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train | _toolkits_select_columns | Same as select columns but redirect runtime error to ToolkitError. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _toolkits_select_columns(dataset, columns):
"""
Same as select columns but redirect runtime error to ToolkitError.
"""
try:
return dataset.select_columns(columns)
except RuntimeError:
missing_features = list(set(columns).difference(set(dataset.column_names())))
raise Tool... | def _toolkits_select_columns(dataset, columns):
"""
Same as select columns but redirect runtime error to ToolkitError.
"""
try:
return dataset.select_columns(columns)
except RuntimeError:
missing_features = list(set(columns).difference(set(dataset.column_names())))
raise Tool... | [
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train | _raise_error_if_column_exists | Check if a column exists in an SFrame with error message. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _raise_error_if_column_exists(dataset, column_name = 'dataset',
dataset_variable_name = 'dataset',
column_name_error_message_name = 'column_name'):
"""
Check if a column exists in an SFrame with error message.
"""
err_msg = 'The SFrame {0} must... | def _raise_error_if_column_exists(dataset, column_name = 'dataset',
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"""
Check if a column exists in an SFrame with error message.
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train | _check_categorical_option_type | Check whether or not the requested option is one of the allowed values. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _check_categorical_option_type(option_name, option_value, possible_values):
"""
Check whether or not the requested option is one of the allowed values.
"""
err_msg = '{0} is not a valid option for {1}. '.format(option_value, option_name)
err_msg += ' Expected one of: '.format(possible_values)
... | def _check_categorical_option_type(option_name, option_value, possible_values):
"""
Check whether or not the requested option is one of the allowed values.
"""
err_msg = '{0} is not a valid option for {1}. '.format(option_value, option_name)
err_msg += ' Expected one of: '.format(possible_values)
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train | _raise_error_if_not_sarray | Check if the input is an SArray. Provide a proper error
message otherwise. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _raise_error_if_not_sarray(dataset, variable_name="SArray"):
"""
Check if the input is an SArray. Provide a proper error
message otherwise.
"""
err_msg = "Input %s is not an SArray."
if not isinstance(dataset, _SArray):
raise ToolkitError(err_msg % variable_name) | def _raise_error_if_not_sarray(dataset, variable_name="SArray"):
"""
Check if the input is an SArray. Provide a proper error
message otherwise.
"""
err_msg = "Input %s is not an SArray."
if not isinstance(dataset, _SArray):
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train | _raise_error_if_not_sframe | Check if the input is an SFrame. Provide a proper error
message otherwise. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _raise_error_if_not_sframe(dataset, variable_name="SFrame"):
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Check if the input is an SFrame. Provide a proper error
message otherwise.
"""
err_msg = "Input %s is not an SFrame. If it is a Pandas DataFrame,"
err_msg += " you may use the to_sframe() function to convert it to an SFrame."
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"""
Check if the input is an SFrame. Provide a proper error
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train | _raise_error_if_sframe_empty | Check if the input is empty. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _raise_error_if_sframe_empty(dataset, variable_name="SFrame"):
"""
Check if the input is empty.
"""
err_msg = "Input %s either has no rows or no columns. A non-empty SFrame "
err_msg += "is required."
if dataset.num_rows() == 0 or dataset.num_columns() == 0:
raise ToolkitError(err_m... | def _raise_error_if_sframe_empty(dataset, variable_name="SFrame"):
"""
Check if the input is empty.
"""
err_msg = "Input %s either has no rows or no columns. A non-empty SFrame "
err_msg += "is required."
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train | _raise_error_evaluation_metric_is_valid | Check if the input is an SFrame. Provide a proper error
message otherwise. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _raise_error_evaluation_metric_is_valid(metric, allowed_metrics):
"""
Check if the input is an SFrame. Provide a proper error
message otherwise.
"""
err_msg = "Evaluation metric '%s' not recognized. The supported evaluation"
err_msg += " metrics are (%s)."
if metric not in allowed_metr... | def _raise_error_evaluation_metric_is_valid(metric, allowed_metrics):
"""
Check if the input is an SFrame. Provide a proper error
message otherwise.
"""
err_msg = "Evaluation metric '%s' not recognized. The supported evaluation"
err_msg += " metrics are (%s)."
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train | _numeric_param_check_range | Checks if numeric parameter is within given range | src/unity/python/turicreate/toolkits/_internal_utils.py | def _numeric_param_check_range(variable_name, variable_value, range_bottom, range_top):
"""
Checks if numeric parameter is within given range
"""
err_msg = "%s must be between %i and %i"
if variable_value < range_bottom or variable_value > range_top:
raise ToolkitError(err_msg % (variable_n... | def _numeric_param_check_range(variable_name, variable_value, range_bottom, range_top):
"""
Checks if numeric parameter is within given range
"""
err_msg = "%s must be between %i and %i"
if variable_value < range_bottom or variable_value > range_top:
raise ToolkitError(err_msg % (variable_n... | [
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train | _validate_data | Validate and canonicalize training and validation data.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable.
features : list[string], optional
List of feature names used.
validation_s... | src/unity/python/turicreate/toolkits/_internal_utils.py | def _validate_data(dataset, target, features=None, validation_set='auto'):
"""
Validate and canonicalize training and validation data.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable.
... | def _validate_data(dataset, target, features=None, validation_set='auto'):
"""
Validate and canonicalize training and validation data.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : string
Name of the column containing the target variable.
... | [
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train | _validate_row_label | Validate a row label column. If the row label is not specified, a column is
created with row numbers, named with the string in the `default_label`
parameter.
Parameters
----------
dataset : SFrame
Input dataset.
label : str, optional
Name of the column containing row labels.
... | src/unity/python/turicreate/toolkits/_internal_utils.py | def _validate_row_label(dataset, label=None, default_label='__id'):
"""
Validate a row label column. If the row label is not specified, a column is
created with row numbers, named with the string in the `default_label`
parameter.
Parameters
----------
dataset : SFrame
Input dataset.... | def _validate_row_label(dataset, label=None, default_label='__id'):
"""
Validate a row label column. If the row label is not specified, a column is
created with row numbers, named with the string in the `default_label`
parameter.
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----------
dataset : SFrame
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train | _mac_ver | Returns Mac version as a tuple of integers, making it easy to do proper
version comparisons. On non-Macs, it returns an empty tuple. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _mac_ver():
"""
Returns Mac version as a tuple of integers, making it easy to do proper
version comparisons. On non-Macs, it returns an empty tuple.
"""
import platform
import sys
if sys.platform == 'darwin':
ver_str = platform.mac_ver()[0]
return tuple([int(v) for v in v... | def _mac_ver():
"""
Returns Mac version as a tuple of integers, making it easy to do proper
version comparisons. On non-Macs, it returns an empty tuple.
"""
import platform
import sys
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train | _print_neural_compute_device | Print a message making it clear to the user what compute resource is used in
neural network training. | src/unity/python/turicreate/toolkits/_internal_utils.py | def _print_neural_compute_device(cuda_gpus, use_mps, cuda_mem_req=None, has_mps_impl=True):
"""
Print a message making it clear to the user what compute resource is used in
neural network training.
"""
num_cuda_gpus = len(cuda_gpus)
if num_cuda_gpus >= 1:
gpu_names = ', '.join(gpu['name'... | def _print_neural_compute_device(cuda_gpus, use_mps, cuda_mem_req=None, has_mps_impl=True):
"""
Print a message making it clear to the user what compute resource is used in
neural network training.
"""
num_cuda_gpus = len(cuda_gpus)
if num_cuda_gpus >= 1:
gpu_names = ', '.join(gpu['name'... | [
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train | _GetMessageFromFactory | Get a proto class from the MessageFactory by name.
Args:
factory: a MessageFactory instance.
full_name: str, the fully qualified name of the proto type.
Returns:
A class, for the type identified by full_name.
Raises:
KeyError, if the proto is not found in the factory's descriptor pool. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/proto_builder.py | def _GetMessageFromFactory(factory, full_name):
"""Get a proto class from the MessageFactory by name.
Args:
factory: a MessageFactory instance.
full_name: str, the fully qualified name of the proto type.
Returns:
A class, for the type identified by full_name.
Raises:
KeyError, if the proto is n... | def _GetMessageFromFactory(factory, full_name):
"""Get a proto class from the MessageFactory by name.
Args:
factory: a MessageFactory instance.
full_name: str, the fully qualified name of the proto type.
Returns:
A class, for the type identified by full_name.
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train | MakeSimpleProtoClass | Create a Protobuf class whose fields are basic types.
Note: this doesn't validate field names!
Args:
fields: dict of {name: field_type} mappings for each field in the proto. If
this is an OrderedDict the order will be maintained, otherwise the
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Note: this doesn't validate field names!
Args:
fields: dict of {name: field_type} mappings for each field in the proto. If
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train | _MakeFileDescriptorProto | Populate FileDescriptorProto for MessageFactory's DescriptorPool. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/proto_builder.py | def _MakeFileDescriptorProto(proto_file_name, full_name, field_items):
"""Populate FileDescriptorProto for MessageFactory's DescriptorPool."""
package, name = full_name.rsplit('.', 1)
file_proto = descriptor_pb2.FileDescriptorProto()
file_proto.name = os.path.join(package.replace('.', '/'), proto_file_name)
f... | def _MakeFileDescriptorProto(proto_file_name, full_name, field_items):
"""Populate FileDescriptorProto for MessageFactory's DescriptorPool."""
package, name = full_name.rsplit('.', 1)
file_proto = descriptor_pb2.FileDescriptorProto()
file_proto.name = os.path.join(package.replace('.', '/'), proto_file_name)
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train | convert | Convert a decision tree model to protobuf format.
Parameters
----------
decision_tree : DecisionTreeClassifier
A trained scikit-learn tree model.
input_name: str
Name of the input columns.
output_name: str
Name of the output columns.
Returns
-------
model_spec... | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_decision_tree_classifier.py | def convert(model, input_name, output_features):
"""Convert a decision tree model to protobuf format.
Parameters
----------
decision_tree : DecisionTreeClassifier
A trained scikit-learn tree model.
input_name: str
Name of the input columns.
output_name: str
Name of the... | def convert(model, input_name, output_features):
"""Convert a decision tree model to protobuf format.
Parameters
----------
decision_tree : DecisionTreeClassifier
A trained scikit-learn tree model.
input_name: str
Name of the input columns.
output_name: str
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train | _get_model_metadata | Returns user-defined metadata, making sure information all models should
have is also available, as a dictionary | src/unity/python/turicreate/toolkits/_coreml_utils.py | def _get_model_metadata(model_class, metadata, version=None):
"""
Returns user-defined metadata, making sure information all models should
have is also available, as a dictionary
"""
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info = {
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train | _set_model_metadata | Sets user-defined metadata, making sure information all models should have
is also available | src/unity/python/turicreate/toolkits/_coreml_utils.py | def _set_model_metadata(mlmodel, model_class, metadata, version=None):
"""
Sets user-defined metadata, making sure information all models should have
is also available
"""
info = _get_model_metadata(model_class, metadata, version)
mlmodel.user_defined_metadata.update(info) | def _set_model_metadata(mlmodel, model_class, metadata, version=None):
"""
Sets user-defined metadata, making sure information all models should have
is also available
"""
info = _get_model_metadata(model_class, metadata, version)
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train | _ToCamelCase | Converts name to camel-case and returns it. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor.py | def _ToCamelCase(name):
"""Converts name to camel-case and returns it."""
capitalize_next = False
result = []
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if c == '_':
if result:
capitalize_next = True
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capitalize_next = False
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result += c
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"""Converts name to camel-case and returns it."""
capitalize_next = False
result = []
for c in name:
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if result:
capitalize_next = True
elif capitalize_next:
result.append(c.upper())
capitalize_next = False
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result += c
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train | _ToJsonName | Converts name to Json name and returns it. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor.py | def _ToJsonName(name):
"""Converts name to Json name and returns it."""
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return ''.join(result) | def _ToJsonName(name):
"""Converts name to Json name and returns it."""
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result = []
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capitalize_next = True
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train | DescriptorBase._SetOptions | Sets the descriptor's options
This function is used in generated proto2 files to update descriptor
options. It must not be used outside proto2. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor.py | def _SetOptions(self, options, options_class_name):
"""Sets the descriptor's options
This function is used in generated proto2 files to update descriptor
options. It must not be used outside proto2.
"""
self._options = options
self._options_class_name = options_class_name
# Does this descr... | def _SetOptions(self, options, options_class_name):
"""Sets the descriptor's options
This function is used in generated proto2 files to update descriptor
options. It must not be used outside proto2.
"""
self._options = options
self._options_class_name = options_class_name
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train | DescriptorBase.GetOptions | Retrieves descriptor options.
This method returns the options set or creates the default options for the
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"""
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"""Retrieves descriptor options.
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"""
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train | _NestedDescriptorBase.CopyToProto | Copies this to the matching proto in descriptor_pb2.
Args:
proto: An empty proto instance from descriptor_pb2.
Raises:
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Args:
proto: An empty proto instance from descriptor_pb2.
Raises:
Error: If self couldnt be serialized, due to to few constructor arguments.
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proto: An empty proto instance from descriptor_pb2.
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train | Descriptor.EnumValueName | Returns the string name of an enum value.
This is just a small helper method to simplify a common operation.
Args:
enum: string name of the Enum.
value: int, value of the enum.
Returns:
string name of the enum value.
Raises:
KeyError if either the Enum doesn't exist or the va... | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/descriptor.py | def EnumValueName(self, enum, value):
"""Returns the string name of an enum value.
This is just a small helper method to simplify a common operation.
Args:
enum: string name of the Enum.
value: int, value of the enum.
Returns:
string name of the enum value.
Raises:
KeyErr... | def EnumValueName(self, enum, value):
"""Returns the string name of an enum value.
This is just a small helper method to simplify a common operation.
Args:
enum: string name of the Enum.
value: int, value of the enum.
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train | resolve_reference | Given a target_reference, made in context of 'project',
returns the AbstractTarget instance that is referred to, as well
as properties explicitly specified for this reference. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def resolve_reference(target_reference, project):
""" Given a target_reference, made in context of 'project',
returns the AbstractTarget instance that is referred to, as well
as properties explicitly specified for this reference.
"""
# Separate target name from properties override
assert isinsta... | def resolve_reference(target_reference, project):
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"""
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train | generate_from_reference | Attempts to generate the target given by target reference, which
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Returns a list consisting of
- usage requirements
- generated virtual targets, if any
target_reference: Target reference
project: Project where the reference is made
prop... | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def generate_from_reference(target_reference, project, property_set_):
""" Attempts to generate the target given by target reference, which
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Returns a list consisting of
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- generated virtual targets, if any
target_reference: Targe... | def generate_from_reference(target_reference, project, property_set_):
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Returns a list consisting of
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train | TargetRegistry.main_target_alternative | Registers the specified target as a main target alternatives.
Returns 'target'. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def main_target_alternative (self, target):
""" Registers the specified target as a main target alternatives.
Returns 'target'.
"""
assert isinstance(target, AbstractTarget)
target.project ().add_alternative (target)
return target | def main_target_alternative (self, target):
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target.project ().add_alternative (target)
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train | TargetRegistry.main_target_sources | Return the list of sources to use, if main target rule is invoked
with 'sources'. If there are any objects in 'sources', they are treated
as main target instances, and the name of such targets are adjusted to
be '<name_of_this_target>__<name_of_source_target>'. Such renaming
is disabled ... | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def main_target_sources (self, sources, main_target_name, no_renaming=0):
"""Return the list of sources to use, if main target rule is invoked
with 'sources'. If there are any objects in 'sources', they are treated
as main target instances, and the name of such targets are adjusted to
be... | def main_target_sources (self, sources, main_target_name, no_renaming=0):
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"""Returns the requirement to use when declaring a main target,
which are obtained by
- translating all specified property paths, and
- refining project requirements with the one specified for the target
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train | TargetRegistry.main_target_default_build | Return the default build value to use when declaring a main target,
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train | TargetRegistry.start_building | Helper rules to detect cycles in main target references. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def start_building (self, main_target_instance):
""" Helper rules to detect cycles in main target references.
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assert isinstance(main_target_instance, MainTarget)
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train | TargetRegistry.create_typed_target | Creates a TypedTarget with the specified properties.
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""" Creates a TypedTarget with the specified properties.
The 'name', 'sources', 'requirements', 'default_build' and
'usage_requirements' are assumed to be in the form specified
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""" Creates a TypedTarget with the specified properties.
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train | ProjectTarget.generate | Generates all possible targets contained in this project. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def generate (self, ps):
""" Generates all possible targets contained in this project.
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assert isinstance(ps, property_set.PropertySet)
self.manager_.targets().log(
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train | ProjectTarget.mark_targets_as_explicit | Add 'target' to the list of targets in this project
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# Record the name of the target, not instance, since this
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train | ProjectTarget.add_alternative | Add new target alternative. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def add_alternative (self, target_instance):
""" Add new target alternative.
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assert isinstance(target_instance, AbstractTarget)
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train | ProjectTarget.has_main_target | Tells if a main target with the specified name exists. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def has_main_target (self, name):
"""Tells if a main target with the specified name exists."""
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self.build_main_targets()
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train | ProjectTarget.create_main_target | Returns a 'MainTarget' class instance corresponding to the 'name'. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def create_main_target (self, name):
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train | ProjectTarget.find_really | Find and return the target with the specified id, treated
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""" Find and return the target with the specified id, treated
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"""
assert isinstance(id, basestring)
result = None
current_location = self.get ('location')
__re_split_project_target = re.compile (r'(.*)//(.*)')
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""" Find and return the target with the specified id, treated
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train | ProjectTarget.add_constant | Adds a new constant for this project.
The constant will be available for use in Jamfile
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"""Adds a new constant for this project.
The constant will be available for use in Jamfile
module for this project. If 'path' is true,
the constant will be interpreted relatively
to the location of project.
"""
assert ... | def add_constant(self, name, value, path=0):
"""Adds a new constant for this project.
The constant will be available for use in Jamfile
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the constant will be interpreted relatively
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train | MainTarget.add_alternative | Add a new alternative for this target. | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def add_alternative (self, target):
""" Add a new alternative for this target.
"""
assert isinstance(target, BasicTarget)
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if self.alternatives_ and self.default_build_ != d:
get_manager().errors()("default build must be identical in all al... | def add_alternative (self, target):
""" Add a new alternative for this target.
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assert isinstance(target, BasicTarget)
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train | MainTarget.__select_alternatives | Returns the best viable alternative for this property_set
See the documentation for selection rules.
# TODO: shouldn't this be 'alternative' (singular)? | deps/src/boost_1_68_0/tools/build/src/build/targets.py | def __select_alternatives (self, property_set_, debug):
""" Returns the best viable alternative for this property_set
See the documentation for selection rules.
# TODO: shouldn't this be 'alternative' (singular)?
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# When selecting alternatives we have to consider defau... | def __select_alternatives (self, property_set_, debug):
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train | BasicTarget.generate_dependency_properties | Takes a target reference, which might be either target id
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train | BasicTarget.compute_usage_requirements | Given the set of generated targets, and refined build
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train | register_globals | Registers all features and variants declared by this module. | deps/src/boost_1_68_0/tools/build/src/tools/builtin.py | def register_globals ():
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train | create | Create a model that makes recommendations using item popularity. When no
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random_seed=0,
verbose=True):
"""
Create a model that makes recommendations using item popularity. When no
target column is provided, the popularity is ... | def create(observation_data,
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user_data=None, item_data=None,
random_seed=0,
verbose=True):
"""
Create a model that makes recommendations using item popularity. When no
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train | XGBModel.get_params | Get parameter.s | src/external/xgboost/python-package/xgboost/sklearn.py | def get_params(self, deep=False):
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if params['missing'] is np.nan:
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"""Get parameter.s"""
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if params['missing'] is np.nan:
params['missing'] = None # sklearn doesn't handle nan. see #4725
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train | XGBModel.get_xgb_params | Get xgboost type parameters. | src/external/xgboost/python-package/xgboost/sklearn.py | def get_xgb_params(self):
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if self.nthread <= 0:
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train | XGBModel.fit | Fit the gradient boosting model
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X : array_like
Feature matrix
y : array_like
Labels
eval_set : list, optional
A list of (X, y) tuple pairs to use as a validation set for
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eval_metric : st... | src/external/xgboost/python-package/xgboost/sklearn.py | def fit(self, X, y, eval_set=None, eval_metric=None,
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# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
Fit the gradient boosting model
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----------
X : array_like
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# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
Fit the gradient boosting model
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train | XGBClassifier.fit | Fit gradient boosting classifier
Parameters
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X : array_like
Feature matrix
y : array_like
Labels
sample_weight : array_like
Weight for each instance
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train | add_grist | Transform a string by bracketing it with "<>". If already bracketed, does nothing.
features: one string or a sequence of strings
return: the gristed string, if features is a string, or a sequence of gristed strings, if features is a sequence | deps/src/boost_1_68_0/tools/build/src/util/utility.py | def add_grist (features):
""" Transform a string by bracketing it with "<>". If already bracketed, does nothing.
features: one string or a sequence of strings
return: the gristed string, if features is a string, or a sequence of gristed strings, if features is a sequence
"""
assert is_iterab... | def add_grist (features):
""" Transform a string by bracketing it with "<>". If already bracketed, does nothing.
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train | replace_grist | Replaces the grist of a string by a new one.
Returns the string with the new grist. | deps/src/boost_1_68_0/tools/build/src/util/utility.py | def replace_grist (features, new_grist):
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"""
assert is_iterable_typed(features, basestring) or isinstance(features, basestring)
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Returns the string with the new grist.
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train | get_value | Gets the value of a property, that is, the part following the grist, if any. | deps/src/boost_1_68_0/tools/build/src/util/utility.py | def get_value (property):
""" Gets the value of a property, that is, the part following the grist, if any.
"""
assert is_iterable_typed(property, basestring) or isinstance(property, basestring)
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""" Gets the value of a property, that is, the part following the grist, if any.
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train | get_grist | Returns the grist of a string.
If value is a sequence, does it for every value and returns the result as a sequence. | deps/src/boost_1_68_0/tools/build/src/util/utility.py | def get_grist (value):
""" Returns the grist of a string.
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"""
assert is_iterable_typed(value, basestring) or isinstance(value, basestring)
def get_grist_one (name):
split = __re_grist_and_value.match (n... | def get_grist (value):
""" Returns the grist of a string.
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train | ungrist | Returns the value without grist.
If value is a sequence, does it for every value and returns the result as a sequence. | deps/src/boost_1_68_0/tools/build/src/util/utility.py | def ungrist (value):
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"""
assert is_iterable_typed(value, basestring) or isinstance(value, basestring)
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train | replace_suffix | Replaces the suffix of name by new_suffix.
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"""
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train | split_action_id | Splits an id in the toolset and specific rule parts. E.g.
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toolset = split [0]
name = ''
if len (split) > 1:
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retu... | def split_action_id (id):
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train | on_windows | Returns true if running on windows, whether in cygwin or not. | deps/src/boost_1_68_0/tools/build/src/util/utility.py | def on_windows ():
""" Returns true if running on windows, whether in cygwin or not.
"""
if bjam.variable("NT"):
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elif bjam.variable("UNIX"):
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if uname and uname[0].startswith("CYGWIN"):
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""" Returns true if running on windows, whether in cygwin or not.
"""
if bjam.variable("NT"):
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train | _validate_dataset | Validate the main Kmeans dataset.
Parameters
----------
dataset: SFrame
Input dataset. | src/unity/python/turicreate/toolkits/clustering/kmeans.py | def _validate_dataset(dataset):
"""
Validate the main Kmeans dataset.
Parameters
----------
dataset: SFrame
Input dataset.
"""
if not (isinstance(dataset, _SFrame)):
raise TypeError("Input 'dataset' must be an SFrame.")
if dataset.num_rows() == 0 or dataset.num_columns(... | def _validate_dataset(dataset):
"""
Validate the main Kmeans dataset.
Parameters
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dataset: SFrame
Input dataset.
"""
if not (isinstance(dataset, _SFrame)):
raise TypeError("Input 'dataset' must be an SFrame.")
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Parameters
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initial_centers : SFrame
Initial cluster center locations, in SFrame form.
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Initial cluster center locations, in SFrame form.
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Specified number of clusters.
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Validate the combination of the `num_clusters` and `initial_centers`
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train | _validate_features | Identify the subset of desired `features` that are valid for the Kmeans
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Desired feature names.
column_type_map : dict[str, type]
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dataset : SFrame
Dataset of new observations. Must include the features used for
... | src/unity/python/turicreate/toolkits/clustering/kmeans.py | def predict(self, dataset, output_type='cluster_id', verbose=True):
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Return predicted cluster label for instances in the new 'dataset'.
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train | KmeansModel._get | Return the value of a given field.
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"""
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Return the value of a given field.
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train | count_ngrams | Return an SArray of ``dict`` type where each element contains the count
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"""
Compute the TF-IDF scores for each word in each document. The collection
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'''
Remove words that occur below a certain number of times in an SArray.
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For a given query and set of documents, compute the BM25 score for each
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train | parse_sparse | Parse a file that's in libSVM format. In libSVM format each line of the text
file represents a document in bag of words format:
num_unique_words_in_doc word_id:count another_id:count
The word_ids have 0-based indexing, i.e. 0 corresponds to the first
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Parameters
---... | src/unity/python/turicreate/toolkits/text_analytics/_util.py | def parse_sparse(filename, vocab_filename):
"""
Parse a file that's in libSVM format. In libSVM format each line of the text
file represents a document in bag of words format:
num_unique_words_in_doc word_id:count another_id:count
The word_ids have 0-based indexing, i.e. 0 corresponds to the first... | def parse_sparse(filename, vocab_filename):
"""
Parse a file that's in libSVM format. In libSVM format each line of the text
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num_unique_words_in_doc word_id:count another_id:count
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train | parse_docword | Parse a file that's in "docword" format. This consists of a 3-line header
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comprised of the document count, the vocabulary count, and the number of
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train | random_split | Utility for performing a random split for text data that is already in
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set.
Parameters
----------
dataset : SArray of type dict, SFrame with columns ... | src/unity/python/turicreate/toolkits/text_analytics/_util.py | def random_split(dataset, prob=.5):
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bag-of-words format. For each (word, count) pair in a particular element,
the counts are uniformly partitioned in either a training set or a test
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----------
dat... | def random_split(dataset, prob=.5):
"""
Utility for performing a random split for text data that is already in
bag-of-words format. For each (word, count) pair in a particular element,
the counts are uniformly partitioned in either a training set or a test
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train | train | Train a booster with given parameters.
Parameters
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params : dict
Booster params.
dtrain : DMatrix
Data to be trained.
num_boost_round: int
Number of boosting iterations.
watchlist (evals): list of pairs (DMatrix, string)
List of items to be evaluated du... | src/external/xgboost/python-package/xgboost/training.py | def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
maximize=False, early_stopping_rounds=None, evals_result=None,
verbose_eval=True, learning_rates=None, xgb_model=None):
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
"""Tra... | def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
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train | mknfold | Make an n-fold list of CVPack from random indices. | src/external/xgboost/python-package/xgboost/training.py | def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None):
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evals = list(evals)
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idset = [randidx[(i * kstep): min(len(randidx),... | def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None):
"""
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"""
evals = list(evals)
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train | aggcv | Aggregate cross-validation results. | src/external/xgboost/python-package/xgboost/training.py | def aggcv(rlist, show_stdv=True, show_progress=None, as_pandas=True):
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Aggregate cross-validation results.
"""
cvmap = {}
idx = rlist[0].split()[0]
for line in rlist:
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Aggregate cross-validation results.
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cvmap = {}
idx = rlist[0].split()[0]
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train | cv | Cross-validation with given paramaters.
Parameters
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params : dict
Booster params.
dtrain : DMatrix
Data to be trained.
num_boost_round : int
Number of boosting iterations.
nfold : int
Number of folds in CV.
metrics : list of strings
Evaluati... | src/external/xgboost/python-package/xgboost/training.py | def cv(params, dtrain, num_boost_round=10, nfold=3, metrics=(),
obj=None, feval=None, fpreproc=None, as_pandas=True,
show_progress=None, show_stdv=True, seed=0):
# pylint: disable = invalid-name
"""Cross-validation with given paramaters.
Parameters
----------
params : dict
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params : dict
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train | create | Create a :class:`ImageClassifier` model.
Parameters
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dataset : SFrame
Input data. The column named by the 'feature' parameter will be
extracted for modeling.
target : string, or int
Name of the column containing the target variable. The values in this
column m... | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def create(dataset, target, feature=None, model = 'resnet-50',
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train | ImageClassifier._get_native_state | Save the model as a dictionary, which can be loaded with the
:py:func:`~turicreate.load_model` method. | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def _get_native_state(self):
"""
Save the model as a dictionary, which can be loaded with the
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"""
state = self.__proxy__.get_state()
state['classifier'] = state['classifier'].__proxy__
del state['feature_extractor']
... | def _get_native_state(self):
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Save the model as a dictionary, which can be loaded with the
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"""
state = self.__proxy__.get_state()
state['classifier'] = state['classifier'].__proxy__
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train | ImageClassifier._load_version | A function to load a previously saved ImageClassifier
instance. | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def _load_version(cls, state, version):
"""
A function to load a previously saved ImageClassifier
instance.
"""
_tkutl._model_version_check(version, cls._PYTHON_IMAGE_CLASSIFIER_VERSION)
from turicreate.toolkits.classifier.logistic_classifier import LogisticClassifier
... | def _load_version(cls, state, version):
"""
A function to load a previously saved ImageClassifier
instance.
"""
_tkutl._model_version_check(version, cls._PYTHON_IMAGE_CLASSIFIER_VERSION)
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train | ImageClassifier.predict | Return predictions for ``dataset``, using the trained logistic
regression model. Predictions can be generated as class labels,
probabilities that the target value is True, or margins (i.e. the
distance of the observations from the hyperplane separating the
classes). `probability_vector` ... | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def predict(self, dataset, output_type='class', batch_size=64):
"""
Return predictions for ``dataset``, using the trained logistic
regression model. Predictions can be generated as class labels,
probabilities that the target value is True, or margins (i.e. the
distance of the obs... | def predict(self, dataset, output_type='class', batch_size=64):
"""
Return predictions for ``dataset``, using the trained logistic
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train | ImageClassifier.predict_topk | Return top-k predictions for the ``dataset``, using the trained model.
Predictions are returned as an SFrame with three columns: `id`,
`class`, and `probability`, `margin`, or `rank`, depending on the ``output_type``
parameter. Input dataset size must be the same as for training of the model.
... | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def predict_topk(self, dataset, output_type="probability", k=3, batch_size=64):
"""
Return top-k predictions for the ``dataset``, using the trained model.
Predictions are returned as an SFrame with three columns: `id`,
`class`, and `probability`, `margin`, or `rank`, depending on the ``... | def predict_topk(self, dataset, output_type="probability", k=3, batch_size=64):
"""
Return top-k predictions for the ``dataset``, using the trained model.
Predictions are returned as an SFrame with three columns: `id`,
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train | ImageClassifier.evaluate | Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include columns with the same
names as the target and features used for model training. Additi... | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include colum... | def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset of new observations. Must include colum... | [
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train | ImageClassifier.export_coreml | Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('myModel.mlmodel') | src/unity/python/turicreate/toolkits/image_classifier/image_classifier.py | def export_coreml(self, filename):
"""
Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('myModel.mlmodel')
"""
import coremltools
# First define three internal helper functions
... | def export_coreml(self, filename):
"""
Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('myModel.mlmodel')
"""
import coremltools
# First define three internal helper functions
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train | NetGraph.make_input_layers | Extract the ordering of the input layers. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology2.py | def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
in_nodes = self.model._inbound_nodes if hasattr(
self.model,'_inbound_nodes') else self.model.inbound_nodes
if hasattr(self.model, 'input_layers'):
... | def make_input_layers(self):
"""
Extract the ordering of the input layers.
"""
self.input_layers = []
in_nodes = self.model._inbound_nodes if hasattr(
self.model,'_inbound_nodes') else self.model.inbound_nodes
if hasattr(self.model, 'input_layers'):
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train | NetGraph.make_output_layers | Extract the ordering of output layers. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology2.py | def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
self.output_layers = []
# import pytest; pytest.set_trace()
if hasattr(self.model, 'output_layers'):
# find corresponding output layers in CoreML model
# assume output lay... | def make_output_layers(self):
"""
Extract the ordering of output layers.
"""
self.output_layers = []
# import pytest; pytest.set_trace()
if hasattr(self.model, 'output_layers'):
# find corresponding output layers in CoreML model
# assume output lay... | [
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train | NetGraph._remove_layer_and_reconnect | Remove the layer, and reconnect each of its predecessor to each of
its successor | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology2.py | def _remove_layer_and_reconnect(self, layer):
""" Remove the layer, and reconnect each of its predecessor to each of
its successor
"""
successors = self.get_successors(layer)
predecessors = self.get_predecessors(layer)
# remove layer's edges
for succ in successors... | def _remove_layer_and_reconnect(self, layer):
""" Remove the layer, and reconnect each of its predecessor to each of
its successor
"""
successors = self.get_successors(layer)
predecessors = self.get_predecessors(layer)
# remove layer's edges
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train | NetGraph.defuse_activation | Defuse the fused activation layers in the network. | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_topology2.py | def defuse_activation(self):
""" Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
if (isinstance(k_layer, _keras.la... | def defuse_activation(self):
""" Defuse the fused activation layers in the network.
"""
idx, nb_layers = 0, len(self.layer_list)
while idx < nb_layers:
layer = self.layer_list[idx]
k_layer = self.keras_layer_map[layer]
if (isinstance(k_layer, _keras.la... | [
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