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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/well_known_types.py | _FieldMaskTree.MergeMessage | def MergeMessage(
self, source, destination,
replace_message, replace_repeated):
"""Merge all fields specified by this tree from source to destination."""
_MergeMessage(
self._root, source, destination, replace_message, replace_repeated) | python | def MergeMessage(
self, source, destination,
replace_message, replace_repeated):
"""Merge all fields specified by this tree from source to destination."""
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_NuSVR.py | convert | def convert(model, feature_names, target):
"""Convert a Nu Support Vector Regression (NuSVR) model to the protobuf spec.
Parameters
----------
model: NuSVR
A trained NuSVR encoder model.
feature_names: [str]
Name of the input columns.
target: str
Name of the output colu... | python | def convert(model, feature_names, target):
"""Convert a Nu Support Vector Regression (NuSVR) model to the protobuf spec.
Parameters
----------
model: NuSVR
A trained NuSVR encoder model.
feature_names: [str]
Name of the input columns.
target: str
Name of the output colu... | [
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apple/turicreate | src/unity/python/turicreate/toolkits/regression/linear_regression.py | create | def create(dataset, target, features=None, l2_penalty=1e-2, l1_penalty=0.0,
solver='auto', feature_rescaling=True,
convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size = _DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'... | python | def create(dataset, target, features=None, l2_penalty=1e-2, l1_penalty=0.0,
solver='auto', feature_rescaling=True,
convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
step_size = _DEFAULT_SOLVER_OPTIONS['step_size'],
lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'... | [
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apple/turicreate | src/unity/python/turicreate/toolkits/regression/linear_regression.py | LinearRegression.export_coreml | def export_coreml(self, filename):
"""
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel")
"""
from ... | python | def export_coreml(self, filename):
"""
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel")
"""
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apple/turicreate | src/unity/python/turicreate/toolkits/regression/linear_regression.py | LinearRegression.predict | def predict(self, dataset, missing_value_action='auto'):
"""
Return target value predictions for ``dataset``, using the trained
linear regression model. This method can be used to get fitted values
for the model by inputting the training dataset.
Parameters
----------
... | python | def predict(self, dataset, missing_value_action='auto'):
"""
Return target value predictions for ``dataset``, using the trained
linear regression model. This method can be used to get fitted values
for the model by inputting the training dataset.
Parameters
----------
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apple/turicreate | src/unity/python/turicreate/toolkits/regression/linear_regression.py | LinearRegression.evaluate | def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
r"""Evaluate the model by making target value predictions and comparing
to actual values.
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is root-mean-squared error (RMSE) while the second is ... | python | def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
r"""Evaluate the model by making target value predictions and comparing
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apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/mel_features.py | frame | def frame(data, window_length, hop_length):
"""Convert array into a sequence of successive possibly overlapping frames.
An n-dimensional array of shape (num_samples, ...) is converted into an
(n+1)-D array of shape (num_frames, window_length, ...), where each frame
starts hop_length points after the preceding ... | python | def frame(data, window_length, hop_length):
"""Convert array into a sequence of successive possibly overlapping frames.
An n-dimensional array of shape (num_samples, ...) is converted into an
(n+1)-D array of shape (num_frames, window_length, ...), where each frame
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apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/mel_features.py | periodic_hann | def periodic_hann(window_length):
"""Calculate a "periodic" Hann window.
The classic Hann window is defined as a raised cosine that starts and
ends on zero, and where every value appears twice, except the middle
point for an odd-length window. Matlab calls this a "symmetric" window
and np.hanning() returns ... | python | def periodic_hann(window_length):
"""Calculate a "periodic" Hann window.
The classic Hann window is defined as a raised cosine that starts and
ends on zero, and where every value appears twice, except the middle
point for an odd-length window. Matlab calls this a "symmetric" window
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apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/mel_features.py | stft_magnitude | def stft_magnitude(signal, fft_length,
hop_length=None,
window_length=None):
"""Calculate the short-time Fourier transform magnitude.
Args:
signal: 1D np.array of the input time-domain signal.
fft_length: Size of the FFT to apply.
hop_length: Advance (in samples) b... | python | def stft_magnitude(signal, fft_length,
hop_length=None,
window_length=None):
"""Calculate the short-time Fourier transform magnitude.
Args:
signal: 1D np.array of the input time-domain signal.
fft_length: Size of the FFT to apply.
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apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/mel_features.py | spectrogram_to_mel_matrix | def spectrogram_to_mel_matrix(num_mel_bins=20,
num_spectrogram_bins=129,
audio_sample_rate=8000,
lower_edge_hertz=125.0,
upper_edge_hertz=3800.0):
"""Return a matrix that can post-multiply spectrogr... | python | def spectrogram_to_mel_matrix(num_mel_bins=20,
num_spectrogram_bins=129,
audio_sample_rate=8000,
lower_edge_hertz=125.0,
upper_edge_hertz=3800.0):
"""Return a matrix that can post-multiply spectrogr... | [
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apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/mel_features.py | log_mel_spectrogram | def log_mel_spectrogram(data,
audio_sample_rate=8000,
log_offset=0.0,
window_length_secs=0.025,
hop_length_secs=0.010,
**kwargs):
"""Convert waveform to a log magnitude mel-frequency spectrogram.
... | python | def log_mel_spectrogram(data,
audio_sample_rate=8000,
log_offset=0.0,
window_length_secs=0.025,
hop_length_secs=0.010,
**kwargs):
"""Convert waveform to a log magnitude mel-frequency spectrogram.
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apple/turicreate | src/unity/python/turicreate/util/_sframe_generation.py | generate_random_sframe | def generate_random_sframe(num_rows, column_codes, random_seed = 0):
"""
Creates a random SFrame with `num_rows` rows and randomly
generated column types determined by `column_codes`. The output
SFrame is deterministic based on `random_seed`.
`column_types` is a string with each character denoti... | python | def generate_random_sframe(num_rows, column_codes, random_seed = 0):
"""
Creates a random SFrame with `num_rows` rows and randomly
generated column types determined by `column_codes`. The output
SFrame is deterministic based on `random_seed`.
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apple/turicreate | src/unity/python/turicreate/util/_sframe_generation.py | generate_random_regression_sframe | def generate_random_regression_sframe(num_rows, column_codes, random_seed = 0, target_noise_level = 0.25):
"""
Creates a random SFrame with `num_rows` rows and randomly
generated column types determined by `column_codes`. The output
SFrame is deterministic based on `random_seed`. In addition, a
ta... | python | def generate_random_regression_sframe(num_rows, column_codes, random_seed = 0, target_noise_level = 0.25):
"""
Creates a random SFrame with `num_rows` rows and randomly
generated column types determined by `column_codes`. The output
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apple/turicreate | src/unity/python/turicreate/util/_sframe_generation.py | generate_random_classification_sframe | def generate_random_classification_sframe(num_rows, column_codes, num_classes,
misclassification_spread = 0.25,
num_extra_class_bins = None,
random_seed = 0):
"""
Creates a random SFrame... | python | def generate_random_classification_sframe(num_rows, column_codes, num_classes,
misclassification_spread = 0.25,
num_extra_class_bins = None,
random_seed = 0):
"""
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/_infer_shapes_nn_mlmodel.py | infer_shapes | def infer_shapes(nn_spec, input_spec, input_shape_dict = None):
"""
Input:
spec : mlmodel spec
input_shape_dict: dictionary of string --> tuple
string: input name
tuple: input shape as a 5 length tuple in order (Seq, Batch, C, H, W)
If inp... | python | def infer_shapes(nn_spec, input_spec, input_shape_dict = None):
"""
Input:
spec : mlmodel spec
input_shape_dict: dictionary of string --> tuple
string: input name
tuple: input shape as a 5 length tuple in order (Seq, Batch, C, H, W)
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/libsvm/_libsvm_converter.py | convert | def convert(libsvm_model, feature_names, target, input_length, probability):
"""Convert a svm model to the protobuf spec.
This currently supports:
* C-SVC
* nu-SVC
* Epsilon-SVR
* nu-SVR
Parameters
----------
model_path: libsvm_model
Libsvm representation of the mode... | python | def convert(libsvm_model, feature_names, target, input_length, probability):
"""Convert a svm model to the protobuf spec.
This currently supports:
* C-SVC
* nu-SVC
* Epsilon-SVR
* nu-SVR
Parameters
----------
model_path: libsvm_model
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | create | def create(dataset, session_id, target, features=None, prediction_window=100,
validation_set='auto', max_iterations=10, batch_size=32, verbose=True):
"""
Create an :class:`ActivityClassifier` model.
Parameters
----------
dataset : SFrame
Input data which consists of `sessions` of... | python | def create(dataset, session_id, target, features=None, prediction_window=100,
validation_set='auto', max_iterations=10, batch_size=32, verbose=True):
"""
Create an :class:`ActivityClassifier` model.
Parameters
----------
dataset : SFrame
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | _encode_target | def _encode_target(data, target, mapping=None):
""" Encode targets to integers in [0, num_classes - 1] """
if mapping is None:
mapping = {t: i for i, t in enumerate(sorted(data[target].unique()))}
data[target] = data[target].apply(lambda t: mapping[t])
return data, mapping | python | def _encode_target(data, target, mapping=None):
""" Encode targets to integers in [0, num_classes - 1] """
if mapping is None:
mapping = {t: i for i, t in enumerate(sorted(data[target].unique()))}
data[target] = data[target].apply(lambda t: mapping[t])
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | ActivityClassifier.export_coreml | def export_coreml(self, filename):
"""
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel")
"""
impor... | python | def export_coreml(self, filename):
"""
Export the model in Core ML format.
Parameters
----------
filename: str
A valid filename where the model can be saved.
Examples
--------
>>> model.export_coreml("MyModel.mlmodel")
"""
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | ActivityClassifier.predict | def predict(self, dataset, output_type='class', output_frequency='per_row'):
"""
Return predictions for ``dataset``, using the trained activity classifier.
Predictions can be generated as class labels, or as a probability
vector with probabilities for each class.
The activity cl... | python | def predict(self, dataset, output_type='class', output_frequency='per_row'):
"""
Return predictions for ``dataset``, using the trained activity classifier.
Predictions can be generated as class labels, or as a probability
vector with probabilities for each class.
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | ActivityClassifier.evaluate | def evaluate(self, dataset, metric='auto'):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include columns with the same
... | python | def evaluate(self, dataset, metric='auto'):
"""
Evaluate the model by making predictions of target values and comparing
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | ActivityClassifier.classify | def classify(self, dataset, output_frequency='per_row'):
"""
Return a classification, for each ``prediction_window`` examples in the
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apple/turicreate | src/unity/python/turicreate/toolkits/activity_classifier/_activity_classifier.py | ActivityClassifier.predict_topk | def predict_topk(self, dataset, output_type='probability', k=3, output_frequency='per_row'):
"""
Return top-k predictions for the ``dataset``, using the trained model.
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Return top-k predictions for the ``dataset``, using the trained model.
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apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/benchmark/char_stat.py | count_characters | def count_characters(root, out):
"""Count the occurrances of the different characters in the files"""
if os.path.isfile(root):
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for char in line:
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... | python | def count_characters(root, out):
"""Count the occurrances of the different characters in the files"""
if os.path.isfile(root):
with open(root, 'rb') as in_f:
for line in in_f:
for char in line:
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apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/benchmark/char_stat.py | main | def main():
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parser = argparse.ArgumentParser(description=desc)
parser.add_argument(
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dest='src',
required=True,
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parser.add_... | python | def main():
"""The main function of the script"""
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | save_spec | def save_spec(spec, filename):
"""
Save a protobuf model specification to file.
Parameters
----------
spec: Model_pb
Protobuf representation of the model
filename: str
File path where the spec gets saved.
Examples
--------
.. sourcecode:: python
>>> corem... | python | def save_spec(spec, filename):
"""
Save a protobuf model specification to file.
Parameters
----------
spec: Model_pb
Protobuf representation of the model
filename: str
File path where the spec gets saved.
Examples
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Load a protobuf model specification from file
Parameters
----------
filename: str
Location on disk (a valid filepath) from which the file is loaded
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Returns
-------
model_spec: Model_pb
Protobuf representation of t... | python | def load_spec(filename):
"""
Load a protobuf model specification from file
Parameters
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filename: str
Location on disk (a valid filepath) from which the file is loaded
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | _get_nn_layers | def _get_nn_layers(spec):
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Returns a list of neural network layers if the model contains any.
Parameters
----------
spec: Model_pb
A model protobuf specification.
Returns
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[NN layer]
list of all layers (including layers from elements of a pipeline
"""
... | python | def _get_nn_layers(spec):
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Returns a list of neural network layers if the model contains any.
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spec: Model_pb
A model protobuf specification.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | evaluate_regressor | def evaluate_regressor(model, data, target="target", verbose=False):
"""
Evaluate a CoreML regression model and compare against predictions
from the original framework (for testing correctness of conversion)
Parameters
----------
filename: [str | MLModel]
File path from which to load th... | python | def evaluate_regressor(model, data, target="target", verbose=False):
"""
Evaluate a CoreML regression model and compare against predictions
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filename: [str | MLModel]
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | evaluate_classifier | def evaluate_classifier(model, data, target='target', verbose=False):
"""
Evaluate a CoreML classifier model and compare against predictions
from the original framework (for testing correctness of conversion). Use
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Parameters
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Evaluate a CoreML classifier model and compare against predictions
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | evaluate_classifier_with_probabilities | def evaluate_classifier_with_probabilities(model, data,
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verbose = False):
"""
Evaluate a classifier specification for testing.
Parameters
----------
filename: [str | Model]
F... | python | def evaluate_classifier_with_probabilities(model, data,
probabilities='probabilities',
verbose = False):
"""
Evaluate a classifier specification for testing.
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----------
filename: [str | Model]
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | rename_feature | def rename_feature(spec, current_name, new_name, rename_inputs=True,
rename_outputs=True):
"""
Rename a feature in the specification.
Parameters
----------
spec: Model_pb
The specification containing the feature to rename.
current_name: str
Current name of th... | python | def rename_feature(spec, current_name, new_name, rename_inputs=True,
rename_outputs=True):
"""
Rename a feature in the specification.
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spec: Model_pb
The specification containing the feature to rename.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | _sanitize_value | def _sanitize_value(x):
"""
Performs cleaning steps on the data so various type comparisons can
be performed correctly.
"""
if isinstance(x, _six.string_types + _six.integer_types + (float,)):
return x
elif _HAS_SKLEARN and _sp.issparse(x):
return x.todense()
elif isinstance(... | python | def _sanitize_value(x):
"""
Performs cleaning steps on the data so various type comparisons can
be performed correctly.
"""
if isinstance(x, _six.string_types + _six.integer_types + (float,)):
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | _element_equal | def _element_equal(x, y):
"""
Performs a robust equality test between elements.
"""
if isinstance(x, _np.ndarray) or isinstance(y, _np.ndarray):
try:
return (abs(_np.asarray(x) - _np.asarray(y)) < 1e-5).all()
except:
return False
elif isinstance(x, dict):
... | python | def _element_equal(x, y):
"""
Performs a robust equality test between elements.
"""
if isinstance(x, _np.ndarray) or isinstance(y, _np.ndarray):
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | evaluate_transformer | def evaluate_transformer(model, input_data, reference_output,
verbose=False):
"""
Evaluate a transformer specification for testing.
Parameters
----------
spec: [str | MLModel]
File from where to load the Model from (OR) a loaded
version of MLModel.
inpu... | python | def evaluate_transformer(model, input_data, reference_output,
verbose=False):
"""
Evaluate a transformer specification for testing.
Parameters
----------
spec: [str | MLModel]
File from where to load the Model from (OR) a loaded
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/utils.py | _get_input_names | def _get_input_names(spec):
"""
Returns a list of the names of the inputs to this model.
:param spec: The model protobuf specification
:return: [str] A list of input feature names
"""
retval = [feature.name for feature in spec.description.input]
return retval | python | def _get_input_names(spec):
"""
Returns a list of the names of the inputs to this model.
:param spec: The model protobuf specification
:return: [str] A list of input feature names
"""
retval = [feature.name for feature in spec.description.input]
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apple/turicreate | src/unity/python/turicreate/toolkits/graph_analytics/degree_counting.py | create | def create(graph, verbose=True):
"""
Compute the in degree, out degree and total degree of each vertex.
Parameters
----------
graph : SGraph
The graph on which to compute degree counts.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out :... | python | def create(graph, verbose=True):
"""
Compute the in degree, out degree and total degree of each vertex.
Parameters
----------
graph : SGraph
The graph on which to compute degree counts.
verbose : bool, optional
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apple/turicreate | deps/src/boost_1_68_0/tools/litre/cplusplus.py | Example.replace_emphasis | def replace_emphasis(self, s, index = 0):
"""replace the index'th emphasized text with s"""
e = self.emphasized[index]
self.body[e[0]:e[1]] = [s]
del self.emphasized[index] | python | def replace_emphasis(self, s, index = 0):
"""replace the index'th emphasized text with s"""
e = self.emphasized[index]
self.body[e[0]:e[1]] = [s]
del self.emphasized[index] | [
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apple/turicreate | deps/src/boost_1_68_0/tools/litre/cplusplus.py | CPlusPlusTranslator._execute | def _execute(self, code):
"""Override of litre._execute; sets up variable context before
evaluating code
"""
self.globals['example'] = self.example
eval(code, self.globals) | python | def _execute(self, code):
"""Override of litre._execute; sets up variable context before
evaluating code
"""
self.globals['example'] = self.example
eval(code, self.globals) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.find | def find(self, name, current_location):
"""Given 'name' which can be project-id or plain directory name,
return project module corresponding to that id or directory.
Returns nothing of project is not found."""
assert isinstance(name, basestring)
assert isinstance(current_location... | python | def find(self, name, current_location):
"""Given 'name' which can be project-id or plain directory name,
return project module corresponding to that id or directory.
Returns nothing of project is not found."""
assert isinstance(name, basestring)
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.module_name | def module_name(self, jamfile_location):
"""Returns the name of module corresponding to 'jamfile-location'.
If no module corresponds to location yet, associates default
module name with that location."""
assert isinstance(jamfile_location, basestring)
module = self.location2modul... | python | def module_name(self, jamfile_location):
"""Returns the name of module corresponding to 'jamfile-location'.
If no module corresponds to location yet, associates default
module name with that location."""
assert isinstance(jamfile_location, basestring)
module = self.location2modul... | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.load_standalone | def load_standalone(self, jamfile_module, file):
"""Loads 'file' as standalone project that has no location
associated with it. This is mostly useful for user-config.jam,
which should be able to define targets, but although it has
some location in filesystem, we do not want any build to... | python | def load_standalone(self, jamfile_module, file):
"""Loads 'file' as standalone project that has no location
associated with it. This is mostly useful for user-config.jam,
which should be able to define targets, but although it has
some location in filesystem, we do not want any build to... | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.inherit_attributes | def inherit_attributes(self, project_module, parent_module):
"""Make 'project-module' inherit attributes of project
root and parent module."""
assert isinstance(project_module, basestring)
assert isinstance(parent_module, basestring)
attributes = self.module2attributes[project_m... | python | def inherit_attributes(self, project_module, parent_module):
"""Make 'project-module' inherit attributes of project
root and parent module."""
assert isinstance(project_module, basestring)
assert isinstance(parent_module, basestring)
attributes = self.module2attributes[project_m... | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.register_id | def register_id(self, id, module):
"""Associate the given id with the given project module."""
assert isinstance(id, basestring)
assert isinstance(module, basestring)
self.id2module[id] = module | python | def register_id(self, id, module):
"""Associate the given id with the given project module."""
assert isinstance(id, basestring)
assert isinstance(module, basestring)
self.id2module[id] = module | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.push_current | def push_current(self, project):
"""Temporary changes the current project to 'project'. Should
be followed by 'pop-current'."""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
self.saved_current_project.append(self.curren... | python | def push_current(self, project):
"""Temporary changes the current project to 'project'. Should
be followed by 'pop-current'."""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
self.saved_current_project.append(self.curren... | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.attribute | def attribute(self, project, attribute):
"""Returns the value of the specified attribute in the
specified jamfile module."""
assert isinstance(project, basestring)
assert isinstance(attribute, basestring)
try:
return self.module2attributes[project].get(attribute)
... | python | def attribute(self, project, attribute):
"""Returns the value of the specified attribute in the
specified jamfile module."""
assert isinstance(project, basestring)
assert isinstance(attribute, basestring)
try:
return self.module2attributes[project].get(attribute)
... | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.attributeDefault | def attributeDefault(self, project, attribute, default):
"""Returns the value of the specified attribute in the
specified jamfile module."""
assert isinstance(project, basestring)
assert isinstance(attribute, basestring)
assert isinstance(default, basestring) or default is None
... | python | def attributeDefault(self, project, attribute, default):
"""Returns the value of the specified attribute in the
specified jamfile module."""
assert isinstance(project, basestring)
assert isinstance(attribute, basestring)
assert isinstance(default, basestring) or default is None
... | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.target | def target(self, project_module):
"""Returns the project target corresponding to the 'project-module'."""
assert isinstance(project_module, basestring)
if project_module not in self.module2target:
self.module2target[project_module] = \
b2.build.targets.ProjectTarget(p... | python | def target(self, project_module):
"""Returns the project target corresponding to the 'project-module'."""
assert isinstance(project_module, basestring)
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.add_rule | def add_rule(self, name, callable_):
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self.project_rules_.add_rule(name, callable_) | python | def add_rule(self, name, callable_):
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.__build_python_module_cache | def __build_python_module_cache(self):
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRegistry.load_module | def load_module(self, name, extra_path=None):
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectAttributes.set | def set(self, attribute, specification, exact=False):
"""Set the named attribute from the specification given by the user.
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assert isinstance(exact, (int, bool))
if __debug__ and not exact:
... | python | def set(self, attribute, specification, exact=False):
"""Set the named attribute from the specification given by the user.
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRules.make_wrapper | def make_wrapper(self, callable_):
"""Given a free-standing function 'callable', return a new
callable that will call 'callable' and report all exceptins,
using 'call_and_report_errors'."""
assert callable(callable_)
def wrapper(*args, **kw):
return self.call_and_repo... | python | def make_wrapper(self, callable_):
"""Given a free-standing function 'callable', return a new
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRules.constant | def constant(self, name, value):
"""Declare and set a project global constant.
Project global constants are normal variables but should
not be changed. They are applied to every child Jamfile."""
assert is_iterable_typed(name, basestring)
assert is_iterable_typed(value, basestrin... | python | def constant(self, name, value):
"""Declare and set a project global constant.
Project global constants are normal variables but should
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assert is_iterable_typed(name, basestring)
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRules.path_constant | def path_constant(self, name, value):
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path is adjusted to be relative to the invocation directory. The given
value path is taken to be either absolute, or relative to this project
root."""
assert is_iterable... | python | def path_constant(self, name, value):
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/project.py | ProjectRules.conditional | def conditional(self, condition, requirements):
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lib x : x.cpp : [ conditional <toolset>gcc <variant>debug :
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"""Calculates conditional requirements for multiple requirements
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/array_feature_extractor.py | create_array_feature_extractor | def create_array_feature_extractor(input_features, output_name, extract_indices,
output_type = None):
"""
Creates a feature extractor from an input array feature, return
input_features is a list of one (name, array) tuple.
extract_indices is either an integer or a li... | python | def create_array_feature_extractor(input_features, output_name, extract_indices,
output_type = None):
"""
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apple/turicreate | deps/src/boost_1_68_0/libs/predef/tools/ci/build_log.py | BuildOutputProcessor.add_input | def add_input(self, input):
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Add a single build XML output file to our data.
'''
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'''
Add a single build XML output file to our data.
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events = xml.dom.pulldom.parse(input)
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Process the target dependency DAG into an ancestry tree so we can look up
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name = self.get_child_data(target_node,tag='name',... | python | def x_build_targets_target( self, node ):
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Process the target dependency DAG into an ancestry tree so we can look up
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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):
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Print the detailed info of failed or always print tests.
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'''
Print the detailed info of failed or always print tests.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | _get_weight_param_summary | def _get_weight_param_summary(wp):
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Args:
wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display
Returns:
a str summary for wp
"""
summary_str = ''
if wp.HasField('quantization'):
nbits = wp... | python | def _get_weight_param_summary(wp):
"""Get a summary of _NeuralNetwork_pb2.WeightParams
Args:
wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display
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layer - an MLModel NeuralNetwork Layer protobuf message
Returns:
layer_type : str - type of layer
layer_name : str - name of the layer
layer_inputs : list[str] - a list of strings representing input blobs of the layer
layer_outputs : li... | python | def _summarize_network_layer_info(layer):
"""
Args:
layer - an MLModel NeuralNetwork Layer protobuf message
Returns:
layer_type : str - type of layer
layer_name : str - name of the layer
layer_inputs : list[str] - a list of strings representing input blobs of the layer
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | summarize_neural_network_spec | def summarize_neural_network_spec(mlmodel_spec):
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Args:
mlmodel_spec : mlmodel spec
Returns:
inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
outputs : list[(str, str)] - a list of two tuple (name, descript... | python | def summarize_neural_network_spec(mlmodel_spec):
""" Summarize network into the following structure.
Args:
mlmodel_spec : mlmodel spec
Returns:
inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/printer.py | print_network_spec | def print_network_spec(mlmodel_spec, interface_only=False):
""" Print the network information summary.
Args:
mlmodel_spec : the mlmodel spec
interface_only : Shows only the input and output of the network
"""
inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec)
print('... | python | def print_network_spec(mlmodel_spec, interface_only=False):
""" Print the network information summary.
Args:
mlmodel_spec : the mlmodel spec
interface_only : Shows only the input and output of the network
"""
inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec)
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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
shared between all SVMs.
"""
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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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_SVC.py | convert | def convert(model, feature_names, target):
"""Convert a Support Vector Classtion (SVC) model to the protobuf spec.
Parameters
----------
model: SVC
A trained SVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (defau... | python | def convert(model, feature_names, target):
"""Convert a Support Vector Classtion (SVC) model to the protobuf spec.
Parameters
----------
model: SVC
A trained SVC encoder model.
feature_names: [str], optional (default=None)
Name of the input columns.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph.make_input_layers | def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
if hasattr(self.model, 'input_layers'):
input_keras_layers = self.model.input_layers[:]
self.input_layers = [None] * len(input_keras_layers)
f... | python | def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
if hasattr(self.model, 'input_layers'):
input_keras_layers = self.model.input_layers[:]
self.input_layers = [None] * len(input_keras_layers)
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph.make_output_layers | def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
# TODO
# use successors == 0 as the criteria for output layer
# will fail when some intermediate layers also generate output.
# However, because the possibility of having inserted layers,... | python | def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
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# will fail when some intermediate layers also generate output.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph.generate_blob_names | def generate_blob_names(self):
"""
Generate blob names for each one of the edge. At this time, Keras does not
support "fork" operation (a layer with more than 1 blob output). So we just
use names of the src layer to identify a blob. We also assume all neural
networks are singly... | python | def generate_blob_names(self):
"""
Generate blob names for each one of the edge. At this time, Keras does not
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph._remove_layer | def _remove_layer(self, layer):
"""
remove the layer and its input/output edges
"""
successors = self.get_successors(layer)
predecessors = self.get_predecessors(layer)
# remove all edges
for succ in successors:
self._remove_edge(layer, succ)
fo... | python | def _remove_layer(self, layer):
"""
remove the layer and its input/output edges
"""
successors = self.get_successors(layer)
predecessors = self.get_predecessors(layer)
# remove all edges
for succ in successors:
self._remove_edge(layer, succ)
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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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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):
"""
Insert the new_layer before layer, whose position is layer_idx. The new layer's
parameter is stored in a Keras layer called new_keras_layer
"""
if snk is None:
insert_pos = self.layer_list.inde... | python | def _insert_layer_between(self, src, snk, new_layer, new_keras_layer):
"""
Insert the new_layer before 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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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph.defuse_activation | def defuse_activation(self):
"""
Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
# unwrap time-distributed... | python | def defuse_activation(self):
"""
Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology.py | NetGraph._get_1d_interface_edges | def _get_1d_interface_edges(self):
"""
Get edges that represents transition from not 1D to 1D, and 1D to not 1D
A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs.
An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs.
"""
... | python | def _get_1d_interface_edges(self):
"""
Get edges that represents transition from not 1D to 1D, and 1D to not 1D
A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs.
An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs.
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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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apple/turicreate | src/unity/python/turicreate/meta/asttools/mutators/replace_mutator.py | replace_nodes | def replace_nodes(root, old, new):
'''
Replace the old node with the new one.
Old must be an indirect child of root
:param root: ast node that contains an indirect reference to old
:param old: node to replace
:param new: node to replace `old` with
'''
rep = Replacer(old, new)
... | python | def replace_nodes(root, old, new):
'''
Replace the old node with the new one.
Old must be an indirect child of root
:param root: ast node that contains an indirect reference to old
:param old: node to replace
:param new: node to replace `old` with
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rep = Replacer(old, new)
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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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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]
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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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apple/turicreate | src/unity/python/turicreate/toolkits/sound_classifier/_audio_feature_extractor.py | VGGishFeatureExtractor._extract_features | def _extract_features(self, preprocessed_data, verbose=True):
"""
Parameters
----------
preprocessed_data : SArray
Returns
-------
numpy array containing the deep features
"""
last_progress_update = _time.time()
progress_header_printed = F... | python | def _extract_features(self, preprocessed_data, verbose=True):
"""
Parameters
----------
preprocessed_data : SArray
Returns
-------
numpy array containing the deep features
"""
last_progress_update = _time.time()
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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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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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apple/turicreate | src/unity/python/turicreate/meta/asttools/mutators/remove_trivial.py | remove_trivial | def remove_trivial(root):
'''
Remove redundant statements.
The statement `a = 1` will be removed::
a = 1
a = 2
The statement `a = 1` will not be removed because `b` depends on it::
a = 1
b = a + 2
a = 2
:param root: ast node
... | python | def remove_trivial(root):
'''
Remove redundant statements.
The statement `a = 1` will be removed::
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | safe_isinstance | def safe_isinstance(value, types=None, class_names=None):
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/__init__.py | value_to_jam | def value_to_jam(value, methods=False):
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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):
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r = []
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r.append(abbreviate(part))
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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]
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"""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
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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):
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Get the decision from this node to a child node.
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child: Node
A child node of this node.
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apple/turicreate | src/unity/python/turicreate/toolkits/_decision_tree.py | Node.to_dict | def to_dict(self):
"""
Return the node as a dictionary.
Returns
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dict: All the attributes of this node as a dictionary (minus the left
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"""
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
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dict: All the attributes of this node as a dictionary (minus the left
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"""
out = {}
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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
-------
dict: A tree in JS... | python | 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.
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apple/turicreate | src/unity/python/turicreate/toolkits/_decision_tree.py | DecisionTree.get_prediction_score | def get_prediction_score(self, node_id):
"""
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Parameters
----------
node_id: id of the node to get the prediction value.
Returns
-------
float or None: returns float v... | python | def get_prediction_score(self, node_id):
"""
Return the prediction score (if leaf node) or None if its an
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node_id: id of the node to get the prediction value.
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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.
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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... | 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.
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/_decision_tree.py#L403-L484 | train | Get the prediction path from this node to the parent node. | Pu7Z6IJCgH3a,vcEHXBQXuDuh,sHOWSIAKtU58,ZVWAAMjVVHHl,qRin5pdYOdbB,IySsVMyKT3tF,FwEHNICjJCy0,yISIa0MMKKfB,GAtvbI59wr0o,OmNM6rT0Sgul,gu1MSKhYvigU,S2TTo9DhhiSh,aaLV7ZjAfkcR,ker4pIJmdvxf,WaQEaQCVMQ03,xV97BFGi0hY9,YnM1HtHE4j7G,X5FyJb4ToTo6,jLmadlzMdunT,GGFwFLsDF9Fv,prtR0Uw1GMh5,oNamnshN4dFG,QZzQeAYvsoum,VHAt7CcYKC2T,cKsTbNGL... |
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):
"""
Given a weighted graph with observed cl... | [
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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
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True if the GLC class is a model a... | [
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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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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.
Returns
----------
A class typ... | [
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A class type for the pickle file to save. | [
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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
None for all non GLC objects.
Parameters
----------
obj: Name of the object whose persistent ID is extracted.
Returns
--------
None if the ob... | [
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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()
self.file = ... | [
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