(self[column_name].head(100)).dropna()
if (len(head_row) == 0):
raise ValueError("Cannot infer column type because there is not enough rows to infer value")
if new_column_type is None:
# we have to perform type inference
if stack_column_type == dict:
# infer key/value type
keys = []; values = []
for row in head_row:
for val in row:
keys.append(val)
if val is not None: values.append(row[val])
new_column_type = [
infer_type_of_list(keys),
infer_type_of_list(values)
]
else:
values = [v for v in itertools.chain.from_iterable(head_row)]
new_column_type = [infer_type_of_list(values)]
with cython_context():
return SFrame(_proxy=self.__proxy__.stack(column_name,
new_column_name,
new_column_type, drop_na)) | [
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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 the value.
The rest of the columns are repeated for each key/value pair.
If the column is array or list type, one new column is created as a
result of stacking. With each row holds one element of the array or list
value, and the rest columns from the same original row repeated.
The returned SFrame includes the newly created column(s) and all
columns other than the one that is stacked.
Parameters
--------------
column_name : str
The column to stack. This column must be of dict/list/array type
new_column_name : str | list of str, optional
The new column name(s). If original column is list/array type,
new_column_name must a string. If original column is dict type,
new_column_name must be a list of two strings. If not given, column
names are generated automatically.
drop_na : boolean, optional
If True, missing values and empty list/array/dict are all dropped
from the resulting column(s). If False, missing values are
maintained in stacked column(s).
new_column_type : type | list of types, optional
The new column types. If original column is a list/array type
new_column_type must be a single type, or a list of one type. If
original column is of dict type, new_column_type must be a list of
two types. If not provided, the types are automatically inferred
from the first 100 values of the SFrame.
Returns
-------
out : SFrame
A new SFrame that contains newly stacked column(s) plus columns in
original SFrame other than the stacked column.
See Also
--------
unstack
Examples
---------
Suppose 'sf' is an SFrame that contains a column of dict type:
>>> sf = turicreate.SFrame({'topic':[1,2,3,4],
... 'words': [{'a':3, 'cat':2},
... {'a':1, 'the':2},
... {'the':1, 'dog':3},
... {}]
... })
+-------+----------------------+
| topic | words |
+-------+----------------------+
| 1 | {'a': 3, 'cat': 2} |
| 2 | {'a': 1, 'the': 2} |
| 3 | {'the': 1, 'dog': 3} |
| 4 | {} |
+-------+----------------------+
[4 rows x 2 columns]
Stack would stack all keys in one column and all values in another
column:
>>> sf.stack('words', new_column_name=['word', 'count'])
+-------+------+-------+
| topic | word | count |
+-------+------+-------+
| 1 | a | 3 |
| 1 | cat | 2 |
| 2 | a | 1 |
| 2 | the | 2 |
| 3 | the | 1 |
| 3 | dog | 3 |
| 4 | None | None |
+-------+------+-------+
[7 rows x 3 columns]
Observe that since topic 4 had no words, an empty row is inserted.
To drop that row, set drop_na=True in the parameters to stack.
Suppose 'sf' is an SFrame that contains a user and his/her friends,
where 'friends' columns is an array type. Stack on 'friends' column
would create a user/friend list for each user/friend pair:
>>> sf = turicreate.SFrame({'topic':[1,2,3],
... 'friends':[[2,3,4], [5,6],
... [4,5,10,None]]
... })
>>> sf
+-------+------------------+
| topic | friends |
+-------+------------------+
| 1 | [2, 3, 4] |
| 2 | [5, 6] |
| 3 | [4, 5, 10, None] |
+----- -+------------------+
[3 rows x 2 columns]
>>> sf.stack('friends', new_column_name='friend')
+-------+--------+
| topic | friend |
+-------+--------+
| 1 | 2 |
| 1 | 3 |
| 1 | 4 |
| 2 | 5 |
| 2 | 6 |
| 3 | 4 |
| 3 | 5 |
| 3 | 10 |
| 3 | None |
+-------+--------+
[9 rows x 2 columns] | [
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29,053 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.unstack | 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
array.array type. If ``column_names`` is a non-numeric column, the new column
will be of list type. If ``column_names`` is a list of two columns, the new
column will be of dict type where the keys are taken from the first
column in the list.
Parameters
----------
column_names : str | [str, str]
The column(s) that is(are) to be concatenated.
If str, then collapsed column type is either array or list.
If [str, str], then collapsed column type is dict
new_column_name : str, optional
New column name. If not given, a name is generated automatically.
Returns
-------
out : SFrame
A new SFrame containing the grouped columns as well as the new
column.
See Also
--------
stack : The inverse of unstack.
groupby : ``unstack`` is a special version of ``groupby`` that uses the
:mod:`~turicreate.aggregate.CONCAT` aggregator
Notes
-----
- There is no guarantee the resulting SFrame maintains the same order as
the original SFrame.
- Missing values are maintained during unstack.
- When unstacking into a dictionary, if there is more than one instance
of a given key for a particular group, an arbitrary value is selected.
Examples
--------
>>> sf = turicreate.SFrame({'count':[4, 2, 1, 1, 2, None],
... 'topic':['cat', 'cat', 'dog', 'elephant', 'elephant', 'fish'],
... 'word':['a', 'c', 'c', 'a', 'b', None]})
>>> sf.unstack(column_names=['word', 'count'], new_column_name='words')
+----------+------------------+
| topic | words |
+----------+------------------+
| elephant | {'a': 1, 'b': 2} |
| dog | {'c': 1} |
| cat | {'a': 4, 'c': 2} |
| fish | None |
+----------+------------------+
[4 rows x 2 columns]
>>> sf = turicreate.SFrame({'friend': [2, 3, 4, 5, 6, 4, 5, 2, 3],
... 'user': [1, 1, 1, 2, 2, 2, 3, 4, 4]})
>>> sf.unstack('friend', new_column_name='new name')
+------+-----------+
| user | new name |
+------+-----------+
| 3 | [5] |
| 1 | [2, 3, 4] |
| 2 | [6, 4, 5] |
| 4 | [2, 3] |
+------+-----------+
[4 rows x 2 columns]
"""
if (type(column_names) != str and len(column_names) != 2):
raise TypeError("'column_names' parameter has to be either a string or a list of two strings.")
with cython_context():
if type(column_names) == str:
key_columns = [i for i in self.column_names() if i != column_names]
if new_column_name is not None:
return self.groupby(key_columns, {new_column_name : aggregate.CONCAT(column_names)})
else:
return self.groupby(key_columns, aggregate.CONCAT(column_names))
elif len(column_names) == 2:
key_columns = [i for i in self.column_names() if i not in column_names]
if new_column_name is not None:
return self.groupby(key_columns, {new_column_name: aggregate.CONCAT(column_names[0], column_names[1])})
else:
return self.groupby(key_columns, aggregate.CONCAT(column_names[0], column_names[1])) | python | 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
array.array type. If ``column_names`` is a non-numeric column, the new column
will be of list type. If ``column_names`` is a list of two columns, the new
column will be of dict type where the keys are taken from the first
column in the list.
Parameters
----------
column_names : str | [str, str]
The column(s) that is(are) to be concatenated.
If str, then collapsed column type is either array or list.
If [str, str], then collapsed column type is dict
new_column_name : str, optional
New column name. If not given, a name is generated automatically.
Returns
-------
out : SFrame
A new SFrame containing the grouped columns as well as the new
column.
See Also
--------
stack : The inverse of unstack.
groupby : ``unstack`` is a special version of ``groupby`` that uses the
:mod:`~turicreate.aggregate.CONCAT` aggregator
Notes
-----
- There is no guarantee the resulting SFrame maintains the same order as
the original SFrame.
- Missing values are maintained during unstack.
- When unstacking into a dictionary, if there is more than one instance
of a given key for a particular group, an arbitrary value is selected.
Examples
--------
>>> sf = turicreate.SFrame({'count':[4, 2, 1, 1, 2, None],
... 'topic':['cat', 'cat', 'dog', 'elephant', 'elephant', 'fish'],
... 'word':['a', 'c', 'c', 'a', 'b', None]})
>>> sf.unstack(column_names=['word', 'count'], new_column_name='words')
+----------+------------------+
| topic | words |
+----------+------------------+
| elephant | {'a': 1, 'b': 2} |
| dog | {'c': 1} |
| cat | {'a': 4, 'c': 2} |
| fish | None |
+----------+------------------+
[4 rows x 2 columns]
>>> sf = turicreate.SFrame({'friend': [2, 3, 4, 5, 6, 4, 5, 2, 3],
... 'user': [1, 1, 1, 2, 2, 2, 3, 4, 4]})
>>> sf.unstack('friend', new_column_name='new name')
+------+-----------+
| user | new name |
+------+-----------+
| 3 | [5] |
| 1 | [2, 3, 4] |
| 2 | [6, 4, 5] |
| 4 | [2, 3] |
+------+-----------+
[4 rows x 2 columns]
"""
if (type(column_names) != str and len(column_names) != 2):
raise TypeError("'column_names' parameter has to be either a string or a list of two strings.")
with cython_context():
if type(column_names) == str:
key_columns = [i for i in self.column_names() if i != column_names]
if new_column_name is not None:
return self.groupby(key_columns, {new_column_name : aggregate.CONCAT(column_names)})
else:
return self.groupby(key_columns, aggregate.CONCAT(column_names))
elif len(column_names) == 2:
key_columns = [i for i in self.column_names() if i not in column_names]
if new_column_name is not None:
return self.groupby(key_columns, {new_column_name: aggregate.CONCAT(column_names[0], column_names[1])})
else:
return self.groupby(key_columns, aggregate.CONCAT(column_names[0], column_names[1])) | [
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The column(s) that is(are) to be concatenated.
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new_column_name : str, optional
New column name. If not given, a name is generated automatically.
Returns
-------
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A new SFrame containing the grouped columns as well as the new
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See Also
--------
stack : The inverse of unstack.
groupby : ``unstack`` is a special version of ``groupby`` that uses the
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Notes
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- There is no guarantee the resulting SFrame maintains the same order as
the original SFrame.
- Missing values are maintained during unstack.
- When unstacking into a dictionary, if there is more than one instance
of a given key for a particular group, an arbitrary value is selected.
Examples
--------
>>> sf = turicreate.SFrame({'count':[4, 2, 1, 1, 2, None],
... 'topic':['cat', 'cat', 'dog', 'elephant', 'elephant', 'fish'],
... 'word':['a', 'c', 'c', 'a', 'b', None]})
>>> sf.unstack(column_names=['word', 'count'], new_column_name='words')
+----------+------------------+
| topic | words |
+----------+------------------+
| elephant | {'a': 1, 'b': 2} |
| dog | {'c': 1} |
| cat | {'a': 4, 'c': 2} |
| fish | None |
+----------+------------------+
[4 rows x 2 columns]
>>> sf = turicreate.SFrame({'friend': [2, 3, 4, 5, 6, 4, 5, 2, 3],
... 'user': [1, 1, 1, 2, 2, 2, 3, 4, 4]})
>>> sf.unstack('friend', new_column_name='new name')
+------+-----------+
| user | new name |
+------+-----------+
| 3 | [5] |
| 1 | [2, 3, 4] |
| 2 | [6, 4, 5] |
| 4 | [2, 3] |
+------+-----------+
[4 rows x 2 columns] | [
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29,054 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.sort | 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) pairs
Names of columns to be sorted. The result will be sorted first by
first column, followed by second column, and so on. All columns will
be sorted in the same order as governed by the `ascending`
parameter. To control the sort ordering for each column
individually, `key_column_names` must be a list of (str, bool) pairs.
Given this case, the first value is the column name and the second
value is a boolean indicating whether the sort order is ascending.
ascending : bool, optional
Sort all columns in the given order.
Returns
-------
out : SFrame
A new SFrame that is sorted according to given sort criteria
See Also
--------
topk
Examples
--------
Suppose 'sf' is an sframe that has three columns 'a', 'b', 'c'.
To sort by column 'a', ascending
>>> sf = turicreate.SFrame({'a':[1,3,2,1],
... 'b':['a','c','b','b'],
... 'c':['x','y','z','y']})
>>> sf
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 3 | c | y |
| 2 | b | z |
| 1 | b | y |
+---+---+---+
[4 rows x 3 columns]
>>> sf.sort('a')
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 1 | b | y |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a', descending
>>> sf.sort('a', ascending = False)
+---+---+---+
| a | b | c |
+---+---+---+
| 3 | c | y |
| 2 | b | z |
| 1 | a | x |
| 1 | b | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a' and 'b', all ascending
>>> sf.sort(['a', 'b'])
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 1 | b | y |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a' ascending, and then by column 'c' descending
>>> sf.sort([('a', True), ('c', False)])
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | b | y |
| 1 | a | x |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
"""
sort_column_names = []
sort_column_orders = []
# validate key_column_names
if (type(key_column_names) == str):
sort_column_names = [key_column_names]
elif (type(key_column_names) == list):
if (len(key_column_names) == 0):
raise ValueError("Please provide at least one column to sort")
first_param_types = set([type(i) for i in key_column_names])
if (len(first_param_types) != 1):
raise ValueError("key_column_names element are not of the same type")
first_param_type = first_param_types.pop()
if (first_param_type == tuple):
sort_column_names = [i[0] for i in key_column_names]
sort_column_orders = [i[1] for i in key_column_names]
elif(first_param_type == str):
sort_column_names = key_column_names
else:
raise TypeError("key_column_names type is not supported")
else:
raise TypeError("key_column_names type is not correct. Supported types are str, list of str or list of (str,bool) pair.")
# use the second parameter if the sort order is not given
if (len(sort_column_orders) == 0):
sort_column_orders = [ascending for i in sort_column_names]
# make sure all column exists
my_column_names = set(self.column_names())
for column in sort_column_names:
if (type(column) != str):
raise TypeError("Only string parameter can be passed in as column names")
if (column not in my_column_names):
raise ValueError("SFrame has no column named: '" + str(column) + "'")
if (self[column].dtype not in (str, int, float,datetime.datetime)):
raise TypeError("Only columns of type (str, int, float) can be sorted")
with cython_context():
return SFrame(_proxy=self.__proxy__.sort(sort_column_names, sort_column_orders)) | python | 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) pairs
Names of columns to be sorted. The result will be sorted first by
first column, followed by second column, and so on. All columns will
be sorted in the same order as governed by the `ascending`
parameter. To control the sort ordering for each column
individually, `key_column_names` must be a list of (str, bool) pairs.
Given this case, the first value is the column name and the second
value is a boolean indicating whether the sort order is ascending.
ascending : bool, optional
Sort all columns in the given order.
Returns
-------
out : SFrame
A new SFrame that is sorted according to given sort criteria
See Also
--------
topk
Examples
--------
Suppose 'sf' is an sframe that has three columns 'a', 'b', 'c'.
To sort by column 'a', ascending
>>> sf = turicreate.SFrame({'a':[1,3,2,1],
... 'b':['a','c','b','b'],
... 'c':['x','y','z','y']})
>>> sf
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 3 | c | y |
| 2 | b | z |
| 1 | b | y |
+---+---+---+
[4 rows x 3 columns]
>>> sf.sort('a')
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 1 | b | y |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a', descending
>>> sf.sort('a', ascending = False)
+---+---+---+
| a | b | c |
+---+---+---+
| 3 | c | y |
| 2 | b | z |
| 1 | a | x |
| 1 | b | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a' and 'b', all ascending
>>> sf.sort(['a', 'b'])
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 1 | b | y |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a' ascending, and then by column 'c' descending
>>> sf.sort([('a', True), ('c', False)])
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | b | y |
| 1 | a | x |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
"""
sort_column_names = []
sort_column_orders = []
# validate key_column_names
if (type(key_column_names) == str):
sort_column_names = [key_column_names]
elif (type(key_column_names) == list):
if (len(key_column_names) == 0):
raise ValueError("Please provide at least one column to sort")
first_param_types = set([type(i) for i in key_column_names])
if (len(first_param_types) != 1):
raise ValueError("key_column_names element are not of the same type")
first_param_type = first_param_types.pop()
if (first_param_type == tuple):
sort_column_names = [i[0] for i in key_column_names]
sort_column_orders = [i[1] for i in key_column_names]
elif(first_param_type == str):
sort_column_names = key_column_names
else:
raise TypeError("key_column_names type is not supported")
else:
raise TypeError("key_column_names type is not correct. Supported types are str, list of str or list of (str,bool) pair.")
# use the second parameter if the sort order is not given
if (len(sort_column_orders) == 0):
sort_column_orders = [ascending for i in sort_column_names]
# make sure all column exists
my_column_names = set(self.column_names())
for column in sort_column_names:
if (type(column) != str):
raise TypeError("Only string parameter can be passed in as column names")
if (column not in my_column_names):
raise ValueError("SFrame has no column named: '" + str(column) + "'")
if (self[column].dtype not in (str, int, float,datetime.datetime)):
raise TypeError("Only columns of type (str, int, float) can be sorted")
with cython_context():
return SFrame(_proxy=self.__proxy__.sort(sort_column_names, sort_column_orders)) | [
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Names of columns to be sorted. The result will be sorted first by
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ascending : bool, optional
Sort all columns in the given order.
Returns
-------
out : SFrame
A new SFrame that is sorted according to given sort criteria
See Also
--------
topk
Examples
--------
Suppose 'sf' is an sframe that has three columns 'a', 'b', 'c'.
To sort by column 'a', ascending
>>> sf = turicreate.SFrame({'a':[1,3,2,1],
... 'b':['a','c','b','b'],
... 'c':['x','y','z','y']})
>>> sf
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 3 | c | y |
| 2 | b | z |
| 1 | b | y |
+---+---+---+
[4 rows x 3 columns]
>>> sf.sort('a')
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 1 | b | y |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a', descending
>>> sf.sort('a', ascending = False)
+---+---+---+
| a | b | c |
+---+---+---+
| 3 | c | y |
| 2 | b | z |
| 1 | a | x |
| 1 | b | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a' and 'b', all ascending
>>> sf.sort(['a', 'b'])
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | a | x |
| 1 | b | y |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
[4 rows x 3 columns]
To sort by column 'a' ascending, and then by column 'c' descending
>>> sf.sort([('a', True), ('c', False)])
+---+---+---+
| a | b | c |
+---+---+---+
| 1 | b | y |
| 1 | a | x |
| 2 | b | z |
| 3 | c | y |
+---+---+---+
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29,055 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.dropna | 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 'all', a row will be removed if all of the columns
in the ``columns`` parameter are missing values.
If the ``columns`` parameter is not specified, the default is to
consider all columns when searching for missing values.
Parameters
----------
columns : list or str, optional
The columns to use when looking for missing values. By default, all
columns are used.
how : {'any', 'all'}, optional
Specifies whether a row should be dropped if at least one column
has missing values, or if all columns have missing values. 'any' is
default.
Returns
-------
out : SFrame
SFrame with missing values removed (according to the given rules).
See Also
--------
dropna_split : Drops missing rows from the SFrame and returns them.
Examples
--------
Drop all missing values.
>>> sf = turicreate.SFrame({'a': [1, None, None], 'b': ['a', 'b', None]})
>>> sf.dropna()
+---+---+
| a | b |
+---+---+
| 1 | a |
+---+---+
[1 rows x 2 columns]
Drop rows where every value is missing.
>>> sf.dropna(any="all")
+------+---+
| a | b |
+------+---+
| 1 | a |
| None | b |
+------+---+
[2 rows x 2 columns]
Drop rows where column 'a' has a missing value.
>>> sf.dropna('a', any="all")
+---+---+
| a | b |
+---+---+
| 1 | a |
+---+---+
[1 rows x 2 columns]
"""
# If the user gives me an empty list (the indicator to use all columns)
# NA values being dropped would not be the expected behavior. This
# is a NOOP, so let's not bother the server
if type(columns) is list and len(columns) == 0:
return SFrame(_proxy=self.__proxy__)
(columns, all_behavior) = self.__dropna_errchk(columns, how)
with cython_context():
return SFrame(_proxy=self.__proxy__.drop_missing_values(columns, all_behavior, False)) | python | 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 'all', a row will be removed if all of the columns
in the ``columns`` parameter are missing values.
If the ``columns`` parameter is not specified, the default is to
consider all columns when searching for missing values.
Parameters
----------
columns : list or str, optional
The columns to use when looking for missing values. By default, all
columns are used.
how : {'any', 'all'}, optional
Specifies whether a row should be dropped if at least one column
has missing values, or if all columns have missing values. 'any' is
default.
Returns
-------
out : SFrame
SFrame with missing values removed (according to the given rules).
See Also
--------
dropna_split : Drops missing rows from the SFrame and returns them.
Examples
--------
Drop all missing values.
>>> sf = turicreate.SFrame({'a': [1, None, None], 'b': ['a', 'b', None]})
>>> sf.dropna()
+---+---+
| a | b |
+---+---+
| 1 | a |
+---+---+
[1 rows x 2 columns]
Drop rows where every value is missing.
>>> sf.dropna(any="all")
+------+---+
| a | b |
+------+---+
| 1 | a |
| None | b |
+------+---+
[2 rows x 2 columns]
Drop rows where column 'a' has a missing value.
>>> sf.dropna('a', any="all")
+---+---+
| a | b |
+---+---+
| 1 | a |
+---+---+
[1 rows x 2 columns]
"""
# If the user gives me an empty list (the indicator to use all columns)
# NA values being dropped would not be the expected behavior. This
# is a NOOP, so let's not bother the server
if type(columns) is list and len(columns) == 0:
return SFrame(_proxy=self.__proxy__)
(columns, all_behavior) = self.__dropna_errchk(columns, how)
with cython_context():
return SFrame(_proxy=self.__proxy__.drop_missing_values(columns, all_behavior, False)) | [
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The columns to use when looking for missing values. By default, all
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how : {'any', 'all'}, optional
Specifies whether a row should be dropped if at least one column
has missing values, or if all columns have missing values. 'any' is
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Returns
-------
out : SFrame
SFrame with missing values removed (according to the given rules).
See Also
--------
dropna_split : Drops missing rows from the SFrame and returns them.
Examples
--------
Drop all missing values.
>>> sf = turicreate.SFrame({'a': [1, None, None], 'b': ['a', 'b', None]})
>>> sf.dropna()
+---+---+
| a | b |
+---+---+
| 1 | a |
+---+---+
[1 rows x 2 columns]
Drop rows where every value is missing.
>>> sf.dropna(any="all")
+------+---+
| a | b |
+------+---+
| 1 | a |
| None | b |
+------+---+
[2 rows x 2 columns]
Drop rows where column 'a' has a missing value.
>>> sf.dropna('a', any="all")
+---+---+
| a | b |
+---+---+
| 1 | a |
+---+---+
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29,056 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.fillna | 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 raised.
Parameters
----------
column_name : str
The name of the column to modify.
value : type convertible to SArray's type
The value used to replace all missing values.
Returns
-------
out : SFrame
A new SFrame with the specified value in place of missing values.
See Also
--------
dropna
Examples
--------
>>> sf = turicreate.SFrame({'a':[1, None, None],
... 'b':['13.1', '17.2', None]})
>>> sf = sf.fillna('a', 0)
>>> sf
+---+------+
| a | b |
+---+------+
| 1 | 13.1 |
| 0 | 17.2 |
| 0 | None |
+---+------+
[3 rows x 2 columns]
"""
# Normal error checking
if type(column_name) is not str:
raise TypeError("column_name must be a str")
ret = self[self.column_names()]
ret[column_name] = ret[column_name].fillna(value)
return ret | python | 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 raised.
Parameters
----------
column_name : str
The name of the column to modify.
value : type convertible to SArray's type
The value used to replace all missing values.
Returns
-------
out : SFrame
A new SFrame with the specified value in place of missing values.
See Also
--------
dropna
Examples
--------
>>> sf = turicreate.SFrame({'a':[1, None, None],
... 'b':['13.1', '17.2', None]})
>>> sf = sf.fillna('a', 0)
>>> sf
+---+------+
| a | b |
+---+------+
| 1 | 13.1 |
| 0 | 17.2 |
| 0 | None |
+---+------+
[3 rows x 2 columns]
"""
# Normal error checking
if type(column_name) is not str:
raise TypeError("column_name must be a str")
ret = self[self.column_names()]
ret[column_name] = ret[column_name].fillna(value)
return ret | [
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See Also
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dropna
Examples
--------
>>> sf = turicreate.SFrame({'a':[1, None, None],
... 'b':['13.1', '17.2', None]})
>>> sf = sf.fillna('a', 0)
>>> sf
+---+------+
| a | b |
+---+------+
| 1 | 13.1 |
| 0 | 17.2 |
| 0 | None |
+---+------+
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29,057 | apple/turicreate | src/unity/python/turicreate/data_structures/sframe.py | SFrame.add_row_number | 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 column name. An error will be raised if the given column
name already exists in the SFrame.
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 : str, optional
The name of the new column that will hold the row numbers.
start : int, optional
The number used to start the row number count.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The new SFrame with a column name
Notes
-----
The range of numbers is constrained by a signed 64-bit integer, so
beware of overflow if you think the results in the row number column
will be greater than 9 quintillion.
Examples
--------
>>> sf = turicreate.SFrame({'a': [1, None, None], 'b': ['a', 'b', None]})
>>> sf.add_row_number()
+----+------+------+
| id | a | b |
+----+------+------+
| 0 | 1 | a |
| 1 | None | b |
| 2 | None | None |
+----+------+------+
[3 rows x 3 columns]
"""
if type(column_name) is not str:
raise TypeError("Must give column_name as strs")
if type(start) is not int:
raise TypeError("Must give start as int")
if column_name in self.column_names():
raise RuntimeError("Column '" + column_name + "' already exists in the current SFrame")
the_col = _create_sequential_sarray(self.num_rows(), start)
# Make sure the row number column is the first column
new_sf = SFrame()
new_sf.add_column(the_col, column_name, inplace=True)
new_sf.add_columns(self, inplace=True)
if inplace:
self.__proxy__ = new_sf.__proxy__
return self
else:
return new_sf | python | 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 column name. An error will be raised if the given column
name already exists in the SFrame.
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 : str, optional
The name of the new column that will hold the row numbers.
start : int, optional
The number used to start the row number count.
inplace : bool, optional. Defaults to False.
Whether the SFrame is modified in place.
Returns
-------
out : SFrame
The new SFrame with a column name
Notes
-----
The range of numbers is constrained by a signed 64-bit integer, so
beware of overflow if you think the results in the row number column
will be greater than 9 quintillion.
Examples
--------
>>> sf = turicreate.SFrame({'a': [1, None, None], 'b': ['a', 'b', None]})
>>> sf.add_row_number()
+----+------+------+
| id | a | b |
+----+------+------+
| 0 | 1 | a |
| 1 | None | b |
| 2 | None | None |
+----+------+------+
[3 rows x 3 columns]
"""
if type(column_name) is not str:
raise TypeError("Must give column_name as strs")
if type(start) is not int:
raise TypeError("Must give start as int")
if column_name in self.column_names():
raise RuntimeError("Column '" + column_name + "' already exists in the current SFrame")
the_col = _create_sequential_sarray(self.num_rows(), start)
# Make sure the row number column is the first column
new_sf = SFrame()
new_sf.add_column(the_col, column_name, inplace=True)
new_sf.add_columns(self, inplace=True)
if inplace:
self.__proxy__ = new_sf.__proxy__
return self
else:
return new_sf | [
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The number used to start the row number count.
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Whether the SFrame is modified in place.
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-----
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will be greater than 9 quintillion.
Examples
--------
>>> sf = turicreate.SFrame({'a': [1, None, None], 'b': ['a', 'b', None]})
>>> sf.add_row_number()
+----+------+------+
| id | a | b |
+----+------+------+
| 0 | 1 | a |
| 1 | None | b |
| 2 | None | None |
+----+------+------+
[3 rows x 3 columns] | [
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29,058 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.AddSerializedFile | 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 import descriptor_pb2
file_desc_proto = descriptor_pb2.FileDescriptorProto.FromString(
serialized_file_desc_proto)
self.Add(file_desc_proto) | python | 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 import descriptor_pb2
file_desc_proto = descriptor_pb2.FileDescriptorProto.FromString(
serialized_file_desc_proto)
self.Add(file_desc_proto) | [
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29,059 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.AddDescriptor | 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 not isinstance(desc, descriptor.Descriptor):
raise TypeError('Expected instance of descriptor.Descriptor.')
self._descriptors[desc.full_name] = desc
self._AddFileDescriptor(desc.file) | python | 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 not isinstance(desc, descriptor.Descriptor):
raise TypeError('Expected instance of descriptor.Descriptor.')
self._descriptors[desc.full_name] = desc
self._AddFileDescriptor(desc.file) | [
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29,060 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.AddServiceDescriptor | def AddServiceDescriptor(self, service_desc):
"""Adds a ServiceDescriptor to the pool.
Args:
service_desc: A ServiceDescriptor.
"""
if not isinstance(service_desc, descriptor.ServiceDescriptor):
raise TypeError('Expected instance of descriptor.ServiceDescriptor.')
self._service_descriptors[service_desc.full_name] = service_desc | python | def AddServiceDescriptor(self, service_desc):
"""Adds a ServiceDescriptor to the pool.
Args:
service_desc: A ServiceDescriptor.
"""
if not isinstance(service_desc, descriptor.ServiceDescriptor):
raise TypeError('Expected instance of descriptor.ServiceDescriptor.')
self._service_descriptors[service_desc.full_name] = service_desc | [
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29,061 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.AddExtensionDescriptor | 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 not a
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"""
if not (isinstance(extension, descriptor.FieldDescriptor) and
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self._toplevel_extensions[extension.full_name] = extension
try:
existing_desc = self._extensions_by_number[
extension.containing_type][extension.number]
except KeyError:
pass
else:
if extension is not existing_desc:
raise AssertionError(
'Extensions "%s" and "%s" both try to extend message type "%s" '
'with field number %d.' %
(extension.full_name, existing_desc.full_name,
extension.containing_type.full_name, extension.number))
self._extensions_by_number[extension.containing_type][
extension.number] = extension
self._extensions_by_name[extension.containing_type][
extension.full_name] = extension
# Also register MessageSet extensions with the type name.
if _IsMessageSetExtension(extension):
self._extensions_by_name[extension.containing_type][
extension.message_type.full_name] = extension | python | 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 not a
descriptor.FieldDescriptor.
"""
if not (isinstance(extension, descriptor.FieldDescriptor) and
extension.is_extension):
raise TypeError('Expected an extension descriptor.')
if extension.extension_scope is None:
self._toplevel_extensions[extension.full_name] = extension
try:
existing_desc = self._extensions_by_number[
extension.containing_type][extension.number]
except KeyError:
pass
else:
if extension is not existing_desc:
raise AssertionError(
'Extensions "%s" and "%s" both try to extend message type "%s" '
'with field number %d.' %
(extension.full_name, existing_desc.full_name,
extension.containing_type.full_name, extension.number))
self._extensions_by_number[extension.containing_type][
extension.number] = extension
self._extensions_by_name[extension.containing_type][
extension.full_name] = extension
# Also register MessageSet extensions with the type name.
if _IsMessageSetExtension(extension):
self._extensions_by_name[extension.containing_type][
extension.message_type.full_name] = extension | [
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29,062 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.FindFileByName | 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._file_descriptors[file_name]
except KeyError:
pass
try:
file_proto = self._internal_db.FindFileByName(file_name)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileByName(file_name)
else:
raise error
if not file_proto:
raise KeyError('Cannot find a file named %s' % file_name)
return self._ConvertFileProtoToFileDescriptor(file_proto) | python | 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._file_descriptors[file_name]
except KeyError:
pass
try:
file_proto = self._internal_db.FindFileByName(file_name)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileByName(file_name)
else:
raise error
if not file_proto:
raise KeyError('Cannot find a file named %s' % file_name)
return self._ConvertFileProtoToFileDescriptor(file_proto) | [
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29,063 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.FindFileContainingSymbol | 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 pool.
"""
symbol = _NormalizeFullyQualifiedName(symbol)
try:
return self._descriptors[symbol].file
except KeyError:
pass
try:
return self._enum_descriptors[symbol].file
except KeyError:
pass
try:
return self._FindFileContainingSymbolInDb(symbol)
except KeyError:
pass
try:
return self._file_desc_by_toplevel_extension[symbol]
except KeyError:
pass
# Try nested extensions inside a message.
message_name, _, extension_name = symbol.rpartition('.')
try:
message = self.FindMessageTypeByName(message_name)
assert message.extensions_by_name[extension_name]
return message.file
except KeyError:
raise KeyError('Cannot find a file containing %s' % symbol) | python | 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 pool.
"""
symbol = _NormalizeFullyQualifiedName(symbol)
try:
return self._descriptors[symbol].file
except KeyError:
pass
try:
return self._enum_descriptors[symbol].file
except KeyError:
pass
try:
return self._FindFileContainingSymbolInDb(symbol)
except KeyError:
pass
try:
return self._file_desc_by_toplevel_extension[symbol]
except KeyError:
pass
# Try nested extensions inside a message.
message_name, _, extension_name = symbol.rpartition('.')
try:
message = self.FindMessageTypeByName(message_name)
assert message.extensions_by_name[extension_name]
return message.file
except KeyError:
raise KeyError('Cannot find a file containing %s' % symbol) | [
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29,064 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.FindMessageTypeByName | 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 = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._descriptors[full_name] | python | 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 = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._descriptors[full_name] | [
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29,065 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.FindEnumTypeByName | 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_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._enum_descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._enum_descriptors[full_name] | python | 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_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._enum_descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._enum_descriptors[full_name] | [
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29,066 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.FindFieldByName | 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_name = _NormalizeFullyQualifiedName(full_name)
message_name, _, field_name = full_name.rpartition('.')
message_descriptor = self.FindMessageTypeByName(message_name)
return message_descriptor.fields_by_name[field_name] | python | 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_name = _NormalizeFullyQualifiedName(full_name)
message_name, _, field_name = full_name.rpartition('.')
message_descriptor = self.FindMessageTypeByName(message_name)
return message_descriptor.fields_by_name[field_name] | [
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29,067 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool.FindServiceByName | 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.
"""
full_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._service_descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._service_descriptors[full_name] | python | 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.
"""
full_name = _NormalizeFullyQualifiedName(full_name)
if full_name not in self._service_descriptors:
self._FindFileContainingSymbolInDb(full_name)
return self._service_descriptors[full_name] | [
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29,068 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._FindFileContainingSymbolInDb | 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 descriptor database.
"""
try:
file_proto = self._internal_db.FindFileContainingSymbol(symbol)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileContainingSymbol(symbol)
else:
raise error
if not file_proto:
raise KeyError('Cannot find a file containing %s' % symbol)
return self._ConvertFileProtoToFileDescriptor(file_proto) | python | 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 descriptor database.
"""
try:
file_proto = self._internal_db.FindFileContainingSymbol(symbol)
except KeyError as error:
if self._descriptor_db:
file_proto = self._descriptor_db.FindFileContainingSymbol(symbol)
else:
raise error
if not file_proto:
raise KeyError('Cannot find a file containing %s' % symbol)
return self._ConvertFileProtoToFileDescriptor(file_proto) | [
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29,069 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._ConvertFileProtoToFileDescriptor | 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.
Returns:
A FileDescriptor matching the passed in proto.
"""
if file_proto.name not in self._file_descriptors:
built_deps = list(self._GetDeps(file_proto.dependency))
direct_deps = [self.FindFileByName(n) for n in file_proto.dependency]
public_deps = [direct_deps[i] for i in file_proto.public_dependency]
file_descriptor = descriptor.FileDescriptor(
pool=self,
name=file_proto.name,
package=file_proto.package,
syntax=file_proto.syntax,
options=_OptionsOrNone(file_proto),
serialized_pb=file_proto.SerializeToString(),
dependencies=direct_deps,
public_dependencies=public_deps)
scope = {}
# This loop extracts all the message and enum types from all the
# dependencies of the file_proto. This is necessary to create the
# scope of available message types when defining the passed in
# file proto.
for dependency in built_deps:
scope.update(self._ExtractSymbols(
dependency.message_types_by_name.values()))
scope.update((_PrefixWithDot(enum.full_name), enum)
for enum in dependency.enum_types_by_name.values())
for message_type in file_proto.message_type:
message_desc = self._ConvertMessageDescriptor(
message_type, file_proto.package, file_descriptor, scope,
file_proto.syntax)
file_descriptor.message_types_by_name[message_desc.name] = (
message_desc)
for enum_type in file_proto.enum_type:
file_descriptor.enum_types_by_name[enum_type.name] = (
self._ConvertEnumDescriptor(enum_type, file_proto.package,
file_descriptor, None, scope))
for index, extension_proto in enumerate(file_proto.extension):
extension_desc = self._MakeFieldDescriptor(
extension_proto, file_proto.package, index, is_extension=True)
extension_desc.containing_type = self._GetTypeFromScope(
file_descriptor.package, extension_proto.extendee, scope)
self._SetFieldType(extension_proto, extension_desc,
file_descriptor.package, scope)
file_descriptor.extensions_by_name[extension_desc.name] = (
extension_desc)
for desc_proto in file_proto.message_type:
self._SetAllFieldTypes(file_proto.package, desc_proto, scope)
if file_proto.package:
desc_proto_prefix = _PrefixWithDot(file_proto.package)
else:
desc_proto_prefix = ''
for desc_proto in file_proto.message_type:
desc = self._GetTypeFromScope(
desc_proto_prefix, desc_proto.name, scope)
file_descriptor.message_types_by_name[desc_proto.name] = desc
for index, service_proto in enumerate(file_proto.service):
file_descriptor.services_by_name[service_proto.name] = (
self._MakeServiceDescriptor(service_proto, index, scope,
file_proto.package, file_descriptor))
self.Add(file_proto)
self._file_descriptors[file_proto.name] = file_descriptor
return self._file_descriptors[file_proto.name] | python | 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.
Returns:
A FileDescriptor matching the passed in proto.
"""
if file_proto.name not in self._file_descriptors:
built_deps = list(self._GetDeps(file_proto.dependency))
direct_deps = [self.FindFileByName(n) for n in file_proto.dependency]
public_deps = [direct_deps[i] for i in file_proto.public_dependency]
file_descriptor = descriptor.FileDescriptor(
pool=self,
name=file_proto.name,
package=file_proto.package,
syntax=file_proto.syntax,
options=_OptionsOrNone(file_proto),
serialized_pb=file_proto.SerializeToString(),
dependencies=direct_deps,
public_dependencies=public_deps)
scope = {}
# This loop extracts all the message and enum types from all the
# dependencies of the file_proto. This is necessary to create the
# scope of available message types when defining the passed in
# file proto.
for dependency in built_deps:
scope.update(self._ExtractSymbols(
dependency.message_types_by_name.values()))
scope.update((_PrefixWithDot(enum.full_name), enum)
for enum in dependency.enum_types_by_name.values())
for message_type in file_proto.message_type:
message_desc = self._ConvertMessageDescriptor(
message_type, file_proto.package, file_descriptor, scope,
file_proto.syntax)
file_descriptor.message_types_by_name[message_desc.name] = (
message_desc)
for enum_type in file_proto.enum_type:
file_descriptor.enum_types_by_name[enum_type.name] = (
self._ConvertEnumDescriptor(enum_type, file_proto.package,
file_descriptor, None, scope))
for index, extension_proto in enumerate(file_proto.extension):
extension_desc = self._MakeFieldDescriptor(
extension_proto, file_proto.package, index, is_extension=True)
extension_desc.containing_type = self._GetTypeFromScope(
file_descriptor.package, extension_proto.extendee, scope)
self._SetFieldType(extension_proto, extension_desc,
file_descriptor.package, scope)
file_descriptor.extensions_by_name[extension_desc.name] = (
extension_desc)
for desc_proto in file_proto.message_type:
self._SetAllFieldTypes(file_proto.package, desc_proto, scope)
if file_proto.package:
desc_proto_prefix = _PrefixWithDot(file_proto.package)
else:
desc_proto_prefix = ''
for desc_proto in file_proto.message_type:
desc = self._GetTypeFromScope(
desc_proto_prefix, desc_proto.name, scope)
file_descriptor.message_types_by_name[desc_proto.name] = desc
for index, service_proto in enumerate(file_proto.service):
file_descriptor.services_by_name[service_proto.name] = (
self._MakeServiceDescriptor(service_proto, index, scope,
file_proto.package, file_descriptor))
self.Add(file_proto)
self._file_descriptors[file_proto.name] = file_descriptor
return self._file_descriptors[file_proto.name] | [
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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. | [
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29,070 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._ConvertMessageDescriptor | 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 should be located in.
file_desc: The file containing this message.
scope: Dict mapping short and full symbols to message and enum types.
syntax: string indicating syntax of the file ("proto2" or "proto3")
Returns:
The added descriptor.
"""
if package:
desc_name = '.'.join((package, desc_proto.name))
else:
desc_name = desc_proto.name
if file_desc is None:
file_name = None
else:
file_name = file_desc.name
if scope is None:
scope = {}
nested = [
self._ConvertMessageDescriptor(
nested, desc_name, file_desc, scope, syntax)
for nested in desc_proto.nested_type]
enums = [
self._ConvertEnumDescriptor(enum, desc_name, file_desc, None, scope)
for enum in desc_proto.enum_type]
fields = [self._MakeFieldDescriptor(field, desc_name, index)
for index, field in enumerate(desc_proto.field)]
extensions = [
self._MakeFieldDescriptor(extension, desc_name, index,
is_extension=True)
for index, extension in enumerate(desc_proto.extension)]
oneofs = [
descriptor.OneofDescriptor(desc.name, '.'.join((desc_name, desc.name)),
index, None, [], desc.options)
for index, desc in enumerate(desc_proto.oneof_decl)]
extension_ranges = [(r.start, r.end) for r in desc_proto.extension_range]
if extension_ranges:
is_extendable = True
else:
is_extendable = False
desc = descriptor.Descriptor(
name=desc_proto.name,
full_name=desc_name,
filename=file_name,
containing_type=None,
fields=fields,
oneofs=oneofs,
nested_types=nested,
enum_types=enums,
extensions=extensions,
options=_OptionsOrNone(desc_proto),
is_extendable=is_extendable,
extension_ranges=extension_ranges,
file=file_desc,
serialized_start=None,
serialized_end=None,
syntax=syntax)
for nested in desc.nested_types:
nested.containing_type = desc
for enum in desc.enum_types:
enum.containing_type = desc
for field_index, field_desc in enumerate(desc_proto.field):
if field_desc.HasField('oneof_index'):
oneof_index = field_desc.oneof_index
oneofs[oneof_index].fields.append(fields[field_index])
fields[field_index].containing_oneof = oneofs[oneof_index]
scope[_PrefixWithDot(desc_name)] = desc
self._descriptors[desc_name] = desc
return desc | python | 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 should be located in.
file_desc: The file containing this message.
scope: Dict mapping short and full symbols to message and enum types.
syntax: string indicating syntax of the file ("proto2" or "proto3")
Returns:
The added descriptor.
"""
if package:
desc_name = '.'.join((package, desc_proto.name))
else:
desc_name = desc_proto.name
if file_desc is None:
file_name = None
else:
file_name = file_desc.name
if scope is None:
scope = {}
nested = [
self._ConvertMessageDescriptor(
nested, desc_name, file_desc, scope, syntax)
for nested in desc_proto.nested_type]
enums = [
self._ConvertEnumDescriptor(enum, desc_name, file_desc, None, scope)
for enum in desc_proto.enum_type]
fields = [self._MakeFieldDescriptor(field, desc_name, index)
for index, field in enumerate(desc_proto.field)]
extensions = [
self._MakeFieldDescriptor(extension, desc_name, index,
is_extension=True)
for index, extension in enumerate(desc_proto.extension)]
oneofs = [
descriptor.OneofDescriptor(desc.name, '.'.join((desc_name, desc.name)),
index, None, [], desc.options)
for index, desc in enumerate(desc_proto.oneof_decl)]
extension_ranges = [(r.start, r.end) for r in desc_proto.extension_range]
if extension_ranges:
is_extendable = True
else:
is_extendable = False
desc = descriptor.Descriptor(
name=desc_proto.name,
full_name=desc_name,
filename=file_name,
containing_type=None,
fields=fields,
oneofs=oneofs,
nested_types=nested,
enum_types=enums,
extensions=extensions,
options=_OptionsOrNone(desc_proto),
is_extendable=is_extendable,
extension_ranges=extension_ranges,
file=file_desc,
serialized_start=None,
serialized_end=None,
syntax=syntax)
for nested in desc.nested_types:
nested.containing_type = desc
for enum in desc.enum_types:
enum.containing_type = desc
for field_index, field_desc in enumerate(desc_proto.field):
if field_desc.HasField('oneof_index'):
oneof_index = field_desc.oneof_index
oneofs[oneof_index].fields.append(fields[field_index])
fields[field_index].containing_oneof = oneofs[oneof_index]
scope[_PrefixWithDot(desc_name)] = desc
self._descriptors[desc_name] = desc
return desc | [
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] | 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.
syntax: string indicating syntax of the file ("proto2" or "proto3")
Returns:
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py#L591-L670 |
29,071 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._SetAllFieldTypes | 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 available types.
"""
package = _PrefixWithDot(package)
main_desc = self._GetTypeFromScope(package, desc_proto.name, scope)
if package == '.':
nested_package = _PrefixWithDot(desc_proto.name)
else:
nested_package = '.'.join([package, desc_proto.name])
for field_proto, field_desc in zip(desc_proto.field, main_desc.fields):
self._SetFieldType(field_proto, field_desc, nested_package, scope)
for extension_proto, extension_desc in (
zip(desc_proto.extension, main_desc.extensions)):
extension_desc.containing_type = self._GetTypeFromScope(
nested_package, extension_proto.extendee, scope)
self._SetFieldType(extension_proto, extension_desc, nested_package, scope)
for nested_type in desc_proto.nested_type:
self._SetAllFieldTypes(nested_package, nested_type, scope) | python | 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 available types.
"""
package = _PrefixWithDot(package)
main_desc = self._GetTypeFromScope(package, desc_proto.name, scope)
if package == '.':
nested_package = _PrefixWithDot(desc_proto.name)
else:
nested_package = '.'.join([package, desc_proto.name])
for field_proto, field_desc in zip(desc_proto.field, main_desc.fields):
self._SetFieldType(field_proto, field_desc, nested_package, scope)
for extension_proto, extension_desc in (
zip(desc_proto.extension, main_desc.extensions)):
extension_desc.containing_type = self._GetTypeFromScope(
nested_package, extension_proto.extendee, scope)
self._SetFieldType(extension_proto, extension_desc, nested_package, scope)
for nested_type in desc_proto.nested_type:
self._SetAllFieldTypes(nested_package, nested_type, scope) | [
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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. | [
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29,072 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._SetFieldType | 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: Enclosing scope of available types.
"""
if field_proto.type_name:
desc = self._GetTypeFromScope(package, field_proto.type_name, scope)
else:
desc = None
if not field_proto.HasField('type'):
if isinstance(desc, descriptor.Descriptor):
field_proto.type = descriptor.FieldDescriptor.TYPE_MESSAGE
else:
field_proto.type = descriptor.FieldDescriptor.TYPE_ENUM
field_desc.cpp_type = descriptor.FieldDescriptor.ProtoTypeToCppProtoType(
field_proto.type)
if (field_proto.type == descriptor.FieldDescriptor.TYPE_MESSAGE
or field_proto.type == descriptor.FieldDescriptor.TYPE_GROUP):
field_desc.message_type = desc
if field_proto.type == descriptor.FieldDescriptor.TYPE_ENUM:
field_desc.enum_type = desc
if field_proto.label == descriptor.FieldDescriptor.LABEL_REPEATED:
field_desc.has_default_value = False
field_desc.default_value = []
elif field_proto.HasField('default_value'):
field_desc.has_default_value = True
if (field_proto.type == descriptor.FieldDescriptor.TYPE_DOUBLE or
field_proto.type == descriptor.FieldDescriptor.TYPE_FLOAT):
field_desc.default_value = float(field_proto.default_value)
elif field_proto.type == descriptor.FieldDescriptor.TYPE_STRING:
field_desc.default_value = field_proto.default_value
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BOOL:
field_desc.default_value = field_proto.default_value.lower() == 'true'
elif field_proto.type == descriptor.FieldDescriptor.TYPE_ENUM:
field_desc.default_value = field_desc.enum_type.values_by_name[
field_proto.default_value].number
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BYTES:
field_desc.default_value = text_encoding.CUnescape(
field_proto.default_value)
else:
# All other types are of the "int" type.
field_desc.default_value = int(field_proto.default_value)
else:
field_desc.has_default_value = False
if (field_proto.type == descriptor.FieldDescriptor.TYPE_DOUBLE or
field_proto.type == descriptor.FieldDescriptor.TYPE_FLOAT):
field_desc.default_value = 0.0
elif field_proto.type == descriptor.FieldDescriptor.TYPE_STRING:
field_desc.default_value = u''
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BOOL:
field_desc.default_value = False
elif field_proto.type == descriptor.FieldDescriptor.TYPE_ENUM:
field_desc.default_value = field_desc.enum_type.values[0].number
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BYTES:
field_desc.default_value = b''
else:
# All other types are of the "int" type.
field_desc.default_value = 0
field_desc.type = field_proto.type | python | 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: Enclosing scope of available types.
"""
if field_proto.type_name:
desc = self._GetTypeFromScope(package, field_proto.type_name, scope)
else:
desc = None
if not field_proto.HasField('type'):
if isinstance(desc, descriptor.Descriptor):
field_proto.type = descriptor.FieldDescriptor.TYPE_MESSAGE
else:
field_proto.type = descriptor.FieldDescriptor.TYPE_ENUM
field_desc.cpp_type = descriptor.FieldDescriptor.ProtoTypeToCppProtoType(
field_proto.type)
if (field_proto.type == descriptor.FieldDescriptor.TYPE_MESSAGE
or field_proto.type == descriptor.FieldDescriptor.TYPE_GROUP):
field_desc.message_type = desc
if field_proto.type == descriptor.FieldDescriptor.TYPE_ENUM:
field_desc.enum_type = desc
if field_proto.label == descriptor.FieldDescriptor.LABEL_REPEATED:
field_desc.has_default_value = False
field_desc.default_value = []
elif field_proto.HasField('default_value'):
field_desc.has_default_value = True
if (field_proto.type == descriptor.FieldDescriptor.TYPE_DOUBLE or
field_proto.type == descriptor.FieldDescriptor.TYPE_FLOAT):
field_desc.default_value = float(field_proto.default_value)
elif field_proto.type == descriptor.FieldDescriptor.TYPE_STRING:
field_desc.default_value = field_proto.default_value
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BOOL:
field_desc.default_value = field_proto.default_value.lower() == 'true'
elif field_proto.type == descriptor.FieldDescriptor.TYPE_ENUM:
field_desc.default_value = field_desc.enum_type.values_by_name[
field_proto.default_value].number
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BYTES:
field_desc.default_value = text_encoding.CUnescape(
field_proto.default_value)
else:
# All other types are of the "int" type.
field_desc.default_value = int(field_proto.default_value)
else:
field_desc.has_default_value = False
if (field_proto.type == descriptor.FieldDescriptor.TYPE_DOUBLE or
field_proto.type == descriptor.FieldDescriptor.TYPE_FLOAT):
field_desc.default_value = 0.0
elif field_proto.type == descriptor.FieldDescriptor.TYPE_STRING:
field_desc.default_value = u''
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BOOL:
field_desc.default_value = False
elif field_proto.type == descriptor.FieldDescriptor.TYPE_ENUM:
field_desc.default_value = field_desc.enum_type.values[0].number
elif field_proto.type == descriptor.FieldDescriptor.TYPE_BYTES:
field_desc.default_value = b''
else:
# All other types are of the "int" type.
field_desc.default_value = 0
field_desc.type = field_proto.type | [
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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. | [
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29,073 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._MakeEnumValueDescriptor | 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 descriptor.EnumValueDescriptor(
name=value_proto.name,
index=index,
number=value_proto.number,
options=_OptionsOrNone(value_proto),
type=None) | python | 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 descriptor.EnumValueDescriptor(
name=value_proto.name,
index=index,
number=value_proto.number,
options=_OptionsOrNone(value_proto),
type=None) | [
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29,074 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._MakeServiceDescriptor | 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 the service in the File.
scope: Dict mapping short and full symbols to message and enum types.
package: Optional package name for the new message EnumDescriptor.
file_desc: The file containing the service descriptor.
Returns:
The added descriptor.
"""
if package:
service_name = '.'.join((package, service_proto.name))
else:
service_name = service_proto.name
methods = [self._MakeMethodDescriptor(method_proto, service_name, package,
scope, index)
for index, method_proto in enumerate(service_proto.method)]
desc = descriptor.ServiceDescriptor(name=service_proto.name,
full_name=service_name,
index=service_index,
methods=methods,
options=_OptionsOrNone(service_proto),
file=file_desc)
self._service_descriptors[service_name] = desc
return desc | python | 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 the service in the File.
scope: Dict mapping short and full symbols to message and enum types.
package: Optional package name for the new message EnumDescriptor.
file_desc: The file containing the service descriptor.
Returns:
The added descriptor.
"""
if package:
service_name = '.'.join((package, service_proto.name))
else:
service_name = service_proto.name
methods = [self._MakeMethodDescriptor(method_proto, service_name, package,
scope, index)
for index, method_proto in enumerate(service_proto.method)]
desc = descriptor.ServiceDescriptor(name=service_proto.name,
full_name=service_name,
index=service_index,
methods=methods,
options=_OptionsOrNone(service_proto),
file=file_desc)
self._service_descriptors[service_name] = desc
return desc | [
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29,075 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._MakeMethodDescriptor | 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 package name to look up for types.
scope: Scope containing available types.
index: Index of the method in the service.
Returns:
An initialized MethodDescriptor object.
"""
full_name = '.'.join((service_name, method_proto.name))
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output_type = self._GetTypeFromScope(
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full_name=full_name,
index=index,
containing_service=None,
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output_type=output_type,
options=_OptionsOrNone(method_proto)) | python | 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 package name to look up for types.
scope: Scope containing available types.
index: Index of the method in the service.
Returns:
An initialized MethodDescriptor object.
"""
full_name = '.'.join((service_name, method_proto.name))
input_type = self._GetTypeFromScope(
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output_type = self._GetTypeFromScope(
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return descriptor.MethodDescriptor(name=method_proto.name,
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index: Index of the method in the service.
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29,076 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._ExtractSymbols | 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.full_name), desc)
for symbol in self._ExtractSymbols(desc.nested_types):
yield symbol
for enum in desc.enum_types:
yield (_PrefixWithDot(enum.full_name), enum) | python | 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.full_name), desc)
for symbol in self._ExtractSymbols(desc.nested_types):
yield symbol
for enum in desc.enum_types:
yield (_PrefixWithDot(enum.full_name), enum) | [
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29,077 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._GetDeps | 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)
yield dep_desc
for parent_dep in dep_desc.dependencies:
yield parent_dep | python | 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)
yield dep_desc
for parent_dep in dep_desc.dependencies:
yield parent_dep | [
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29,078 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor_pool.py | DescriptorPool._GetTypeFromScope | 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 descriptor for the requested type.
"""
if type_name not in scope:
components = _PrefixWithDot(package).split('.')
while components:
possible_match = '.'.join(components + [type_name])
if possible_match in scope:
type_name = possible_match
break
else:
components.pop(-1)
return scope[type_name] | python | 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 descriptor for the requested type.
"""
if type_name not in scope:
components = _PrefixWithDot(package).split('.')
while components:
possible_match = '.'.join(components + [type_name])
if possible_match in scope:
type_name = possible_match
break
else:
components.pop(-1)
return scope[type_name] | [
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29,079 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/sequence.py | max_element | def max_element (elements, ordered = None):
""" Returns the maximum number in 'elements'. Uses 'ordered' for comparisons,
or '<' is none is provided.
"""
assert is_iterable(elements)
assert callable(ordered) or ordered is None
if not ordered: ordered = operator.lt
max = elements [0]
for e in elements [1:]:
if ordered (max, e):
max = e
return max | python | def max_element (elements, ordered = None):
""" Returns the maximum number in 'elements'. Uses 'ordered' for comparisons,
or '<' is none is provided.
"""
assert is_iterable(elements)
assert callable(ordered) or ordered is None
if not ordered: ordered = operator.lt
max = elements [0]
for e in elements [1:]:
if ordered (max, e):
max = e
return max | [
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29,080 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/sequence.py | select_highest_ranked | 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_element (ranks)
result = []
while elements:
if ranks [0] == max_rank:
result.append (elements [0])
elements = elements [1:]
ranks = ranks [1:]
return result | python | 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_element (ranks)
result = []
while elements:
if ranks [0] == max_rank:
result.append (elements [0])
elements = elements [1:]
ranks = ranks [1:]
return result | [
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29,081 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/message.py | Message.CopyFrom | 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:
return
self.Clear()
self.MergeFrom(other_msg) | python | 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:
return
self.Clear()
self.MergeFrom(other_msg) | [
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29,082 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/xgboost/_tree_ensemble.py | convert_tree_ensemble | 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 model is or
a handle to the XGboost model.
feature_names : list of strings or None
Names of each of the features. When set to None, the feature names are
extracted from the model.
target: str,
Name of the output column.
force_32bit_float: bool
If True, then the resulting CoreML model will use 32 bit floats internally.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model
"""
if not(_HAS_XGBOOST):
raise RuntimeError('xgboost not found. xgboost conversion API is disabled.')
import json
import os
feature_map = None
if isinstance(model, (_xgboost.core.Booster, _xgboost.XGBRegressor)):
# Testing a few corner cases that we don't support
if isinstance(model, _xgboost.XGBRegressor):
try:
objective = model.get_xgb_params()["objective"]
except:
objective = None
if objective in ["reg:gamma", "reg:tweedie"]:
raise ValueError("Regression objective '%s' not supported for export." % objective)
# Now use the booster API.
if isinstance(model, _xgboost.XGBRegressor):
# Name change in 0.7
if hasattr(model, 'get_booster'):
model = model.get_booster()
else:
model = model.booster()
# Xgboost sometimes has feature names in there. Sometimes does not.
if (feature_names is None) and (model.feature_names is None):
raise ValueError("Feature names not present in the model. Must be provided during conversion.")
feature_names = model.feature_names
if feature_names is None:
feature_names = model.feature_names
xgb_model_str = model.get_dump(with_stats=True, dump_format = 'json')
if model.feature_names:
feature_map = {f:i for i,f in enumerate(model.feature_names)}
# Path on the file system where the XGboost model exists.
elif isinstance(model, str):
if not os.path.exists(model):
raise TypeError("Invalid path %s." % model)
with open(model) as f:
xgb_model_str = json.load(f)
feature_map = {f:i for i,f in enumerate(feature_names)}
else:
raise TypeError("Unexpected type. Expecting XGBoost model.")
mlkit_tree = _TreeEnsembleRegressor(feature_names, target)
mlkit_tree.set_default_prediction_value(0.5)
for xgb_tree_id, xgb_tree_str in enumerate(xgb_model_str):
xgb_tree_json = json.loads(xgb_tree_str)
recurse_json(mlkit_tree, xgb_tree_json, xgb_tree_id, node_id = 0,
feature_map = feature_map, force_32bit_float = force_32bit_float)
return mlkit_tree.spec | python | 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 model is or
a handle to the XGboost model.
feature_names : list of strings or None
Names of each of the features. When set to None, the feature names are
extracted from the model.
target: str,
Name of the output column.
force_32bit_float: bool
If True, then the resulting CoreML model will use 32 bit floats internally.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model
"""
if not(_HAS_XGBOOST):
raise RuntimeError('xgboost not found. xgboost conversion API is disabled.')
import json
import os
feature_map = None
if isinstance(model, (_xgboost.core.Booster, _xgboost.XGBRegressor)):
# Testing a few corner cases that we don't support
if isinstance(model, _xgboost.XGBRegressor):
try:
objective = model.get_xgb_params()["objective"]
except:
objective = None
if objective in ["reg:gamma", "reg:tweedie"]:
raise ValueError("Regression objective '%s' not supported for export." % objective)
# Now use the booster API.
if isinstance(model, _xgboost.XGBRegressor):
# Name change in 0.7
if hasattr(model, 'get_booster'):
model = model.get_booster()
else:
model = model.booster()
# Xgboost sometimes has feature names in there. Sometimes does not.
if (feature_names is None) and (model.feature_names is None):
raise ValueError("Feature names not present in the model. Must be provided during conversion.")
feature_names = model.feature_names
if feature_names is None:
feature_names = model.feature_names
xgb_model_str = model.get_dump(with_stats=True, dump_format = 'json')
if model.feature_names:
feature_map = {f:i for i,f in enumerate(model.feature_names)}
# Path on the file system where the XGboost model exists.
elif isinstance(model, str):
if not os.path.exists(model):
raise TypeError("Invalid path %s." % model)
with open(model) as f:
xgb_model_str = json.load(f)
feature_map = {f:i for i,f in enumerate(feature_names)}
else:
raise TypeError("Unexpected type. Expecting XGBoost model.")
mlkit_tree = _TreeEnsembleRegressor(feature_names, target)
mlkit_tree.set_default_prediction_value(0.5)
for xgb_tree_id, xgb_tree_str in enumerate(xgb_model_str):
xgb_tree_json = json.loads(xgb_tree_str)
recurse_json(mlkit_tree, xgb_tree_json, xgb_tree_id, node_id = 0,
feature_map = feature_map, force_32bit_float = force_32bit_float)
return mlkit_tree.spec | [
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] | 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 strings or None
Names of each of the features. When set to None, the feature names are
extracted from the model.
target: str,
Name of the output column.
force_32bit_float: bool
If True, then the resulting CoreML model will use 32 bit floats internally.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model | [
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29,083 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_one_hot_encoder.py | update_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(model, lambda m: hasattr(m, 'active_features_'))
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'n_values_'))
if model.categorical_features == 'all':
return len(model.active_features_)
else:
out_dimension = (len(model.active_features_)
+ (input_dimension - len(model.n_values_)))
return out_dimension | python | def update_dimension(model, input_dimension):
"""
Given a model that takes an array of dimension input_dimension, returns
the output dimension.
"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'active_features_'))
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'n_values_'))
if model.categorical_features == 'all':
return len(model.active_features_)
else:
out_dimension = (len(model.active_features_)
+ (input_dimension - len(model.n_values_)))
return out_dimension | [
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29,084 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_elementwise_mul_scalar | 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: NeuralNetworkBuilder
A neural network builder object.
"""
import numpy
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
mult = literal_eval(param['scalar'])
builder.add_scale(name=name,
W=numpy.array([mult]),
b=0,
has_bias=False,
input_name=input_name,
output_name=output_name) | python | 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: NeuralNetworkBuilder
A neural network builder object.
"""
import numpy
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
mult = literal_eval(param['scalar'])
builder.add_scale(name=name,
W=numpy.array([mult]),
b=0,
has_bias=False,
input_name=input_name,
output_name=output_name) | [
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Parameters
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net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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29,085 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_dense | 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 network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
has_bias = True
name = node['name']
inputs = node['inputs']
args, _ = module.get_params()
W = args[_get_node_name(net, inputs[1][0])].asnumpy()
if has_bias:
Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
else:
Wb = None
nC, nB = W.shape
builder.add_inner_product(
name=name,
W=W,
b=Wb,
input_channels=nB,
output_channels=nC,
has_bias=has_bias,
input_name=input_name,
output_name=output_name
) | python | 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 network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
has_bias = True
name = node['name']
inputs = node['inputs']
args, _ = module.get_params()
W = args[_get_node_name(net, inputs[1][0])].asnumpy()
if has_bias:
Wb = args[_get_node_name(net, inputs[2][0])].asnumpy()
else:
Wb = None
nC, nB = W.shape
builder.add_inner_product(
name=name,
W=W,
b=Wb,
input_channels=nB,
output_channels=nC,
has_bias=has_bias,
input_name=input_name,
output_name=output_name
) | [
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Parameters
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net: network
A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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29,086 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_padding | 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 neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
pad = literal_eval(param['pad_width'])
pad_left = pad[4]
pad_top = pad[5]
pad_right = pad[6]
pad_bottom = pad[7]
if param['mode'] == 'reflect':
builder.add_padding(
name=name,
top=pad_top,
bottom=pad_bottom,
left=pad_left,
right=pad_right,
padding_type='reflection',
input_name=input_name,
output_name=output_name
)
else:
raise TypeError("Padding type %s not supported" % param['mode']) | python | 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 neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
pad = literal_eval(param['pad_width'])
pad_left = pad[4]
pad_top = pad[5]
pad_right = pad[6]
pad_bottom = pad[7]
if param['mode'] == 'reflect':
builder.add_padding(
name=name,
top=pad_top,
bottom=pad_bottom,
left=pad_left,
right=pad_right,
padding_type='reflection',
input_name=input_name,
output_name=output_name
)
else:
raise TypeError("Padding type %s not supported" % param['mode']) | [
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A mxnet network object.
node: layer
Node to convert.
module: module
An module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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29,087 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_upsample | 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 neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
inputs = node['inputs']
args, _ = module.get_params()
scale = literal_eval(param['scale'])
#method
if 'sample_type' in param.keys():
method = param['sample_type']
if method == 'nearest':
mode = 'NN'
elif method == '':
mode = 'BILINEAR'
builder.add_upsample(name, scaling_factor_h=scale, scaling_factor_w=scale,
input_name=input_name, output_name=output_name, mode=mode) | python | 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 neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
inputs = node['inputs']
args, _ = module.get_params()
scale = literal_eval(param['scale'])
#method
if 'sample_type' in param.keys():
method = param['sample_type']
if method == 'nearest':
mode = 'NN'
elif method == '':
mode = 'BILINEAR'
builder.add_upsample(name, scaling_factor_h=scale, scaling_factor_w=scale,
input_name=input_name, output_name=output_name, mode=mode) | [
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29,088 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_custom | def convert_custom(net, node, module, builder):
"""Convert highly specific ops"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
if param['op_type'] == 'special-darknet-maxpool':
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=2,
width=2,
stride_height=1,
stride_width=1,
layer_type='MAX',
padding_type='SAME',
is_global=False,
same_padding_asymmetry_mode='BOTTOM_RIGHT_HEAVY',
input_name=input_name,
output_name=output_name
)
else:
raise TypeError("MXNet layer of type Custom is not supported.") | python | def convert_custom(net, node, module, builder):
"""Convert highly specific ops"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
if param['op_type'] == 'special-darknet-maxpool':
_add_pooling.add_pooling_with_padding_types(
builder=builder,
name=name,
height=2,
width=2,
stride_height=1,
stride_width=1,
layer_type='MAX',
padding_type='SAME',
is_global=False,
same_padding_asymmetry_mode='BOTTOM_RIGHT_HEAVY',
input_name=input_name,
output_name=output_name
)
else:
raise TypeError("MXNet layer of type Custom is not supported.") | [
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29,089 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_embedding | 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 neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
outputs = node['outputs']
arg_params, aux_params = model.get_params()
W = arg_params[_get_node_name(net, inputs[1][0])].asnumpy()
if not ONE_HOT_ENCODE_HACK:
nC, nB = W.shape
W = W.T
builder.add_embedding(name = name,
W = W,
b = None,
input_dim = nC,
output_channels = nB,
has_bias = False,
input_name = input_name,
output_name = output_name)
else:
W = W.T
nC, nB = W.shape
builder.add_inner_product(name = name,
W = W,
b = None,
input_channels = nB,
output_channels = nC,
has_bias = False,
input_name = input_name,
output_name = output_name) | python | 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 neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
outputs = node['outputs']
arg_params, aux_params = model.get_params()
W = arg_params[_get_node_name(net, inputs[1][0])].asnumpy()
if not ONE_HOT_ENCODE_HACK:
nC, nB = W.shape
W = W.T
builder.add_embedding(name = name,
W = W,
b = None,
input_dim = nC,
output_channels = nB,
has_bias = False,
input_name = input_name,
output_name = output_name)
else:
W = W.T
nC, nB = W.shape
builder.add_inner_product(name = name,
W = W,
b = None,
input_channels = nB,
output_channels = nC,
has_bias = False,
input_name = input_name,
output_name = output_name) | [
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A mxnet network object.
node: layer
Node to convert.
model: model
An model for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | [
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29,090 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_scalar_add | 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 neural network builder object.
"""
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
mode = 'ADD'
alpha = _np.array([float(param['scalar'])])
builder.add_scale(name = name, input_name = input_name,
output_name = output_name, W = _np.array([1.0]), b = alpha, has_bias=True) | python | 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 neural network builder object.
"""
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
mode = 'ADD'
alpha = _np.array([float(param['scalar'])])
builder.add_scale(name = name, input_name = input_name,
output_name = output_name, W = _np.array([1.0]), b = alpha, has_bias=True) | [
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29,091 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_scalar_multiply | 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
A neural network builder object.
"""
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
alpha = _np.array([float(param['scalar'])])
builder.add_scale(name = name, input_name = input_name,
output_name = output_name, W = alpha, has_bias=False, b=None) | python | 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
A neural network builder object.
"""
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
param = _get_attr(node)
alpha = _np.array([float(param['scalar'])])
builder.add_scale(name = name, input_name = input_name,
output_name = output_name, W = alpha, has_bias=False, b=None) | [
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A mxnet network object.
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An model for MXNet
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A neural network builder object. | [
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29,092 | apple/turicreate | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_layers.py | convert_instancenorm | 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
A neural network builder object.
"""
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
outputs = node['outputs']
data_blob_name = _get_node_name(net, inputs[0][0])
gamma_blob_name = _get_node_name(net, inputs[1][0])
beta_blob_name = _get_node_name(net, inputs[2][0])
channels = _get_node_channels(net, inputs[0][0])
bn_output_name = output_name + '_bn_'
builder.add_batchnorm(
name = name + '_normalize',
channels = channels,
gamma = _np.ones((channels, )),
beta = _np.zeros((channels, )),
mean = None,
variance = None,
input_name = input_name,
output_name = bn_output_name,
compute_mean_var = True,
instance_normalization = True)
gamma_input_names = [bn_output_name, gamma_blob_name]
gamma_output_name = output_name + '_mult_gamma'
builder.add_elementwise(name=name+'_mult_gamma', input_names=gamma_input_names,
output_name = gamma_output_name, mode='MULTIPLY', alpha = None)
beta_input_names = [gamma_output_name, beta_blob_name]
builder.add_elementwise(name=name+'_add_beta', input_names=beta_input_names,
output_name = output_name, mode='ADD', alpha=None) | python | 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
A neural network builder object.
"""
import numpy as _np
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
inputs = node['inputs']
outputs = node['outputs']
data_blob_name = _get_node_name(net, inputs[0][0])
gamma_blob_name = _get_node_name(net, inputs[1][0])
beta_blob_name = _get_node_name(net, inputs[2][0])
channels = _get_node_channels(net, inputs[0][0])
bn_output_name = output_name + '_bn_'
builder.add_batchnorm(
name = name + '_normalize',
channels = channels,
gamma = _np.ones((channels, )),
beta = _np.zeros((channels, )),
mean = None,
variance = None,
input_name = input_name,
output_name = bn_output_name,
compute_mean_var = True,
instance_normalization = True)
gamma_input_names = [bn_output_name, gamma_blob_name]
gamma_output_name = output_name + '_mult_gamma'
builder.add_elementwise(name=name+'_mult_gamma', input_names=gamma_input_names,
output_name = gamma_output_name, mode='MULTIPLY', alpha = None)
beta_input_names = [gamma_output_name, beta_blob_name]
builder.add_elementwise(name=name+'_add_beta', input_names=beta_input_names,
output_name = output_name, mode='ADD', alpha=None) | [
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Node to convert.
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29,093 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | _get_aws_credentials | 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]
The first string of the tuple is the value of the AWS_ACCESS_KEY_ID
environment variable. The second string of the tuple is the value of the
AWS_SECRET_ACCESS_KEY environment variable.
Examples
--------
>>> turicreate.aws.get_credentials()
('RBZH792CTQPP7T435BGQ', '7x2hMqplWsLpU/qQCN6xAPKcmWo46TlPJXYTvKcv')
"""
if (not 'AWS_ACCESS_KEY_ID' in _os.environ):
raise KeyError('No access key found. Please set the environment variable AWS_ACCESS_KEY_ID.')
if (not 'AWS_SECRET_ACCESS_KEY' in _os.environ):
raise KeyError('No secret key found. Please set the environment variable AWS_SECRET_ACCESS_KEY.')
return (_os.environ['AWS_ACCESS_KEY_ID'], _os.environ['AWS_SECRET_ACCESS_KEY']) | python | 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]
The first string of the tuple is the value of the AWS_ACCESS_KEY_ID
environment variable. The second string of the tuple is the value of the
AWS_SECRET_ACCESS_KEY environment variable.
Examples
--------
>>> turicreate.aws.get_credentials()
('RBZH792CTQPP7T435BGQ', '7x2hMqplWsLpU/qQCN6xAPKcmWo46TlPJXYTvKcv')
"""
if (not 'AWS_ACCESS_KEY_ID' in _os.environ):
raise KeyError('No access key found. Please set the environment variable AWS_ACCESS_KEY_ID.')
if (not 'AWS_SECRET_ACCESS_KEY' in _os.environ):
raise KeyError('No secret key found. Please set the environment variable AWS_SECRET_ACCESS_KEY.')
return (_os.environ['AWS_ACCESS_KEY_ID'], _os.environ['AWS_SECRET_ACCESS_KEY']) | [
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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 value of the AWS_ACCESS_KEY_ID
environment variable. The second string of the tuple is the value of the
AWS_SECRET_ACCESS_KEY environment variable.
Examples
--------
>>> turicreate.aws.get_credentials()
('RBZH792CTQPP7T435BGQ', '7x2hMqplWsLpU/qQCN6xAPKcmWo46TlPJXYTvKcv') | [
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29,094 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | is_directory_archive | 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
----------
path : string
Directory to evaluate.
Returns
-------
True if path provided is an archive location, False otherwise
"""
if path is None:
return False
if not _os.path.isdir(path):
return False
ini_path = '/'.join([_convert_slashes(path), 'dir_archive.ini'])
if not _os.path.exists(ini_path):
return False
if _os.path.isfile(ini_path):
return True
return False | python | 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
----------
path : string
Directory to evaluate.
Returns
-------
True if path provided is an archive location, False otherwise
"""
if path is None:
return False
if not _os.path.isdir(path):
return False
ini_path = '/'.join([_convert_slashes(path), 'dir_archive.ini'])
if not _os.path.exists(ini_path):
return False
if _os.path.isfile(ini_path):
return True
return False | [
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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 evaluate.
Returns
-------
True if path provided is an archive location, False otherwise | [
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29,095 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | get_archive_type | 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(path):
raise TypeError('Unable to determine the type of archive at path: %s' % path)
try:
ini_path = '/'.join([_convert_slashes(path), 'dir_archive.ini'])
parser = _ConfigParser.SafeConfigParser()
parser.read(ini_path)
contents = parser.get('metadata', 'contents')
return contents
except Exception as e:
raise TypeError('Unable to determine type of archive for path: %s' % path, e) | python | 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(path):
raise TypeError('Unable to determine the type of archive at path: %s' % path)
try:
ini_path = '/'.join([_convert_slashes(path), 'dir_archive.ini'])
parser = _ConfigParser.SafeConfigParser()
parser.read(ini_path)
contents = parser.get('metadata', 'contents')
return contents
except Exception as e:
raise TypeError('Unable to determine type of archive for path: %s' % path, e) | [
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----------
path : string
Directory to evaluate.
Returns
-------
Returns a string of: sframe, sgraph, raises TypeError for anything else | [
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29,096 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | crossproduct | 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 will be a column for each key in the provided dictionary,
and a row for each unique combination of all values.
Example
-------
settings = {'argument_1':[0, 1],
'argument_2':['a', 'b', 'c']}
print crossproduct(settings)
+------------+------------+
| argument_2 | argument_1 |
+------------+------------+
| a | 0 |
| a | 1 |
| b | 0 |
| b | 1 |
| c | 0 |
| c | 1 |
+------------+------------+
[6 rows x 2 columns]
"""
from .. import SArray
d = [list(zip(list(d.keys()), x)) for x in _itertools.product(*list(d.values()))]
sa = [{k:v for (k,v) in x} for x in d]
return SArray(sa).unpack(column_name_prefix='') | python | 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 will be a column for each key in the provided dictionary,
and a row for each unique combination of all values.
Example
-------
settings = {'argument_1':[0, 1],
'argument_2':['a', 'b', 'c']}
print crossproduct(settings)
+------------+------------+
| argument_2 | argument_1 |
+------------+------------+
| a | 0 |
| a | 1 |
| b | 0 |
| b | 1 |
| c | 0 |
| c | 1 |
+------------+------------+
[6 rows x 2 columns]
"""
from .. import SArray
d = [list(zip(list(d.keys()), x)) for x in _itertools.product(*list(d.values()))]
sa = [{k:v for (k,v) in x} for x in d]
return SArray(sa).unpack(column_name_prefix='') | [
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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 the provided dictionary,
and a row for each unique combination of all values.
Example
-------
settings = {'argument_1':[0, 1],
'argument_2':['a', 'b', 'c']}
print crossproduct(settings)
+------------+------------+
| argument_2 | argument_1 |
+------------+------------+
| a | 0 |
| a | 1 |
| b | 0 |
| b | 1 |
| c | 0 |
| c | 1 |
+------------+------------+
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29,097 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | _assert_sframe_equal | 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 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 with another, with
the exception of `check_column_order` and `check_column_names`, we must use
one of these to determine which columns to compare with one another.
Parameters
----------
sf1 : SFrame
sf2 : SFrame
check_column_names : bool
If true, assert if the data values in two columns are the same, but
they have different names. If False, column order is used to determine
which columns to compare.
check_column_order : bool
If true, assert if the data values in two columns are the same, but are
not in the same column position (one is the i-th column and the other
is the j-th column, i != j). If False, column names are used to
determine which columns to compare.
check_row_order : bool
If true, assert if all rows in the first SFrame exist in the second
SFrame, but they are not in the same order.
float_column_delta : float
The acceptable delta that two float values can be and still be
considered "equal". When this is None, only exact equality is accepted.
This is the default behavior since columns of all Nones are often of
float type. Applies to all float columns.
"""
from .. import SFrame as _SFrame
if (type(sf1) is not _SFrame) or (type(sf2) is not _SFrame):
raise TypeError("Cannot function on types other than SFrames.")
if not check_column_order and not check_column_names:
raise ValueError("Cannot ignore both column order and column names.")
sf1.__materialize__()
sf2.__materialize__()
if sf1.num_columns() != sf2.num_columns():
raise AssertionError("Number of columns mismatched: " +
str(sf1.num_columns()) + " != " + str(sf2.num_columns()))
s1_names = sf1.column_names()
s2_names = sf2.column_names()
sorted_s1_names = sorted(s1_names)
sorted_s2_names = sorted(s2_names)
if check_column_names:
if (check_column_order and (s1_names != s2_names)) or (sorted_s1_names != sorted_s2_names):
raise AssertionError("SFrame does not have same column names: " +
str(sf1.column_names()) + " != " + str(sf2.column_names()))
if sf1.num_rows() != sf2.num_rows():
raise AssertionError("Number of rows mismatched: " +
str(sf1.num_rows()) + " != " + str(sf2.num_rows()))
if not check_row_order and (sf1.num_rows() > 1):
sf1 = sf1.sort(s1_names)
sf2 = sf2.sort(s2_names)
names_to_check = None
if check_column_names:
names_to_check = list(zip(sorted_s1_names, sorted_s2_names))
else:
names_to_check = list(zip(s1_names, s2_names))
for i in names_to_check:
col1 = sf1[i[0]]
col2 = sf2[i[1]]
if col1.dtype != col2.dtype:
raise AssertionError("Columns " + str(i) + " types mismatched.")
compare_ary = None
if col1.dtype == float and float_column_delta is not None:
dt = float_column_delta
compare_ary = ((col1 > col2-dt) & (col1 < col2+dt))
else:
compare_ary = (sf1[i[0]] == sf2[i[1]])
if not compare_ary.all():
count = 0
for j in compare_ary:
if not j:
first_row = count
break
count += 1
raise AssertionError("Columns " + str(i) +
" are not equal! First differing element is at row " +
str(first_row) + ": " + str((col1[first_row],col2[first_row]))) | python | 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 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 with another, with
the exception of `check_column_order` and `check_column_names`, we must use
one of these to determine which columns to compare with one another.
Parameters
----------
sf1 : SFrame
sf2 : SFrame
check_column_names : bool
If true, assert if the data values in two columns are the same, but
they have different names. If False, column order is used to determine
which columns to compare.
check_column_order : bool
If true, assert if the data values in two columns are the same, but are
not in the same column position (one is the i-th column and the other
is the j-th column, i != j). If False, column names are used to
determine which columns to compare.
check_row_order : bool
If true, assert if all rows in the first SFrame exist in the second
SFrame, but they are not in the same order.
float_column_delta : float
The acceptable delta that two float values can be and still be
considered "equal". When this is None, only exact equality is accepted.
This is the default behavior since columns of all Nones are often of
float type. Applies to all float columns.
"""
from .. import SFrame as _SFrame
if (type(sf1) is not _SFrame) or (type(sf2) is not _SFrame):
raise TypeError("Cannot function on types other than SFrames.")
if not check_column_order and not check_column_names:
raise ValueError("Cannot ignore both column order and column names.")
sf1.__materialize__()
sf2.__materialize__()
if sf1.num_columns() != sf2.num_columns():
raise AssertionError("Number of columns mismatched: " +
str(sf1.num_columns()) + " != " + str(sf2.num_columns()))
s1_names = sf1.column_names()
s2_names = sf2.column_names()
sorted_s1_names = sorted(s1_names)
sorted_s2_names = sorted(s2_names)
if check_column_names:
if (check_column_order and (s1_names != s2_names)) or (sorted_s1_names != sorted_s2_names):
raise AssertionError("SFrame does not have same column names: " +
str(sf1.column_names()) + " != " + str(sf2.column_names()))
if sf1.num_rows() != sf2.num_rows():
raise AssertionError("Number of rows mismatched: " +
str(sf1.num_rows()) + " != " + str(sf2.num_rows()))
if not check_row_order and (sf1.num_rows() > 1):
sf1 = sf1.sort(s1_names)
sf2 = sf2.sort(s2_names)
names_to_check = None
if check_column_names:
names_to_check = list(zip(sorted_s1_names, sorted_s2_names))
else:
names_to_check = list(zip(s1_names, s2_names))
for i in names_to_check:
col1 = sf1[i[0]]
col2 = sf2[i[1]]
if col1.dtype != col2.dtype:
raise AssertionError("Columns " + str(i) + " types mismatched.")
compare_ary = None
if col1.dtype == float and float_column_delta is not None:
dt = float_column_delta
compare_ary = ((col1 > col2-dt) & (col1 < col2+dt))
else:
compare_ary = (sf1[i[0]] == sf2[i[1]])
if not compare_ary.all():
count = 0
for j in compare_ary:
if not j:
first_row = count
break
count += 1
raise AssertionError("Columns " + str(i) +
" are not equal! First differing element is at row " +
str(first_row) + ": " + str((col1[first_row],col2[first_row]))) | [
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] | 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 with another, with
the exception of `check_column_order` and `check_column_names`, we must use
one of these to determine which columns to compare with one another.
Parameters
----------
sf1 : SFrame
sf2 : SFrame
check_column_names : bool
If true, assert if the data values in two columns are the same, but
they have different names. If False, column order is used to determine
which columns to compare.
check_column_order : bool
If true, assert if the data values in two columns are the same, but are
not in the same column position (one is the i-th column and the other
is the j-th column, i != j). If False, column names are used to
determine which columns to compare.
check_row_order : bool
If true, assert if all rows in the first SFrame exist in the second
SFrame, but they are not in the same order.
float_column_delta : float
The acceptable delta that two float values can be and still be
considered "equal". When this is None, only exact equality is accepted.
This is the default behavior since columns of all Nones are often of
float type. Applies to all float columns. | [
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/util/__init__.py#L263-L365 |
29,098 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | _make_temp_directory | 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 = _make_temp_filename(prefix=str(prefix))
_os.makedirs(temp_dir)
return temp_dir | python | 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 = _make_temp_filename(prefix=str(prefix))
_os.makedirs(temp_dir)
return temp_dir | [
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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 | [
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29,099 | apple/turicreate | src/unity/python/turicreate/util/__init__.py | _make_temp_filename | 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
'''
temp_location = _get_temp_file_location()
temp_file_name = '/'.join([temp_location, str(prefix)+str(_uuid.uuid4())])
return temp_file_name | python | 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
'''
temp_location = _get_temp_file_location()
temp_file_name = '/'.join([temp_location, str(prefix)+str(_uuid.uuid4())])
return temp_file_name | [
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Caller is expected to clean up the temp file as soon as the file is no
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