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apple/turicreate | src/unity/python/turicreate/visualization/show.py | box_plot | def box_plot(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots the data in `x` on the X axis and the data in `y` on the Y axis
in a 2d box and whiskers plot, and returns the resulting Plot object.
The function x as SArray of dtype str and y as SArray of dtype: int, float.
Parameters
----------
x : SArray
The data to plot on the X axis of the box and whiskers plot.
Must be an SArray with dtype string.
y : SArray
The data to plot on the Y axis of the box and whiskers plot.
Must be numeric (int/float) and must be the same length as `x`.
xlabel : str (optional)
The text label for the X axis. Defaults to "X".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Y".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the box and whiskers plot.
Examples
--------
Make a box and whiskers plot.
>>> bp = turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0]))
"""
if (not isinstance(x, tc.data_structures.sarray.SArray) or
not isinstance(y, tc.data_structures.sarray.SArray) or
x.dtype != str or y.dtype not in [int, float]):
raise ValueError("turicreate.visualization.box_plot supports " +
"x as SArray of dtype str and y as SArray of dtype: int, float." +
"\nExample: turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0]))")
title = _get_title(title)
plt_ref = tc.extensions.plot_boxes_and_whiskers(x, y,
xlabel, ylabel, title)
return Plot(plt_ref) | python | def box_plot(x, y, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots the data in `x` on the X axis and the data in `y` on the Y axis
in a 2d box and whiskers plot, and returns the resulting Plot object.
The function x as SArray of dtype str and y as SArray of dtype: int, float.
Parameters
----------
x : SArray
The data to plot on the X axis of the box and whiskers plot.
Must be an SArray with dtype string.
y : SArray
The data to plot on the Y axis of the box and whiskers plot.
Must be numeric (int/float) and must be the same length as `x`.
xlabel : str (optional)
The text label for the X axis. Defaults to "X".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Y".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the box and whiskers plot.
Examples
--------
Make a box and whiskers plot.
>>> bp = turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0]))
"""
if (not isinstance(x, tc.data_structures.sarray.SArray) or
not isinstance(y, tc.data_structures.sarray.SArray) or
x.dtype != str or y.dtype not in [int, float]):
raise ValueError("turicreate.visualization.box_plot supports " +
"x as SArray of dtype str and y as SArray of dtype: int, float." +
"\nExample: turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0]))")
title = _get_title(title)
plt_ref = tc.extensions.plot_boxes_and_whiskers(x, y,
xlabel, ylabel, title)
return Plot(plt_ref) | [
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in a 2d box and whiskers plot, and returns the resulting Plot object.
The function x as SArray of dtype str and y as SArray of dtype: int, float.
Parameters
----------
x : SArray
The data to plot on the X axis of the box and whiskers plot.
Must be an SArray with dtype string.
y : SArray
The data to plot on the Y axis of the box and whiskers plot.
Must be numeric (int/float) and must be the same length as `x`.
xlabel : str (optional)
The text label for the X axis. Defaults to "X".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Y".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the box and whiskers plot.
Examples
--------
Make a box and whiskers plot.
>>> bp = turicreate.visualization.box_plot(tc.SArray(['a','b','c','a','a']),tc.SArray([4.0,3.25,2.1,2.0,1.0])) | [
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apple/turicreate | src/unity/python/turicreate/visualization/show.py | columnwise_summary | def columnwise_summary(sf):
"""
Plots a columnwise summary of the sframe provided as input,
and returns the resulting Plot object.
The function supports SFrames.
Parameters
----------
sf : SFrame
The data to get a columnwise summary for.
Returns
-------
out : Plot
A :class: Plot object that is the columnwise summary plot.
Examples
--------
Make a columnwise summary of an SFrame.
>>> x = turicreate.SArray([1,2,3,4,5])
>>> s = turicreate.SArray(['a','b','c','a','a'])
>>> sf_test = turicreate.SFrame([x,x,x,x,s,s,s,x,s,x,s,s,s,x,x])
>>> colsum = turicreate.visualization.columnwise_summary(sf_test)
"""
if not isinstance(sf, tc.data_structures.sframe.SFrame):
raise ValueError("turicreate.visualization.columnwise_summary " +
"supports SFrame")
plt_ref = tc.extensions.plot_columnwise_summary(sf)
return Plot(plt_ref) | python | def columnwise_summary(sf):
"""
Plots a columnwise summary of the sframe provided as input,
and returns the resulting Plot object.
The function supports SFrames.
Parameters
----------
sf : SFrame
The data to get a columnwise summary for.
Returns
-------
out : Plot
A :class: Plot object that is the columnwise summary plot.
Examples
--------
Make a columnwise summary of an SFrame.
>>> x = turicreate.SArray([1,2,3,4,5])
>>> s = turicreate.SArray(['a','b','c','a','a'])
>>> sf_test = turicreate.SFrame([x,x,x,x,s,s,s,x,s,x,s,s,s,x,x])
>>> colsum = turicreate.visualization.columnwise_summary(sf_test)
"""
if not isinstance(sf, tc.data_structures.sframe.SFrame):
raise ValueError("turicreate.visualization.columnwise_summary " +
"supports SFrame")
plt_ref = tc.extensions.plot_columnwise_summary(sf)
return Plot(plt_ref) | [
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The data to get a columnwise summary for.
Returns
-------
out : Plot
A :class: Plot object that is the columnwise summary plot.
Examples
--------
Make a columnwise summary of an SFrame.
>>> x = turicreate.SArray([1,2,3,4,5])
>>> s = turicreate.SArray(['a','b','c','a','a'])
>>> sf_test = turicreate.SFrame([x,x,x,x,s,s,s,x,s,x,s,s,s,x,x])
>>> colsum = turicreate.visualization.columnwise_summary(sf_test) | [
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apple/turicreate | src/unity/python/turicreate/visualization/show.py | histogram | def histogram(sa, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots a histogram of the sarray provided as input, and returns the
resulting Plot object.
The function supports numeric SArrays with dtypes int or float.
Parameters
----------
sa : SArray
The data to get a histogram for. Must be numeric (int/float).
xlabel : str (optional)
The text label for the X axis. Defaults to "Values".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Count".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the histogram.
Examples
--------
Make a histogram of an SArray.
>>> x = turicreate.SArray([1,2,3,4,5,1,1,1,1,2,2,3,2,3,1,1,1,4])
>>> hist = turicreate.visualization.histogram(x)
"""
if (not isinstance(sa, tc.data_structures.sarray.SArray) or
sa.dtype not in [int, float]):
raise ValueError("turicreate.visualization.histogram supports " +
"SArrays of dtypes: int, float")
title = _get_title(title)
plt_ref = tc.extensions.plot_histogram(sa,
xlabel, ylabel, title)
return Plot(plt_ref) | python | def histogram(sa, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots a histogram of the sarray provided as input, and returns the
resulting Plot object.
The function supports numeric SArrays with dtypes int or float.
Parameters
----------
sa : SArray
The data to get a histogram for. Must be numeric (int/float).
xlabel : str (optional)
The text label for the X axis. Defaults to "Values".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Count".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the histogram.
Examples
--------
Make a histogram of an SArray.
>>> x = turicreate.SArray([1,2,3,4,5,1,1,1,1,2,2,3,2,3,1,1,1,4])
>>> hist = turicreate.visualization.histogram(x)
"""
if (not isinstance(sa, tc.data_structures.sarray.SArray) or
sa.dtype not in [int, float]):
raise ValueError("turicreate.visualization.histogram supports " +
"SArrays of dtypes: int, float")
title = _get_title(title)
plt_ref = tc.extensions.plot_histogram(sa,
xlabel, ylabel, title)
return Plot(plt_ref) | [
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The text label for the X axis. Defaults to "Values".
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The text label for the Y axis. Defaults to "Count".
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The title of the plot. Defaults to LABEL_DEFAULT. If the value is
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Returns
-------
out : Plot
A :class: Plot object that is the histogram.
Examples
--------
Make a histogram of an SArray.
>>> x = turicreate.SArray([1,2,3,4,5,1,1,1,1,2,2,3,2,3,1,1,1,4])
>>> hist = turicreate.visualization.histogram(x) | [
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apple/turicreate | src/unity/python/turicreate/visualization/show.py | item_frequency | def item_frequency(sa, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots an item frequency of the sarray provided as input, and returns the
resulting Plot object.
The function supports SArrays with dtype str.
Parameters
----------
sa : SArray
The data to get an item frequency for. Must have dtype str
xlabel : str (optional)
The text label for the X axis. Defaults to "Values".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Count".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the item frequency plot.
Examples
--------
Make an item frequency of an SArray.
>>> x = turicreate.SArray(['a','ab','acd','ab','a','a','a','ab','cd'])
>>> ifplt = turicreate.visualization.item_frequency(x)
"""
if (not isinstance(sa, tc.data_structures.sarray.SArray) or
sa.dtype != str):
raise ValueError("turicreate.visualization.item_frequency supports " +
"SArrays of dtype str")
title = _get_title(title)
plt_ref = tc.extensions.plot_item_frequency(sa,
xlabel, ylabel, title)
return Plot(plt_ref) | python | def item_frequency(sa, xlabel=LABEL_DEFAULT, ylabel=LABEL_DEFAULT, title=LABEL_DEFAULT):
"""
Plots an item frequency of the sarray provided as input, and returns the
resulting Plot object.
The function supports SArrays with dtype str.
Parameters
----------
sa : SArray
The data to get an item frequency for. Must have dtype str
xlabel : str (optional)
The text label for the X axis. Defaults to "Values".
ylabel : str (optional)
The text label for the Y axis. Defaults to "Count".
title : str (optional)
The title of the plot. Defaults to LABEL_DEFAULT. If the value is
LABEL_DEFAULT, the title will be "<xlabel> vs. <ylabel>". If the value
is None, the title will be omitted. Otherwise, the string passed in as the
title will be used as the plot title.
Returns
-------
out : Plot
A :class: Plot object that is the item frequency plot.
Examples
--------
Make an item frequency of an SArray.
>>> x = turicreate.SArray(['a','ab','acd','ab','a','a','a','ab','cd'])
>>> ifplt = turicreate.visualization.item_frequency(x)
"""
if (not isinstance(sa, tc.data_structures.sarray.SArray) or
sa.dtype != str):
raise ValueError("turicreate.visualization.item_frequency supports " +
"SArrays of dtype str")
title = _get_title(title)
plt_ref = tc.extensions.plot_item_frequency(sa,
xlabel, ylabel, title)
return Plot(plt_ref) | [
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The text label for the X axis. Defaults to "Values".
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The text label for the Y axis. Defaults to "Count".
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Returns
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A :class: Plot object that is the item frequency plot.
Examples
--------
Make an item frequency of an SArray.
>>> x = turicreate.SArray(['a','ab','acd','ab','a','a','a','ab','cd'])
>>> ifplt = turicreate.visualization.item_frequency(x) | [
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apple/turicreate | deps/src/libevent-2.0.18-stable/event_rpcgen.py | Parse | def Parse(factory, file):
"""
Parses the input file and returns C code and corresponding header file.
"""
entities = []
while 1:
# Just gets the whole struct nicely formatted
data = GetNextStruct(file)
if not data:
break
entities.extend(ProcessStruct(factory, data))
return entities | python | def Parse(factory, file):
"""
Parses the input file and returns C code and corresponding header file.
"""
entities = []
while 1:
# Just gets the whole struct nicely formatted
data = GetNextStruct(file)
if not data:
break
entities.extend(ProcessStruct(factory, data))
return entities | [
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apple/turicreate | deps/src/libevent-2.0.18-stable/event_rpcgen.py | Struct.EntryTagName | def EntryTagName(self, entry):
"""Creates the name inside an enumeration for distinguishing data
types."""
name = "%s_%s" % (self._name, entry.Name())
return name.upper() | python | def EntryTagName(self, entry):
"""Creates the name inside an enumeration for distinguishing data
types."""
name = "%s_%s" % (self._name, entry.Name())
return name.upper() | [
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apple/turicreate | deps/src/libevent-2.0.18-stable/event_rpcgen.py | Struct.PrintIndented | def PrintIndented(self, file, ident, code):
"""Takes an array, add indentation to each entry and prints it."""
for entry in code:
print >>file, '%s%s' % (ident, entry) | python | def PrintIndented(self, file, ident, code):
"""Takes an array, add indentation to each entry and prints it."""
for entry in code:
print >>file, '%s%s' % (ident, entry) | [
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apple/turicreate | deps/src/libevent-2.0.18-stable/event_rpcgen.py | StructCCode.PrintTags | def PrintTags(self, file):
"""Prints the tag definitions for a structure."""
print >>file, '/* Tag definition for %s */' % self._name
print >>file, 'enum %s_ {' % self._name.lower()
for entry in self._entries:
print >>file, ' %s=%d,' % (self.EntryTagName(entry),
entry.Tag())
print >>file, ' %s_MAX_TAGS' % (self._name.upper())
print >>file, '};\n' | python | def PrintTags(self, file):
"""Prints the tag definitions for a structure."""
print >>file, '/* Tag definition for %s */' % self._name
print >>file, 'enum %s_ {' % self._name.lower()
for entry in self._entries:
print >>file, ' %s=%d,' % (self.EntryTagName(entry),
entry.Tag())
print >>file, ' %s_MAX_TAGS' % (self._name.upper())
print >>file, '};\n' | [
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apple/turicreate | src/unity/python/turicreate/aggregate.py | QUANTILE | def QUANTILE(src_column, *args):
"""
Builtin approximate quantile aggregator for groupby.
Accepts as an argument, one or more of a list of quantiles to query.
For instance:
To extract the median
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.5)})
To extract a few quantiles
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', [0.25,0.5,0.75])})
Or equivalently
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.25,0.5,0.75)})
The returned quantiles are guaranteed to have 0.5% accuracy. That is to say,
if the requested quantile is 0.50, the resultant quantile value may be
between 0.495 and 0.505 of the true quantile.
"""
if len(args) == 1:
quantiles = args[0]
else:
quantiles = list(args)
if not _is_non_string_iterable(quantiles):
quantiles = [quantiles]
query = ",".join([str(i) for i in quantiles])
return ("__builtin__quantile__[" + query + "]", [src_column]) | python | def QUANTILE(src_column, *args):
"""
Builtin approximate quantile aggregator for groupby.
Accepts as an argument, one or more of a list of quantiles to query.
For instance:
To extract the median
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.5)})
To extract a few quantiles
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', [0.25,0.5,0.75])})
Or equivalently
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.25,0.5,0.75)})
The returned quantiles are guaranteed to have 0.5% accuracy. That is to say,
if the requested quantile is 0.50, the resultant quantile value may be
between 0.495 and 0.505 of the true quantile.
"""
if len(args) == 1:
quantiles = args[0]
else:
quantiles = list(args)
if not _is_non_string_iterable(quantiles):
quantiles = [quantiles]
query = ",".join([str(i) for i in quantiles])
return ("__builtin__quantile__[" + query + "]", [src_column]) | [
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Accepts as an argument, one or more of a list of quantiles to query.
For instance:
To extract the median
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.5)})
To extract a few quantiles
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', [0.25,0.5,0.75])})
Or equivalently
>>> sf.groupby("user",
... {'rating_quantiles':tc.aggregate.QUANTILE('rating', 0.25,0.5,0.75)})
The returned quantiles are guaranteed to have 0.5% accuracy. That is to say,
if the requested quantile is 0.50, the resultant quantile value may be
between 0.495 and 0.505 of the true quantile. | [
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apple/turicreate | src/unity/python/turicreate/toolkits/graph_analytics/kcore.py | create | def create(graph, kmin=0, kmax=10, verbose=True):
"""
Compute the K-core decomposition of the graph. Return a model object with
total number of cores as well as the core id for each vertex in the graph.
Parameters
----------
graph : SGraph
The graph on which to compute the k-core decomposition.
kmin : int, optional
Minimum core id. Vertices having smaller core id than `kmin` will be
assigned with core_id = `kmin`.
kmax : int, optional
Maximum core id. Vertices having larger core id than `kmax` will be
assigned with core_id=`kmax`.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : KcoreModel
References
----------
- Alvarez-Hamelin, J.I., et al. (2005) `K-Core Decomposition: A Tool for the
Visualization of Large Networks <http://arxiv.org/abs/cs/0504107>`_.
Examples
--------
If given an :class:`~turicreate.SGraph` ``g``, we can create
a :class:`~turicreate.kcore.KcoreModel` as follows:
>>> g = turicreate.load_sgraph('http://snap.stanford.edu/data/email-Enron.txt.gz', format='snap')
>>> kc = turicreate.kcore.create(g)
We can obtain the ``core id`` corresponding to each vertex in the graph
``g`` using:
>>> kcore_id = kc['core_id'] # SFrame
We can add the new core id field to the original graph g using:
>>> g.vertices['core_id'] = kc['graph'].vertices['core_id']
Note that the task above does not require a join because the vertex
ordering is preserved through ``create()``.
See Also
--------
KcoreModel
"""
from turicreate._cython.cy_server import QuietProgress
if not isinstance(graph, _SGraph):
raise TypeError('graph input must be a SGraph object.')
opts = {'graph': graph.__proxy__, 'kmin': kmin, 'kmax': kmax}
with QuietProgress(verbose):
params = _tc.extensions._toolkits.graph.kcore.create(opts)
return KcoreModel(params['model']) | python | def create(graph, kmin=0, kmax=10, verbose=True):
"""
Compute the K-core decomposition of the graph. Return a model object with
total number of cores as well as the core id for each vertex in the graph.
Parameters
----------
graph : SGraph
The graph on which to compute the k-core decomposition.
kmin : int, optional
Minimum core id. Vertices having smaller core id than `kmin` will be
assigned with core_id = `kmin`.
kmax : int, optional
Maximum core id. Vertices having larger core id than `kmax` will be
assigned with core_id=`kmax`.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : KcoreModel
References
----------
- Alvarez-Hamelin, J.I., et al. (2005) `K-Core Decomposition: A Tool for the
Visualization of Large Networks <http://arxiv.org/abs/cs/0504107>`_.
Examples
--------
If given an :class:`~turicreate.SGraph` ``g``, we can create
a :class:`~turicreate.kcore.KcoreModel` as follows:
>>> g = turicreate.load_sgraph('http://snap.stanford.edu/data/email-Enron.txt.gz', format='snap')
>>> kc = turicreate.kcore.create(g)
We can obtain the ``core id`` corresponding to each vertex in the graph
``g`` using:
>>> kcore_id = kc['core_id'] # SFrame
We can add the new core id field to the original graph g using:
>>> g.vertices['core_id'] = kc['graph'].vertices['core_id']
Note that the task above does not require a join because the vertex
ordering is preserved through ``create()``.
See Also
--------
KcoreModel
"""
from turicreate._cython.cy_server import QuietProgress
if not isinstance(graph, _SGraph):
raise TypeError('graph input must be a SGraph object.')
opts = {'graph': graph.__proxy__, 'kmin': kmin, 'kmax': kmax}
with QuietProgress(verbose):
params = _tc.extensions._toolkits.graph.kcore.create(opts)
return KcoreModel(params['model']) | [
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Parameters
----------
graph : SGraph
The graph on which to compute the k-core decomposition.
kmin : int, optional
Minimum core id. Vertices having smaller core id than `kmin` will be
assigned with core_id = `kmin`.
kmax : int, optional
Maximum core id. Vertices having larger core id than `kmax` will be
assigned with core_id=`kmax`.
verbose : bool, optional
If True, print progress updates.
Returns
-------
out : KcoreModel
References
----------
- Alvarez-Hamelin, J.I., et al. (2005) `K-Core Decomposition: A Tool for the
Visualization of Large Networks <http://arxiv.org/abs/cs/0504107>`_.
Examples
--------
If given an :class:`~turicreate.SGraph` ``g``, we can create
a :class:`~turicreate.kcore.KcoreModel` as follows:
>>> g = turicreate.load_sgraph('http://snap.stanford.edu/data/email-Enron.txt.gz', format='snap')
>>> kc = turicreate.kcore.create(g)
We can obtain the ``core id`` corresponding to each vertex in the graph
``g`` using:
>>> kcore_id = kc['core_id'] # SFrame
We can add the new core id field to the original graph g using:
>>> g.vertices['core_id'] = kc['graph'].vertices['core_id']
Note that the task above does not require a join because the vertex
ordering is preserved through ``create()``.
See Also
--------
KcoreModel | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_utils.py | raise_error_unsupported_categorical_option | def raise_error_unsupported_categorical_option(option_name, option_value, layer_type, layer_name):
"""
Raise an error if an option is not supported.
"""
raise RuntimeError("Unsupported option %s=%s in layer %s(%s)" % (option_name, option_value,
layer_type, layer_name)) | python | def raise_error_unsupported_categorical_option(option_name, option_value, layer_type, layer_name):
"""
Raise an error if an option is not supported.
"""
raise RuntimeError("Unsupported option %s=%s in layer %s(%s)" % (option_name, option_value,
layer_type, layer_name)) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/_feature_management.py | process_or_validate_classifier_output_features | def process_or_validate_classifier_output_features(
output_features, class_labels, supports_class_scores = True):
"""
Given a list of class labels and a list of output_features, validate the
list and return a valid version of output_features with all the correct
data type information included.
"""
def raise_error(msg):
raise ValueError("Classifier error: %s" % msg)
class_labels = list(class_labels)
# First, we need to determine the type of the classes.
_int_types = _integer_types + (bool, _np.bool_, _np.int32, _np.int64)
if all(isinstance(cl, _int_types) for cl in class_labels):
output_class_type = datatypes.Int64()
elif all(isinstance(cl, _string_types) for cl in class_labels):
output_class_type = datatypes.String()
else:
raise ValueError('Class labels must be all of type int or all of type string.')
if output_features is None:
out = [("classLabel", output_class_type)]
if supports_class_scores:
out += [("classProbability", datatypes.Dictionary(output_class_type))]
elif isinstance(output_features, _string_types):
out = [(output_features, output_class_type)]
if supports_class_scores:
out += [("classProbability", datatypes.Dictionary(output_class_type))]
elif (isinstance(output_features, (list, tuple))
and all(isinstance(fn, _string_types) for fn in output_features)
and len(output_features) == 2):
if supports_class_scores:
out = [(output_features[0], output_class_type),
(output_features[1], datatypes.Dictionary(output_class_type))]
else:
raise ValueError("Classifier model (as trained) does not support output scores for classes.")
elif is_valid_feature_list(output_features):
output_features = [(k, datatypes._normalize_datatype(dt)) for k, dt in output_features]
if len(output_features) == 1 or not supports_class_scores:
if not output_features[0][1] == output_class_type:
raise ValueError("Type of output class feature does not match type of class labels.")
else:
# Make sure the first two output features specified give the output
# class field and the output class scores dictionary field
if (isinstance(output_features[0][1], datatypes.Dictionary)
and isinstance(output_features[1][1], output_class_type)):
output_features[0], output_features[1] = output_features[1], output_features[0]
if not isinstance(output_features[1][1], datatypes.Dictionary):
raise_error("Output features class scores should be dictionary type.")
if output_features[1][1].key_type != output_class_type:
raise_error("Class scores dictionary key type does not match type of class labels.")
if output_features[0][1] != output_class_type:
raise_error("Specified type of output class does not match type of class labels.")
# NOTE: We are intentionally allowing the case where additional fields are allowed
# beyond the original two features.
out = output_features
else:
raise_error("Form of output features not recognized")
return out | python | def process_or_validate_classifier_output_features(
output_features, class_labels, supports_class_scores = True):
"""
Given a list of class labels and a list of output_features, validate the
list and return a valid version of output_features with all the correct
data type information included.
"""
def raise_error(msg):
raise ValueError("Classifier error: %s" % msg)
class_labels = list(class_labels)
# First, we need to determine the type of the classes.
_int_types = _integer_types + (bool, _np.bool_, _np.int32, _np.int64)
if all(isinstance(cl, _int_types) for cl in class_labels):
output_class_type = datatypes.Int64()
elif all(isinstance(cl, _string_types) for cl in class_labels):
output_class_type = datatypes.String()
else:
raise ValueError('Class labels must be all of type int or all of type string.')
if output_features is None:
out = [("classLabel", output_class_type)]
if supports_class_scores:
out += [("classProbability", datatypes.Dictionary(output_class_type))]
elif isinstance(output_features, _string_types):
out = [(output_features, output_class_type)]
if supports_class_scores:
out += [("classProbability", datatypes.Dictionary(output_class_type))]
elif (isinstance(output_features, (list, tuple))
and all(isinstance(fn, _string_types) for fn in output_features)
and len(output_features) == 2):
if supports_class_scores:
out = [(output_features[0], output_class_type),
(output_features[1], datatypes.Dictionary(output_class_type))]
else:
raise ValueError("Classifier model (as trained) does not support output scores for classes.")
elif is_valid_feature_list(output_features):
output_features = [(k, datatypes._normalize_datatype(dt)) for k, dt in output_features]
if len(output_features) == 1 or not supports_class_scores:
if not output_features[0][1] == output_class_type:
raise ValueError("Type of output class feature does not match type of class labels.")
else:
# Make sure the first two output features specified give the output
# class field and the output class scores dictionary field
if (isinstance(output_features[0][1], datatypes.Dictionary)
and isinstance(output_features[1][1], output_class_type)):
output_features[0], output_features[1] = output_features[1], output_features[0]
if not isinstance(output_features[1][1], datatypes.Dictionary):
raise_error("Output features class scores should be dictionary type.")
if output_features[1][1].key_type != output_class_type:
raise_error("Class scores dictionary key type does not match type of class labels.")
if output_features[0][1] != output_class_type:
raise_error("Specified type of output class does not match type of class labels.")
# NOTE: We are intentionally allowing the case where additional fields are allowed
# beyond the original two features.
out = output_features
else:
raise_error("Form of output features not recognized")
return out | [
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list and return a valid version of output_features with all the correct
data type information included. | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/models/_feature_management.py | process_or_validate_features | def process_or_validate_features(features, num_dimensions = None, feature_type_map = {}):
"""
Puts features into a standard form from a number of different possible forms.
The standard form is a list of 2-tuples of (name, datatype) pairs. The name
is a string and the datatype is an object as defined in the _datatype module.
The possible input forms are as follows:
* A list of strings. in this case, the overall dimension is assumed to be
the length of the list. If neighboring names are identical, they are
assumed to be an input array of that length. For example:
["a", "b", "c"]
resolves to
[("a", Double), ("b", Double), ("c", Double)].
And:
["a", "a", "b"]
resolves to
[("a", Array(2)), ("b", Double)].
* A dictionary of keys to indices or ranges of feature indices.
In this case, it's presented as a mapping from keys to indices or
ranges of contiguous indices. For example,
{"a" : 0, "b" : [2,3], "c" : 1}
Resolves to
[("a", Double), ("c", Double), ("b", Array(2))].
Note that the ordering is determined by the indices.
* A single string. In this case, the input is assumed to be a single array,
with the number of dimensions set using num_dimensions.
Notes:
If the features variable is in the standard form, it is simply checked and
returned.
If num_dimensions is given, it is used to check against the existing features,
or fill in missing information in the case when features is a single string.
"""
original_features = copy(features)
if num_dimensions is not None and not isinstance(num_dimensions, _integer_types):
raise TypeError("num_dimensions must be None, an integer or a long, not '%s'"
% str(type(num_dimensions)))
def raise_type_error(additional_msg):
raise TypeError("Error processing feature list: %s\nfeatures = %s"
% (additional_msg, str(original_features)))
if type(features) is dict and is_valid_feature_list(features.items()):
features = features.items()
# First, see if the features are already in the correct form. If they are,
# then we
if is_valid_feature_list(features):
if num_dimensions is not None:
try:
feature_dims = dimension_of_array_features(features)
except ValueError:
feature_dims = None
if feature_dims is not None and feature_dims != num_dimensions:
raise_type_error("Dimension mismatch.")
# We may need to translate some parts of this back to the actual
# datatype class -- e.g. translate str to datatypes.String().
return [(k, datatypes._normalize_datatype(dt)) for k, dt in features]
if isinstance(features, _string_types):
if num_dimensions is None:
raise_type_error("If a single feature name is given, then "
"num_dimensions must be provided.")
features = {features : range(num_dimensions)}
if isinstance(features, (list, tuple, _np.ndarray)):
# Change this into a dictionary
mapping = defaultdict(lambda: [])
for i, k in enumerate(features):
if not isinstance(k, _string_types):
raise_type_error("List of feature names must be list of strings.")
if num_dimensions is not None and len(features) != num_dimensions:
raise_type_error(("List of feature names has wrong length; "
"%d required, %d provided.")
% (num_dimensions, len(features)))
for i, k in enumerate(features):
mapping[k].append(i)
# Replace the features
features = mapping
if not isinstance(features, dict):
raise_type_error("features must be either a list of feature names "
"or a dictionary of feature names to ranges.")
# We'll be invasive here so make a copy.
features = copy(features)
for k, v in list(features.items()):
if not isinstance(k, str):
raise_type_error("Feature names must be strings.")
def test_index(val):
error = False
try:
if val != int(val):
error = True
except:
error = True
if error:
raise_type_error("Specified indices for feature %s must be integers." % k)
if val < 0 or (num_dimensions is not None and val >= num_dimensions):
raise_type_error("Index in feature %s out of range." % k)
iterable_types = [tuple, list, set]
if _PY3:
iterable_types.append(range)
else:
iterable_types.append(xrange)
if isinstance(v, tuple(iterable_types)):
for idx in v:
test_index(idx)
# Replace and update
features[k] = v = list(sorted(v))
elif isinstance(v, (int, long)):
test_index(v)
features[k] = v = [v]
else:
raise_type_error(("Value type for feature %s not recognized; "
"values must be either integers, lists or range objects.") % k)
# check to make sure things are contiguous
if v != list(range(v[0], v[-1] + 1)):
raise_type_error("Index list for feature %s must consist of "
"a contiguous range of indices." % k)
if len(set(v)) != len(v):
raise_type_error("Index list for feature %s contains duplicates." % k)
# Now, set num dimensions from the list if it's actually None
if num_dimensions is None:
from itertools import chain
num_dimensions = 1 + max(chain(*[il for k, il in features.items()]))
if (set().union(*features.values()) != set(range(num_dimensions))
or sum(len(v) for v in features.values()) != num_dimensions):
raise_type_error("Supplied indices must cover entire range of 0, ..., num_dimensions-1.")
# Define the output feature types
output_features = [None]*len(features)
# Finally, go through and map all these things out as types.
# Sort by first value of the index range.
for i, (k, v) in enumerate(sorted(features.items(), key = lambda t: t[1][0])):
if k in feature_type_map:
output_features[i] = (k, feature_type_map[k])
elif len(v) == 1:
output_features[i] = (k, datatypes.Double())
else:
output_features[i] = (k, datatypes.Array(len(v)))
return output_features | python | def process_or_validate_features(features, num_dimensions = None, feature_type_map = {}):
"""
Puts features into a standard form from a number of different possible forms.
The standard form is a list of 2-tuples of (name, datatype) pairs. The name
is a string and the datatype is an object as defined in the _datatype module.
The possible input forms are as follows:
* A list of strings. in this case, the overall dimension is assumed to be
the length of the list. If neighboring names are identical, they are
assumed to be an input array of that length. For example:
["a", "b", "c"]
resolves to
[("a", Double), ("b", Double), ("c", Double)].
And:
["a", "a", "b"]
resolves to
[("a", Array(2)), ("b", Double)].
* A dictionary of keys to indices or ranges of feature indices.
In this case, it's presented as a mapping from keys to indices or
ranges of contiguous indices. For example,
{"a" : 0, "b" : [2,3], "c" : 1}
Resolves to
[("a", Double), ("c", Double), ("b", Array(2))].
Note that the ordering is determined by the indices.
* A single string. In this case, the input is assumed to be a single array,
with the number of dimensions set using num_dimensions.
Notes:
If the features variable is in the standard form, it is simply checked and
returned.
If num_dimensions is given, it is used to check against the existing features,
or fill in missing information in the case when features is a single string.
"""
original_features = copy(features)
if num_dimensions is not None and not isinstance(num_dimensions, _integer_types):
raise TypeError("num_dimensions must be None, an integer or a long, not '%s'"
% str(type(num_dimensions)))
def raise_type_error(additional_msg):
raise TypeError("Error processing feature list: %s\nfeatures = %s"
% (additional_msg, str(original_features)))
if type(features) is dict and is_valid_feature_list(features.items()):
features = features.items()
# First, see if the features are already in the correct form. If they are,
# then we
if is_valid_feature_list(features):
if num_dimensions is not None:
try:
feature_dims = dimension_of_array_features(features)
except ValueError:
feature_dims = None
if feature_dims is not None and feature_dims != num_dimensions:
raise_type_error("Dimension mismatch.")
# We may need to translate some parts of this back to the actual
# datatype class -- e.g. translate str to datatypes.String().
return [(k, datatypes._normalize_datatype(dt)) for k, dt in features]
if isinstance(features, _string_types):
if num_dimensions is None:
raise_type_error("If a single feature name is given, then "
"num_dimensions must be provided.")
features = {features : range(num_dimensions)}
if isinstance(features, (list, tuple, _np.ndarray)):
# Change this into a dictionary
mapping = defaultdict(lambda: [])
for i, k in enumerate(features):
if not isinstance(k, _string_types):
raise_type_error("List of feature names must be list of strings.")
if num_dimensions is not None and len(features) != num_dimensions:
raise_type_error(("List of feature names has wrong length; "
"%d required, %d provided.")
% (num_dimensions, len(features)))
for i, k in enumerate(features):
mapping[k].append(i)
# Replace the features
features = mapping
if not isinstance(features, dict):
raise_type_error("features must be either a list of feature names "
"or a dictionary of feature names to ranges.")
# We'll be invasive here so make a copy.
features = copy(features)
for k, v in list(features.items()):
if not isinstance(k, str):
raise_type_error("Feature names must be strings.")
def test_index(val):
error = False
try:
if val != int(val):
error = True
except:
error = True
if error:
raise_type_error("Specified indices for feature %s must be integers." % k)
if val < 0 or (num_dimensions is not None and val >= num_dimensions):
raise_type_error("Index in feature %s out of range." % k)
iterable_types = [tuple, list, set]
if _PY3:
iterable_types.append(range)
else:
iterable_types.append(xrange)
if isinstance(v, tuple(iterable_types)):
for idx in v:
test_index(idx)
# Replace and update
features[k] = v = list(sorted(v))
elif isinstance(v, (int, long)):
test_index(v)
features[k] = v = [v]
else:
raise_type_error(("Value type for feature %s not recognized; "
"values must be either integers, lists or range objects.") % k)
# check to make sure things are contiguous
if v != list(range(v[0], v[-1] + 1)):
raise_type_error("Index list for feature %s must consist of "
"a contiguous range of indices." % k)
if len(set(v)) != len(v):
raise_type_error("Index list for feature %s contains duplicates." % k)
# Now, set num dimensions from the list if it's actually None
if num_dimensions is None:
from itertools import chain
num_dimensions = 1 + max(chain(*[il for k, il in features.items()]))
if (set().union(*features.values()) != set(range(num_dimensions))
or sum(len(v) for v in features.values()) != num_dimensions):
raise_type_error("Supplied indices must cover entire range of 0, ..., num_dimensions-1.")
# Define the output feature types
output_features = [None]*len(features)
# Finally, go through and map all these things out as types.
# Sort by first value of the index range.
for i, (k, v) in enumerate(sorted(features.items(), key = lambda t: t[1][0])):
if k in feature_type_map:
output_features[i] = (k, feature_type_map[k])
elif len(v) == 1:
output_features[i] = (k, datatypes.Double())
else:
output_features[i] = (k, datatypes.Array(len(v)))
return output_features | [
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is a string and the datatype is an object as defined in the _datatype module.
The possible input forms are as follows:
* A list of strings. in this case, the overall dimension is assumed to be
the length of the list. If neighboring names are identical, they are
assumed to be an input array of that length. For example:
["a", "b", "c"]
resolves to
[("a", Double), ("b", Double), ("c", Double)].
And:
["a", "a", "b"]
resolves to
[("a", Array(2)), ("b", Double)].
* A dictionary of keys to indices or ranges of feature indices.
In this case, it's presented as a mapping from keys to indices or
ranges of contiguous indices. For example,
{"a" : 0, "b" : [2,3], "c" : 1}
Resolves to
[("a", Double), ("c", Double), ("b", Array(2))].
Note that the ordering is determined by the indices.
* A single string. In this case, the input is assumed to be a single array,
with the number of dimensions set using num_dimensions.
Notes:
If the features variable is in the standard form, it is simply checked and
returned.
If num_dimensions is given, it is used to check against the existing features,
or fill in missing information in the case when features is a single string. | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/kernel/bootstrap.py | bootstrap | def bootstrap(root_path):
"""Performs python-side bootstrapping of Boost.Build/Python.
This function arranges for 'b2.whatever' package names to work, while also
allowing to put python files alongside corresponding jam modules.
"""
m = imp.new_module("b2")
# Note that:
# 1. If __path__ is not list of strings, nothing will work
# 2. root_path is already list of strings.
m.__path__ = root_path
sys.modules["b2"] = m
import b2.build_system
return b2.build_system.main() | python | def bootstrap(root_path):
"""Performs python-side bootstrapping of Boost.Build/Python.
This function arranges for 'b2.whatever' package names to work, while also
allowing to put python files alongside corresponding jam modules.
"""
m = imp.new_module("b2")
# Note that:
# 1. If __path__ is not list of strings, nothing will work
# 2. root_path is already list of strings.
m.__path__ = root_path
sys.modules["b2"] = m
import b2.build_system
return b2.build_system.main() | [
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apple/turicreate | deps/src/boost_1_68_0/libs/metaparse/tools/build_environment.py | main | def main():
"""The main function of the utility"""
parser = argparse.ArgumentParser(
description='Manage the build environment of Boost.Metaparse'
)
parser.add_argument(
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required=True,
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parser.add_argument(
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parser.add_argument(
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help='The Boost repository to clone'
)
parser.add_argument(
'--ref',
required=False,
default='origin/master',
help='The reference to set to in update'
)
args = parser.parse_args()
build_environment(
args.dep_json,
args.out,
ChildProcess([args.git]),
args.boost_repository,
args.action,
args.ref
) | python | def main():
"""The main function of the utility"""
parser = argparse.ArgumentParser(
description='Manage the build environment of Boost.Metaparse'
)
parser.add_argument(
'--dep_json',
required=True,
help='The json file describing the dependencies'
)
parser.add_argument(
'--git',
required=False,
default='git',
help='The git command to use'
)
parser.add_argument(
'--out',
required=False,
default='boost',
help='The directory to clone into'
)
parser.add_argument(
'--action',
required=True,
choices=['update', 'checkout'],
help='The action to do with the dependencies'
)
parser.add_argument(
'--boost_repository',
required=False,
default='https://github.com/boostorg/boost.git',
help='The Boost repository to clone'
)
parser.add_argument(
'--ref',
required=False,
default='origin/master',
help='The reference to set to in update'
)
args = parser.parse_args()
build_environment(
args.dep_json,
args.out,
ChildProcess([args.git]),
args.boost_repository,
args.action,
args.ref
) | [
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apple/turicreate | src/unity/python/turicreate/data_structures/sarray_builder.py | SArrayBuilder.read_history | def read_history(self, num=10, segment=0):
"""
Outputs the last `num` elements that were appended either by `append` or
`append_multiple`.
Returns
-------
out : list
"""
if num < 0:
num = 0
if segment < 0:
raise TypeError("segment must be >= 0")
return self._builder.read_history(num, segment) | python | def read_history(self, num=10, segment=0):
"""
Outputs the last `num` elements that were appended either by `append` or
`append_multiple`.
Returns
-------
out : list
"""
if num < 0:
num = 0
if segment < 0:
raise TypeError("segment must be >= 0")
return self._builder.read_history(num, segment) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/toolset.py | flags | def flags(rule_or_module, variable_name, condition, values = []):
""" Specifies the flags (variables) that must be set on targets under certain
conditions, described by arguments.
rule_or_module: If contains dot, should be a rule name.
The flags will be applied when that rule is
used to set up build actions.
If does not contain dot, should be a module name.
The flags will be applied for all rules in that
module.
If module for rule is different from the calling
module, an error is issued.
variable_name: Variable that should be set on target
condition A condition when this flag should be applied.
Should be set of property sets. If one of
those property sets is contained in build
properties, the flag will be used.
Implied values are not allowed:
"<toolset>gcc" should be used, not just
"gcc". Subfeatures, like in "<toolset>gcc-3.2"
are allowed. If left empty, the flag will
always used.
Propery sets may use value-less properties
('<a>' vs. '<a>value') to match absent
properties. This allows to separately match
<architecture>/<address-model>64
<architecture>ia64/<address-model>
Where both features are optional. Without this
syntax we'd be forced to define "default" value.
values: The value to add to variable. If <feature>
is specified, then the value of 'feature'
will be added.
"""
assert isinstance(rule_or_module, basestring)
assert isinstance(variable_name, basestring)
assert is_iterable_typed(condition, basestring)
assert is_iterable(values) and all(isinstance(v, (basestring, type(None))) for v in values)
caller = bjam.caller()
if not '.' in rule_or_module and caller and caller[:-1].startswith("Jamfile"):
# Unqualified rule name, used inside Jamfile. Most likely used with
# 'make' or 'notfile' rules. This prevents setting flags on the entire
# Jamfile module (this will be considered as rule), but who cares?
# Probably, 'flags' rule should be split into 'flags' and
# 'flags-on-module'.
rule_or_module = qualify_jam_action(rule_or_module, caller)
else:
# FIXME: revive checking that we don't set flags for a different
# module unintentionally
pass
if condition and not replace_grist (condition, ''):
# We have condition in the form '<feature>', that is, without
# value. That's a previous syntax:
#
# flags gcc.link RPATH <dll-path> ;
# for compatibility, convert it to
# flags gcc.link RPATH : <dll-path> ;
values = [ condition ]
condition = None
if condition:
transformed = []
for c in condition:
# FIXME: 'split' might be a too raw tool here.
pl = [property.create_from_string(s,False,True) for s in c.split('/')]
pl = feature.expand_subfeatures(pl);
transformed.append(property_set.create(pl))
condition = transformed
property.validate_property_sets(condition)
__add_flag (rule_or_module, variable_name, condition, values) | python | def flags(rule_or_module, variable_name, condition, values = []):
""" Specifies the flags (variables) that must be set on targets under certain
conditions, described by arguments.
rule_or_module: If contains dot, should be a rule name.
The flags will be applied when that rule is
used to set up build actions.
If does not contain dot, should be a module name.
The flags will be applied for all rules in that
module.
If module for rule is different from the calling
module, an error is issued.
variable_name: Variable that should be set on target
condition A condition when this flag should be applied.
Should be set of property sets. If one of
those property sets is contained in build
properties, the flag will be used.
Implied values are not allowed:
"<toolset>gcc" should be used, not just
"gcc". Subfeatures, like in "<toolset>gcc-3.2"
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always used.
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<architecture>/<address-model>64
<architecture>ia64/<address-model>
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syntax we'd be forced to define "default" value.
values: The value to add to variable. If <feature>
is specified, then the value of 'feature'
will be added.
"""
assert isinstance(rule_or_module, basestring)
assert isinstance(variable_name, basestring)
assert is_iterable_typed(condition, basestring)
assert is_iterable(values) and all(isinstance(v, (basestring, type(None))) for v in values)
caller = bjam.caller()
if not '.' in rule_or_module and caller and caller[:-1].startswith("Jamfile"):
# Unqualified rule name, used inside Jamfile. Most likely used with
# 'make' or 'notfile' rules. This prevents setting flags on the entire
# Jamfile module (this will be considered as rule), but who cares?
# Probably, 'flags' rule should be split into 'flags' and
# 'flags-on-module'.
rule_or_module = qualify_jam_action(rule_or_module, caller)
else:
# FIXME: revive checking that we don't set flags for a different
# module unintentionally
pass
if condition and not replace_grist (condition, ''):
# We have condition in the form '<feature>', that is, without
# value. That's a previous syntax:
#
# flags gcc.link RPATH <dll-path> ;
# for compatibility, convert it to
# flags gcc.link RPATH : <dll-path> ;
values = [ condition ]
condition = None
if condition:
transformed = []
for c in condition:
# FIXME: 'split' might be a too raw tool here.
pl = [property.create_from_string(s,False,True) for s in c.split('/')]
pl = feature.expand_subfeatures(pl);
transformed.append(property_set.create(pl))
condition = transformed
property.validate_property_sets(condition)
__add_flag (rule_or_module, variable_name, condition, values) | [
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If does not contain dot, should be a module name.
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If module for rule is different from the calling
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variable_name: Variable that should be set on target
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Should be set of property sets. If one of
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properties, the flag will be used.
Implied values are not allowed:
"<toolset>gcc" should be used, not just
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Propery sets may use value-less properties
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properties. This allows to separately match
<architecture>/<address-model>64
<architecture>ia64/<address-model>
Where both features are optional. Without this
syntax we'd be forced to define "default" value.
values: The value to add to variable. If <feature>
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/toolset.py | find_satisfied_condition | def find_satisfied_condition(conditions, ps):
"""Returns the first element of 'property-sets' which is a subset of
'properties', or an empty list if no such element exists."""
assert is_iterable_typed(conditions, property_set.PropertySet)
assert isinstance(ps, property_set.PropertySet)
for condition in conditions:
found_all = True
for i in condition.all():
if i.value:
found = i.value in ps.get(i.feature)
else:
# Handle value-less properties like '<architecture>' (compare with
# '<architecture>x86').
# If $(i) is a value-less property it should match default
# value of an optional property. See the first line in the
# example below:
#
# property set properties result
# <a> <b>foo <b>foo match
# <a> <b>foo <a>foo <b>foo no match
# <a>foo <b>foo <b>foo no match
# <a>foo <b>foo <a>foo <b>foo match
found = not ps.get(i.feature)
found_all = found_all and found
if found_all:
return condition
return None | python | def find_satisfied_condition(conditions, ps):
"""Returns the first element of 'property-sets' which is a subset of
'properties', or an empty list if no such element exists."""
assert is_iterable_typed(conditions, property_set.PropertySet)
assert isinstance(ps, property_set.PropertySet)
for condition in conditions:
found_all = True
for i in condition.all():
if i.value:
found = i.value in ps.get(i.feature)
else:
# Handle value-less properties like '<architecture>' (compare with
# '<architecture>x86').
# If $(i) is a value-less property it should match default
# value of an optional property. See the first line in the
# example below:
#
# property set properties result
# <a> <b>foo <b>foo match
# <a> <b>foo <a>foo <b>foo no match
# <a>foo <b>foo <b>foo no match
# <a>foo <b>foo <a>foo <b>foo match
found = not ps.get(i.feature)
found_all = found_all and found
if found_all:
return condition
return None | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/toolset.py | inherit_flags | def inherit_flags(toolset, base, prohibited_properties = []):
"""Brings all flag definitions from the 'base' toolset into the 'toolset'
toolset. Flag definitions whose conditions make use of properties in
'prohibited-properties' are ignored. Don't confuse property and feature, for
example <debug-symbols>on and <debug-symbols>off, so blocking one of them does
not block the other one.
The flag conditions are not altered at all, so if a condition includes a name,
or version of a base toolset, it won't ever match the inheriting toolset. When
such flag settings must be inherited, define a rule in base toolset module and
call it as needed."""
assert isinstance(toolset, basestring)
assert isinstance(base, basestring)
assert is_iterable_typed(prohibited_properties, basestring)
for f in __module_flags.get(base, []):
if not f.condition or b2.util.set.difference(f.condition, prohibited_properties):
match = __re_first_group.match(f.rule)
rule_ = None
if match:
rule_ = match.group(1)
new_rule_or_module = ''
if rule_:
new_rule_or_module = toolset + '.' + rule_
else:
new_rule_or_module = toolset
__add_flag (new_rule_or_module, f.variable_name, f.condition, f.values) | python | def inherit_flags(toolset, base, prohibited_properties = []):
"""Brings all flag definitions from the 'base' toolset into the 'toolset'
toolset. Flag definitions whose conditions make use of properties in
'prohibited-properties' are ignored. Don't confuse property and feature, for
example <debug-symbols>on and <debug-symbols>off, so blocking one of them does
not block the other one.
The flag conditions are not altered at all, so if a condition includes a name,
or version of a base toolset, it won't ever match the inheriting toolset. When
such flag settings must be inherited, define a rule in base toolset module and
call it as needed."""
assert isinstance(toolset, basestring)
assert isinstance(base, basestring)
assert is_iterable_typed(prohibited_properties, basestring)
for f in __module_flags.get(base, []):
if not f.condition or b2.util.set.difference(f.condition, prohibited_properties):
match = __re_first_group.match(f.rule)
rule_ = None
if match:
rule_ = match.group(1)
new_rule_or_module = ''
if rule_:
new_rule_or_module = toolset + '.' + rule_
else:
new_rule_or_module = toolset
__add_flag (new_rule_or_module, f.variable_name, f.condition, f.values) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/toolset.py | __set_target_variables_aux | def __set_target_variables_aux (manager, rule_or_module, ps):
""" Given a rule name and a property set, returns a list of tuples of
variables names and values, which must be set on targets for that
rule/properties combination.
"""
assert isinstance(rule_or_module, basestring)
assert isinstance(ps, property_set.PropertySet)
result = []
for f in __flags.get(rule_or_module, []):
if not f.condition or find_satisfied_condition (f.condition, ps):
processed = []
for v in f.values:
# The value might be <feature-name> so needs special
# treatment.
processed += __handle_flag_value (manager, v, ps)
for r in processed:
result.append ((f.variable_name, r))
# strip away last dot separated part and recurse.
next = __re_split_last_segment.match(rule_or_module)
if next:
result.extend(__set_target_variables_aux(
manager, next.group(1), ps))
return result | python | def __set_target_variables_aux (manager, rule_or_module, ps):
""" Given a rule name and a property set, returns a list of tuples of
variables names and values, which must be set on targets for that
rule/properties combination.
"""
assert isinstance(rule_or_module, basestring)
assert isinstance(ps, property_set.PropertySet)
result = []
for f in __flags.get(rule_or_module, []):
if not f.condition or find_satisfied_condition (f.condition, ps):
processed = []
for v in f.values:
# The value might be <feature-name> so needs special
# treatment.
processed += __handle_flag_value (manager, v, ps)
for r in processed:
result.append ((f.variable_name, r))
# strip away last dot separated part and recurse.
next = __re_split_last_segment.match(rule_or_module)
if next:
result.extend(__set_target_variables_aux(
manager, next.group(1), ps))
return result | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/toolset.py | __add_flag | def __add_flag (rule_or_module, variable_name, condition, values):
""" Adds a new flag setting with the specified values.
Does no checking.
"""
assert isinstance(rule_or_module, basestring)
assert isinstance(variable_name, basestring)
assert is_iterable_typed(condition, property_set.PropertySet)
assert is_iterable(values) and all(
isinstance(v, (basestring, type(None))) for v in values)
f = Flag(variable_name, values, condition, rule_or_module)
# Grab the name of the module
m = __re_first_segment.match (rule_or_module)
assert m
module = m.group(1)
__module_flags.setdefault(module, []).append(f)
__flags.setdefault(rule_or_module, []).append(f) | python | def __add_flag (rule_or_module, variable_name, condition, values):
""" Adds a new flag setting with the specified values.
Does no checking.
"""
assert isinstance(rule_or_module, basestring)
assert isinstance(variable_name, basestring)
assert is_iterable_typed(condition, property_set.PropertySet)
assert is_iterable(values) and all(
isinstance(v, (basestring, type(None))) for v in values)
f = Flag(variable_name, values, condition, rule_or_module)
# Grab the name of the module
m = __re_first_segment.match (rule_or_module)
assert m
module = m.group(1)
__module_flags.setdefault(module, []).append(f)
__flags.setdefault(rule_or_module, []).append(f) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_logistic_regression.py | convert | def convert(model, feature_names, target):
"""Convert a Logistic Regression model to the protobuf spec.
Parameters
----------
model: LogisticRegression
A trained LogisticRegression model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (default=None)
Name of the output column.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model
"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
_sklearn_util.check_expected_type(model, LogisticRegression)
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'coef_'))
return _MLModel(_convert(model, feature_names, target)) | python | def convert(model, feature_names, target):
"""Convert a Logistic Regression model to the protobuf spec.
Parameters
----------
model: LogisticRegression
A trained LogisticRegression model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (default=None)
Name of the output column.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model
"""
if not(_HAS_SKLEARN):
raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
_sklearn_util.check_expected_type(model, LogisticRegression)
_sklearn_util.check_fitted(model, lambda m: hasattr(m, 'coef_'))
return _MLModel(_convert(model, feature_names, target)) | [
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model: LogisticRegression
A trained LogisticRegression model.
feature_names: [str], optional (default=None)
Name of the input columns.
target: str, optional (default=None)
Name of the output column.
Returns
-------
model_spec: An object of type Model_pb.
Protobuf representation of the model | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/path.py | root | def root (path, root):
""" If 'path' is relative, it is rooted at 'root'. Otherwise, it's unchanged.
"""
if os.path.isabs (path):
return path
else:
return os.path.join (root, path) | python | def root (path, root):
""" If 'path' is relative, it is rooted at 'root'. Otherwise, it's unchanged.
"""
if os.path.isabs (path):
return path
else:
return os.path.join (root, path) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/path.py | reverse | def reverse(path):
"""Returns path2 such that `os.path.join(path, path2) == '.'`.
`path` may not contain '..' or be rooted.
Args:
path (str): the path to reverse
Returns:
the string of the reversed path
Example:
>>> p1 = 'path/to/somewhere'
>>> p2 = reverse('path/to/somewhere')
>>> p2
'../../..'
>>> os.path.normpath(os.path.join(p1, p2))
'.'
"""
if is_rooted(path) or '..' in path:
from b2.manager import get_manager
get_manager().errors()(
'reverse(path): path is either rooted or contains ".." in the path')
if path == '.':
return path
path = os.path.normpath(path)
# os.sep.join() is being used over os.path.join() due
# to an extra '..' that is created by os.path.join()
return os.sep.join('..' for t in path.split(os.sep)) | python | def reverse(path):
"""Returns path2 such that `os.path.join(path, path2) == '.'`.
`path` may not contain '..' or be rooted.
Args:
path (str): the path to reverse
Returns:
the string of the reversed path
Example:
>>> p1 = 'path/to/somewhere'
>>> p2 = reverse('path/to/somewhere')
>>> p2
'../../..'
>>> os.path.normpath(os.path.join(p1, p2))
'.'
"""
if is_rooted(path) or '..' in path:
from b2.manager import get_manager
get_manager().errors()(
'reverse(path): path is either rooted or contains ".." in the path')
if path == '.':
return path
path = os.path.normpath(path)
# os.sep.join() is being used over os.path.join() due
# to an extra '..' that is created by os.path.join()
return os.sep.join('..' for t in path.split(os.sep)) | [
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Example:
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/path.py | glob | def glob (dirs, patterns):
""" Returns the list of files matching the given pattern in the
specified directory. Both directories and patterns are
supplied as portable paths. Each pattern should be non-absolute
path, and can't contain "." or ".." elements. Each slash separated
element of pattern can contain the following special characters:
- '?', which match any character
- '*', which matches arbitrary number of characters.
A file $(d)/e1/e2/e3 (where 'd' is in $(dirs)) matches pattern p1/p2/p3
if and only if e1 matches p1, e2 matches p2 and so on.
For example:
[ glob . : *.cpp ]
[ glob . : */build/Jamfile ]
"""
# {
# local result ;
# if $(patterns:D)
# {
# # When a pattern has a directory element, we first glob for
# # directory, and then glob for file name is the found directories.
# for local p in $(patterns)
# {
# # First glob for directory part.
# local globbed-dirs = [ glob $(dirs) : $(p:D) ] ;
# result += [ glob $(globbed-dirs) : $(p:D="") ] ;
# }
# }
# else
# {
# # When a pattern has not directory, we glob directly.
# # Take care of special ".." value. The "GLOB" rule simply ignores
# # the ".." element (and ".") element in directory listings. This is
# # needed so that
# #
# # [ glob libs/*/Jamfile ]
# #
# # don't return
# #
# # libs/../Jamfile (which is the same as ./Jamfile)
# #
# # On the other hand, when ".." is explicitly present in the pattern
# # we need to return it.
# #
# for local dir in $(dirs)
# {
# for local p in $(patterns)
# {
# if $(p) != ".."
# {
# result += [ sequence.transform make
# : [ GLOB [ native $(dir) ] : $(p) ] ] ;
# }
# else
# {
# result += [ path.join $(dir) .. ] ;
# }
# }
# }
# }
# return $(result) ;
# }
#
# TODO: (PF) I replaced the code above by this. I think it should work but needs to be tested.
result = []
dirs = to_seq (dirs)
patterns = to_seq (patterns)
splitdirs = []
for dir in dirs:
splitdirs += dir.split (os.pathsep)
for dir in splitdirs:
for pattern in patterns:
p = os.path.join (dir, pattern)
import glob
result.extend (glob.glob (p))
return result | python | def glob (dirs, patterns):
""" Returns the list of files matching the given pattern in the
specified directory. Both directories and patterns are
supplied as portable paths. Each pattern should be non-absolute
path, and can't contain "." or ".." elements. Each slash separated
element of pattern can contain the following special characters:
- '?', which match any character
- '*', which matches arbitrary number of characters.
A file $(d)/e1/e2/e3 (where 'd' is in $(dirs)) matches pattern p1/p2/p3
if and only if e1 matches p1, e2 matches p2 and so on.
For example:
[ glob . : *.cpp ]
[ glob . : */build/Jamfile ]
"""
# {
# local result ;
# if $(patterns:D)
# {
# # When a pattern has a directory element, we first glob for
# # directory, and then glob for file name is the found directories.
# for local p in $(patterns)
# {
# # First glob for directory part.
# local globbed-dirs = [ glob $(dirs) : $(p:D) ] ;
# result += [ glob $(globbed-dirs) : $(p:D="") ] ;
# }
# }
# else
# {
# # When a pattern has not directory, we glob directly.
# # Take care of special ".." value. The "GLOB" rule simply ignores
# # the ".." element (and ".") element in directory listings. This is
# # needed so that
# #
# # [ glob libs/*/Jamfile ]
# #
# # don't return
# #
# # libs/../Jamfile (which is the same as ./Jamfile)
# #
# # On the other hand, when ".." is explicitly present in the pattern
# # we need to return it.
# #
# for local dir in $(dirs)
# {
# for local p in $(patterns)
# {
# if $(p) != ".."
# {
# result += [ sequence.transform make
# : [ GLOB [ native $(dir) ] : $(p) ] ] ;
# }
# else
# {
# result += [ path.join $(dir) .. ] ;
# }
# }
# }
# }
# return $(result) ;
# }
#
# TODO: (PF) I replaced the code above by this. I think it should work but needs to be tested.
result = []
dirs = to_seq (dirs)
patterns = to_seq (patterns)
splitdirs = []
for dir in dirs:
splitdirs += dir.split (os.pathsep)
for dir in splitdirs:
for pattern in patterns:
p = os.path.join (dir, pattern)
import glob
result.extend (glob.glob (p))
return result | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/path.py | glob | def glob(dirs, patterns, exclude_patterns=None):
"""Returns the list of files matching the given pattern in the
specified directory. Both directories and patterns are
supplied as portable paths. Each pattern should be non-absolute
path, and can't contain '.' or '..' elements. Each slash separated
element of pattern can contain the following special characters:
- '?', which match any character
- '*', which matches arbitrary number of characters.
A file $(d)/e1/e2/e3 (where 'd' is in $(dirs)) matches pattern p1/p2/p3
if and only if e1 matches p1, e2 matches p2 and so on.
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[ glob . : *.cpp ]
[ glob . : */build/Jamfile ]
"""
assert(isinstance(patterns, list))
assert(isinstance(dirs, list))
if not exclude_patterns:
exclude_patterns = []
else:
assert(isinstance(exclude_patterns, list))
real_patterns = [os.path.join(d, p) for p in patterns for d in dirs]
real_exclude_patterns = [os.path.join(d, p) for p in exclude_patterns
for d in dirs]
inc = [os.path.normpath(name) for p in real_patterns
for name in builtin_glob(p)]
exc = [os.path.normpath(name) for p in real_exclude_patterns
for name in builtin_glob(p)]
return [x for x in inc if x not in exc] | python | def glob(dirs, patterns, exclude_patterns=None):
"""Returns the list of files matching the given pattern in the
specified directory. Both directories and patterns are
supplied as portable paths. Each pattern should be non-absolute
path, and can't contain '.' or '..' elements. Each slash separated
element of pattern can contain the following special characters:
- '?', which match any character
- '*', which matches arbitrary number of characters.
A file $(d)/e1/e2/e3 (where 'd' is in $(dirs)) matches pattern p1/p2/p3
if and only if e1 matches p1, e2 matches p2 and so on.
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[ glob . : *.cpp ]
[ glob . : */build/Jamfile ]
"""
assert(isinstance(patterns, list))
assert(isinstance(dirs, list))
if not exclude_patterns:
exclude_patterns = []
else:
assert(isinstance(exclude_patterns, list))
real_patterns = [os.path.join(d, p) for p in patterns for d in dirs]
real_exclude_patterns = [os.path.join(d, p) for p in exclude_patterns
for d in dirs]
inc = [os.path.normpath(name) for p in real_patterns
for name in builtin_glob(p)]
exc = [os.path.normpath(name) for p in real_exclude_patterns
for name in builtin_glob(p)]
return [x for x in inc if x not in exc] | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/path.py | glob_tree | def glob_tree(roots, patterns, exclude_patterns=None):
"""Recursive version of GLOB. Builds the glob of files while
also searching in the subdirectories of the given roots. An
optional set of exclusion patterns will filter out the
matching entries from the result. The exclusions also apply
to the subdirectory scanning, such that directories that
match the exclusion patterns will not be searched."""
if not exclude_patterns:
exclude_patterns = []
result = glob(roots, patterns, exclude_patterns)
subdirs = [s for s in glob(roots, ["*"], exclude_patterns) if s != "." and s != ".." and os.path.isdir(s)]
if subdirs:
result.extend(glob_tree(subdirs, patterns, exclude_patterns))
return result | python | def glob_tree(roots, patterns, exclude_patterns=None):
"""Recursive version of GLOB. Builds the glob of files while
also searching in the subdirectories of the given roots. An
optional set of exclusion patterns will filter out the
matching entries from the result. The exclusions also apply
to the subdirectory scanning, such that directories that
match the exclusion patterns will not be searched."""
if not exclude_patterns:
exclude_patterns = []
result = glob(roots, patterns, exclude_patterns)
subdirs = [s for s in glob(roots, ["*"], exclude_patterns) if s != "." and s != ".." and os.path.isdir(s)]
if subdirs:
result.extend(glob_tree(subdirs, patterns, exclude_patterns))
return result | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/util/path.py | glob_in_parents | def glob_in_parents(dir, patterns, upper_limit=None):
"""Recursive version of GLOB which glob sall parent directories
of dir until the first match is found. Returns an empty result if no match
is found"""
assert(isinstance(dir, str))
assert(isinstance(patterns, list))
result = []
absolute_dir = os.path.join(os.getcwd(), dir)
absolute_dir = os.path.normpath(absolute_dir)
while absolute_dir:
new_dir = os.path.split(absolute_dir)[0]
if new_dir == absolute_dir:
break
result = glob([new_dir], patterns)
if result:
break
absolute_dir = new_dir
return result | python | def glob_in_parents(dir, patterns, upper_limit=None):
"""Recursive version of GLOB which glob sall parent directories
of dir until the first match is found. Returns an empty result if no match
is found"""
assert(isinstance(dir, str))
assert(isinstance(patterns, list))
result = []
absolute_dir = os.path.join(os.getcwd(), dir)
absolute_dir = os.path.normpath(absolute_dir)
while absolute_dir:
new_dir = os.path.split(absolute_dir)[0]
if new_dir == absolute_dir:
break
result = glob([new_dir], patterns)
if result:
break
absolute_dir = new_dir
return result | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _wrap_function_return | def _wrap_function_return(val):
"""
Recursively walks each thing in val, opening lists and dictionaries,
converting all occurrences of UnityGraphProxy to an SGraph,
UnitySFrameProxy to SFrame, and UnitySArrayProxy to SArray.
"""
if type(val) is _UnityGraphProxy:
return _SGraph(_proxy = val)
elif type(val) is _UnitySFrameProxy:
return _SFrame(_proxy = val)
elif type(val) is _UnitySArrayProxy:
return _SArray(_proxy = val)
elif type(val) is _UnityModel:
# we need to cast it up to the appropriate type
uid = val.get_uid()
if uid in class_uid_to_class:
return class_uid_to_class[uid](_proxy=val)
else:
return val
elif type(val) is list:
return [_wrap_function_return(i) for i in val]
elif type(val) is dict:
return dict( (i, _wrap_function_return(val[i])) for i in val)
else:
return val | python | def _wrap_function_return(val):
"""
Recursively walks each thing in val, opening lists and dictionaries,
converting all occurrences of UnityGraphProxy to an SGraph,
UnitySFrameProxy to SFrame, and UnitySArrayProxy to SArray.
"""
if type(val) is _UnityGraphProxy:
return _SGraph(_proxy = val)
elif type(val) is _UnitySFrameProxy:
return _SFrame(_proxy = val)
elif type(val) is _UnitySArrayProxy:
return _SArray(_proxy = val)
elif type(val) is _UnityModel:
# we need to cast it up to the appropriate type
uid = val.get_uid()
if uid in class_uid_to_class:
return class_uid_to_class[uid](_proxy=val)
else:
return val
elif type(val) is list:
return [_wrap_function_return(i) for i in val]
elif type(val) is dict:
return dict( (i, _wrap_function_return(val[i])) for i in val)
else:
return val | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _setattr_wrapper | def _setattr_wrapper(mod, key, value):
"""
A setattr wrapper call used only by _publish(). This ensures that anything
published into this module is also published into tc.extensions
"""
setattr(mod, key, value)
if mod == _thismodule:
setattr(_sys.modules[__name__], key, value) | python | def _setattr_wrapper(mod, key, value):
"""
A setattr wrapper call used only by _publish(). This ensures that anything
published into this module is also published into tc.extensions
"""
setattr(mod, key, value)
if mod == _thismodule:
setattr(_sys.modules[__name__], key, value) | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _run_toolkit_function | def _run_toolkit_function(fnname, arguments, args, kwargs):
"""
Dispatches arguments to a toolkit function.
Parameters
----------
fnname : string
The toolkit function to run
arguments : list[string]
The list of all the arguments the function takes.
args : list
The arguments that were passed
kwargs : dictionary
The keyword arguments that were passed
"""
# scan for all the arguments in args
num_args_got = len(args) + len(kwargs)
num_args_required = len(arguments)
if num_args_got != num_args_required:
raise TypeError("Expecting " + str(num_args_required) + " arguments, got " + str(num_args_got))
## fill the dict first with the regular args
argument_dict = {}
for i in range(len(args)):
argument_dict[arguments[i]] = args[i]
# now fill with the kwargs.
for k in kwargs.keys():
if k in argument_dict:
raise TypeError("Got multiple values for keyword argument '" + k + "'")
argument_dict[k] = kwargs[k]
# unwrap it
with cython_context():
ret = _get_unity().run_toolkit(fnname, argument_dict)
# handle errors
if not ret[0]:
if len(ret[1]) > 0:
raise _ToolkitError(ret[1])
else:
raise _ToolkitError("Toolkit failed with unknown error")
ret = _wrap_function_return(ret[2])
if type(ret) is dict and 'return_value' in ret:
return ret['return_value']
else:
return ret | python | def _run_toolkit_function(fnname, arguments, args, kwargs):
"""
Dispatches arguments to a toolkit function.
Parameters
----------
fnname : string
The toolkit function to run
arguments : list[string]
The list of all the arguments the function takes.
args : list
The arguments that were passed
kwargs : dictionary
The keyword arguments that were passed
"""
# scan for all the arguments in args
num_args_got = len(args) + len(kwargs)
num_args_required = len(arguments)
if num_args_got != num_args_required:
raise TypeError("Expecting " + str(num_args_required) + " arguments, got " + str(num_args_got))
## fill the dict first with the regular args
argument_dict = {}
for i in range(len(args)):
argument_dict[arguments[i]] = args[i]
# now fill with the kwargs.
for k in kwargs.keys():
if k in argument_dict:
raise TypeError("Got multiple values for keyword argument '" + k + "'")
argument_dict[k] = kwargs[k]
# unwrap it
with cython_context():
ret = _get_unity().run_toolkit(fnname, argument_dict)
# handle errors
if not ret[0]:
if len(ret[1]) > 0:
raise _ToolkitError(ret[1])
else:
raise _ToolkitError("Toolkit failed with unknown error")
ret = _wrap_function_return(ret[2])
if type(ret) is dict and 'return_value' in ret:
return ret['return_value']
else:
return ret | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _class_instance_from_name | def _class_instance_from_name(class_name, *arg, **kwarg):
"""
class_name is of the form modA.modB.modC.class module_path splits on "."
and the import_path is then ['modA','modB','modC'] the __import__ call is
really annoying but essentially it reads like:
import class from modA.modB.modC
- Then the module variable points to modC
- Then you get the class from the module.
"""
# we first look in tc.extensions for the class name
module_path = class_name.split('.')
import_path = module_path[0:-1]
module = __import__('.'.join(import_path), fromlist=[module_path[-1]])
class_ = getattr(module, module_path[-1])
instance = class_(*arg, **kwarg)
return instance | python | def _class_instance_from_name(class_name, *arg, **kwarg):
"""
class_name is of the form modA.modB.modC.class module_path splits on "."
and the import_path is then ['modA','modB','modC'] the __import__ call is
really annoying but essentially it reads like:
import class from modA.modB.modC
- Then the module variable points to modC
- Then you get the class from the module.
"""
# we first look in tc.extensions for the class name
module_path = class_name.split('.')
import_path = module_path[0:-1]
module = __import__('.'.join(import_path), fromlist=[module_path[-1]])
class_ = getattr(module, module_path[-1])
instance = class_(*arg, **kwarg)
return instance | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _create_class_instance | def _create_class_instance(class_name, _proxy):
"""
Look for the class in .extensions in case it has already been
imported (perhaps as a builtin extensions hard compiled into unity_server).
"""
try:
return _class_instance_from_name('turicreate.extensions.' + class_name, _proxy=_proxy)
except:
pass
return _class_instance_from_name(class_name, _proxy=_proxy) | python | def _create_class_instance(class_name, _proxy):
"""
Look for the class in .extensions in case it has already been
imported (perhaps as a builtin extensions hard compiled into unity_server).
"""
try:
return _class_instance_from_name('turicreate.extensions.' + class_name, _proxy=_proxy)
except:
pass
return _class_instance_from_name(class_name, _proxy=_proxy) | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _publish | def _publish():
import copy
"""
Publishes all functions and classes registered in unity_server.
The functions and classes will appear in the module turicreate.extensions
"""
unity = _get_unity()
fnlist = unity.list_toolkit_functions()
# Loop through all the functions and inject it into
# turicreate.extensions.[blah]
# Note that [blah] may be somemodule.somefunction
# and so the injection has to be
# turicreate.extensions.somemodule.somefunction
for fn in fnlist:
props = unity.describe_toolkit_function(fn)
# quit if there is nothing we can process
if 'arguments' not in props:
continue
arguments = props['arguments']
newfunc = _make_injected_function(fn, arguments)
newfunc.__doc__ = "Name: " + fn + "\nParameters: " + str(arguments) + "\n"
if 'documentation' in props:
newfunc.__doc__ += props['documentation'] + "\n"
newfunc.__dict__['__glmeta__'] = {'extension_name':fn}
modpath = fn.split('.')
# walk the module tree
mod = _thismodule
for path in modpath[:-1]:
try:
getattr(mod, path)
except:
_setattr_wrapper(mod, path, _types.ModuleType(name=path))
mod = getattr(mod, path)
_setattr_wrapper(mod, modpath[-1], newfunc)
# Repeat for classes
tkclasslist = unity.list_toolkit_classes()
for tkclass in tkclasslist:
m = unity.describe_toolkit_class(tkclass)
# of v2 type
if not ('functions' in m and 'get_properties' in m and 'set_properties' in m and 'uid' in m):
continue
# create a new class
if _version_info.major == 3:
new_class = _ToolkitClass.__dict__.copy()
del new_class['__dict__']
del new_class['__weakref__']
else:
new_class = copy.deepcopy(_ToolkitClass.__dict__)
new_class['__init__'] = _types.FunctionType(new_class['__init__'].__code__,
new_class['__init__'].__globals__,
name='__init__',
argdefs=(),
closure=())
# rewrite the init method to add the toolkit class name so it will
# default construct correctly
new_class['__init__'].tkclass_name = tkclass
newclass = _class_type(tkclass, (), new_class)
setattr(newclass, '__glmeta__', {'extension_name':tkclass})
class_uid_to_class[m['uid']] = newclass
modpath = tkclass.split('.')
# walk the module tree
mod = _thismodule
for path in modpath[:-1]:
try:
getattr(mod, path)
except:
_setattr_wrapper(mod, path, _types.ModuleType(name=path))
mod = getattr(mod, path)
_setattr_wrapper(mod, modpath[-1], newclass) | python | def _publish():
import copy
"""
Publishes all functions and classes registered in unity_server.
The functions and classes will appear in the module turicreate.extensions
"""
unity = _get_unity()
fnlist = unity.list_toolkit_functions()
# Loop through all the functions and inject it into
# turicreate.extensions.[blah]
# Note that [blah] may be somemodule.somefunction
# and so the injection has to be
# turicreate.extensions.somemodule.somefunction
for fn in fnlist:
props = unity.describe_toolkit_function(fn)
# quit if there is nothing we can process
if 'arguments' not in props:
continue
arguments = props['arguments']
newfunc = _make_injected_function(fn, arguments)
newfunc.__doc__ = "Name: " + fn + "\nParameters: " + str(arguments) + "\n"
if 'documentation' in props:
newfunc.__doc__ += props['documentation'] + "\n"
newfunc.__dict__['__glmeta__'] = {'extension_name':fn}
modpath = fn.split('.')
# walk the module tree
mod = _thismodule
for path in modpath[:-1]:
try:
getattr(mod, path)
except:
_setattr_wrapper(mod, path, _types.ModuleType(name=path))
mod = getattr(mod, path)
_setattr_wrapper(mod, modpath[-1], newfunc)
# Repeat for classes
tkclasslist = unity.list_toolkit_classes()
for tkclass in tkclasslist:
m = unity.describe_toolkit_class(tkclass)
# of v2 type
if not ('functions' in m and 'get_properties' in m and 'set_properties' in m and 'uid' in m):
continue
# create a new class
if _version_info.major == 3:
new_class = _ToolkitClass.__dict__.copy()
del new_class['__dict__']
del new_class['__weakref__']
else:
new_class = copy.deepcopy(_ToolkitClass.__dict__)
new_class['__init__'] = _types.FunctionType(new_class['__init__'].__code__,
new_class['__init__'].__globals__,
name='__init__',
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# rewrite the init method to add the toolkit class name so it will
# default construct correctly
new_class['__init__'].tkclass_name = tkclass
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setattr(newclass, '__glmeta__', {'extension_name':tkclass})
class_uid_to_class[m['uid']] = newclass
modpath = tkclass.split('.')
# walk the module tree
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for path in modpath[:-1]:
try:
getattr(mod, path)
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_setattr_wrapper(mod, path, _types.ModuleType(name=path))
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apple/turicreate | src/unity/python/turicreate/extensions.py | ext_import | def ext_import(soname, module_subpath=""):
"""
Loads a turicreate toolkit module (a shared library) into the
tc.extensions namespace.
Toolkit module created via SDK can either be directly imported,
e.g. ``import example`` or via this function, e.g. ``turicreate.ext_import("example.so")``.
Use ``ext_import`` when you need more namespace control, or when
the shared library is not local, e.g. in http, s3 or hdfs.
Parameters
----------
soname : string
The filename of the shared library to load.
This can be a URL, or a HDFS location. For instance if soname is
somewhere/outthere/toolkit.so
The functions in toolkit.so will appear in tc.extensions.toolkit.*
module_subpath : string, optional
Any additional module paths to prepend to the toolkit module after
it is imported. For instance if soname is
somewhere/outthere/toolkit.so, by default
the functions in toolkit.so will appear in tc.extensions.toolkit.*.
However, if I module_subpath="somewhere.outthere", the functions
in toolkit.so will appear in tc.extensions.somewhere.outthere.toolkit.*
Returns
-------
out : a list of functions and classes loaded.
Examples
--------
For instance, given a module which implements the function "square_root",
.. code-block:: c++
#include <cmath>
#include <turicreate/sdk/toolkit_function_macros.hpp>
double square_root(double a) {
return sqrt(a);
}
BEGIN_FUNCTION_REGISTRATION
REGISTER_FUNCTION(square_root, "a");
END_FUNCTION_REGISTRATION
compiled into example.so
>>> turicreate.ext_import('example1.so')
['example1.square_root']
>>> turicreate.extensions.example1.square_root(9)
3.0
We can customize the import location with module_subpath which can be
used to avoid namespace conflicts when you have multiple toolkits with the
same filename.
>>> turicreate.ext_import('example1.so', 'math')
['math.example1.square_root']
>>> turicreate.extensions.math.example1.square_root(9)
3.0
The module can also be imported directly, but turicreate *must* be imported
first. turicreate will intercept the module loading process to load the
toolkit.
>>> import turicreate
>>> import example1 #searches for example1.so in all the python paths
>>> example1.square_root(9)
3.0
"""
unity = _get_unity()
import os
if os.path.exists(soname):
soname = os.path.abspath(soname)
else:
soname = _make_internal_url(soname)
ret = unity.load_toolkit(soname, module_subpath)
if len(ret) > 0:
raise RuntimeError(ret)
_publish()
# push the functions into the corresponding module namespace
return unity.list_toolkit_functions_in_dynamic_module(soname) + unity.list_toolkit_classes_in_dynamic_module(soname) | python | def ext_import(soname, module_subpath=""):
"""
Loads a turicreate toolkit module (a shared library) into the
tc.extensions namespace.
Toolkit module created via SDK can either be directly imported,
e.g. ``import example`` or via this function, e.g. ``turicreate.ext_import("example.so")``.
Use ``ext_import`` when you need more namespace control, or when
the shared library is not local, e.g. in http, s3 or hdfs.
Parameters
----------
soname : string
The filename of the shared library to load.
This can be a URL, or a HDFS location. For instance if soname is
somewhere/outthere/toolkit.so
The functions in toolkit.so will appear in tc.extensions.toolkit.*
module_subpath : string, optional
Any additional module paths to prepend to the toolkit module after
it is imported. For instance if soname is
somewhere/outthere/toolkit.so, by default
the functions in toolkit.so will appear in tc.extensions.toolkit.*.
However, if I module_subpath="somewhere.outthere", the functions
in toolkit.so will appear in tc.extensions.somewhere.outthere.toolkit.*
Returns
-------
out : a list of functions and classes loaded.
Examples
--------
For instance, given a module which implements the function "square_root",
.. code-block:: c++
#include <cmath>
#include <turicreate/sdk/toolkit_function_macros.hpp>
double square_root(double a) {
return sqrt(a);
}
BEGIN_FUNCTION_REGISTRATION
REGISTER_FUNCTION(square_root, "a");
END_FUNCTION_REGISTRATION
compiled into example.so
>>> turicreate.ext_import('example1.so')
['example1.square_root']
>>> turicreate.extensions.example1.square_root(9)
3.0
We can customize the import location with module_subpath which can be
used to avoid namespace conflicts when you have multiple toolkits with the
same filename.
>>> turicreate.ext_import('example1.so', 'math')
['math.example1.square_root']
>>> turicreate.extensions.math.example1.square_root(9)
3.0
The module can also be imported directly, but turicreate *must* be imported
first. turicreate will intercept the module loading process to load the
toolkit.
>>> import turicreate
>>> import example1 #searches for example1.so in all the python paths
>>> example1.square_root(9)
3.0
"""
unity = _get_unity()
import os
if os.path.exists(soname):
soname = os.path.abspath(soname)
else:
soname = _make_internal_url(soname)
ret = unity.load_toolkit(soname, module_subpath)
if len(ret) > 0:
raise RuntimeError(ret)
_publish()
# push the functions into the corresponding module namespace
return unity.list_toolkit_functions_in_dynamic_module(soname) + unity.list_toolkit_classes_in_dynamic_module(soname) | [
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The functions in toolkit.so will appear in tc.extensions.toolkit.*
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Returns
-------
out : a list of functions and classes loaded.
Examples
--------
For instance, given a module which implements the function "square_root",
.. code-block:: c++
#include <cmath>
#include <turicreate/sdk/toolkit_function_macros.hpp>
double square_root(double a) {
return sqrt(a);
}
BEGIN_FUNCTION_REGISTRATION
REGISTER_FUNCTION(square_root, "a");
END_FUNCTION_REGISTRATION
compiled into example.so
>>> turicreate.ext_import('example1.so')
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>>> turicreate.extensions.example1.square_root(9)
3.0
We can customize the import location with module_subpath which can be
used to avoid namespace conflicts when you have multiple toolkits with the
same filename.
>>> turicreate.ext_import('example1.so', 'math')
['math.example1.square_root']
>>> turicreate.extensions.math.example1.square_root(9)
3.0
The module can also be imported directly, but turicreate *must* be imported
first. turicreate will intercept the module loading process to load the
toolkit.
>>> import turicreate
>>> import example1 #searches for example1.so in all the python paths
>>> example1.square_root(9)
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apple/turicreate | src/unity/python/turicreate/extensions.py | _get_argument_list_from_toolkit_function_name | def _get_argument_list_from_toolkit_function_name(fn):
"""
Given a toolkit function name, return the argument list
"""
unity = _get_unity()
fnprops = unity.describe_toolkit_function(fn)
argnames = fnprops['arguments']
return argnames | python | def _get_argument_list_from_toolkit_function_name(fn):
"""
Given a toolkit function name, return the argument list
"""
unity = _get_unity()
fnprops = unity.describe_toolkit_function(fn)
argnames = fnprops['arguments']
return argnames | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _descend_namespace | def _descend_namespace(caller_globals, name):
"""
Given a globals dictionary, and a name of the form "a.b.c.d", recursively
walk the globals expanding caller_globals['a']['b']['c']['d'] returning
the result. Raises an exception (IndexError) on failure.
"""
names = name.split('.')
cur = caller_globals
for i in names:
if type(cur) is dict:
cur = cur[i]
else:
cur = getattr(cur, i)
return cur | python | def _descend_namespace(caller_globals, name):
"""
Given a globals dictionary, and a name of the form "a.b.c.d", recursively
walk the globals expanding caller_globals['a']['b']['c']['d'] returning
the result. Raises an exception (IndexError) on failure.
"""
names = name.split('.')
cur = caller_globals
for i in names:
if type(cur) is dict:
cur = cur[i]
else:
cur = getattr(cur, i)
return cur | [
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apple/turicreate | src/unity/python/turicreate/extensions.py | _build_native_function_call | def _build_native_function_call(fn):
"""
If fn can be interpreted and handled as a native function: i.e.
fn is one of the extensions, or fn is a simple lambda closure using one of
the extensions.
fn = tc.extensions.add
fn = lambda x: tc.extensions.add(5)
Then, this returns a closure object, which describes the function call
which can then be passed to C++.
Returns a _Closure object on success, raises an exception on failure.
"""
# See if fn is the native function itself
native_function_name = _get_toolkit_function_name_from_function(fn)
if native_function_name != "":
# yup!
# generate an "identity" argument list
argnames = _get_argument_list_from_toolkit_function_name(native_function_name)
arglist = [[0, i] for i in range(len(argnames))]
return _Closure(native_function_name, arglist)
# ok. its not a native function
from .util.lambda_closure_capture import translate
from .util.lambda_closure_capture import Parameter
# Lets see if it is a simple lambda
capture = translate(fn)
# ok. build up the closure arguments
# Try to pick up the lambda
function = _descend_namespace(capture.caller_globals, capture.closure_fn_name)
native_function_name = _get_toolkit_function_name_from_function(function)
if native_function_name == "":
raise RuntimeError("Lambda does not contain a native function")
argnames = _get_argument_list_from_toolkit_function_name(native_function_name)
# ok. build up the argument list. this is mildly annoying due to the mix of
# positional and named arguments
# make an argument list with a placeholder for everything first
arglist = [[-1, i] for i in argnames]
# loop through the positional arguments
for i in range(len(capture.positional_args)):
arg = capture.positional_args[i]
if type(arg) is Parameter:
# This is a lambda argument
# arg.name is the actual string of the argument
# here we need the index
arglist[i] = [0, capture.input_arg_names.index(arg.name)]
else:
# this is a captured value
arglist[i] = [1, arg]
# now. the named arguments are somewhat annoying
for i in capture.named_args:
arg = capture.named_args[i]
if type(arg) is Parameter:
# This is a lambda argument
# arg.name is the actual string of the argument
# here we need the index
arglist[argnames.index(i)] = [0, capture.input_arg_names.index(arg.name)]
else:
# this is a captured value
arglist[argnames.index(i)] = [1, arg]
# done. Make sure all arguments are filled
for i in arglist:
if i[0] == -1:
raise RuntimeError("Incomplete function specification")
# attempt to recursively break down any other functions
import inspect
for i in range(len(arglist)):
if arglist[i][0] == 1 and inspect.isfunction(arglist[i][1]):
try:
arglist[i][1] = _build_native_function_call(arglist[i][1])
except:
pass
return _Closure(native_function_name, arglist) | python | def _build_native_function_call(fn):
"""
If fn can be interpreted and handled as a native function: i.e.
fn is one of the extensions, or fn is a simple lambda closure using one of
the extensions.
fn = tc.extensions.add
fn = lambda x: tc.extensions.add(5)
Then, this returns a closure object, which describes the function call
which can then be passed to C++.
Returns a _Closure object on success, raises an exception on failure.
"""
# See if fn is the native function itself
native_function_name = _get_toolkit_function_name_from_function(fn)
if native_function_name != "":
# yup!
# generate an "identity" argument list
argnames = _get_argument_list_from_toolkit_function_name(native_function_name)
arglist = [[0, i] for i in range(len(argnames))]
return _Closure(native_function_name, arglist)
# ok. its not a native function
from .util.lambda_closure_capture import translate
from .util.lambda_closure_capture import Parameter
# Lets see if it is a simple lambda
capture = translate(fn)
# ok. build up the closure arguments
# Try to pick up the lambda
function = _descend_namespace(capture.caller_globals, capture.closure_fn_name)
native_function_name = _get_toolkit_function_name_from_function(function)
if native_function_name == "":
raise RuntimeError("Lambda does not contain a native function")
argnames = _get_argument_list_from_toolkit_function_name(native_function_name)
# ok. build up the argument list. this is mildly annoying due to the mix of
# positional and named arguments
# make an argument list with a placeholder for everything first
arglist = [[-1, i] for i in argnames]
# loop through the positional arguments
for i in range(len(capture.positional_args)):
arg = capture.positional_args[i]
if type(arg) is Parameter:
# This is a lambda argument
# arg.name is the actual string of the argument
# here we need the index
arglist[i] = [0, capture.input_arg_names.index(arg.name)]
else:
# this is a captured value
arglist[i] = [1, arg]
# now. the named arguments are somewhat annoying
for i in capture.named_args:
arg = capture.named_args[i]
if type(arg) is Parameter:
# This is a lambda argument
# arg.name is the actual string of the argument
# here we need the index
arglist[argnames.index(i)] = [0, capture.input_arg_names.index(arg.name)]
else:
# this is a captured value
arglist[argnames.index(i)] = [1, arg]
# done. Make sure all arguments are filled
for i in arglist:
if i[0] == -1:
raise RuntimeError("Incomplete function specification")
# attempt to recursively break down any other functions
import inspect
for i in range(len(arglist)):
if arglist[i][0] == 1 and inspect.isfunction(arglist[i][1]):
try:
arglist[i][1] = _build_native_function_call(arglist[i][1])
except:
pass
return _Closure(native_function_name, arglist) | [
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fn = lambda x: tc.extensions.add(5)
Then, this returns a closure object, which describes the function call
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apple/turicreate | src/unity/python/turicreate/extensions.py | _ExtMetaPath.find_module | def find_module(self, fullname, submodule_path=None):
"""
We have to see if fullname refers to a module we can import.
Some care is needed here because:
import xxx # tries to load xxx.so from any of the python import paths
import aaa.bbb.xxx # tries to load aaa/bbb/xxx.so from any of the python import paths
"""
# first see if we have this particular so has been loaded by
# turicreate's extension library before
ret = self.try_find_module(fullname, submodule_path)
if ret is not None:
return ret
# nope. has not been loaded before
# lets try to find a ".so" or a ".dylib" if any of the python
# locations
import sys
import os
# This drops the last "." So if I am importing aaa.bbb.xxx
# module_subpath is aaa.bbb
module_subpath = ".".join(fullname.split('.')[:-1])
for path in sys.path:
# joins the path to aaa/bbb/xxx
pathname = os.path.join(path, os.sep.join(fullname.split('.')))
# try to laod the ".so" extension
try:
if os.path.exists(pathname + '.so'):
ext_import(pathname + '.so', module_subpath)
break
except:
pass
# try to laod the ".dylib" extension
try:
if os.path.exists(pathname + '.dylib'):
ext_import(pathname + '.dylib', module_subpath)
break
except:
pass
ret = self.try_find_module(fullname, submodule_path)
if ret is not None:
return ret | python | def find_module(self, fullname, submodule_path=None):
"""
We have to see if fullname refers to a module we can import.
Some care is needed here because:
import xxx # tries to load xxx.so from any of the python import paths
import aaa.bbb.xxx # tries to load aaa/bbb/xxx.so from any of the python import paths
"""
# first see if we have this particular so has been loaded by
# turicreate's extension library before
ret = self.try_find_module(fullname, submodule_path)
if ret is not None:
return ret
# nope. has not been loaded before
# lets try to find a ".so" or a ".dylib" if any of the python
# locations
import sys
import os
# This drops the last "." So if I am importing aaa.bbb.xxx
# module_subpath is aaa.bbb
module_subpath = ".".join(fullname.split('.')[:-1])
for path in sys.path:
# joins the path to aaa/bbb/xxx
pathname = os.path.join(path, os.sep.join(fullname.split('.')))
# try to laod the ".so" extension
try:
if os.path.exists(pathname + '.so'):
ext_import(pathname + '.so', module_subpath)
break
except:
pass
# try to laod the ".dylib" extension
try:
if os.path.exists(pathname + '.dylib'):
ext_import(pathname + '.dylib', module_subpath)
break
except:
pass
ret = self.try_find_module(fullname, submodule_path)
if ret is not None:
return ret | [
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Some care is needed here because:
import xxx # tries to load xxx.so from any of the python import paths
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apple/turicreate | src/unity/python/doc/source/sphinx_turicreate_ext/pycon.py | main | def main():
"""
Print lines of input along with output.
"""
source_lines = (line.rstrip() for line in sys.stdin)
console = InteractiveInterpreter()
console.runsource('import turicreate')
source = ''
try:
while True:
source = source_lines.next()
more = console.runsource(source)
while more:
next_line = source_lines.next()
print '...', next_line
source += '\n' + next_line
more = console.runsource(source)
except StopIteration:
if more:
print '... '
more = console.runsource(source + '\n') | python | def main():
"""
Print lines of input along with output.
"""
source_lines = (line.rstrip() for line in sys.stdin)
console = InteractiveInterpreter()
console.runsource('import turicreate')
source = ''
try:
while True:
source = source_lines.next()
more = console.runsource(source)
while more:
next_line = source_lines.next()
print '...', next_line
source += '\n' + next_line
more = console.runsource(source)
except StopIteration:
if more:
print '... '
more = console.runsource(source + '\n') | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/type_checkers.py | GetTypeChecker | def GetTypeChecker(field):
"""Returns a type checker for a message field of the specified types.
Args:
field: FieldDescriptor object for this field.
Returns:
An instance of TypeChecker which can be used to verify the types
of values assigned to a field of the specified type.
"""
if (field.cpp_type == _FieldDescriptor.CPPTYPE_STRING and
field.type == _FieldDescriptor.TYPE_STRING):
return UnicodeValueChecker()
if field.cpp_type == _FieldDescriptor.CPPTYPE_ENUM:
if SupportsOpenEnums(field):
# When open enums are supported, any int32 can be assigned.
return _VALUE_CHECKERS[_FieldDescriptor.CPPTYPE_INT32]
else:
return EnumValueChecker(field.enum_type)
return _VALUE_CHECKERS[field.cpp_type] | python | def GetTypeChecker(field):
"""Returns a type checker for a message field of the specified types.
Args:
field: FieldDescriptor object for this field.
Returns:
An instance of TypeChecker which can be used to verify the types
of values assigned to a field of the specified type.
"""
if (field.cpp_type == _FieldDescriptor.CPPTYPE_STRING and
field.type == _FieldDescriptor.TYPE_STRING):
return UnicodeValueChecker()
if field.cpp_type == _FieldDescriptor.CPPTYPE_ENUM:
if SupportsOpenEnums(field):
# When open enums are supported, any int32 can be assigned.
return _VALUE_CHECKERS[_FieldDescriptor.CPPTYPE_INT32]
else:
return EnumValueChecker(field.enum_type)
return _VALUE_CHECKERS[field.cpp_type] | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/type_checkers.py | TypeChecker.CheckValue | def CheckValue(self, proposed_value):
"""Type check the provided value and return it.
The returned value might have been normalized to another type.
"""
if not isinstance(proposed_value, self._acceptable_types):
message = ('%.1024r has type %s, but expected one of: %s' %
(proposed_value, type(proposed_value), self._acceptable_types))
raise TypeError(message)
return proposed_value | python | def CheckValue(self, proposed_value):
"""Type check the provided value and return it.
The returned value might have been normalized to another type.
"""
if not isinstance(proposed_value, self._acceptable_types):
message = ('%.1024r has type %s, but expected one of: %s' %
(proposed_value, type(proposed_value), self._acceptable_types))
raise TypeError(message)
return proposed_value | [
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apple/turicreate | src/unity/python/turicreate/meta/asttools/visitors/pysourcegen.py | python_source | def python_source(ast, file=sys.stdout):
'''
Generate executable python source code from an ast node.
:param ast: ast node
:param file: file to write output to.
'''
gen = SourceGen()
gen.visit(ast)
gen.dump(file) | python | def python_source(ast, file=sys.stdout):
'''
Generate executable python source code from an ast node.
:param ast: ast node
:param file: file to write output to.
'''
gen = SourceGen()
gen.visit(ast)
gen.dump(file) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/text_encoding.py | CEscape | def CEscape(text, as_utf8):
"""Escape a bytes string for use in an ascii protocol buffer.
text.encode('string_escape') does not seem to satisfy our needs as it
encodes unprintable characters using two-digit hex escapes whereas our
C++ unescaping function allows hex escapes to be any length. So,
"\0011".encode('string_escape') ends up being "\\x011", which will be
decoded in C++ as a single-character string with char code 0x11.
Args:
text: A byte string to be escaped
as_utf8: Specifies if result should be returned in UTF-8 encoding
Returns:
Escaped string
"""
# PY3 hack: make Ord work for str and bytes:
# //platforms/networking/data uses unicode here, hence basestring.
Ord = ord if isinstance(text, six.string_types) else lambda x: x
if as_utf8:
return ''.join(_cescape_utf8_to_str[Ord(c)] for c in text)
return ''.join(_cescape_byte_to_str[Ord(c)] for c in text) | python | def CEscape(text, as_utf8):
"""Escape a bytes string for use in an ascii protocol buffer.
text.encode('string_escape') does not seem to satisfy our needs as it
encodes unprintable characters using two-digit hex escapes whereas our
C++ unescaping function allows hex escapes to be any length. So,
"\0011".encode('string_escape') ends up being "\\x011", which will be
decoded in C++ as a single-character string with char code 0x11.
Args:
text: A byte string to be escaped
as_utf8: Specifies if result should be returned in UTF-8 encoding
Returns:
Escaped string
"""
# PY3 hack: make Ord work for str and bytes:
# //platforms/networking/data uses unicode here, hence basestring.
Ord = ord if isinstance(text, six.string_types) else lambda x: x
if as_utf8:
return ''.join(_cescape_utf8_to_str[Ord(c)] for c in text)
return ''.join(_cescape_byte_to_str[Ord(c)] for c in text) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/text_encoding.py | CUnescape | def CUnescape(text):
"""Unescape a text string with C-style escape sequences to UTF-8 bytes."""
def ReplaceHex(m):
# Only replace the match if the number of leading back slashes is odd. i.e.
# the slash itself is not escaped.
if len(m.group(1)) & 1:
return m.group(1) + 'x0' + m.group(2)
return m.group(0)
# This is required because the 'string_escape' encoding doesn't
# allow single-digit hex escapes (like '\xf').
result = _CUNESCAPE_HEX.sub(ReplaceHex, text)
if str is bytes: # PY2
return result.decode('string_escape')
result = ''.join(_cescape_highbit_to_str[ord(c)] for c in result)
return (result.encode('ascii') # Make it bytes to allow decode.
.decode('unicode_escape')
# Make it bytes again to return the proper type.
.encode('raw_unicode_escape')) | python | def CUnescape(text):
"""Unescape a text string with C-style escape sequences to UTF-8 bytes."""
def ReplaceHex(m):
# Only replace the match if the number of leading back slashes is odd. i.e.
# the slash itself is not escaped.
if len(m.group(1)) & 1:
return m.group(1) + 'x0' + m.group(2)
return m.group(0)
# This is required because the 'string_escape' encoding doesn't
# allow single-digit hex escapes (like '\xf').
result = _CUNESCAPE_HEX.sub(ReplaceHex, text)
if str is bytes: # PY2
return result.decode('string_escape')
result = ''.join(_cescape_highbit_to_str[ord(c)] for c in result)
return (result.encode('ascii') # Make it bytes to allow decode.
.decode('unicode_escape')
# Make it bytes again to return the proper type.
.encode('raw_unicode_escape')) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | reset | def reset ():
""" Clear the module state. This is mainly for testing purposes.
Note that this must be called _after_ resetting the module 'feature'.
"""
global __prefixes_suffixes, __suffixes_to_types, __types, __rule_names_to_types, __target_suffixes_cache
__register_features ()
# Stores suffixes for generated targets.
__prefixes_suffixes = [property.PropertyMap(), property.PropertyMap()]
# Maps suffixes to types
__suffixes_to_types = {}
# A map with all the registered types, indexed by the type name
# Each entry is a dictionary with following values:
# 'base': the name of base type or None if type has no base
# 'derived': a list of names of type which derive from this one
# 'scanner': the scanner class registered for this type, if any
__types = {}
# Caches suffixes for targets with certain properties.
__target_suffixes_cache = {} | python | def reset ():
""" Clear the module state. This is mainly for testing purposes.
Note that this must be called _after_ resetting the module 'feature'.
"""
global __prefixes_suffixes, __suffixes_to_types, __types, __rule_names_to_types, __target_suffixes_cache
__register_features ()
# Stores suffixes for generated targets.
__prefixes_suffixes = [property.PropertyMap(), property.PropertyMap()]
# Maps suffixes to types
__suffixes_to_types = {}
# A map with all the registered types, indexed by the type name
# Each entry is a dictionary with following values:
# 'base': the name of base type or None if type has no base
# 'derived': a list of names of type which derive from this one
# 'scanner': the scanner class registered for this type, if any
__types = {}
# Caches suffixes for targets with certain properties.
__target_suffixes_cache = {} | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | register | def register (type, suffixes = [], base_type = None):
""" Registers a target type, possibly derived from a 'base-type'.
If 'suffixes' are provided, they list all the suffixes that mean a file is of 'type'.
Also, the first element gives the suffix to be used when constructing and object of
'type'.
type: a string
suffixes: None or a sequence of strings
base_type: None or a string
"""
# Type names cannot contain hyphens, because when used as
# feature-values they will be interpreted as composite features
# which need to be decomposed.
if __re_hyphen.search (type):
raise BaseException ('type name "%s" contains a hyphen' % type)
# it's possible for a type to be registered with a
# base type that hasn't been registered yet. in the
# check for base_type below and the following calls to setdefault()
# the key `type` will be added to __types. When the base type
# actually gets registered, it would fail after the simple check
# of "type in __types"; thus the check for "'base' in __types[type]"
if type in __types and 'base' in __types[type]:
raise BaseException ('Type "%s" is already registered.' % type)
entry = __types.setdefault(type, {})
entry['base'] = base_type
entry.setdefault('derived', [])
entry.setdefault('scanner', None)
if base_type:
__types.setdefault(base_type, {}).setdefault('derived', []).append(type)
if len (suffixes) > 0:
# Generated targets of 'type' will use the first of 'suffixes'
# (this may be overriden)
set_generated_target_suffix (type, [], suffixes [0])
# Specify mapping from suffixes to type
register_suffixes (suffixes, type)
feature.extend('target-type', [type])
feature.extend('main-target-type', [type])
feature.extend('base-target-type', [type])
if base_type:
feature.compose ('<target-type>' + type, [replace_grist (base_type, '<base-target-type>')])
feature.compose ('<base-target-type>' + type, ['<base-target-type>' + base_type])
import b2.build.generators as generators
# Adding a new derived type affects generator selection so we need to
# make the generator selection module update any of its cached
# information related to a new derived type being defined.
generators.update_cached_information_with_a_new_type(type)
# FIXME: resolving recursive dependency.
from b2.manager import get_manager
get_manager().projects().project_rules().add_rule_for_type(type) | python | def register (type, suffixes = [], base_type = None):
""" Registers a target type, possibly derived from a 'base-type'.
If 'suffixes' are provided, they list all the suffixes that mean a file is of 'type'.
Also, the first element gives the suffix to be used when constructing and object of
'type'.
type: a string
suffixes: None or a sequence of strings
base_type: None or a string
"""
# Type names cannot contain hyphens, because when used as
# feature-values they will be interpreted as composite features
# which need to be decomposed.
if __re_hyphen.search (type):
raise BaseException ('type name "%s" contains a hyphen' % type)
# it's possible for a type to be registered with a
# base type that hasn't been registered yet. in the
# check for base_type below and the following calls to setdefault()
# the key `type` will be added to __types. When the base type
# actually gets registered, it would fail after the simple check
# of "type in __types"; thus the check for "'base' in __types[type]"
if type in __types and 'base' in __types[type]:
raise BaseException ('Type "%s" is already registered.' % type)
entry = __types.setdefault(type, {})
entry['base'] = base_type
entry.setdefault('derived', [])
entry.setdefault('scanner', None)
if base_type:
__types.setdefault(base_type, {}).setdefault('derived', []).append(type)
if len (suffixes) > 0:
# Generated targets of 'type' will use the first of 'suffixes'
# (this may be overriden)
set_generated_target_suffix (type, [], suffixes [0])
# Specify mapping from suffixes to type
register_suffixes (suffixes, type)
feature.extend('target-type', [type])
feature.extend('main-target-type', [type])
feature.extend('base-target-type', [type])
if base_type:
feature.compose ('<target-type>' + type, [replace_grist (base_type, '<base-target-type>')])
feature.compose ('<base-target-type>' + type, ['<base-target-type>' + base_type])
import b2.build.generators as generators
# Adding a new derived type affects generator selection so we need to
# make the generator selection module update any of its cached
# information related to a new derived type being defined.
generators.update_cached_information_with_a_new_type(type)
# FIXME: resolving recursive dependency.
from b2.manager import get_manager
get_manager().projects().project_rules().add_rule_for_type(type) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | register_suffixes | def register_suffixes (suffixes, type):
""" Specifies that targets with suffix from 'suffixes' have the type 'type'.
If a different type is already specified for any of syffixes, issues an error.
"""
assert is_iterable_typed(suffixes, basestring)
assert isinstance(type, basestring)
for s in suffixes:
if s in __suffixes_to_types:
old_type = __suffixes_to_types [s]
if old_type != type:
raise BaseException ('Attempting to specify type for suffix "%s"\nOld type: "%s", New type "%s"' % (s, old_type, type))
else:
__suffixes_to_types [s] = type | python | def register_suffixes (suffixes, type):
""" Specifies that targets with suffix from 'suffixes' have the type 'type'.
If a different type is already specified for any of syffixes, issues an error.
"""
assert is_iterable_typed(suffixes, basestring)
assert isinstance(type, basestring)
for s in suffixes:
if s in __suffixes_to_types:
old_type = __suffixes_to_types [s]
if old_type != type:
raise BaseException ('Attempting to specify type for suffix "%s"\nOld type: "%s", New type "%s"' % (s, old_type, type))
else:
__suffixes_to_types [s] = type | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | set_scanner | def set_scanner (type, scanner):
""" Sets a scanner class that will be used for this 'type'.
"""
if __debug__:
from .scanner import Scanner
assert isinstance(type, basestring)
assert issubclass(scanner, Scanner)
validate (type)
__types [type]['scanner'] = scanner | python | def set_scanner (type, scanner):
""" Sets a scanner class that will be used for this 'type'.
"""
if __debug__:
from .scanner import Scanner
assert isinstance(type, basestring)
assert issubclass(scanner, Scanner)
validate (type)
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | get_scanner | def get_scanner (type, prop_set):
""" Returns a scanner instance appropriate to 'type' and 'property_set'.
"""
if __debug__:
from .property_set import PropertySet
assert isinstance(type, basestring)
assert isinstance(prop_set, PropertySet)
if registered (type):
scanner_type = __types [type]['scanner']
if scanner_type:
return scanner.get (scanner_type, prop_set.raw ())
pass
return None | python | def get_scanner (type, prop_set):
""" Returns a scanner instance appropriate to 'type' and 'property_set'.
"""
if __debug__:
from .property_set import PropertySet
assert isinstance(type, basestring)
assert isinstance(prop_set, PropertySet)
if registered (type):
scanner_type = __types [type]['scanner']
if scanner_type:
return scanner.get (scanner_type, prop_set.raw ())
pass
return None | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | all_bases | def all_bases (type):
""" Returns type and all of its bases, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = []
while type:
result.append (type)
type = __types [type]['base']
return result | python | def all_bases (type):
""" Returns type and all of its bases, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = []
while type:
result.append (type)
type = __types [type]['base']
return result | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | all_derived | def all_derived (type):
""" Returns type and all classes that derive from it, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = [type]
for d in __types [type]['derived']:
result.extend (all_derived (d))
return result | python | def all_derived (type):
""" Returns type and all classes that derive from it, in the order of their distance from type.
"""
assert isinstance(type, basestring)
result = [type]
for d in __types [type]['derived']:
result.extend (all_derived (d))
return result | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | is_derived | def is_derived (type, base):
""" Returns true if 'type' is 'base' or has 'base' as its direct or indirect base.
"""
assert isinstance(type, basestring)
assert isinstance(base, basestring)
# TODO: this isn't very efficient, especially for bases close to type
if base in all_bases (type):
return True
else:
return False | python | def is_derived (type, base):
""" Returns true if 'type' is 'base' or has 'base' as its direct or indirect base.
"""
assert isinstance(type, basestring)
assert isinstance(base, basestring)
# TODO: this isn't very efficient, especially for bases close to type
if base in all_bases (type):
return True
else:
return False | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | is_subtype | def is_subtype (type, base):
""" Same as is_derived. Should be removed.
"""
assert isinstance(type, basestring)
assert isinstance(base, basestring)
# TODO: remove this method
return is_derived (type, base) | python | def is_subtype (type, base):
""" Same as is_derived. Should be removed.
"""
assert isinstance(type, basestring)
assert isinstance(base, basestring)
# TODO: remove this method
return is_derived (type, base) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | set_generated_target_suffix | def set_generated_target_suffix (type, properties, suffix):
""" Sets a target suffix that should be used when generating target
of 'type' with the specified properties. Can be called with
empty properties if no suffix for 'type' was specified yet.
This does not automatically specify that files 'suffix' have
'type' --- two different types can use the same suffix for
generating, but only one type should be auto-detected for
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The 'suffix' parameter can be empty string ("") to indicate that
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"""
assert isinstance(type, basestring)
assert is_iterable_typed(properties, basestring)
assert isinstance(suffix, basestring)
set_generated_target_ps(1, type, properties, suffix) | python | def set_generated_target_suffix (type, properties, suffix):
""" Sets a target suffix that should be used when generating target
of 'type' with the specified properties. Can be called with
empty properties if no suffix for 'type' was specified yet.
This does not automatically specify that files 'suffix' have
'type' --- two different types can use the same suffix for
generating, but only one type should be auto-detected for
a file with that suffix. User should explicitly specify which
one.
The 'suffix' parameter can be empty string ("") to indicate that
no suffix should be used.
"""
assert isinstance(type, basestring)
assert is_iterable_typed(properties, basestring)
assert isinstance(suffix, basestring)
set_generated_target_ps(1, type, properties, suffix) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | change_generated_target_suffix | def change_generated_target_suffix (type, properties, suffix):
""" Change the suffix previously registered for this type/properties
combination. If suffix is not yet specified, sets it.
"""
assert isinstance(type, basestring)
assert is_iterable_typed(properties, basestring)
assert isinstance(suffix, basestring)
change_generated_target_ps(1, type, properties, suffix) | python | def change_generated_target_suffix (type, properties, suffix):
""" Change the suffix previously registered for this type/properties
combination. If suffix is not yet specified, sets it.
"""
assert isinstance(type, basestring)
assert is_iterable_typed(properties, basestring)
assert isinstance(suffix, basestring)
change_generated_target_ps(1, type, properties, suffix) | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | generated_target_ps | def generated_target_ps(is_suffix, type, prop_set):
""" Returns suffix that should be used when generating target of 'type',
with the specified properties. If not suffix were specified for
'type', returns suffix for base type, if any.
"""
if __debug__:
from .property_set import PropertySet
assert isinstance(is_suffix, (int, bool))
assert isinstance(type, basestring)
assert isinstance(prop_set, PropertySet)
key = (is_suffix, type, prop_set)
v = __target_suffixes_cache.get(key, None)
if not v:
v = generated_target_ps_real(is_suffix, type, prop_set.raw())
__target_suffixes_cache [key] = v
return v | python | def generated_target_ps(is_suffix, type, prop_set):
""" Returns suffix that should be used when generating target of 'type',
with the specified properties. If not suffix were specified for
'type', returns suffix for base type, if any.
"""
if __debug__:
from .property_set import PropertySet
assert isinstance(is_suffix, (int, bool))
assert isinstance(type, basestring)
assert isinstance(prop_set, PropertySet)
key = (is_suffix, type, prop_set)
v = __target_suffixes_cache.get(key, None)
if not v:
v = generated_target_ps_real(is_suffix, type, prop_set.raw())
__target_suffixes_cache [key] = v
return v | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | type | def type(filename):
""" Returns file type given it's name. If there are several dots in filename,
tries each suffix. E.g. for name of "file.so.1.2" suffixes "2", "1", and
"so" will be tried.
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assert isinstance(filename, basestring)
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if not suffix: return None
suffix = suffix[1:]
if suffix in __suffixes_to_types:
return __suffixes_to_types[suffix] | python | def type(filename):
""" Returns file type given it's name. If there are several dots in filename,
tries each suffix. E.g. for name of "file.so.1.2" suffixes "2", "1", and
"so" will be tried.
"""
assert isinstance(filename, basestring)
while 1:
filename, suffix = os.path.splitext (filename)
if not suffix: return None
suffix = suffix[1:]
if suffix in __suffixes_to_types:
return __suffixes_to_types[suffix] | [
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apple/turicreate | deps/src/boost_1_68_0/tools/build/src/build/type.py | register_type | def register_type (type, suffixes, base_type = None, os = []):
""" Register the given type on the specified OSes, or on remaining OSes
if os is not specified. This rule is injected into each of the type
modules for the sake of convenience.
"""
assert isinstance(type, basestring)
assert is_iterable_typed(suffixes, basestring)
assert isinstance(base_type, basestring) or base_type is None
assert is_iterable_typed(os, basestring)
if registered (type):
return
if not os or os_name () in os:
register (type, suffixes, base_type) | python | def register_type (type, suffixes, base_type = None, os = []):
""" Register the given type on the specified OSes, or on remaining OSes
if os is not specified. This rule is injected into each of the type
modules for the sake of convenience.
"""
assert isinstance(type, basestring)
assert is_iterable_typed(suffixes, basestring)
assert isinstance(base_type, basestring) or base_type is None
assert is_iterable_typed(os, basestring)
if registered (type):
return
if not os or os_name () in os:
register (type, suffixes, base_type) | [
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apple/turicreate | src/unity/python/turicreate/util/_progress_table_printer.py | ProgressTablePrinter.print_row | def print_row(self, **kwargs):
'''
keys of kwargs must be the names passed to __init__(...) as `column_names`
'''
meta_string = '|'
for key in self.column_names:
float_specifier = ''
if isinstance(kwargs[key], float):
float_specifier = '.3f'
meta_string += " {%s:<{width}%s}|" % (key, float_specifier)
kwargs['width'] = self.column_width - 1
print(meta_string.format(**kwargs))
print(self.hr) | python | def print_row(self, **kwargs):
'''
keys of kwargs must be the names passed to __init__(...) as `column_names`
'''
meta_string = '|'
for key in self.column_names:
float_specifier = ''
if isinstance(kwargs[key], float):
float_specifier = '.3f'
meta_string += " {%s:<{width}%s}|" % (key, float_specifier)
kwargs['width'] = self.column_width - 1
print(meta_string.format(**kwargs))
print(self.hr) | [
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apple/turicreate | src/unity/python/turicreate/toolkits/_feature_engineering/_internal_utils.py | process_features | def process_features(features, exclude):
"""
Parameters
----------
features : list[str] | str | None, optional
Column names of features to be transformed. If None, all columns
are selected. If string, that column is transformed. If list of strings,
this list of column names is selected.
exclude : list[str] | str | None, optional
Column names of features to be ignored in transformation. Can be string
or list of strings. Either 'exclude' or 'features' can be passed, but
not both.
Returns
-------
(features, exclude) that are processed.
"""
# Check types
_raise_error_if_not_of_type(features, [NoneType, str, list], 'features')
_raise_error_if_not_of_type(exclude, [NoneType, str, list], 'exclude')
# Make a copy of the parameters.
_features = _copy.copy(features)
_exclude = _copy.copy(exclude)
# Check of both are None or empty.
if _features and _exclude:
raise ValueError("The parameters 'features' and 'exclude' cannot both be set."
" Please set one or the other.")
if _features == [] and not _exclude:
raise ValueError("Features cannot be an empty list.")
# Allow a single list
_features = [_features] if type(_features) == str else _features
_exclude = [_exclude] if type(_exclude) == str else _exclude
# Type check each feature/exclude
if _features:
for f in _features:
_raise_error_if_not_of_type(f, str, "Feature names")
if _exclude:
for e in _exclude:
_raise_error_if_not_of_type(e, str, "Excluded feature names")
if _exclude is not None and _features is not None:
feature_set = set(_features)
for col_name in _exclude:
if col_name in feature_set:
raise ValueError("'%s' appears in both features and excluded_features." % col_name)
return _features, _exclude | python | def process_features(features, exclude):
"""
Parameters
----------
features : list[str] | str | None, optional
Column names of features to be transformed. If None, all columns
are selected. If string, that column is transformed. If list of strings,
this list of column names is selected.
exclude : list[str] | str | None, optional
Column names of features to be ignored in transformation. Can be string
or list of strings. Either 'exclude' or 'features' can be passed, but
not both.
Returns
-------
(features, exclude) that are processed.
"""
# Check types
_raise_error_if_not_of_type(features, [NoneType, str, list], 'features')
_raise_error_if_not_of_type(exclude, [NoneType, str, list], 'exclude')
# Make a copy of the parameters.
_features = _copy.copy(features)
_exclude = _copy.copy(exclude)
# Check of both are None or empty.
if _features and _exclude:
raise ValueError("The parameters 'features' and 'exclude' cannot both be set."
" Please set one or the other.")
if _features == [] and not _exclude:
raise ValueError("Features cannot be an empty list.")
# Allow a single list
_features = [_features] if type(_features) == str else _features
_exclude = [_exclude] if type(_exclude) == str else _exclude
# Type check each feature/exclude
if _features:
for f in _features:
_raise_error_if_not_of_type(f, str, "Feature names")
if _exclude:
for e in _exclude:
_raise_error_if_not_of_type(e, str, "Excluded feature names")
if _exclude is not None and _features is not None:
feature_set = set(_features)
for col_name in _exclude:
if col_name in feature_set:
raise ValueError("'%s' appears in both features and excluded_features." % col_name)
return _features, _exclude | [
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exclude : list[str] | str | None, optional
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apple/turicreate | src/unity/python/turicreate/toolkits/_feature_engineering/_internal_utils.py | pretty_print_list | def pretty_print_list(lst, name = 'features', repr_format=True):
""" Pretty print a list to be readable.
"""
if not lst or len(lst) < 8:
if repr_format:
return lst.__repr__()
else:
return ', '.join(map(str, lst))
else:
topk = ', '.join(map(str, lst[:3]))
if repr_format:
lst_separator = "["
lst_end_separator = "]"
else:
lst_separator = ""
lst_end_separator = ""
return "{start}{topk}, ... {last}{end} (total {size} {name})".format(\
topk = topk, last = lst[-1], name = name, size = len(lst),
start = lst_separator, end = lst_end_separator) | python | def pretty_print_list(lst, name = 'features', repr_format=True):
""" Pretty print a list to be readable.
"""
if not lst or len(lst) < 8:
if repr_format:
return lst.__repr__()
else:
return ', '.join(map(str, lst))
else:
topk = ', '.join(map(str, lst[:3]))
if repr_format:
lst_separator = "["
lst_end_separator = "]"
else:
lst_separator = ""
lst_end_separator = ""
return "{start}{topk}, ... {last}{end} (total {size} {name})".format(\
topk = topk, last = lst[-1], name = name, size = len(lst),
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | _get_elementwise_name_from_keras_layer | def _get_elementwise_name_from_keras_layer(keras_layer):
"""
Get the keras layer name from the activation name.
"""
mode = keras_layer.mode
if mode == 'sum':
return 'ADD'
elif mode == 'mul':
return 'MULTIPLY'
elif mode == 'concat':
if len(keras_layer.input_shape[0]) == 3 and (keras_layer.concat_axis == 1 or keras_layer.concat_axis == -2):
return 'SEQUENCE_CONCAT'
elif len(keras_layer.input_shape[0]) == 4 and (keras_layer.concat_axis == 3 or keras_layer.concat_axis == -1):
return 'CONCAT'
elif len(keras_layer.input_shape[0]) == 2 and (keras_layer.concat_axis == 1 or keras_layer.concat_axis == -1):
return 'CONCAT'
else:
option = "input_shape = %s concat_axis = %s" % (str(keras_layer.input_shape[0]), str(keras_layer.concat_axis))
_utils.raise_error_unsupported_option(option, mode, keras_layer.name)
elif mode == 'cos':
if len(keras_layer.input_shape[0]) == 2:
return 'COS'
else:
option = "input_shape = %s" % (str(keras_layer.input_shape[0]))
_utils.raise_error_unsupported_option(option, mode, keras_layer.name)
elif mode == 'dot':
if len(keras_layer.input_shape[0]) == 2:
return 'DOT'
else:
option = "input_shape = %s" % (str(keras_layer.input_shape[0]))
_utils.raise_error_unsupported_option(option, mode, keras_layer.name)
elif mode == 'max':
return 'MAX'
elif mode == 'ave':
return 'AVE'
else:
_utils.raise_error_unsupported_categorical_option('mode', mode, 'Merge',
keras_layer.name) | python | def _get_elementwise_name_from_keras_layer(keras_layer):
"""
Get the keras layer name from the activation name.
"""
mode = keras_layer.mode
if mode == 'sum':
return 'ADD'
elif mode == 'mul':
return 'MULTIPLY'
elif mode == 'concat':
if len(keras_layer.input_shape[0]) == 3 and (keras_layer.concat_axis == 1 or keras_layer.concat_axis == -2):
return 'SEQUENCE_CONCAT'
elif len(keras_layer.input_shape[0]) == 4 and (keras_layer.concat_axis == 3 or keras_layer.concat_axis == -1):
return 'CONCAT'
elif len(keras_layer.input_shape[0]) == 2 and (keras_layer.concat_axis == 1 or keras_layer.concat_axis == -1):
return 'CONCAT'
else:
option = "input_shape = %s concat_axis = %s" % (str(keras_layer.input_shape[0]), str(keras_layer.concat_axis))
_utils.raise_error_unsupported_option(option, mode, keras_layer.name)
elif mode == 'cos':
if len(keras_layer.input_shape[0]) == 2:
return 'COS'
else:
option = "input_shape = %s" % (str(keras_layer.input_shape[0]))
_utils.raise_error_unsupported_option(option, mode, keras_layer.name)
elif mode == 'dot':
if len(keras_layer.input_shape[0]) == 2:
return 'DOT'
else:
option = "input_shape = %s" % (str(keras_layer.input_shape[0]))
_utils.raise_error_unsupported_option(option, mode, keras_layer.name)
elif mode == 'max':
return 'MAX'
elif mode == 'ave':
return 'AVE'
else:
_utils.raise_error_unsupported_categorical_option('mode', mode, 'Merge',
keras_layer.name) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | _same_elements_per_channel | def _same_elements_per_channel(x):
"""
Test if a 3D (H,W,C) matrix x has the same element in each (H,W) matrix for each channel
"""
eps = 1e-5
dims = x.shape
for c in range(dims[-1]):
xc = x[:,:,c].flatten()
if not np.all(np.absolute(xc - xc[0]) < eps):
return False
return True | python | def _same_elements_per_channel(x):
"""
Test if a 3D (H,W,C) matrix x has the same element in each (H,W) matrix for each channel
"""
eps = 1e-5
dims = x.shape
for c in range(dims[-1]):
xc = x[:,:,c].flatten()
if not np.all(np.absolute(xc - xc[0]) < eps):
return False
return True | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_dense | def convert_dense(builder, layer, input_names, output_names, keras_layer):
"""Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
has_bias = keras_layer.bias
# Get the weights from keras
W = keras_layer.get_weights ()[0].T
Wb = keras_layer.get_weights ()[1].T if has_bias else None
builder.add_inner_product(name = layer,
W = W,
b = Wb,
input_channels = keras_layer.input_dim,
output_channels = keras_layer.output_dim,
has_bias = has_bias,
input_name = input_name,
output_name = output_name) | python | def convert_dense(builder, layer, input_names, output_names, keras_layer):
"""Convert a dense layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
has_bias = keras_layer.bias
# Get the weights from keras
W = keras_layer.get_weights ()[0].T
Wb = keras_layer.get_weights ()[1].T if has_bias else None
builder.add_inner_product(name = layer,
W = W,
b = Wb,
input_channels = keras_layer.input_dim,
output_channels = keras_layer.output_dim,
has_bias = has_bias,
input_name = input_name,
output_name = output_name) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_activation | def convert_activation(builder, layer, input_names, output_names, keras_layer):
"""Convert an activation layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
non_linearity = _get_activation_name_from_keras_layer(keras_layer)
# Add a non-linearity layer
if non_linearity == 'SOFTMAX':
builder.add_softmax(name = layer, input_name = input_name,
output_name = output_name)
return
params = None
if non_linearity == 'LEAKYRELU':
params = [keras_layer.alpha]
elif non_linearity == 'PRELU':
# In Keras 1.2 PReLU layer's weights are stored as a
# backend tensor, not a numpy array as it claims in documentation.
shared_axes = list(keras_layer.shared_axes)
if not (shared_axes == [1,2,3] or shared_axes == [1,2]):
_utils.raise_error_unsupported_scenario("Shared axis not being [1,2,3] or [1,2]", 'parametric_relu', layer)
params = keras.backend.eval(keras_layer.weights[0])
elif non_linearity == 'ELU':
params = keras_layer.alpha
elif non_linearity == 'PARAMETRICSOFTPLUS':
# In Keras 1.2 Parametric Softplus layer's weights are stored as a
# backend tensor, not a numpy array as it claims in documentation.
alphas = keras.backend.eval(keras_layer.weights[0])
betas = keras.backend.eval(keras_layer.weights[1])
if len(alphas.shape) == 3: # (H,W,C)
if not (_same_elements_per_channel(alphas) and _same_elements_per_channel(betas)):
_utils.raise_error_unsupported_scenario("Different parameter values", 'parametric_softplus', layer)
alphas = alphas[0,0,:]
betas = betas[0,0,:]
params = [alphas, betas]
elif non_linearity == 'THRESHOLDEDRELU':
params = keras_layer.theta
else:
pass # do nothing to parameters
builder.add_activation(name = layer,
non_linearity = non_linearity,
input_name = input_name, output_name = output_name,
params = params) | python | def convert_activation(builder, layer, input_names, output_names, keras_layer):
"""Convert an activation layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
non_linearity = _get_activation_name_from_keras_layer(keras_layer)
# Add a non-linearity layer
if non_linearity == 'SOFTMAX':
builder.add_softmax(name = layer, input_name = input_name,
output_name = output_name)
return
params = None
if non_linearity == 'LEAKYRELU':
params = [keras_layer.alpha]
elif non_linearity == 'PRELU':
# In Keras 1.2 PReLU layer's weights are stored as a
# backend tensor, not a numpy array as it claims in documentation.
shared_axes = list(keras_layer.shared_axes)
if not (shared_axes == [1,2,3] or shared_axes == [1,2]):
_utils.raise_error_unsupported_scenario("Shared axis not being [1,2,3] or [1,2]", 'parametric_relu', layer)
params = keras.backend.eval(keras_layer.weights[0])
elif non_linearity == 'ELU':
params = keras_layer.alpha
elif non_linearity == 'PARAMETRICSOFTPLUS':
# In Keras 1.2 Parametric Softplus layer's weights are stored as a
# backend tensor, not a numpy array as it claims in documentation.
alphas = keras.backend.eval(keras_layer.weights[0])
betas = keras.backend.eval(keras_layer.weights[1])
if len(alphas.shape) == 3: # (H,W,C)
if not (_same_elements_per_channel(alphas) and _same_elements_per_channel(betas)):
_utils.raise_error_unsupported_scenario("Different parameter values", 'parametric_softplus', layer)
alphas = alphas[0,0,:]
betas = betas[0,0,:]
params = [alphas, betas]
elif non_linearity == 'THRESHOLDEDRELU':
params = keras_layer.theta
else:
pass # do nothing to parameters
builder.add_activation(name = layer,
non_linearity = non_linearity,
input_name = input_name, output_name = output_name,
params = params) | [
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Parameters
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keras_layer: layer
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builder: NeuralNetworkBuilder
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_padding | def convert_padding(builder, layer, input_names, output_names, keras_layer):
"""Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
if isinstance(keras_layer, keras.layers.convolutional.ZeroPadding1D):
left, right = keras_layer.padding
top, bottom = (0, 0)
else: # 2D
top, left = keras_layer.padding
bottom, right = keras_layer.padding
# Now add the layer
builder.add_padding(name = layer,
left = left, right=right, top=top, bottom=bottom, value = 0,
input_name = input_name, output_name=output_name
) | python | def convert_padding(builder, layer, input_names, output_names, keras_layer):
"""Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
if isinstance(keras_layer, keras.layers.convolutional.ZeroPadding1D):
left, right = keras_layer.padding
top, bottom = (0, 0)
else: # 2D
top, left = keras_layer.padding
bottom, right = keras_layer.padding
# Now add the layer
builder.add_padding(name = layer,
left = left, right=right, top=top, bottom=bottom, value = 0,
input_name = input_name, output_name=output_name
) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_cropping | def convert_cropping(builder, layer, input_names, output_names, keras_layer):
"""Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
if isinstance(keras_layer, keras.layers.convolutional.Cropping1D):
left, right = keras_layer.cropping
top, bottom = (0, 0)
else: # 2D
left, right = keras_layer.cropping[0]
top, bottom = keras_layer.cropping[1]
# Now add the layer
builder.add_crop(name = layer,
left = left, right=right, top=top, bottom=bottom, offset = [0,0],
input_names = [input_name], output_name=output_name
) | python | def convert_cropping(builder, layer, input_names, output_names, keras_layer):
"""Convert padding layer from keras to coreml.
Keras only supports zero padding at this time.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
if isinstance(keras_layer, keras.layers.convolutional.Cropping1D):
left, right = keras_layer.cropping
top, bottom = (0, 0)
else: # 2D
left, right = keras_layer.cropping[0]
top, bottom = keras_layer.cropping[1]
# Now add the layer
builder.add_crop(name = layer,
left = left, right=right, top=top, bottom=bottom, offset = [0,0],
input_names = [input_name], output_name=output_name
) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_upsample | def convert_upsample(builder, layer, input_names, output_names, keras_layer):
"""Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
if isinstance(keras_layer, keras.layers.convolutional.UpSampling1D):
fh, fw = 1, keras_layer.length
else: # 2D
fh, fw = keras_layer.size
builder.add_upsample(name = layer,
scaling_factor_h = fh,
scaling_factor_w = fw,
input_name = input_name,
output_name = output_name) | python | def convert_upsample(builder, layer, input_names, output_names, keras_layer):
"""Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
if isinstance(keras_layer, keras.layers.convolutional.UpSampling1D):
fh, fw = 1, keras_layer.length
else: # 2D
fh, fw = keras_layer.size
builder.add_upsample(name = layer,
scaling_factor_h = fh,
scaling_factor_w = fw,
input_name = input_name,
output_name = output_name) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_convolution | def convert_convolution(builder, layer, input_names, output_names, keras_layer):
"""Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
has_bias = keras_layer.bias
is_deconv = isinstance(keras_layer, keras.layers.convolutional.Deconvolution2D)
# Get the weights from keras.
# Keras stores convolution weights as list of numpy arrays
weightList = keras_layer.get_weights()
output_shape = list(filter(None, keras_layer.output_shape))[:-1]
# Parameter
height, width, channels, n_filters = weightList[0].shape
stride_height, stride_width = keras_layer.subsample
# Weights and bias terms
W = weightList[0]
b = weightList[1] if has_bias else None
# dilation factors
dilation_factors = [1,1]
if isinstance(keras_layer, keras.layers.convolutional.AtrousConvolution2D):
dilation_factors = list(keras_layer.atrous_rate)
builder.add_convolution(name = layer,
kernel_channels = channels,
output_channels = n_filters,
height = height,
width = width,
stride_height = stride_height,
stride_width = stride_width,
border_mode = keras_layer.border_mode,
groups = 1,
W = W,
b = b,
has_bias = has_bias,
is_deconv = is_deconv,
output_shape = output_shape,
input_name = input_name,
output_name = output_name,
dilation_factors = dilation_factors) | python | def convert_convolution(builder, layer, input_names, output_names, keras_layer):
"""Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
has_bias = keras_layer.bias
is_deconv = isinstance(keras_layer, keras.layers.convolutional.Deconvolution2D)
# Get the weights from keras.
# Keras stores convolution weights as list of numpy arrays
weightList = keras_layer.get_weights()
output_shape = list(filter(None, keras_layer.output_shape))[:-1]
# Parameter
height, width, channels, n_filters = weightList[0].shape
stride_height, stride_width = keras_layer.subsample
# Weights and bias terms
W = weightList[0]
b = weightList[1] if has_bias else None
# dilation factors
dilation_factors = [1,1]
if isinstance(keras_layer, keras.layers.convolutional.AtrousConvolution2D):
dilation_factors = list(keras_layer.atrous_rate)
builder.add_convolution(name = layer,
kernel_channels = channels,
output_channels = n_filters,
height = height,
width = width,
stride_height = stride_height,
stride_width = stride_width,
border_mode = keras_layer.border_mode,
groups = 1,
W = W,
b = b,
has_bias = has_bias,
is_deconv = is_deconv,
output_shape = output_shape,
input_name = input_name,
output_name = output_name,
dilation_factors = dilation_factors) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_convolution1d | def convert_convolution1d(builder, layer, input_names, output_names, keras_layer):
"""Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
has_bias = keras_layer.bias
# Get the weights from keras.
# Keras stores convolution weights as list of numpy arrays
weightList = keras_layer.get_weights()
output_shape = list(filter(None, keras_layer.output_shape))[:-1]
# Parameter
# weightList[0].shape = [kernel_length, input_length(time_step), input_dim, num_kernels]
filter_length, input_length, input_dim, n_filters = weightList[0].shape
stride_width = keras_layer.subsample[0]
# Weights and bias terms
W = weightList[0]
b = weightList[1] if has_bias else None
dilation_factors = [1,1]
if isinstance(keras_layer, keras.layers.convolutional.AtrousConvolution1D):
dilation_factors[-1] = keras_layer.atrous_rate
builder.add_convolution(name = layer,
kernel_channels = input_dim,
output_channels = n_filters,
height = 1,
width = filter_length,
stride_height = 1,
stride_width = stride_width,
border_mode = keras_layer.border_mode,
groups = 1,
W = W,
b = b,
has_bias = has_bias,
is_deconv = False,
output_shape = output_shape,
input_name = input_name,
output_name = output_name,
dilation_factors = dilation_factors) | python | def convert_convolution1d(builder, layer, input_names, output_names, keras_layer):
"""Convert convolution layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
has_bias = keras_layer.bias
# Get the weights from keras.
# Keras stores convolution weights as list of numpy arrays
weightList = keras_layer.get_weights()
output_shape = list(filter(None, keras_layer.output_shape))[:-1]
# Parameter
# weightList[0].shape = [kernel_length, input_length(time_step), input_dim, num_kernels]
filter_length, input_length, input_dim, n_filters = weightList[0].shape
stride_width = keras_layer.subsample[0]
# Weights and bias terms
W = weightList[0]
b = weightList[1] if has_bias else None
dilation_factors = [1,1]
if isinstance(keras_layer, keras.layers.convolutional.AtrousConvolution1D):
dilation_factors[-1] = keras_layer.atrous_rate
builder.add_convolution(name = layer,
kernel_channels = input_dim,
output_channels = n_filters,
height = 1,
width = filter_length,
stride_height = 1,
stride_width = stride_width,
border_mode = keras_layer.border_mode,
groups = 1,
W = W,
b = b,
has_bias = has_bias,
is_deconv = False,
output_shape = output_shape,
input_name = input_name,
output_name = output_name,
dilation_factors = dilation_factors) | [
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Parameters
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keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_lstm | def convert_lstm(builder, layer, input_names, output_names, keras_layer):
"""Convert an LSTM layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
hidden_size = keras_layer.output_dim
input_size = keras_layer.input_shape[-1]
if keras_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
output_all = keras_layer.return_sequences
reverse_input = keras_layer.go_backwards
# Keras: I C F O; W_x, W_h, b
# CoreML: I F O G; W_h and W_x are separated
W_h, W_x, b = ([], [], [])
if keras_layer.consume_less == 'cpu':
W_h.append(keras_layer.get_weights()[1].T)
W_h.append(keras_layer.get_weights()[7].T)
W_h.append(keras_layer.get_weights()[10].T)
W_h.append(keras_layer.get_weights()[4].T)
W_x.append(keras_layer.get_weights()[0].T)
W_x.append(keras_layer.get_weights()[6].T)
W_x.append(keras_layer.get_weights()[9].T)
W_x.append(keras_layer.get_weights()[3].T)
b.append(keras_layer.get_weights()[2])
b.append(keras_layer.get_weights()[8])
b.append(keras_layer.get_weights()[11])
b.append(keras_layer.get_weights()[5])
else:
keras_W_h = keras_layer.get_weights()[1].T
W_h.append(keras_W_h[0 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[1 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[3 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[2 * hidden_size:][:hidden_size])
keras_W_x = keras_layer.get_weights()[0].T
W_x.append(keras_W_x[0 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[1 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[3 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[2 * hidden_size:][:hidden_size])
keras_b = keras_layer.get_weights()[2]
b.append(keras_b[0 * hidden_size:][:hidden_size])
b.append(keras_b[1 * hidden_size:][:hidden_size])
b.append(keras_b[3 * hidden_size:][:hidden_size])
b.append(keras_b[2 * hidden_size:][:hidden_size])
# Set activation type
inner_activation_str = _get_recurrent_activation_name_from_keras(keras_layer.inner_activation)
activation_str = _get_recurrent_activation_name_from_keras(keras_layer.activation)
# Add to the network
builder.add_unilstm(
name = layer,
W_h = W_h, W_x = W_x, b = b,
hidden_size = hidden_size,
input_size = input_size,
input_names = input_names,
output_names = output_names,
inner_activation = inner_activation_str,
cell_state_update_activation = activation_str,
output_activation = activation_str,
output_all = output_all,
reverse_input = reverse_input) | python | def convert_lstm(builder, layer, input_names, output_names, keras_layer):
"""Convert an LSTM layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
hidden_size = keras_layer.output_dim
input_size = keras_layer.input_shape[-1]
if keras_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
output_all = keras_layer.return_sequences
reverse_input = keras_layer.go_backwards
# Keras: I C F O; W_x, W_h, b
# CoreML: I F O G; W_h and W_x are separated
W_h, W_x, b = ([], [], [])
if keras_layer.consume_less == 'cpu':
W_h.append(keras_layer.get_weights()[1].T)
W_h.append(keras_layer.get_weights()[7].T)
W_h.append(keras_layer.get_weights()[10].T)
W_h.append(keras_layer.get_weights()[4].T)
W_x.append(keras_layer.get_weights()[0].T)
W_x.append(keras_layer.get_weights()[6].T)
W_x.append(keras_layer.get_weights()[9].T)
W_x.append(keras_layer.get_weights()[3].T)
b.append(keras_layer.get_weights()[2])
b.append(keras_layer.get_weights()[8])
b.append(keras_layer.get_weights()[11])
b.append(keras_layer.get_weights()[5])
else:
keras_W_h = keras_layer.get_weights()[1].T
W_h.append(keras_W_h[0 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[1 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[3 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[2 * hidden_size:][:hidden_size])
keras_W_x = keras_layer.get_weights()[0].T
W_x.append(keras_W_x[0 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[1 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[3 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[2 * hidden_size:][:hidden_size])
keras_b = keras_layer.get_weights()[2]
b.append(keras_b[0 * hidden_size:][:hidden_size])
b.append(keras_b[1 * hidden_size:][:hidden_size])
b.append(keras_b[3 * hidden_size:][:hidden_size])
b.append(keras_b[2 * hidden_size:][:hidden_size])
# Set activation type
inner_activation_str = _get_recurrent_activation_name_from_keras(keras_layer.inner_activation)
activation_str = _get_recurrent_activation_name_from_keras(keras_layer.activation)
# Add to the network
builder.add_unilstm(
name = layer,
W_h = W_h, W_x = W_x, b = b,
hidden_size = hidden_size,
input_size = input_size,
input_names = input_names,
output_names = output_names,
inner_activation = inner_activation_str,
cell_state_update_activation = activation_str,
output_activation = activation_str,
output_all = output_all,
reverse_input = reverse_input) | [
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keras_layer: layer
A keras layer object.
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_simple_rnn | def convert_simple_rnn(builder, layer, input_names, output_names, keras_layer):
"""Convert an SimpleRNN layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
hidden_size = keras_layer.output_dim
input_size = keras_layer.input_shape[-1]
output_all = keras_layer.return_sequences
reverse_input = keras_layer.go_backwards
if keras_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
W_h = np.zeros((hidden_size, hidden_size))
W_x = np.zeros((hidden_size, input_size))
b = np.zeros((hidden_size,))
if keras_layer.consume_less == 'cpu':
W_h = keras_layer.get_weights()[1].T
W_x = keras_layer.get_weights()[0].T
b = keras_layer.get_weights()[2]
else:
W_h = keras_layer.get_weights()[1].T
W_x = keras_layer.get_weights()[0].T
b = keras_layer.get_weights()[2]
# Set actication type
activation_str = _get_recurrent_activation_name_from_keras(keras_layer.activation)
# Add to the network
builder.add_simple_rnn(
name = layer,
W_h = W_h, W_x = W_x, b = b,
hidden_size = hidden_size,
input_size = input_size,
activation = activation_str,
input_names = input_names,
output_names = output_names,
output_all=output_all,
reverse_input=reverse_input) | python | def convert_simple_rnn(builder, layer, input_names, output_names, keras_layer):
"""Convert an SimpleRNN layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
hidden_size = keras_layer.output_dim
input_size = keras_layer.input_shape[-1]
output_all = keras_layer.return_sequences
reverse_input = keras_layer.go_backwards
if keras_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
W_h = np.zeros((hidden_size, hidden_size))
W_x = np.zeros((hidden_size, input_size))
b = np.zeros((hidden_size,))
if keras_layer.consume_less == 'cpu':
W_h = keras_layer.get_weights()[1].T
W_x = keras_layer.get_weights()[0].T
b = keras_layer.get_weights()[2]
else:
W_h = keras_layer.get_weights()[1].T
W_x = keras_layer.get_weights()[0].T
b = keras_layer.get_weights()[2]
# Set actication type
activation_str = _get_recurrent_activation_name_from_keras(keras_layer.activation)
# Add to the network
builder.add_simple_rnn(
name = layer,
W_h = W_h, W_x = W_x, b = b,
hidden_size = hidden_size,
input_size = input_size,
activation = activation_str,
input_names = input_names,
output_names = output_names,
output_all=output_all,
reverse_input=reverse_input) | [
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Parameters
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A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object. | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_gru | def convert_gru(builder, layer, input_names, output_names, keras_layer):
"""Convert a GRU layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
hidden_size = keras_layer.output_dim
input_size = keras_layer.input_shape[-1]
output_all = keras_layer.return_sequences
reverse_input = keras_layer.go_backwards
if keras_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
# Keras: Z R O
# CoreML: Z R O
W_h, W_x, b = ([], [], [])
if keras_layer.consume_less == 'cpu':
W_x.append(keras_layer.get_weights()[0].T)
W_x.append(keras_layer.get_weights()[3].T)
W_x.append(keras_layer.get_weights()[6].T)
W_h.append(keras_layer.get_weights()[1].T)
W_h.append(keras_layer.get_weights()[4].T)
W_h.append(keras_layer.get_weights()[7].T)
b.append(keras_layer.get_weights()[2])
b.append(keras_layer.get_weights()[5])
b.append(keras_layer.get_weights()[8])
else:
print('consume less not implemented')
# Set actication type
inner_activation_str = _get_recurrent_activation_name_from_keras(keras_layer.inner_activation)
activation_str = _get_recurrent_activation_name_from_keras(keras_layer.activation)
# Add to the network
builder.add_gru(
name = layer,
W_h = W_h, W_x = W_x, b = b,
input_size = input_size,
hidden_size = hidden_size,
input_names = input_names,
output_names = output_names,
activation = activation_str,
inner_activation = inner_activation_str,
output_all=output_all,
reverse_input = reverse_input) | python | def convert_gru(builder, layer, input_names, output_names, keras_layer):
"""Convert a GRU layer from keras to coreml.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
hidden_size = keras_layer.output_dim
input_size = keras_layer.input_shape[-1]
output_all = keras_layer.return_sequences
reverse_input = keras_layer.go_backwards
if keras_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
# Keras: Z R O
# CoreML: Z R O
W_h, W_x, b = ([], [], [])
if keras_layer.consume_less == 'cpu':
W_x.append(keras_layer.get_weights()[0].T)
W_x.append(keras_layer.get_weights()[3].T)
W_x.append(keras_layer.get_weights()[6].T)
W_h.append(keras_layer.get_weights()[1].T)
W_h.append(keras_layer.get_weights()[4].T)
W_h.append(keras_layer.get_weights()[7].T)
b.append(keras_layer.get_weights()[2])
b.append(keras_layer.get_weights()[5])
b.append(keras_layer.get_weights()[8])
else:
print('consume less not implemented')
# Set actication type
inner_activation_str = _get_recurrent_activation_name_from_keras(keras_layer.inner_activation)
activation_str = _get_recurrent_activation_name_from_keras(keras_layer.activation)
# Add to the network
builder.add_gru(
name = layer,
W_h = W_h, W_x = W_x, b = b,
input_size = input_size,
hidden_size = hidden_size,
input_names = input_names,
output_names = output_names,
activation = activation_str,
inner_activation = inner_activation_str,
output_all=output_all,
reverse_input = reverse_input) | [
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Parameters
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A keras layer object.
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A neural network builder object. | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_bidirectional | def convert_bidirectional(builder, layer, input_names, output_names, keras_layer):
"""Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_size = keras_layer.input_shape[-1]
lstm_layer = keras_layer.forward_layer
if (type(lstm_layer) != keras.layers.recurrent.LSTM):
raise TypeError('Bidirectional layers only supported with LSTM')
if lstm_layer.go_backwards:
raise TypeError(' \'go_backwards\' mode not supported with Bidirectional layers')
output_all = keras_layer.return_sequences
hidden_size = lstm_layer.output_dim
#output_size = lstm_layer.output_dim * 2
if lstm_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
# Keras: I C F O; W_x, W_h, b
# CoreML: I F O G; W_h and W_x are separated
# Keras has all forward weights, followed by backward in the same order
W_h, W_x, b = ([], [], [])
if lstm_layer.consume_less == 'cpu':
W_h.append(keras_layer.get_weights()[1].T)
W_h.append(keras_layer.get_weights()[7].T)
W_h.append(keras_layer.get_weights()[10].T)
W_h.append(keras_layer.get_weights()[4].T)
W_x.append(keras_layer.get_weights()[0].T)
W_x.append(keras_layer.get_weights()[6].T)
W_x.append(keras_layer.get_weights()[9].T)
W_x.append(keras_layer.get_weights()[3].T)
b.append(keras_layer.get_weights()[2])
b.append(keras_layer.get_weights()[8])
b.append(keras_layer.get_weights()[11])
b.append(keras_layer.get_weights()[5])
else:
keras_W_h = keras_layer.get_weights()[1].T
W_h.append(keras_W_h[0 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[1 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[3 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[2 * hidden_size:][:hidden_size])
keras_W_x = keras_layer.get_weights()[0].T
W_x.append(keras_W_x[0 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[1 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[3 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[2 * hidden_size:][:hidden_size])
keras_b = keras_layer.get_weights()[2]
b.append(keras_b[0 * hidden_size:][:hidden_size])
b.append(keras_b[1 * hidden_size:][:hidden_size])
b.append(keras_b[3 * hidden_size:][:hidden_size])
b.append(keras_b[2 * hidden_size:][:hidden_size])
W_h_back, W_x_back, b_back = ([],[],[])
if keras_layer.backward_layer.consume_less == 'cpu':
back_weights = keras_layer.backward_layer.get_weights()
W_h_back.append(back_weights[1].T)
W_h_back.append(back_weights[7].T)
W_h_back.append(back_weights[10].T)
W_h_back.append(back_weights[4].T)
W_x_back.append(back_weights[0].T)
W_x_back.append(back_weights[6].T)
W_x_back.append(back_weights[9].T)
W_x_back.append(back_weights[3].T)
b_back.append(back_weights[2])
b_back.append(back_weights[8])
b_back.append(back_weights[11])
b_back.append(back_weights[5])
else:
keras_W_h = keras_layer.backward_layer.get_weights()[1].T
W_h_back.append(keras_W_h[0 * hidden_size:][:hidden_size])
W_h_back.append(keras_W_h[1 * hidden_size:][:hidden_size])
W_h_back.append(keras_W_h[3 * hidden_size:][:hidden_size])
W_h_back.append(keras_W_h[2 * hidden_size:][:hidden_size])
keras_W_x = keras_layer.backward_layer.get_weights()[0].T
W_x_back.append(keras_W_x[0 * hidden_size:][:hidden_size])
W_x_back.append(keras_W_x[1 * hidden_size:][:hidden_size])
W_x_back.append(keras_W_x[3 * hidden_size:][:hidden_size])
W_x_back.append(keras_W_x[2 * hidden_size:][:hidden_size])
keras_b = keras_layer.backward_layer.get_weights()[2]
b_back.append(keras_b[0 * hidden_size:][:hidden_size])
b_back.append(keras_b[1 * hidden_size:][:hidden_size])
b_back.append(keras_b[3 * hidden_size:][:hidden_size])
b_back.append(keras_b[2 * hidden_size:][:hidden_size])
# Set activation type
inner_activation_str = _get_recurrent_activation_name_from_keras(lstm_layer.inner_activation)
activation_str = _get_recurrent_activation_name_from_keras(lstm_layer.activation)
# Add to the network
builder.add_bidirlstm(
name = layer,
W_h = W_h, W_x = W_x, b = b,
W_h_back = W_h_back, W_x_back = W_x_back, b_back = b_back,
hidden_size=hidden_size,
input_size=input_size,
input_names=input_names,
output_names=output_names,
inner_activation = inner_activation_str,
cell_state_update_activation = activation_str,
output_activation = activation_str,
output_all = output_all) | python | def convert_bidirectional(builder, layer, input_names, output_names, keras_layer):
"""Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_size = keras_layer.input_shape[-1]
lstm_layer = keras_layer.forward_layer
if (type(lstm_layer) != keras.layers.recurrent.LSTM):
raise TypeError('Bidirectional layers only supported with LSTM')
if lstm_layer.go_backwards:
raise TypeError(' \'go_backwards\' mode not supported with Bidirectional layers')
output_all = keras_layer.return_sequences
hidden_size = lstm_layer.output_dim
#output_size = lstm_layer.output_dim * 2
if lstm_layer.consume_less not in ['cpu', 'gpu']:
raise ValueError('Cannot convert Keras layer with consume_less = %s' % keras_layer.consume_less)
# Keras: I C F O; W_x, W_h, b
# CoreML: I F O G; W_h and W_x are separated
# Keras has all forward weights, followed by backward in the same order
W_h, W_x, b = ([], [], [])
if lstm_layer.consume_less == 'cpu':
W_h.append(keras_layer.get_weights()[1].T)
W_h.append(keras_layer.get_weights()[7].T)
W_h.append(keras_layer.get_weights()[10].T)
W_h.append(keras_layer.get_weights()[4].T)
W_x.append(keras_layer.get_weights()[0].T)
W_x.append(keras_layer.get_weights()[6].T)
W_x.append(keras_layer.get_weights()[9].T)
W_x.append(keras_layer.get_weights()[3].T)
b.append(keras_layer.get_weights()[2])
b.append(keras_layer.get_weights()[8])
b.append(keras_layer.get_weights()[11])
b.append(keras_layer.get_weights()[5])
else:
keras_W_h = keras_layer.get_weights()[1].T
W_h.append(keras_W_h[0 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[1 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[3 * hidden_size:][:hidden_size])
W_h.append(keras_W_h[2 * hidden_size:][:hidden_size])
keras_W_x = keras_layer.get_weights()[0].T
W_x.append(keras_W_x[0 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[1 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[3 * hidden_size:][:hidden_size])
W_x.append(keras_W_x[2 * hidden_size:][:hidden_size])
keras_b = keras_layer.get_weights()[2]
b.append(keras_b[0 * hidden_size:][:hidden_size])
b.append(keras_b[1 * hidden_size:][:hidden_size])
b.append(keras_b[3 * hidden_size:][:hidden_size])
b.append(keras_b[2 * hidden_size:][:hidden_size])
W_h_back, W_x_back, b_back = ([],[],[])
if keras_layer.backward_layer.consume_less == 'cpu':
back_weights = keras_layer.backward_layer.get_weights()
W_h_back.append(back_weights[1].T)
W_h_back.append(back_weights[7].T)
W_h_back.append(back_weights[10].T)
W_h_back.append(back_weights[4].T)
W_x_back.append(back_weights[0].T)
W_x_back.append(back_weights[6].T)
W_x_back.append(back_weights[9].T)
W_x_back.append(back_weights[3].T)
b_back.append(back_weights[2])
b_back.append(back_weights[8])
b_back.append(back_weights[11])
b_back.append(back_weights[5])
else:
keras_W_h = keras_layer.backward_layer.get_weights()[1].T
W_h_back.append(keras_W_h[0 * hidden_size:][:hidden_size])
W_h_back.append(keras_W_h[1 * hidden_size:][:hidden_size])
W_h_back.append(keras_W_h[3 * hidden_size:][:hidden_size])
W_h_back.append(keras_W_h[2 * hidden_size:][:hidden_size])
keras_W_x = keras_layer.backward_layer.get_weights()[0].T
W_x_back.append(keras_W_x[0 * hidden_size:][:hidden_size])
W_x_back.append(keras_W_x[1 * hidden_size:][:hidden_size])
W_x_back.append(keras_W_x[3 * hidden_size:][:hidden_size])
W_x_back.append(keras_W_x[2 * hidden_size:][:hidden_size])
keras_b = keras_layer.backward_layer.get_weights()[2]
b_back.append(keras_b[0 * hidden_size:][:hidden_size])
b_back.append(keras_b[1 * hidden_size:][:hidden_size])
b_back.append(keras_b[3 * hidden_size:][:hidden_size])
b_back.append(keras_b[2 * hidden_size:][:hidden_size])
# Set activation type
inner_activation_str = _get_recurrent_activation_name_from_keras(lstm_layer.inner_activation)
activation_str = _get_recurrent_activation_name_from_keras(lstm_layer.activation)
# Add to the network
builder.add_bidirlstm(
name = layer,
W_h = W_h, W_x = W_x, b = b,
W_h_back = W_h_back, W_x_back = W_x_back, b_back = b_back,
hidden_size=hidden_size,
input_size=input_size,
input_names=input_names,
output_names=output_names,
inner_activation = inner_activation_str,
cell_state_update_activation = activation_str,
output_activation = activation_str,
output_all = output_all) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_batchnorm | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
# Currently CoreML supports only per-channel batch-norm
if keras_layer.mode != 0:
raise NotImplementedError(
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axis = keras_layer.axis
nb_channels = keras_layer.input_shape[axis]
# Set parameters
# Parameter arrangement in Keras: gamma, beta, mean, variance
gamma = keras_layer.get_weights()[0]
beta = keras_layer.get_weights()[1]
mean = keras_layer.get_weights()[2]
std = keras_layer.get_weights()[3]
# compute adjusted parameters
variance = std * std
f = 1.0 / np.sqrt(std + keras_layer.epsilon)
gamma1 = gamma*f
beta1 = beta - gamma*mean*f
mean[:] = 0.0 #mean
variance[:] = 1.0 - .00001 #stddev
builder.add_batchnorm(
name = layer,
channels = nb_channels,
gamma = gamma1,
beta = beta1,
mean = mean,
variance = variance,
input_name = input_name,
output_name = output_name) | python | def convert_batchnorm(builder, layer, input_names, output_names, keras_layer):
"""
Parameters
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
# Get input and output names
input_name, output_name = (input_names[0], output_names[0])
# Currently CoreML supports only per-channel batch-norm
if keras_layer.mode != 0:
raise NotImplementedError(
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axis = keras_layer.axis
nb_channels = keras_layer.input_shape[axis]
# Set parameters
# Parameter arrangement in Keras: gamma, beta, mean, variance
gamma = keras_layer.get_weights()[0]
beta = keras_layer.get_weights()[1]
mean = keras_layer.get_weights()[2]
std = keras_layer.get_weights()[3]
# compute adjusted parameters
variance = std * std
f = 1.0 / np.sqrt(std + keras_layer.epsilon)
gamma1 = gamma*f
beta1 = beta - gamma*mean*f
mean[:] = 0.0 #mean
variance[:] = 1.0 - .00001 #stddev
builder.add_batchnorm(
name = layer,
channels = nb_channels,
gamma = gamma1,
beta = beta1,
mean = mean,
variance = variance,
input_name = input_name,
output_name = output_name) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_flatten | def convert_flatten(builder, layer, input_names, output_names, keras_layer):
"""Convert a flatten layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = (input_names[0], output_names[0])
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blob_order = 0
# using keras_layer.input.shape have a "?" (Dimension[None] at the front),
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if len(keras_layer.input.shape) == 4:
blob_order = 1
builder.add_flatten(name=layer, mode=blob_order, input_name=input_name, output_name=output_name) | python | def convert_flatten(builder, layer, input_names, output_names, keras_layer):
"""Convert a flatten layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = (input_names[0], output_names[0])
# blob_order == 0 if the input blob needs not be rearranged
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blob_order = 0
# using keras_layer.input.shape have a "?" (Dimension[None] at the front),
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_softmax | def convert_softmax(builder, layer, input_names, output_names, keras_layer):
"""Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = (input_names[0], output_names[0])
builder.add_softmax(name = layer, input_name = input_name,
output_name = output_name) | python | def convert_softmax(builder, layer, input_names, output_names, keras_layer):
"""Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = (input_names[0], output_names[0])
builder.add_softmax(name = layer, input_name = input_name,
output_name = output_name) | [
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apple/turicreate | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers.py | convert_permute | def convert_permute(builder, layer, input_names, output_names, keras_layer):
"""Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = (input_names[0], output_names[0])
keras_dims = keras_layer.dims
# Keras permute layer index begins at 1
if len(keras_dims) == 3:
# Keras input tensor interpret as (H,W,C)
x = list(np.array(keras_dims))
i1, i2, i3 = x.index(1), x.index(2), x.index(3)
x[i1], x[i2], x[i3] = 2, 3, 1
# add a sequence axis
x = [0] + x
dim = tuple(x)
elif len(keras_dims) == 4:
# Here we use Keras converter as a place holder for inserting
# permutations - the values here are not valid Keras dim parameters
# but parameters we need to use to convert to CoreML model
dim = keras_dims
else:
raise NotImplementedError('Supports only 3d permutation.')
builder.add_permute(name = layer, dim=dim, input_name = input_name,
output_name = output_name) | python | def convert_permute(builder, layer, input_names, output_names, keras_layer):
"""Convert a softmax layer from keras to coreml.
Parameters
keras_layer: layer
----------
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = (input_names[0], output_names[0])
keras_dims = keras_layer.dims
# Keras permute layer index begins at 1
if len(keras_dims) == 3:
# Keras input tensor interpret as (H,W,C)
x = list(np.array(keras_dims))
i1, i2, i3 = x.index(1), x.index(2), x.index(3)
x[i1], x[i2], x[i3] = 2, 3, 1
# add a sequence axis
x = [0] + x
dim = tuple(x)
elif len(keras_dims) == 4:
# Here we use Keras converter as a place holder for inserting
# permutations - the values here are not valid Keras dim parameters
# but parameters we need to use to convert to CoreML model
dim = keras_dims
else:
raise NotImplementedError('Supports only 3d permutation.')
builder.add_permute(name = layer, dim=dim, input_name = input_name,
output_name = output_name) | [
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apple/turicreate | src/external/xgboost/subtree/rabit/tracker/rabit_sge.py | sge_submit | def sge_submit(nslave, worker_args, worker_envs):
"""
customized submit script, that submit nslave jobs, each must contain args as parameter
note this can be a lambda function containing additional parameters in input
Parameters
nslave number of slave process to start up
args arguments to launch each job
this usually includes the parameters of master_uri and parameters passed into submit
"""
env_arg = ','.join(['%s=\"%s\"' % (k, str(v)) for k, v in worker_envs.items()])
cmd = 'qsub -cwd -t 1-%d -S /bin/bash' % nslave
if args.queue != 'default':
cmd += '-q %s' % args.queue
cmd += ' -N %s ' % args.jobname
cmd += ' -e %s -o %s' % (args.logdir, args.logdir)
cmd += ' -pe orte %d' % (args.vcores)
cmd += ' -v %s,PATH=${PATH}:.' % env_arg
cmd += ' %s %s' % (runscript, ' '.join(args.command + worker_args))
print cmd
subprocess.check_call(cmd, shell = True)
print 'Waiting for the jobs to get up...' | python | def sge_submit(nslave, worker_args, worker_envs):
"""
customized submit script, that submit nslave jobs, each must contain args as parameter
note this can be a lambda function containing additional parameters in input
Parameters
nslave number of slave process to start up
args arguments to launch each job
this usually includes the parameters of master_uri and parameters passed into submit
"""
env_arg = ','.join(['%s=\"%s\"' % (k, str(v)) for k, v in worker_envs.items()])
cmd = 'qsub -cwd -t 1-%d -S /bin/bash' % nslave
if args.queue != 'default':
cmd += '-q %s' % args.queue
cmd += ' -N %s ' % args.jobname
cmd += ' -e %s -o %s' % (args.logdir, args.logdir)
cmd += ' -pe orte %d' % (args.vcores)
cmd += ' -v %s,PATH=${PATH}:.' % env_arg
cmd += ' %s %s' % (runscript, ' '.join(args.command + worker_args))
print cmd
subprocess.check_call(cmd, shell = True)
print 'Waiting for the jobs to get up...' | [
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nslave number of slave process to start up
args arguments to launch each job
this usually includes the parameters of master_uri and parameters passed into submit | [
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apple/turicreate | src/unity/python/turicreate/meta/asttools/visitors/print_visitor.py | dump_ast | def dump_ast(ast, indent=' ', newline='\n'):
'''
Returns a string representing the ast.
:param ast: the ast to print.
:param indent: how far to indent a newline.
:param newline: The newline character.
'''
visitor = ASTPrinter(indent=indent, level=0, newline=newline)
visitor.visit(ast)
return visitor.dumps() | python | def dump_ast(ast, indent=' ', newline='\n'):
'''
Returns a string representing the ast.
:param ast: the ast to print.
:param indent: how far to indent a newline.
:param newline: The newline character.
'''
visitor = ASTPrinter(indent=indent, level=0, newline=newline)
visitor.visit(ast)
return visitor.dumps() | [
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apple/turicreate | src/unity/python/turicreate/meta/asttools/visitors/print_visitor.py | print_ast | def print_ast(ast, indent=' ', initlevel=0, newline='\n', file=sys.stdout):
'''
Pretty print an ast node.
:param ast: the ast to print.
:param indent: how far to indent a newline.
:param initlevel: starting indent level
:param newline: The newline character.
:param file: file object to print to
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node = ast.parse(source)
print_ast(node, indent='', newline='')
'''
visitor = ASTPrinter(indent=indent, level=initlevel, newline=newline)
visitor.visit(ast)
visitor.dump(file=file) | python | def print_ast(ast, indent=' ', initlevel=0, newline='\n', file=sys.stdout):
'''
Pretty print an ast node.
:param ast: the ast to print.
:param indent: how far to indent a newline.
:param initlevel: starting indent level
:param newline: The newline character.
:param file: file object to print to
To print a short ast you may want to use::
node = ast.parse(source)
print_ast(node, indent='', newline='')
'''
visitor = ASTPrinter(indent=indent, level=initlevel, newline=newline)
visitor.visit(ast)
visitor.dump(file=file) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | ctypes2numpy | def ctypes2numpy(cptr, length, dtype):
"""Convert a ctypes pointer array to a numpy array.
"""
if not isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
raise RuntimeError('expected float pointer')
res = np.zeros(length, dtype=dtype)
if not ctypes.memmove(res.ctypes.data, cptr, length * res.strides[0]):
raise RuntimeError('memmove failed')
return res | python | def ctypes2numpy(cptr, length, dtype):
"""Convert a ctypes pointer array to a numpy array.
"""
if not isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
raise RuntimeError('expected float pointer')
res = np.zeros(length, dtype=dtype)
if not ctypes.memmove(res.ctypes.data, cptr, length * res.strides[0]):
raise RuntimeError('memmove failed')
return res | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | _maybe_from_pandas | def _maybe_from_pandas(data, feature_names, feature_types):
""" Extract internal data from pd.DataFrame """
try:
import pandas as pd
except ImportError:
return data, feature_names, feature_types
if not isinstance(data, pd.DataFrame):
return data, feature_names, feature_types
dtypes = data.dtypes
if not all(dtype.name in ('int64', 'float64', 'bool') for dtype in dtypes):
raise ValueError('DataFrame.dtypes must be int, float or bool')
if feature_names is None:
feature_names = data.columns.format()
if feature_types is None:
mapper = {'int64': 'int', 'float64': 'q', 'bool': 'i'}
feature_types = [mapper[dtype.name] for dtype in dtypes]
data = data.values.astype('float')
return data, feature_names, feature_types | python | def _maybe_from_pandas(data, feature_names, feature_types):
""" Extract internal data from pd.DataFrame """
try:
import pandas as pd
except ImportError:
return data, feature_names, feature_types
if not isinstance(data, pd.DataFrame):
return data, feature_names, feature_types
dtypes = data.dtypes
if not all(dtype.name in ('int64', 'float64', 'bool') for dtype in dtypes):
raise ValueError('DataFrame.dtypes must be int, float or bool')
if feature_names is None:
feature_names = data.columns.format()
if feature_types is None:
mapper = {'int64': 'int', 'float64': 'q', 'bool': 'i'}
feature_types = [mapper[dtype.name] for dtype in dtypes]
data = data.values.astype('float')
return data, feature_names, feature_types | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix._init_from_csr | def _init_from_csr(self, csr):
"""
Initialize data from a CSR matrix.
"""
if len(csr.indices) != len(csr.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
self.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromCSR(c_array(ctypes.c_ulong, csr.indptr),
c_array(ctypes.c_uint, csr.indices),
c_array(ctypes.c_float, csr.data),
len(csr.indptr), len(csr.data),
ctypes.byref(self.handle))) | python | def _init_from_csr(self, csr):
"""
Initialize data from a CSR matrix.
"""
if len(csr.indices) != len(csr.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csr.indices), len(csr.data)))
self.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromCSR(c_array(ctypes.c_ulong, csr.indptr),
c_array(ctypes.c_uint, csr.indices),
c_array(ctypes.c_float, csr.data),
len(csr.indptr), len(csr.data),
ctypes.byref(self.handle))) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix._init_from_csc | def _init_from_csc(self, csc):
"""
Initialize data from a CSC matrix.
"""
if len(csc.indices) != len(csc.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
self.handle = ctypes.c_void_p()
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c_array(ctypes.c_float, csc.data),
len(csc.indptr), len(csc.data),
ctypes.byref(self.handle))) | python | def _init_from_csc(self, csc):
"""
Initialize data from a CSC matrix.
"""
if len(csc.indices) != len(csc.data):
raise ValueError('length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
self.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromCSC(c_array(ctypes.c_ulong, csc.indptr),
c_array(ctypes.c_uint, csc.indices),
c_array(ctypes.c_float, csc.data),
len(csc.indptr), len(csc.data),
ctypes.byref(self.handle))) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix._init_from_npy2d | def _init_from_npy2d(self, mat, missing):
"""
Initialize data from a 2-D numpy matrix.
"""
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
data = np.array(mat.reshape(mat.size), dtype=np.float32)
self.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromMat(data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
mat.shape[0], mat.shape[1],
ctypes.c_float(missing),
ctypes.byref(self.handle))) | python | def _init_from_npy2d(self, mat, missing):
"""
Initialize data from a 2-D numpy matrix.
"""
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
data = np.array(mat.reshape(mat.size), dtype=np.float32)
self.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixCreateFromMat(data.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
mat.shape[0], mat.shape[1],
ctypes.c_float(missing),
ctypes.byref(self.handle))) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.get_float_info | def get_float_info(self, field):
"""Get float property from the DMatrix.
Parameters
----------
field: str
The field name of the information
Returns
-------
info : array
a numpy array of float information of the data
"""
length = ctypes.c_ulong()
ret = ctypes.POINTER(ctypes.c_float)()
_check_call(_LIB.XGDMatrixGetFloatInfo(self.handle,
c_str(field),
ctypes.byref(length),
ctypes.byref(ret)))
return ctypes2numpy(ret, length.value, np.float32) | python | def get_float_info(self, field):
"""Get float property from the DMatrix.
Parameters
----------
field: str
The field name of the information
Returns
-------
info : array
a numpy array of float information of the data
"""
length = ctypes.c_ulong()
ret = ctypes.POINTER(ctypes.c_float)()
_check_call(_LIB.XGDMatrixGetFloatInfo(self.handle,
c_str(field),
ctypes.byref(length),
ctypes.byref(ret)))
return ctypes2numpy(ret, length.value, np.float32) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.get_uint_info | def get_uint_info(self, field):
"""Get unsigned integer property from the DMatrix.
Parameters
----------
field: str
The field name of the information
Returns
-------
info : array
a numpy array of float information of the data
"""
length = ctypes.c_ulong()
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c_str(field),
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return ctypes2numpy(ret, length.value, np.uint32) | python | def get_uint_info(self, field):
"""Get unsigned integer property from the DMatrix.
Parameters
----------
field: str
The field name of the information
Returns
-------
info : array
a numpy array of float information of the data
"""
length = ctypes.c_ulong()
ret = ctypes.POINTER(ctypes.c_uint)()
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.set_float_info | def set_float_info(self, field, data):
"""Set float type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array ofdata to be set
"""
_check_call(_LIB.XGDMatrixSetFloatInfo(self.handle,
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c_array(ctypes.c_float, data),
len(data))) | python | def set_float_info(self, field, data):
"""Set float type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array ofdata to be set
"""
_check_call(_LIB.XGDMatrixSetFloatInfo(self.handle,
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.set_uint_info | def set_uint_info(self, field, data):
"""Set uint type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array ofdata to be set
"""
_check_call(_LIB.XGDMatrixSetUIntInfo(self.handle,
c_str(field),
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len(data))) | python | def set_uint_info(self, field, data):
"""Set uint type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array ofdata to be set
"""
_check_call(_LIB.XGDMatrixSetUIntInfo(self.handle,
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.save_binary | def save_binary(self, fname, silent=True):
"""Save DMatrix to an XGBoost buffer.
Parameters
----------
fname : string
Name of the output buffer file.
silent : bool (optional; default: True)
If set, the output is suppressed.
"""
_check_call(_LIB.XGDMatrixSaveBinary(self.handle,
c_str(fname),
int(silent))) | python | def save_binary(self, fname, silent=True):
"""Save DMatrix to an XGBoost buffer.
Parameters
----------
fname : string
Name of the output buffer file.
silent : bool (optional; default: True)
If set, the output is suppressed.
"""
_check_call(_LIB.XGDMatrixSaveBinary(self.handle,
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.num_row | def num_row(self):
"""Get the number of rows in the DMatrix.
Returns
-------
number of rows : int
"""
ret = ctypes.c_ulong()
_check_call(_LIB.XGDMatrixNumRow(self.handle,
ctypes.byref(ret)))
return ret.value | python | def num_row(self):
"""Get the number of rows in the DMatrix.
Returns
-------
number of rows : int
"""
ret = ctypes.c_ulong()
_check_call(_LIB.XGDMatrixNumRow(self.handle,
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Returns
-------
number of rows : int | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.num_col | def num_col(self):
"""Get the number of columns (features) in the DMatrix.
Returns
-------
number of columns : int
"""
ret = ctypes.c_uint()
_check_call(_LIB.XGDMatrixNumCol(self.handle,
ctypes.byref(ret)))
return ret.value | python | def num_col(self):
"""Get the number of columns (features) in the DMatrix.
Returns
-------
number of columns : int
"""
ret = ctypes.c_uint()
_check_call(_LIB.XGDMatrixNumCol(self.handle,
ctypes.byref(ret)))
return ret.value | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.slice | def slice(self, rindex):
"""Slice the DMatrix and return a new DMatrix that only contains `rindex`.
Parameters
----------
rindex : list
List of indices to be selected.
Returns
-------
res : DMatrix
A new DMatrix containing only selected indices.
"""
res = DMatrix(None, feature_names=self.feature_names)
res.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixSliceDMatrix(self.handle,
c_array(ctypes.c_int, rindex),
len(rindex),
ctypes.byref(res.handle)))
return res | python | def slice(self, rindex):
"""Slice the DMatrix and return a new DMatrix that only contains `rindex`.
Parameters
----------
rindex : list
List of indices to be selected.
Returns
-------
res : DMatrix
A new DMatrix containing only selected indices.
"""
res = DMatrix(None, feature_names=self.feature_names)
res.handle = ctypes.c_void_p()
_check_call(_LIB.XGDMatrixSliceDMatrix(self.handle,
c_array(ctypes.c_int, rindex),
len(rindex),
ctypes.byref(res.handle)))
return res | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.feature_names | def feature_names(self, feature_names):
"""Set feature names (column labels).
Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names
"""
if not feature_names is None:
# validate feature name
if not isinstance(feature_names, list):
feature_names = list(feature_names)
if len(feature_names) != len(set(feature_names)):
raise ValueError('feature_names must be unique')
if len(feature_names) != self.num_col():
msg = 'feature_names must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
if not all(isinstance(f, STRING_TYPES) and f.isalnum()
for f in feature_names):
raise ValueError('all feature_names must be alphanumerics')
else:
# reset feature_types also
self.feature_types = None
self._feature_names = feature_names | python | def feature_names(self, feature_names):
"""Set feature names (column labels).
Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names
"""
if not feature_names is None:
# validate feature name
if not isinstance(feature_names, list):
feature_names = list(feature_names)
if len(feature_names) != len(set(feature_names)):
raise ValueError('feature_names must be unique')
if len(feature_names) != self.num_col():
msg = 'feature_names must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
if not all(isinstance(f, STRING_TYPES) and f.isalnum()
for f in feature_names):
raise ValueError('all feature_names must be alphanumerics')
else:
# reset feature_types also
self.feature_types = None
self._feature_names = feature_names | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | DMatrix.feature_types | def feature_types(self, feature_types):
"""Set feature types (column types).
This is for displaying the results and unrelated
to the learning process.
Parameters
----------
feature_types : list or None
Labels for features. None will reset existing feature names
"""
if not feature_types is None:
if self.feature_names is None:
msg = 'Unable to set feature types before setting names'
raise ValueError(msg)
if isinstance(feature_types, STRING_TYPES):
# single string will be applied to all columns
feature_types = [feature_types] * self.num_col()
if not isinstance(feature_types, list):
feature_types = list(feature_types)
if len(feature_types) != self.num_col():
msg = 'feature_types must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
valid = ('q', 'i', 'int', 'float')
if not all(isinstance(f, STRING_TYPES) and f in valid
for f in feature_types):
raise ValueError('all feature_names must be {i, q, int, float}')
self._feature_types = feature_types | python | def feature_types(self, feature_types):
"""Set feature types (column types).
This is for displaying the results and unrelated
to the learning process.
Parameters
----------
feature_types : list or None
Labels for features. None will reset existing feature names
"""
if not feature_types is None:
if self.feature_names is None:
msg = 'Unable to set feature types before setting names'
raise ValueError(msg)
if isinstance(feature_types, STRING_TYPES):
# single string will be applied to all columns
feature_types = [feature_types] * self.num_col()
if not isinstance(feature_types, list):
feature_types = list(feature_types)
if len(feature_types) != self.num_col():
msg = 'feature_types must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
valid = ('q', 'i', 'int', 'float')
if not all(isinstance(f, STRING_TYPES) and f in valid
for f in feature_types):
raise ValueError('all feature_names must be {i, q, int, float}')
self._feature_types = feature_types | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | Booster.update | def update(self, dtrain, iteration, fobj=None):
"""
Update for one iteration, with objective function calculated internally.
Parameters
----------
dtrain : DMatrix
Training data.
iteration : int
Current iteration number.
fobj : function
Customized objective function.
"""
if not isinstance(dtrain, DMatrix):
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
self._validate_features(dtrain)
if fobj is None:
_check_call(_LIB.XGBoosterUpdateOneIter(self.handle, iteration, dtrain.handle))
else:
pred = self.predict(dtrain)
grad, hess = fobj(pred, dtrain)
self.boost(dtrain, grad, hess) | python | def update(self, dtrain, iteration, fobj=None):
"""
Update for one iteration, with objective function calculated internally.
Parameters
----------
dtrain : DMatrix
Training data.
iteration : int
Current iteration number.
fobj : function
Customized objective function.
"""
if not isinstance(dtrain, DMatrix):
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
self._validate_features(dtrain)
if fobj is None:
_check_call(_LIB.XGBoosterUpdateOneIter(self.handle, iteration, dtrain.handle))
else:
pred = self.predict(dtrain)
grad, hess = fobj(pred, dtrain)
self.boost(dtrain, grad, hess) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | Booster.boost | def boost(self, dtrain, grad, hess):
"""
Boost the booster for one iteration, with customized gradient statistics.
Parameters
----------
dtrain : DMatrix
The training DMatrix.
grad : list
The first order of gradient.
hess : list
The second order of gradient.
"""
if len(grad) != len(hess):
raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
if not isinstance(dtrain, DMatrix):
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
self._validate_features(dtrain)
_check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
c_array(ctypes.c_float, grad),
c_array(ctypes.c_float, hess),
len(grad))) | python | def boost(self, dtrain, grad, hess):
"""
Boost the booster for one iteration, with customized gradient statistics.
Parameters
----------
dtrain : DMatrix
The training DMatrix.
grad : list
The first order of gradient.
hess : list
The second order of gradient.
"""
if len(grad) != len(hess):
raise ValueError('grad / hess length mismatch: {} / {}'.format(len(grad), len(hess)))
if not isinstance(dtrain, DMatrix):
raise TypeError('invalid training matrix: {}'.format(type(dtrain).__name__))
self._validate_features(dtrain)
_check_call(_LIB.XGBoosterBoostOneIter(self.handle, dtrain.handle,
c_array(ctypes.c_float, grad),
c_array(ctypes.c_float, hess),
len(grad))) | [
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apple/turicreate | src/external/xgboost/python-package/xgboost/core.py | Booster.eval_set | def eval_set(self, evals, iteration=0, feval=None):
# pylint: disable=invalid-name
"""Evaluate a set of data.
Parameters
----------
evals : list of tuples (DMatrix, string)
List of items to be evaluated.
iteration : int
Current iteration.
feval : function
Custom evaluation function.
Returns
-------
result: str
Evaluation result string.
"""
if feval is None:
for d in evals:
if not isinstance(d[0], DMatrix):
raise TypeError('expected DMatrix, got {}'.format(type(d[0]).__name__))
if not isinstance(d[1], STRING_TYPES):
raise TypeError('expected string, got {}'.format(type(d[1]).__name__))
self._validate_features(d[0])
dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
msg = ctypes.c_char_p()
_check_call(_LIB.XGBoosterEvalOneIter(self.handle, iteration,
dmats, evnames, len(evals),
ctypes.byref(msg)))
return msg.value
else:
res = '[%d]' % iteration
for dmat, evname in evals:
name, val = feval(self.predict(dmat), dmat)
res += '\t%s-%s:%f' % (evname, name, val)
return res | python | def eval_set(self, evals, iteration=0, feval=None):
# pylint: disable=invalid-name
"""Evaluate a set of data.
Parameters
----------
evals : list of tuples (DMatrix, string)
List of items to be evaluated.
iteration : int
Current iteration.
feval : function
Custom evaluation function.
Returns
-------
result: str
Evaluation result string.
"""
if feval is None:
for d in evals:
if not isinstance(d[0], DMatrix):
raise TypeError('expected DMatrix, got {}'.format(type(d[0]).__name__))
if not isinstance(d[1], STRING_TYPES):
raise TypeError('expected string, got {}'.format(type(d[1]).__name__))
self._validate_features(d[0])
dmats = c_array(ctypes.c_void_p, [d[0].handle for d in evals])
evnames = c_array(ctypes.c_char_p, [c_str(d[1]) for d in evals])
msg = ctypes.c_char_p()
_check_call(_LIB.XGBoosterEvalOneIter(self.handle, iteration,
dmats, evnames, len(evals),
ctypes.byref(msg)))
return msg.value
else:
res = '[%d]' % iteration
for dmat, evname in evals:
name, val = feval(self.predict(dmat), dmat)
res += '\t%s-%s:%f' % (evname, name, val)
return res | [
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Parameters
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evals : list of tuples (DMatrix, string)
List of items to be evaluated.
iteration : int
Current iteration.
feval : function
Custom evaluation function.
Returns
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result: str
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] | 74514c3f99e25b46f22c6e02977fe3da69221c2e | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/external/xgboost/python-package/xgboost/core.py#L712-L750 | train |
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