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28,900 | apple/turicreate | deps/src/boost_1_68_0/libs/predef/tools/ci/build_log.py | BuildConsoleSummaryReport.print_action | def print_action(self, test_succeed, action):
'''
Print the detailed info of failed or always print tests.
'''
#self.info_print(">>> {0}",action.keys())
if not test_succeed or action['info']['always_show_run_output']:
output = action['output'].strip()
if o... | python | def print_action(self, test_succeed, action):
'''
Print the detailed info of failed or always print tests.
'''
#self.info_print(">>> {0}",action.keys())
if not test_succeed or action['info']['always_show_run_output']:
output = action['output'].strip()
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28,901 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_SVC.py | _generate_base_svm_classifier_spec | def _generate_base_svm_classifier_spec(model):
"""
Takes an SVM classifier produces a starting spec using the parts. that are
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"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
check_fitted(model, la... | python | def _generate_base_svm_classifier_spec(model):
"""
Takes an SVM classifier produces a starting spec using the parts. that are
shared between all SVMs.
"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
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28,902 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph._insert_layer_after | def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer):
"""
Insert the new_layer after layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer
"""
# reminder: new_keras_layer is not part of the original Keras n... | python | def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer):
"""
Insert the new_layer after layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer
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28,903 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph._insert_layer_between | def _insert_layer_between(self, src, snk, new_layer, new_keras_layer):
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Insert the new_layer before layer, whose position is layer_idx. The new layer's
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"""
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insert_pos = self.layer_list.inde... | python | def _insert_layer_between(self, src, snk, new_layer, new_keras_layer):
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Insert the new_layer before layer, whose position is layer_idx. The new layer's
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28,904 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph.insert_1d_permute_layers | def insert_1d_permute_layers(self):
"""
Insert permutation layers before a 1D start point or after 1D end point
"""
idx, nb_layers = 0, len(self.layer_list)
in_edges, out_edges = self._get_1d_interface_edges()
# Hacky Warning: (1) use a 4-D permute, which is not likely t... | python | def insert_1d_permute_layers(self):
"""
Insert permutation layers before a 1D start point or after 1D end point
"""
idx, nb_layers = 0, len(self.layer_list)
in_edges, out_edges = self._get_1d_interface_edges()
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28,905 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/configure.py | log_component_configuration | def log_component_configuration(component, message):
"""Report something about component configuration that the user should better know."""
assert isinstance(component, basestring)
assert isinstance(message, basestring)
__component_logs.setdefault(component, []).append(message) | python | def log_component_configuration(component, message):
"""Report something about component configuration that the user should better know."""
assert isinstance(component, basestring)
assert isinstance(message, basestring)
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28,906 | apple/turicreate | src/unity/python/turicreate/toolkits/_feature_engineering/__init__.py | create | def create(dataset, transformers):
"""
Create a Transformer object to transform data for feature engineering.
Parameters
----------
dataset : SFrame
The dataset to use for training the model.
transformers: Transformer | list[Transformer]
An Transformer or a list of Transformer... | python | def create(dataset, transformers):
"""
Create a Transformer object to transform data for feature engineering.
Parameters
----------
dataset : SFrame
The dataset to use for training the model.
transformers: Transformer | list[Transformer]
An Transformer or a list of Transformer... | [
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28,907 | apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | VGGishFeatureExtractor._preprocess_data | def _preprocess_data(audio_data, verbose=True):
'''
Preprocess each example, breaking it up into frames.
Returns two numpy arrays: preprocessed frame and their indexes
'''
from .vggish_input import waveform_to_examples
last_progress_update = _time.time()
progres... | python | def _preprocess_data(audio_data, verbose=True):
'''
Preprocess each example, breaking it up into frames.
Returns two numpy arrays: preprocessed frame and their indexes
'''
from .vggish_input import waveform_to_examples
last_progress_update = _time.time()
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28,908 | apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | VGGishFeatureExtractor.get_deep_features | def get_deep_features(self, audio_data, verbose):
'''
Performs both audio preprocessing and VGGish deep feature extraction.
'''
preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose)
deep_features = self._extract_features(preprocessed_data, verbose)
outp... | python | def get_deep_features(self, audio_data, verbose):
'''
Performs both audio preprocessing and VGGish deep feature extraction.
'''
preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose)
deep_features = self._extract_features(preprocessed_data, verbose)
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28,909 | apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | VGGishFeatureExtractor.get_spec | def get_spec(self):
"""
Return the Core ML spec
"""
if _mac_ver() >= (10, 14):
return self.vggish_model.get_spec()
else:
vggish_model_file = VGGish()
coreml_model_path = vggish_model_file.get_model_path(format='coreml')
return MLMod... | python | def get_spec(self):
"""
Return the Core ML spec
"""
if _mac_ver() >= (10, 14):
return self.vggish_model.get_spec()
else:
vggish_model_file = VGGish()
coreml_model_path = vggish_model_file.get_model_path(format='coreml')
return MLMod... | [
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28,910 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | value_to_jam | def value_to_jam(value, methods=False):
"""Makes a token to refer to a Python value inside Jam language code.
The token is merely a string that can be passed around in Jam code and
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"""Makes a token to refer to a Python value inside Jam language code.
The token is merely a string that can be passed around in Jam code and
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28,911 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | abbreviate_dashed | def abbreviate_dashed(s):
"""Abbreviates each part of string that is delimited by a '-'."""
r = []
for part in s.split('-'):
r.append(abbreviate(part))
return '-'.join(r) | python | def abbreviate_dashed(s):
"""Abbreviates each part of string that is delimited by a '-'."""
r = []
for part in s.split('-'):
r.append(abbreviate(part))
return '-'.join(r) | [
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28,912 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | abbreviate | def abbreviate(s):
"""Apply a set of standard transformations to string to produce an
abbreviation no more than 4 characters long.
"""
if not s:
return ''
# check the cache
if s in abbreviate.abbreviations:
return abbreviate.abbreviations[s]
# anything less than 4 characters ... | python | def abbreviate(s):
"""Apply a set of standard transformations to string to produce an
abbreviation no more than 4 characters long.
"""
if not s:
return ''
# check the cache
if s in abbreviate.abbreviations:
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28,913 | apple/turicreate | src/unity/python/turicreate/toolkits/_decision_tree.py | Node.get_decision | def get_decision(self, child, is_missing = False):
"""
Get the decision from this node to a child node.
Parameters
----------
child: Node
A child node of this node.
Returns
-------
dict: A dictionary that describes how to get from this node t... | python | def get_decision(self, child, is_missing = False):
"""
Get the decision from this node to a child node.
Parameters
----------
child: Node
A child node of this node.
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/_decision_tree.py#L80-L123 |
28,914 | apple/turicreate | src/unity/python/turicreate/toolkits/_decision_tree.py | Node.to_dict | def to_dict(self):
"""
Return the node as a dictionary.
Returns
-------
dict: All the attributes of this node as a dictionary (minus the left
and right).
"""
out = {}
for key in self.__dict__.keys():
if key not in ['left', 'right... | python | def to_dict(self):
"""
Return the node as a dictionary.
Returns
-------
dict: All the attributes of this node as a dictionary (minus the left
and right).
"""
out = {}
for key in self.__dict__.keys():
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28,915 | apple/turicreate | src/unity/python/turicreate/toolkits/_decision_tree.py | DecisionTree.to_json | def to_json(self, root_id = 0, output = {}):
"""
Recursive function to dump this tree as a json blob.
Parameters
----------
root_id: Root id of the sub-tree
output: Carry over output from the previous sub-trees.
Returns
-------
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"""
Recursive function to dump this tree as a json blob.
Parameters
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root_id: Root id of the sub-tree
output: Carry over output from the previous sub-trees.
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28,916 | apple/turicreate | src/unity/python/turicreate/toolkits/_decision_tree.py | DecisionTree.get_prediction_path | def get_prediction_path(self, node_id, missing_id = []):
"""
Return the prediction path from this node to the parent node.
Parameters
----------
node_id : id of the node to get the prediction path.
missing_id : Additional info that contains nodes with missing features... | python | def get_prediction_path(self, node_id, missing_id = []):
"""
Return the prediction path from this node to the parent node.
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node_id : id of the node to get the prediction path.
missing_id : Additional info that contains nodes with missing features... | [
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28,917 | apple/turicreate | src/unity/python/turicreate/toolkits/graph_analytics/label_propagation.py | create | def create(graph, label_field,
threshold=1e-3,
weight_field='',
self_weight=1.0,
undirected=False,
max_iterations=None,
_single_precision=False,
_distributed='auto',
verbose=True):
"""
Given a weighted graph with observed cl... | python | def create(graph, label_field,
threshold=1e-3,
weight_field='',
self_weight=1.0,
undirected=False,
max_iterations=None,
_single_precision=False,
_distributed='auto',
verbose=True):
"""
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28,918 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | _is_not_pickle_safe_gl_model_class | def _is_not_pickle_safe_gl_model_class(obj_class):
"""
Check if a Turi create model is pickle safe.
The function does it by checking that _CustomModel is the base class.
Parameters
----------
obj_class : Class to be checked.
Returns
----------
True if the GLC class is a model a... | python | def _is_not_pickle_safe_gl_model_class(obj_class):
"""
Check if a Turi create model is pickle safe.
The function does it by checking that _CustomModel is the base class.
Parameters
----------
obj_class : Class to be checked.
Returns
----------
True if the GLC class is a model a... | [
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Returns
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28,919 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | _is_not_pickle_safe_gl_class | def _is_not_pickle_safe_gl_class(obj_class):
"""
Check if class is a Turi create model.
The function does it by checking the method resolution order (MRO) of the
class and verifies that _Model is the base class.
Parameters
----------
obj_class : Class to be checked.
Returns
---... | python | def _is_not_pickle_safe_gl_class(obj_class):
"""
Check if class is a Turi create model.
The function does it by checking the method resolution order (MRO) of the
class and verifies that _Model is the base class.
Parameters
----------
obj_class : Class to be checked.
Returns
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28,920 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | _get_gl_class_type | def _get_gl_class_type(obj_class):
"""
Internal util to get the type of the GLC class. The pickle file stores
this name so that it knows how to construct the object on unpickling.
Parameters
----------
obj_class : Class which has to be categorized.
Returns
----------
A class typ... | python | def _get_gl_class_type(obj_class):
"""
Internal util to get the type of the GLC class. The pickle file stores
this name so that it knows how to construct the object on unpickling.
Parameters
----------
obj_class : Class which has to be categorized.
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----------
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28,921 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | _get_gl_object_from_persistent_id | def _get_gl_object_from_persistent_id(type_tag, gl_archive_abs_path):
"""
Internal util to get a GLC object from a persistent ID in the pickle file.
Parameters
----------
type_tag : The name of the glc class as saved in the GLC pickler.
gl_archive_abs_path: An absolute path to the GLC archive ... | python | def _get_gl_object_from_persistent_id(type_tag, gl_archive_abs_path):
"""
Internal util to get a GLC object from a persistent ID in the pickle file.
Parameters
----------
type_tag : The name of the glc class as saved in the GLC pickler.
gl_archive_abs_path: An absolute path to the GLC archive ... | [
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28,922 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | GLPickler.persistent_id | def persistent_id(self, obj):
"""
Provide a persistent ID for "saving" GLC objects by reference. Return
None for all non GLC objects.
Parameters
----------
obj: Name of the object whose persistent ID is extracted.
Returns
--------
None if the ob... | python | def persistent_id(self, obj):
"""
Provide a persistent ID for "saving" GLC objects by reference. Return
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obj: Name of the object whose persistent ID is extracted.
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28,923 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | GLPickler.close | def close(self):
"""
Close the pickle file, and the zip archive file. The single zip archive
file can now be shipped around to be loaded by the unpickler.
"""
if self.file is None:
return
# Close the pickle file.
self.file.close()
self.file = ... | python | def close(self):
"""
Close the pickle file, and the zip archive file. The single zip archive
file can now be shipped around to be loaded by the unpickler.
"""
if self.file is None:
return
# Close the pickle file.
self.file.close()
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28,924 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | GLUnpickler.persistent_load | def persistent_load(self, pid):
"""
Reconstruct a GLC object using the persistent ID.
This method should not be used externally. It is required by the unpickler super class.
Parameters
----------
pid : The persistent ID used in pickle file to save the GLC object.
... | python | def persistent_load(self, pid):
"""
Reconstruct a GLC object using the persistent ID.
This method should not be used externally. It is required by the unpickler super class.
Parameters
----------
pid : The persistent ID used in pickle file to save the GLC object.
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28,925 | apple/turicreate | src/unity/python/turicreate/_gl_pickle.py | GLUnpickler.close | def close(self):
"""
Clean up files that were created.
"""
if self.file:
self.file.close()
self.file = None
# If temp_file is a folder, we do not remove it because we may
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if self.tmp_file and... | python | def close(self):
"""
Clean up files that were created.
"""
if self.file:
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28,926 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_converter.py | convert | def convert(sk_obj, input_features = None,
output_feature_names = None):
"""
Convert scikit-learn pipeline, classifier, or regressor to Core ML format.
Parameters
----------
sk_obj: model | [model] of scikit-learn format.
Scikit learn model(s) to convert to a Core ML format.
... | python | def convert(sk_obj, input_features = None,
output_feature_names = None):
"""
Convert scikit-learn pipeline, classifier, or regressor to Core ML format.
Parameters
----------
sk_obj: model | [model] of scikit-learn format.
Scikit learn model(s) to convert to a Core ML format.
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28,927 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/reflection.py | ParseMessage | def ParseMessage(descriptor, byte_str):
"""Generate a new Message instance from this Descriptor and a byte string.
Args:
descriptor: Protobuf Descriptor object
byte_str: Serialized protocol buffer byte string
Returns:
Newly created protobuf Message object.
"""
result_class = MakeClass(descriptor... | python | def ParseMessage(descriptor, byte_str):
"""Generate a new Message instance from this Descriptor and a byte string.
Args:
descriptor: Protobuf Descriptor object
byte_str: Serialized protocol buffer byte string
Returns:
Newly created protobuf Message object.
"""
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28,928 | apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/reflection.py | MakeClass | def MakeClass(descriptor):
"""Construct a class object for a protobuf described by descriptor.
Composite descriptors are handled by defining the new class as a member of the
parent class, recursing as deep as necessary.
This is the dynamic equivalent to:
class Parent(message.Message):
__metaclass__ = Ge... | python | def MakeClass(descriptor):
"""Construct a class object for a protobuf described by descriptor.
Composite descriptors are handled by defining the new class as a member of the
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class Parent(message.Message):
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28,929 | apple/turicreate | src/unity/python/turicreate/toolkits/image_analysis/image_analysis.py | load_images | def load_images(url, format='auto', with_path=True, recursive=True, ignore_failure=True, random_order=False):
"""
Loads images from a directory. JPEG and PNG images are supported.
Parameters
----------
url : str
The string of the path where all the images are stored.
format : {'PNG' | ... | python | def load_images(url, format='auto', with_path=True, recursive=True, ignore_failure=True, random_order=False):
"""
Loads images from a directory. JPEG and PNG images are supported.
Parameters
----------
url : str
The string of the path where all the images are stored.
format : {'PNG' | ... | [
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28,930 | apple/turicreate | src/unity/python/turicreate/toolkits/image_analysis/image_analysis.py | _decode | def _decode(image_data):
"""
Internal helper function for decoding a single Image or an SArray of Images
"""
from ...data_structures.sarray import SArray as _SArray
from ... import extensions as _extensions
if type(image_data) is _SArray:
return _extensions.decode_image_sarray(image_data... | python | def _decode(image_data):
"""
Internal helper function for decoding a single Image or an SArray of Images
"""
from ...data_structures.sarray import SArray as _SArray
from ... import extensions as _extensions
if type(image_data) is _SArray:
return _extensions.decode_image_sarray(image_data... | [
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28,931 | apple/turicreate | src/unity/python/turicreate/toolkits/image_analysis/image_analysis.py | resize | def resize(image, width, height, channels=None, decode=False,
resample='nearest'):
"""
Resizes the image or SArray of Images to a specific width, height, and
number of channels.
Parameters
----------
image : turicreate.Image | SArray
The image or SArray of images to be resiz... | python | def resize(image, width, height, channels=None, decode=False,
resample='nearest'):
"""
Resizes the image or SArray of Images to a specific width, height, and
number of channels.
Parameters
----------
image : turicreate.Image | SArray
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28,932 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _convert_1bit_array_to_byte_array | def _convert_1bit_array_to_byte_array(arr):
"""
Convert bit array to byte array.
:param arr: list
Bits as a list where each element is an integer of 0 or 1
Returns
-------
numpy.array
1D numpy array of type uint8
"""
# Padding if necessary
while len(arr) < 8 or len(... | python | def _convert_1bit_array_to_byte_array(arr):
"""
Convert bit array to byte array.
:param arr: list
Bits as a list where each element is an integer of 0 or 1
Returns
-------
numpy.array
1D numpy array of type uint8
"""
# Padding if necessary
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28,933 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _decompose_bytes_to_bit_arr | def _decompose_bytes_to_bit_arr(arr):
"""
Unpack bytes to bits
:param arr: list
Byte Stream, as a list of uint8 values
Returns
-------
bit_arr: list
Decomposed bit stream as a list of 0/1s of length (len(arr) * 8)
"""
bit_arr = []
for idx in range(len(arr)):
... | python | def _decompose_bytes_to_bit_arr(arr):
"""
Unpack bytes to bits
:param arr: list
Byte Stream, as a list of uint8 values
Returns
-------
bit_arr: list
Decomposed bit stream as a list of 0/1s of length (len(arr) * 8)
"""
bit_arr = []
for idx in range(len(arr)):
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28,934 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _get_linear_lookup_table_and_weight | def _get_linear_lookup_table_and_weight(nbits, wp):
"""
Generate a linear lookup table.
:param nbits: int
Number of bits to represent a quantized weight value
:param wp: numpy.array
Weight blob to be quantized
Returns
-------
lookup_table: numpy.array
Lookup table ... | python | def _get_linear_lookup_table_and_weight(nbits, wp):
"""
Generate a linear lookup table.
:param nbits: int
Number of bits to represent a quantized weight value
:param wp: numpy.array
Weight blob to be quantized
Returns
-------
lookup_table: numpy.array
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28,935 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _get_kmeans_lookup_table_and_weight | def _get_kmeans_lookup_table_and_weight(nbits, w, init='k-means++', tol=1e-2, n_init=1, rand_seed=0):
"""
Generate K-Means lookup table given a weight parameter field
:param nbits:
Number of bits for quantization
:param w:
Weight as numpy array
Returns
-------
lut: numpy.a... | python | def _get_kmeans_lookup_table_and_weight(nbits, w, init='k-means++', tol=1e-2, n_init=1, rand_seed=0):
"""
Generate K-Means lookup table given a weight parameter field
:param nbits:
Number of bits for quantization
:param w:
Weight as numpy array
Returns
-------
lut: numpy.a... | [
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28,936 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _quantize_channelwise_linear | def _quantize_channelwise_linear(weight, nbits, axis=0):
"""
Linearly quantize weight blob.
:param weight: numpy.array
Weight to be quantized.
:param nbits: int
Number of bits per weight element
:param axis: int
Axis of the weight blob to compute channel-wise quantization,... | python | def _quantize_channelwise_linear(weight, nbits, axis=0):
"""
Linearly quantize weight blob.
:param weight: numpy.array
Weight to be quantized.
:param nbits: int
Number of bits per weight element
:param axis: int
Axis of the weight blob to compute channel-wise quantization,... | [
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28,937 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _quantize_wp | def _quantize_wp(wp, nbits, qm, axis=0, **kwargs):
"""
Quantize the weight blob
:param wp: numpy.array
Weight parameters
:param nbits: int
Number of bits
:param qm:
Quantization mode
:param lut_function: (``callable function``)
Python callable representing a look... | python | def _quantize_wp(wp, nbits, qm, axis=0, **kwargs):
"""
Quantize the weight blob
:param wp: numpy.array
Weight parameters
:param nbits: int
Number of bits
:param qm:
Quantization mode
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28,938 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | _quantize_wp_field | def _quantize_wp_field(wp, nbits, qm, shape, axis=0, **kwargs):
"""
Quantize WeightParam field in Neural Network Protobuf
:param wp: MLModel.NeuralNetwork.WeightParam
WeightParam field
:param nbits: int
Number of bits to be quantized
:param qm: str
Quantization mode
:pa... | python | def _quantize_wp_field(wp, nbits, qm, shape, axis=0, **kwargs):
"""
Quantize WeightParam field in Neural Network Protobuf
:param wp: MLModel.NeuralNetwork.WeightParam
WeightParam field
:param nbits: int
Number of bits to be quantized
:param qm: str
Quantization mode
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28,939 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/quantization_utils.py | compare_models | def compare_models(full_precision_model, quantized_model,
sample_data):
"""
Utility function to compare the performance of a full precision vs quantized model
:param full_precision_model: MLModel
The full precision model with float32 weights
:param quantized_model... | python | def compare_models(full_precision_model, quantized_model,
sample_data):
"""
Utility function to compare the performance of a full precision vs quantized model
:param full_precision_model: MLModel
The full precision model with float32 weights
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28,940 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/item_similarity_recommender.py | create | def create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
nearest_items=None,
similarity_type='jaccard',
threshold=0.001,
only_top_k=64,
verbose=True,
target_memory_usage = 8*102... | python | def create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
nearest_items=None,
similarity_type='jaccard',
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28,941 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | convert_advanced_relu | def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer):
"""
Convert an ReLU layer with maximum value from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
... | python | def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer):
"""
Convert an ReLU layer with maximum value from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
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28,942 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | convert_separable_convolution | def convert_separable_convolution(builder, layer, input_names, output_names, keras_layer):
"""
Convert separable convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.... | python | def convert_separable_convolution(builder, layer, input_names, output_names, keras_layer):
"""
Convert separable convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
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28,943 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | convert_batchnorm | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Convert a Batch Normalization layer.
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
i... | python | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Convert a Batch Normalization layer.
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
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28,944 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | convert_merge | def convert_merge(builder, layer, input_names, output_names, keras_layer):
"""
Convert concat layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and ou... | python | def convert_merge(builder, layer, input_names, output_names, keras_layer):
"""
Convert concat layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
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28,945 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | convert_pooling | def convert_pooling(builder, layer, input_names, output_names, keras_layer):
"""
Convert pooling layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
_check_data_for... | python | def convert_pooling(builder, layer, input_names, output_names, keras_layer):
"""
Convert pooling layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
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28,946 | apple/turicreate | src/external/xgboost/subtree/rabit/wrapper/rabit.py | _loadlib | def _loadlib(lib='standard'):
"""Load rabit library."""
global _LIB
if _LIB is not None:
warnings.warn('rabit.int call was ignored because it has'\
' already been initialized', level=2)
return
if lib == 'standard':
_LIB = ctypes.cdll.LoadLibrary(WRAPPER_... | python | def _loadlib(lib='standard'):
"""Load rabit library."""
global _LIB
if _LIB is not None:
warnings.warn('rabit.int call was ignored because it has'\
' already been initialized', level=2)
return
if lib == 'standard':
_LIB = ctypes.cdll.LoadLibrary(WRAPPER_... | [
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28,947 | apple/turicreate | src/external/xgboost/subtree/rabit/wrapper/rabit.py | init | def init(args=None, lib='standard'):
"""Intialize the rabit module, call this once before using anything.
Parameters
----------
args: list of str, optional
The list of arguments used to initialized the rabit
usually you need to pass in sys.argv.
Defaults to sys.argv when it is N... | python | def init(args=None, lib='standard'):
"""Intialize the rabit module, call this once before using anything.
Parameters
----------
args: list of str, optional
The list of arguments used to initialized the rabit
usually you need to pass in sys.argv.
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28,948 | apple/turicreate | src/external/xgboost/subtree/rabit/wrapper/rabit.py | allreduce | def allreduce(data, op, prepare_fun=None):
"""Perform allreduce, return the result.
Parameters
----------
data: numpy array
Input data.
op: int
Reduction operators, can be MIN, MAX, SUM, BITOR
prepare_fun: function
Lazy preprocessing function, if it is not None, prepare_... | python | def allreduce(data, op, prepare_fun=None):
"""Perform allreduce, return the result.
Parameters
----------
data: numpy array
Input data.
op: int
Reduction operators, can be MIN, MAX, SUM, BITOR
prepare_fun: function
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28,949 | apple/turicreate | src/external/xgboost/subtree/rabit/wrapper/rabit.py | load_checkpoint | def load_checkpoint(with_local=False):
"""Load latest check point.
Parameters
----------
with_local: bool, optional
whether the checkpoint contains local model
Returns
-------
tuple : tuple
if with_local: return (version, gobal_model, local_model)
else return (versi... | python | def load_checkpoint(with_local=False):
"""Load latest check point.
Parameters
----------
with_local: bool, optional
whether the checkpoint contains local model
Returns
-------
tuple : tuple
if with_local: return (version, gobal_model, local_model)
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28,950 | apple/turicreate | src/external/xgboost/subtree/rabit/wrapper/rabit.py | checkpoint | def checkpoint(global_model, local_model=None):
"""Checkpoint the model.
This means we finished a stage of execution.
Every time we call check point, there is a version number which will increase by one.
Parameters
----------
global_model: anytype that can be pickled
globally shared mo... | python | def checkpoint(global_model, local_model=None):
"""Checkpoint the model.
This means we finished a stage of execution.
Every time we call check point, there is a version number which will increase by one.
Parameters
----------
global_model: anytype that can be pickled
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28,951 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/ranking_factorization_recommender.py | create | def create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
num_factors=32,
regularization=1e-9,
linear_regularization=1e-9,
side_data_factorization=True,
ranking_regularization=0.25,
... | python | def create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
num_factors=32,
regularization=1e-9,
linear_regularization=1e-9,
side_data_factorization=True,
ranking_regularization=0.25,
... | [
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28,952 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_converter_internal.py | _get_converter_module | def _get_converter_module(sk_obj):
"""
Returns the module holding the conversion functions for a
particular model).
"""
try:
cv_idx = _converter_lookup[sk_obj.__class__]
except KeyError:
raise ValueError(
"Transformer '%s' not supported; supported transformers are... | python | def _get_converter_module(sk_obj):
"""
Returns the module holding the conversion functions for a
particular model).
"""
try:
cv_idx = _converter_lookup[sk_obj.__class__]
except KeyError:
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28,953 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/tree_ensemble.py | TreeEnsembleBase.set_post_evaluation_transform | def set_post_evaluation_transform(self, value):
r"""
Set the post processing transform applied after the prediction value
from the tree ensemble.
Parameters
----------
value: str
A value denoting the transform applied. Possible values are:
- "... | python | def set_post_evaluation_transform(self, value):
r"""
Set the post processing transform applied after the prediction value
from the tree ensemble.
Parameters
----------
value: str
A value denoting the transform applied. Possible values are:
- "... | [
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Set the post processing transform applied after the prediction value
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28,954 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/tree_ensemble.py | TreeEnsembleBase.add_branch_node | def add_branch_node(self, tree_id, node_id, feature_index, feature_value,
branch_mode, true_child_id, false_child_id, relative_hit_rate = None,
missing_value_tracks_true_child = False):
"""
Add a branch node to the tree ensemble.
Parameters
----------
tre... | python | def add_branch_node(self, tree_id, node_id, feature_index, feature_value,
branch_mode, true_child_id, false_child_id, relative_hit_rate = None,
missing_value_tracks_true_child = False):
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Add a branch node to the tree ensemble.
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28,955 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/tree_ensemble.py | TreeEnsembleBase.add_leaf_node | def add_leaf_node(self, tree_id, node_id, values, relative_hit_rate = None):
"""
Add a leaf node to the tree ensemble.
Parameters
----------
tree_id: int
ID of the tree to add the node to.
node_id: int
ID of the node within the tree.
val... | python | def add_leaf_node(self, tree_id, node_id, values, relative_hit_rate = None):
"""
Add a leaf node to the tree ensemble.
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----------
tree_id: int
ID of the tree to add the node to.
node_id: int
ID of the node within the tree.
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28,956 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | create | def create (raw_properties = []):
""" Creates a new 'PropertySet' instance for the given raw properties,
or returns an already existing one.
"""
assert (is_iterable_typed(raw_properties, property.Property)
or is_iterable_typed(raw_properties, basestring))
# FIXME: propagate to caller... | python | def create (raw_properties = []):
""" Creates a new 'PropertySet' instance for the given raw properties,
or returns an already existing one.
"""
assert (is_iterable_typed(raw_properties, property.Property)
or is_iterable_typed(raw_properties, basestring))
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28,957 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | create_with_validation | def create_with_validation (raw_properties):
""" Creates new 'PropertySet' instances after checking
that all properties are valid and converting implicit
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"""
assert is_iterable_typed(raw_properties, basestring)
properties = [property.create_from_string(s) fo... | python | def create_with_validation (raw_properties):
""" Creates new 'PropertySet' instances after checking
that all properties are valid and converting implicit
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"""
assert is_iterable_typed(raw_properties, basestring)
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28,958 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | create_from_user_input | def create_from_user_input(raw_properties, jamfile_module, location):
"""Creates a property-set from the input given by the user, in the
context of 'jamfile-module' at 'location'"""
assert is_iterable_typed(raw_properties, basestring)
assert isinstance(jamfile_module, basestring)
assert isinstance(l... | python | def create_from_user_input(raw_properties, jamfile_module, location):
"""Creates a property-set from the input given by the user, in the
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assert is_iterable_typed(raw_properties, basestring)
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28,959 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.base | def base (self):
""" Returns properties that are neither incidental nor free.
"""
result = [p for p in self.lazy_properties
if not(p.feature.incidental or p.feature.free)]
result.extend(self.base_)
return result | python | def base (self):
""" Returns properties that are neither incidental nor free.
"""
result = [p for p in self.lazy_properties
if not(p.feature.incidental or p.feature.free)]
result.extend(self.base_)
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28,960 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.free | def free (self):
""" Returns free properties which are not dependency properties.
"""
result = [p for p in self.lazy_properties
if not p.feature.incidental and p.feature.free]
result.extend(self.free_)
return result | python | def free (self):
""" Returns free properties which are not dependency properties.
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result = [p for p in self.lazy_properties
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result.extend(self.free_)
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28,961 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.dependency | def dependency (self):
""" Returns dependency properties.
"""
result = [p for p in self.lazy_properties if p.feature.dependency]
result.extend(self.dependency_)
return self.dependency_ | python | def dependency (self):
""" Returns dependency properties.
"""
result = [p for p in self.lazy_properties if p.feature.dependency]
result.extend(self.dependency_)
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28,962 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.non_dependency | def non_dependency (self):
""" Returns properties that are not dependencies.
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""" Returns properties that are not dependencies.
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28,963 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.incidental | def incidental (self):
""" Returns incidental properties.
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""" Returns incidental properties.
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28,964 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.refine | def refine (self, requirements):
""" Refines this set's properties using the requirements passed as an argument.
"""
assert isinstance(requirements, PropertySet)
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r = property.refine(self.all_, requirements.all_)
self.refined_... | python | def refine (self, requirements):
""" Refines this set's properties using the requirements passed as an argument.
"""
assert isinstance(requirements, PropertySet)
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r = property.refine(self.all_, requirements.all_)
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28,965 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.target_path | def target_path (self):
""" Computes the target path that should be used for
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Returns a tuple of
- the computed path
- if the path is relative to build directory, a value of
'true'.
"""
if not self.t... | python | def target_path (self):
""" Computes the target path that should be used for
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Returns a tuple of
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- if the path is relative to build directory, a value of
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28,966 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.add | def add (self, ps):
""" Creates a new property set containing the properties in this one,
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"""
assert isinstance(ps, PropertySet)
if ps not in self.added_:
self.added_[ps] = create(self.all_ + ps.all())
... | python | def add (self, ps):
""" Creates a new property set containing the properties in this one,
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assert isinstance(ps, PropertySet)
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self.added_[ps] = create(self.all_ + ps.all())
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28,967 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.get | def get (self, feature):
""" Returns all values of 'feature'.
"""
if type(feature) == type([]):
feature = feature[0]
if not isinstance(feature, b2.build.feature.Feature):
feature = b2.build.feature.get(feature)
assert isinstance(feature, b2.build.feature.F... | python | def get (self, feature):
""" Returns all values of 'feature'.
"""
if type(feature) == type([]):
feature = feature[0]
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28,968 | apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | PropertySet.get_properties | def get_properties(self, feature):
"""Returns all contained properties associated with 'feature'"""
if not isinstance(feature, b2.build.feature.Feature):
feature = b2.build.feature.get(feature)
assert isinstance(feature, b2.build.feature.Feature)
result = []
for p in... | python | def get_properties(self, feature):
"""Returns all contained properties associated with 'feature'"""
if not isinstance(feature, b2.build.feature.Feature):
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assert isinstance(feature, b2.build.feature.Feature)
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28,969 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _create | def _create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
ranking=True,
verbose=True):
"""
A unified interface for training recommender models. Based on simple
characteristics of the data, a type of model is s... | python | def _create(observation_data,
user_id='user_id', item_id='item_id', target=None,
user_data=None, item_data=None,
ranking=True,
verbose=True):
"""
A unified interface for training recommender models. Based on simple
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28,970 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | compare_models | def compare_models(dataset, models, model_names=None, user_sample=1.0,
metric='auto',
target=None,
exclude_known_for_precision_recall=True,
make_plot=False,
verbose=True,
**kwargs):
"""
Compare the ... | python | def compare_models(dataset, models, model_names=None, user_sample=1.0,
metric='auto',
target=None,
exclude_known_for_precision_recall=True,
make_plot=False,
verbose=True,
**kwargs):
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28,971 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | precision_recall_by_user | def precision_recall_by_user(observed_user_items,
recommendations,
cutoffs=[10]):
"""
Compute precision and recall at a given cutoff for each user. In information
retrieval terms, precision represents the ratio of relevant, retrieved items
to the... | python | def precision_recall_by_user(observed_user_items,
recommendations,
cutoffs=[10]):
"""
Compute precision and recall at a given cutoff for each user. In information
retrieval terms, precision represents the ratio of relevant, retrieved items
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28,972 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | random_split_by_user | def random_split_by_user(dataset,
user_id='user_id',
item_id='item_id',
max_num_users=1000,
item_test_proportion=.2,
random_seed=0):
"""Create a recommender-friendly train-test split of the p... | python | def random_split_by_user(dataset,
user_id='user_id',
item_id='item_id',
max_num_users=1000,
item_test_proportion=.2,
random_seed=0):
"""Create a recommender-friendly train-test split of the p... | [
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28,973 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender._list_fields | def _list_fields(self):
"""
Get the current settings of the model. The keys depend on the type of
model.
Returns
-------
out : list
A list of fields that can be queried using the ``get`` method.
"""
response = self.__proxy__.list_fields()
... | python | def _list_fields(self):
"""
Get the current settings of the model. The keys depend on the type of
model.
Returns
-------
out : list
A list of fields that can be queried using the ``get`` method.
"""
response = self.__proxy__.list_fields()
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28,974 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender._set_current_options | def _set_current_options(self, options):
"""
Set current options for a model.
Parameters
----------
options : dict
A dictionary of the desired option settings. The key should be the name
of the option and each value is the desired value of the option.
... | python | def _set_current_options(self, options):
"""
Set current options for a model.
Parameters
----------
options : dict
A dictionary of the desired option settings. The key should be the name
of the option and each value is the desired value of the option.
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28,975 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.__prepare_dataset_parameter | def __prepare_dataset_parameter(self, dataset):
"""
Processes the dataset parameter for type correctness.
Returns it as an SFrame.
"""
# Translate the dataset argument into the proper type
if not isinstance(dataset, _SFrame):
def raise_dataset_type_exception(... | python | def __prepare_dataset_parameter(self, dataset):
"""
Processes the dataset parameter for type correctness.
Returns it as an SFrame.
"""
# Translate the dataset argument into the proper type
if not isinstance(dataset, _SFrame):
def raise_dataset_type_exception(... | [
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28,976 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.predict | def predict(self, dataset,
new_observation_data=None, new_user_data=None, new_item_data=None):
"""
Return a score prediction for the user ids and item ids in the provided
data set.
Parameters
----------
dataset : SFrame
Dataset in the same for... | python | def predict(self, dataset,
new_observation_data=None, new_user_data=None, new_item_data=None):
"""
Return a score prediction for the user ids and item ids in the provided
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Parameters
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dataset : SFrame
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28,977 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.get_similar_items | def get_similar_items(self, items=None, k=10, verbose=False):
"""
Get the k most similar items for each item in items.
Each type of recommender has its own model for the similarity
between items. For example, the item_similarity_recommender will
return the most similar items acc... | python | def get_similar_items(self, items=None, k=10, verbose=False):
"""
Get the k most similar items for each item in items.
Each type of recommender has its own model for the similarity
between items. For example, the item_similarity_recommender will
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28,978 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.get_similar_users | def get_similar_users(self, users=None, k=10):
"""Get the k most similar users for each entry in `users`.
Each type of recommender has its own model for the similarity
between users. For example, the factorization_recommender will
return the nearest users based on the cosine similarity
... | python | def get_similar_users(self, users=None, k=10):
"""Get the k most similar users for each entry in `users`.
Each type of recommender has its own model for the similarity
between users. For example, the factorization_recommender will
return the nearest users based on the cosine similarity
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28,979 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.recommend_from_interactions | def recommend_from_interactions(
self, observed_items, k=10, exclude=None, items=None,
new_user_data=None, new_item_data=None,
exclude_known=True, diversity=0, random_seed=None,
verbose=True):
"""
Recommend the ``k`` highest scored items based on the
... | python | def recommend_from_interactions(
self, observed_items, k=10, exclude=None, items=None,
new_user_data=None, new_item_data=None,
exclude_known=True, diversity=0, random_seed=None,
verbose=True):
"""
Recommend the ``k`` highest scored items based on the
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28,980 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.evaluate_precision_recall | def evaluate_precision_recall(self, dataset, cutoffs=list(range(1,11,1))+list(range(11,50,5)),
skip_set=None, exclude_known=True,
verbose=True, **kwargs):
"""
Compute a model's precision and recall scores for a particular dataset.
... | python | def evaluate_precision_recall(self, dataset, cutoffs=list(range(1,11,1))+list(range(11,50,5)),
skip_set=None, exclude_known=True,
verbose=True, **kwargs):
"""
Compute a model's precision and recall scores for a particular dataset.
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28,981 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.evaluate_rmse | def evaluate_rmse(self, dataset, target):
"""
Evaluate the prediction error for each user-item pair in the given data
set.
Parameters
----------
dataset : SFrame
An SFrame in the same format as the one used during training.
target : str
T... | python | def evaluate_rmse(self, dataset, target):
"""
Evaluate the prediction error for each user-item pair in the given data
set.
Parameters
----------
dataset : SFrame
An SFrame in the same format as the one used during training.
target : str
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28,982 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender.evaluate | def evaluate(self, dataset, metric='auto',
exclude_known_for_precision_recall=True,
target=None,
verbose=True, **kwargs):
r"""
Evaluate the model's ability to make rating predictions or
recommendations.
If the model is trained to predic... | python | def evaluate(self, dataset, metric='auto',
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target=None,
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28,983 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender._get_popularity_baseline | def _get_popularity_baseline(self):
"""
Returns a new popularity model matching the data set this model was
trained with. Can be used for comparison purposes.
"""
response = self.__proxy__.get_popularity_baseline()
from .popularity_recommender import PopularityRecommen... | python | def _get_popularity_baseline(self):
"""
Returns a new popularity model matching the data set this model was
trained with. Can be used for comparison purposes.
"""
response = self.__proxy__.get_popularity_baseline()
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28,984 | apple/turicreate | src/unity/python/turicreate/toolkits/recommender/util.py | _Recommender._get_item_intersection_info | def _get_item_intersection_info(self, item_pairs):
"""
For a collection of item -> item pairs, returns information about the
users in that intersection.
Parameters
----------
item_pairs : 2-column SFrame of two item columns, or a list of
(item_1, item_2) tupl... | python | def _get_item_intersection_info(self, item_pairs):
"""
For a collection of item -> item pairs, returns information about the
users in that intersection.
Parameters
----------
item_pairs : 2-column SFrame of two item columns, or a list of
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28,985 | apple/turicreate | deps/src/boost_1_68_0/libs/predef/tools/ci/common.py | utils.query_boost_version | def query_boost_version(boost_root):
'''
Read in the Boost version from a given boost_root.
'''
boost_version = None
if os.path.exists(os.path.join(boost_root,'Jamroot')):
with codecs.open(os.path.join(boost_root,'Jamroot'), 'r', 'utf-8') as f:
for lin... | python | def query_boost_version(boost_root):
'''
Read in the Boost version from a given boost_root.
'''
boost_version = None
if os.path.exists(os.path.join(boost_root,'Jamroot')):
with codecs.open(os.path.join(boost_root,'Jamroot'), 'r', 'utf-8') as f:
for lin... | [
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28,986 | apple/turicreate | deps/src/boost_1_68_0/libs/predef/tools/ci/common.py | utils.git_clone | def git_clone(sub_repo, branch, commit = None, cwd = None, no_submodules = False):
'''
This clone mimicks the way Travis-CI clones a project's repo. So far
Travis-CI is the most limiting in the sense of only fetching partial
history of the repo.
'''
if not cwd:
... | python | def git_clone(sub_repo, branch, commit = None, cwd = None, no_submodules = False):
'''
This clone mimicks the way Travis-CI clones a project's repo. So far
Travis-CI is the most limiting in the sense of only fetching partial
history of the repo.
'''
if not cwd:
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28,987 | apple/turicreate | deps/src/boost_1_68_0/libs/predef/tools/ci/common.py | ci_travis.install_toolset | def install_toolset(self, toolset):
'''
Installs specific toolset on CI system.
'''
info = toolset_info[toolset]
if sys.platform.startswith('linux'):
os.chdir(self.work_dir)
if 'ppa' in info:
for ppa in info['ppa']:
util... | python | def install_toolset(self, toolset):
'''
Installs specific toolset on CI system.
'''
info = toolset_info[toolset]
if sys.platform.startswith('linux'):
os.chdir(self.work_dir)
if 'ppa' in info:
for ppa in info['ppa']:
util... | [
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28,988 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_keras2_converter.py | _load_keras_model | def _load_keras_model(model_network_path, model_weight_path, custom_objects=None):
"""Load a keras model from disk
Parameters
----------
model_network_path: str
Path where the model network path is (json file)
model_weight_path: str
Path where the model network weights are (hd5 fil... | python | def _load_keras_model(model_network_path, model_weight_path, custom_objects=None):
"""Load a keras model from disk
Parameters
----------
model_network_path: str
Path where the model network path is (json file)
model_weight_path: str
Path where the model network weights are (hd5 fil... | [
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Path where the model network path is (json file)
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Path where the model network weights are (hd5 file)
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28,989 | apple/turicreate | src/unity/python/turicreate/visualization/_plot.py | Plot.show | def show(self):
"""
A method for displaying the Plot object
Notes
-----
- The plot will render either inline in a Jupyter Notebook, or in a
native GUI window, depending on the value provided in
`turicreate.visualization.set_target` (defaults to 'auto').
... | python | def show(self):
"""
A method for displaying the Plot object
Notes
-----
- The plot will render either inline in a Jupyter Notebook, or in a
native GUI window, depending on the value provided in
`turicreate.visualization.set_target` (defaults to 'auto').
... | [
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28,990 | apple/turicreate | src/unity/python/turicreate/visualization/_plot.py | Plot.save | def save(self, filepath):
"""
A method for saving the Plot object in a vega representation
Parameters
----------
filepath: string
The destination filepath where the plot object must be saved as.
The extension of this filepath determines what format the pl... | python | def save(self, filepath):
"""
A method for saving the Plot object in a vega representation
Parameters
----------
filepath: string
The destination filepath where the plot object must be saved as.
The extension of this filepath determines what format the pl... | [
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28,991 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_tree_ensemble.py | _get_value | def _get_value(scikit_value, mode = 'regressor', scaling = 1.0, n_classes = 2, tree_index = 0):
""" Get the right value from the scikit-tree
"""
# Regression
if mode == 'regressor':
return scikit_value[0] * scaling
# Binary classification
if n_classes == 2:
# Decision tree
... | python | def _get_value(scikit_value, mode = 'regressor', scaling = 1.0, n_classes = 2, tree_index = 0):
""" Get the right value from the scikit-tree
"""
# Regression
if mode == 'regressor':
return scikit_value[0] * scaling
# Binary classification
if n_classes == 2:
# Decision tree
... | [
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28,992 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_tree_ensemble.py | convert_tree_ensemble | def convert_tree_ensemble(model, input_features,
output_features = ('predicted_class', float),
mode = 'regressor',
base_prediction = None,
class_labels = None,
post_evaluation_transform = No... | python | def convert_tree_ensemble(model, input_features,
output_features = ('predicted_class', float),
mode = 'regressor',
base_prediction = None,
class_labels = None,
post_evaluation_transform = No... | [
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28,993 | apple/turicreate | src/unity/python/turicreate/toolkits/style_transfer/style_transfer.py | StyleTransfer.get_styles | def get_styles(self, style=None):
"""
Returns SFrame of style images used for training the model
Parameters
----------
style: int or list, optional
The selected style or list of styles to return. If `None`, all
styles will be returned
See Also
... | python | def get_styles(self, style=None):
"""
Returns SFrame of style images used for training the model
Parameters
----------
style: int or list, optional
The selected style or list of styles to return. If `None`, all
styles will be returned
See Also
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28,994 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/libsvm/_libsvm_util.py | load_model | def load_model(model_path):
"""Load a libsvm model from a path on disk.
This currently supports:
* C-SVC
* NU-SVC
* Epsilon-SVR
* NU-SVR
Parameters
----------
model_path: str
Path on disk where the libsvm model representation is.
Returns
-------
model: ... | python | def load_model(model_path):
"""Load a libsvm model from a path on disk.
This currently supports:
* C-SVC
* NU-SVC
* Epsilon-SVR
* NU-SVR
Parameters
----------
model_path: str
Path on disk where the libsvm model representation is.
Returns
-------
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28,995 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | add_enumerated_multiarray_shapes | def add_enumerated_multiarray_shapes(spec, feature_name, shapes):
"""
Annotate an input or output multiArray feature in a Neural Network spec to
to accommodate a list of enumerated array shapes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
Th... | python | def add_enumerated_multiarray_shapes(spec, feature_name, shapes):
"""
Annotate an input or output multiArray feature in a Neural Network spec to
to accommodate a list of enumerated array shapes
:param spec: MLModel
The MLModel spec containing the feature
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py#L291-L370 |
28,996 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | add_enumerated_image_sizes | def add_enumerated_image_sizes(spec, feature_name, sizes):
"""
Annotate an input or output image feature in a Neural Network spec to
to accommodate a list of enumerated image sizes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the... | python | def add_enumerated_image_sizes(spec, feature_name, sizes):
"""
Annotate an input or output image feature in a Neural Network spec to
to accommodate a list of enumerated image sizes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the... | [
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28,997 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | update_image_size_range | def update_image_size_range(spec, feature_name, size_range):
"""
Annotate an input or output Image feature in a Neural Network spec to
to accommodate a range of image sizes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the Image f... | python | def update_image_size_range(spec, feature_name, size_range):
"""
Annotate an input or output Image feature in a Neural Network spec to
to accommodate a range of image sizes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the Image f... | [
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28,998 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | update_multiarray_shape_range | def update_multiarray_shape_range(spec, feature_name, shape_range):
"""
Annotate an input or output MLMultiArray feature in a Neural Network spec
to accommodate a range of shapes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the f... | python | def update_multiarray_shape_range(spec, feature_name, shape_range):
"""
Annotate an input or output MLMultiArray feature in a Neural Network spec
to accommodate a range of shapes
:param spec: MLModel
The MLModel spec containing the feature
:param feature_name: str
The name of the f... | [
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The name of the feature for which to add shape range
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28,999 | apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | get_allowed_shape_ranges | def get_allowed_shape_ranges(spec):
"""
For a given model specification, returns a dictionary with a shape range object for each input feature name.
"""
shaper = NeuralNetworkShaper(spec, False)
inputs = _get_input_names(spec)
output = {}
for input in inputs:
output[input] = shaper... | python | def get_allowed_shape_ranges(spec):
"""
For a given model specification, returns a dictionary with a shape range object for each input feature name.
"""
shaper = NeuralNetworkShaper(spec, False)
inputs = _get_input_names(spec)
output = {}
for input in inputs:
output[input] = shaper... | [
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