JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Performs cleaning steps on the data so various type comparisons can be performed correctly. def _sanitize_value(x): """ Performs cleaning steps on the data so various type comparisons can be performed correctly. """ if isinstance(x, _six.string_types + _six.integer_types + (float,)): re...
Performs a robust equality test between elements. def _element_equal(x, y): """ Performs a robust equality test between elements. """ if isinstance(x, _np.ndarray) or isinstance(y, _np.ndarray): try: return (abs(_np.asarray(x) - _np.asarray(y)) < 1e-5).all() except: ...
Evaluate a transformer specification for testing. Parameters ---------- spec: [str \| MLModel] File from where to load the Model from (OR) a loaded version of MLModel. input_data: list[dict] Test data on which to evaluate the models. reference_output: list[dict] Exp...
Returns a list of the names of the inputs to this model. :param spec: The model protobuf specification :return: [str] A list of input feature names def _get_input_names(spec): """ Returns a list of the names of the inputs to this model. :param spec: The model protobuf specification :return: [st...
Compute the in degree, out degree and total degree of each vertex. Parameters ---------- graph : SGraph The graph on which to compute degree counts. verbose : bool, optional If True, print progress updates. Returns ------- out : DegreeCountingModel Examples ------...
replace the index'th emphasized text with s def replace_emphasis(self, s, index = 0): """replace the index'th emphasized text with s""" e = self.emphasized[index] self.body[e[0]:e[1]] = [s] del self.emphasized[index]
Override of litre._execute; sets up variable context before evaluating code def _execute(self, code): """Override of litre._execute; sets up variable context before evaluating code """ self.globals['example'] = self.example eval(code, self.globals)
Compile examples on the stack, whose topmost item is the last example seen but not yet handled so far. :howmany: How many of the topmost examples on the stack to compile. You can pass a number, or 'all' to indicate that all examples should be compiled. :pop: How many of t...
Loads jamfile at the given location. After loading, project global file and jamfile needed by the loaded one will be loaded recursively. If the jamfile at that location is loaded already, does nothing. Returns the project module for the Jamfile. def load (self, jamfile_location): """Loa...
Loads parent of Jamfile at 'location'. Issues an error if nothing is found. def load_parent(self, location): """Loads parent of Jamfile at 'location'. Issues an error if nothing is found.""" assert isinstance(location, basestring) found = b2.util.path.glob_in_parents( ...
Given 'name' which can be project-id or plain directory name, return project module corresponding to that id or directory. Returns nothing of project is not found. def find(self, name, current_location): """Given 'name' which can be project-id or plain directory name, return project mod...
Returns the name of module corresponding to 'jamfile-location'. If no module corresponds to location yet, associates default module name with that location. def module_name(self, jamfile_location): """Returns the name of module corresponding to 'jamfile-location'. If no module correspon...
Find the Jamfile at the given location. This returns the exact names of all the Jamfiles in the given directory. The optional parent-root argument causes this to search not the given directory but the ones above it up to the directory given in it. def find_jamfile (self, dir, parent_root=0, no_...
Load a Jamfile at the given directory. Returns nothing. Will attempt to load the file as indicated by the JAMFILE patterns. Effect of calling this rule twice with the same 'dir' is underfined. def load_jamfile(self, dir, jamfile_module): """Load a Jamfile at the given directory. Returns nothing...
Loads 'file' as standalone project that has no location associated with it. This is mostly useful for user-config.jam, which should be able to define targets, but although it has some location in filesystem, we do not want any build to happen in user's HOME, for example. The ca...
Initialize the module for a project. module-name is the name of the project module. location is the location (directory) of the project to initialize. If not specified, standalone project will be initialized standalone_path is the path to the source-location. ...
Make 'project-module' inherit attributes of project root and parent module. def inherit_attributes(self, project_module, parent_module): """Make 'project-module' inherit attributes of project root and parent module.""" assert isinstance(project_module, basestring) assert isinsta...
Associate the given id with the given project module. def register_id(self, id, module): """Associate the given id with the given project module.""" assert isinstance(id, basestring) assert isinstance(module, basestring) self.id2module[id] = module
Temporary changes the current project to 'project'. Should be followed by 'pop-current'. def push_current(self, project): """Temporary changes the current project to 'project'. Should be followed by 'pop-current'.""" if __debug__: from .targets import ProjectTarget ...
Returns the value of the specified attribute in the specified jamfile module. def attribute(self, project, attribute): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(attribute, basestri...
Returns the value of the specified attribute in the specified jamfile module. def attributeDefault(self, project, attribute, default): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(att...
Returns the project target corresponding to the 'project-module'. def target(self, project_module): """Returns the project target corresponding to the 'project-module'.""" assert isinstance(project_module, basestring) if project_module not in self.module2target: self.module2target[p...
Makes rule 'name' available to all subsequently loaded Jamfiles. Calling that rule wil relay to 'callable'. def add_rule(self, name, callable_): """Makes rule 'name' available to all subsequently loaded Jamfiles. Calling that rule wil relay to 'callable'.""" assert isinstance(name, ba...
Recursively walks through the b2/src subdirectories and creates an index of base module name to package name. The index is stored within self.__python_module_cache and allows for an O(1) module lookup. For example, given the base module name `toolset`, self.__python_module_cache...
Load a Python module that should be useable from Jamfiles. There are generally two types of modules Jamfiles might want to use: - Core Boost.Build. Those are imported using plain names, e.g. 'toolset', so this function checks if we have module named b2.package.module already. ...
Set the named attribute from the specification given by the user. The value actually set may be different. def set(self, attribute, specification, exact=False): """Set the named attribute from the specification given by the user. The value actually set may be different.""" assert isinst...
Prints the project attributes. def dump(self): """Prints the project attributes.""" id = self.get("id") if not id: id = "(none)" else: id = id[0] parent = self.get("parent") if not parent: parent = "(none)" else: p...
Given a free-standing function 'callable', return a new callable that will call 'callable' and report all exceptins, using 'call_and_report_errors'. def make_wrapper(self, callable_): """Given a free-standing function 'callable', return a new callable that will call 'callable' and repor...
Declare and set a project global constant. Project global constants are normal variables but should not be changed. They are applied to every child Jamfile. def constant(self, name, value): """Declare and set a project global constant. Project global constants are normal variables but s...
Declare and set a project global constant, whose value is a path. The path is adjusted to be relative to the invocation directory. The given value path is taken to be either absolute, or relative to this project root. def path_constant(self, name, value): """Declare and set a project gl...
Calculates conditional requirements for multiple requirements at once. This is a shorthand to be reduce duplication and to keep an inline declarative syntax. For example: lib x : x.cpp : [ conditional <toolset>gcc <variant>debug : <define>DEBUG_EXCEPTION <define>DEBUG_TRACE ...
Creates a feature extractor from an input array feature, return input_features is a list of one (name, array) tuple. extract_indices is either an integer or a list. If it's an integer, the output type is by default a double (but may also be an integer). If a list, the output type is an array. def cr...
Add a single build XML output file to our data. def add_input(self, input): ''' Add a single build XML output file to our data. ''' events = xml.dom.pulldom.parse(input) context = [] for (event,node) in events: if event == xml.dom.pulldom.START_ELEMENT: ...
Process the target dependency DAG into an ancestry tree so we can look up which top-level library and test targets specific build actions correspond to. def x_build_targets_target( self, node ): ''' Process the target dependency DAG into an ancestry tree so we can look up which top-leve...
Given a build action log, process into the corresponding test log and specific test log sub-part. def x_build_action( self, node ): ''' Given a build action log, process into the corresponding test log and specific test log sub-part. ''' action_node = node name =...
The time-stamp goes to the corresponding attribute in the result. def x_build_timestamp( self, node ): ''' The time-stamp goes to the corresponding attribute in the result. ''' self.timestamps.append(self.get_data(node).strip()) return None
Print the detailed info of failed or always print tests. def print_action(self, test_succeed, action): ''' Print the detailed info of failed or always print tests. ''' #self.info_print(">>> {0}",action.keys()) if not test_succeed or action['info']['always_show_run_output']: ...
Get a summary of _NeuralNetwork_pb2.WeightParams Args: wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display Returns: a str summary for wp def _get_weight_param_summary(wp): """Get a summary of _NeuralNetwork_pb2.WeightParams Args: wp : _NeuralNetwork...
Args: layer - an MLModel NeuralNetwork Layer protobuf message Returns: layer_type : str - type of layer layer_name : str - name of the layer layer_inputs : list[str] - a list of strings representing input blobs of the layer layer_outputs : list[str] - a list of strings representing output blobs ...
Summarize network into the following structure. Args: mlmodel_spec : mlmodel spec Returns: inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob. outputs : list[(str, str)] - a list of two tuple (name, descriptor) for each output blob layers : list[(str, list[str...
Print the network information summary. Args: mlmodel_spec : the mlmodel spec interface_only : Shows only the input and output of the network def print_network_spec(mlmodel_spec, interface_only=False): """ Print the network information summary. Args: mlmodel_spec : the mlmodel spec interface...
Takes an SVM classifier produces a starting spec using the parts. that are shared between all SVMs. def _generate_base_svm_classifier_spec(model): """ Takes an SVM classifier produces a starting spec using the parts. that are shared between all SVMs. """ if not(_HAS_SKLEARN): raise Ru...
Convert a Support Vector Classtion (SVC) model to the protobuf spec. Parameters ---------- model: SVC A trained SVC encoder model. feature_names: [str], optional (default=None) Name of the input columns. target: str, optional (default=None) Name of the output column. R...
Extract the ordering of the input layers. def make_input_layers(self): """ Extract the ordering of the input layers. """ self.input_layers = [] if hasattr(self.model, 'input_layers'): input_keras_layers = self.model.input_layers[:] self.input_layers = [No...
Extract the ordering of output layers. def make_output_layers(self): """ Extract the ordering of output layers. """ # TODO # use successors == 0 as the criteria for output layer # will fail when some intermediate layers also generate output. # However, because th...
Generate blob names for each one of the edge. At this time, Keras does not support "fork" operation (a layer with more than 1 blob output). So we just use names of the src layer to identify a blob. We also assume all neural networks are singly-connected graphs - which should be the case. def ...
remove the layer and its input/output edges def _remove_layer(self, layer): """ remove the layer and its input/output edges """ successors = self.get_successors(layer) predecessors = self.get_predecessors(layer) # remove all edges for succ in successors: ...
Insert the new_layer after layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer): """ Insert the new_layer after layer, whose position is layer_idx. The new layer's ...
Insert the new_layer before layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer def _insert_layer_between(self, src, snk, new_layer, new_keras_layer): """ Insert the new_layer before layer, whose position is layer_idx. The new layer's ...
Defuse the fused activation layers in the network. 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_la...
Get edges that represents transition from not 1D to 1D, and 1D to not 1D A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs. An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs. def _get_1d_interface_edges(self): """ Get edges t...
Insert permutation layers before a 1D start point or after 1D end point def insert_1d_permute_layers(self): """ Insert permutation layers before a 1D start point or after 1D end point """ idx, nb_layers = 0, len(self.layer_list) in_edges, out_edges = self._get_1d_interface_edges...
Replace the old node with the new one. Old must be an indirect child of root :param root: ast node that contains an indirect reference to old :param old: node to replace :param new: node to replace `old` with def replace_nodes(root, old, new): ''' Replace the old node with the new one. ...
Report something about component configuration that the user should better know. def log_component_configuration(component, message): """Report something about component configuration that the user should better know.""" assert isinstance(component, basestring) assert isinstance(message, basestring) __...
Create a Transformer object to transform data for feature engineering. Parameters ---------- dataset : SFrame The dataset to use for training the model. transformers: Transformer \| list[Transformer] An Transformer or a list of Transformers. See Also -------- turicreate.to...
Preprocess each example, breaking it up into frames. Returns two numpy arrays: preprocessed frame and their indexes def _preprocess_data(audio_data, verbose=True): ''' Preprocess each example, breaking it up into frames. Returns two numpy arrays: preprocessed frame and their indexes ...
Parameters ---------- preprocessed_data : SArray Returns ------- numpy array containing the deep features def _extract_features(self, preprocessed_data, verbose=True): """ Parameters ---------- preprocessed_data : SArray Returns ...
Performs both audio preprocessing and VGGish deep feature extraction. def get_deep_features(self, audio_data, verbose): ''' Performs both audio preprocessing and VGGish deep feature extraction. ''' preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose) deep_feat...
Return the Core ML spec def get_spec(self): """ Return the Core ML spec """ if _mac_ver() >= (10, 14): return self.vggish_model.get_spec() else: vggish_model_file = VGGish() coreml_model_path = vggish_model_file.get_model_path(format='coreml')...
Remove redundant statements. The statement `a = 1` will be removed:: a = 1 a = 2 The statement `a = 1` will not be removed because `b` depends on it:: a = 1 b = a + 2 a = 2 :param root: ast node def remove_trivial(root): ''' R...
To prevent circular imports, this extends isinstance() by checking also if `value` has a particular class name (or inherits from a particular class name). This check is safe in that an AttributeError is not raised in case `value` doesn't have a __class__ attribute. def safe_isinstance(value, types=None, cl...
Makes a token to refer to a Python value inside Jam language code. The token is merely a string that can be passed around in Jam code and eventually passed back. For example, we might want to pass PropertySet instance to a tag function and it might eventually call back to virtual_target.add_suffix_and_...
Abbreviates each part of string that is delimited by a '-'. def abbreviate_dashed(s): """Abbreviates each part of string that is delimited by a '-'.""" r = [] for part in s.split('-'): r.append(abbreviate(part)) return '-'.join(r)
Apply a set of standard transformations to string to produce an abbreviation no more than 4 characters long. def abbreviate(s): """Apply a set of standard transformations to string to produce an abbreviation no more than 4 characters long. """ if not s: return '' # check the cache i...
Get the decision from this node to a child node. Parameters ---------- child: Node A child node of this node. Returns ------- dict: A dictionary that describes how to get from this node to the child node. def get_decision(self, child, is_missing = F...
Return the node as a dictionary. Returns ------- dict: All the attributes of this node as a dictionary (minus the left and right). def to_dict(self): """ Return the node as a dictionary. Returns ------- dict: All the attributes of this nod...
Recursive function to dump this tree as a json blob. Parameters ---------- root_id: Root id of the sub-tree output: Carry over output from the previous sub-trees. Returns ------- dict: A tree in JSON format. Starts at the root node and recursively repres...
Return the prediction score (if leaf node) or None if its an intermediate node. Parameters ---------- node_id: id of the node to get the prediction value. Returns ------- float or None: returns float value of prediction if leaf node and None if not. ...
Return the prediction path from this node to the parent node. Parameters ---------- node_id : id of the node to get the prediction path. missing_id : Additional info that contains nodes with missing features. Returns ------- list: The list of decisions (top t...
Given a weighted graph with observed class labels of a subset of vertices, infer the label probability for the unobserved vertices using the "label propagation" algorithm. The algorithm iteratively updates the label probability of current vertex as a weighted sum of label probability of self and the ne...
Check if a Turi create model is pickle safe. The function does it by checking that _CustomModel is the base class. Parameters ---------- obj_class : Class to be checked. Returns ---------- True if the GLC class is a model and is pickle safe. def _is_not_pickle_safe_gl_model_class(obj_...
Check if class is a Turi create model. The function does it by checking the method resolution order (MRO) of the class and verifies that _Model is the base class. Parameters ---------- obj_class : Class to be checked. Returns ---------- True if the class is a GLC Model. def _is_no...
Internal util to get the type of the GLC class. The pickle file stores this name so that it knows how to construct the object on unpickling. Parameters ---------- obj_class : Class which has to be categorized. Returns ---------- A class type for the pickle file to save. def _get_gl_cla...
Internal util to get a GLC object from a persistent ID in the pickle file. Parameters ---------- type_tag : The name of the glc class as saved in the GLC pickler. gl_archive_abs_path: An absolute path to the GLC archive where the object was saved. Returns ---------- ...
Provide a persistent ID for "saving" GLC objects by reference. Return None for all non GLC objects. Parameters ---------- obj: Name of the object whose persistent ID is extracted. Returns -------- None if the object is not a GLC object. (ClassName, relative pat...
Close the pickle file, and the zip archive file. The single zip archive file can now be shipped around to be loaded by the unpickler. def close(self): """ Close the pickle file, and the zip archive file. The single zip archive file can now be shipped around to be loaded by the unpickler...
Reconstruct a GLC object using the persistent ID. This method should not be used externally. It is required by the unpickler super class. Parameters ---------- pid : The persistent ID used in pickle file to save the GLC object. Returns ---------- The GLC o...
Clean up files that were created. def close(self): """ Clean up files that were created. """ if self.file: self.file.close() self.file = None # If temp_file is a folder, we do not remove it because we may # still need it after the unpickler is di...
Convert scikit-learn pipeline, classifier, or regressor to Core ML format. Parameters ---------- sk_obj: model \| [model] of scikit-learn format. Scikit learn model(s) to convert to a Core ML format. The input model may be a single scikit learn model, a scikit learn pipeline model, ...
Generate a new Message instance from this Descriptor and a byte string. Args: descriptor: Protobuf Descriptor object byte_str: Serialized protocol buffer byte string Returns: Newly created protobuf Message object. def ParseMessage(descriptor, byte_str): """Generate a new Message instance from this ...
Construct a class object for a protobuf described by descriptor. Composite descriptors are handled by defining the new class as a member of the parent class, recursing as deep as necessary. This is the dynamic equivalent to: class Parent(message.Message): __metaclass__ = GeneratedProtocolMessageType D...
Loads images from a directory. JPEG and PNG images are supported. Parameters ---------- url : str The string of the path where all the images are stored. format : {'PNG' \| 'JPG' \| 'auto'}, optional The format of the images in the directory. The default 'auto' parameter value tr...
Internal helper function for decoding a single Image or an SArray of Images def _decode(image_data): """ Internal helper function for decoding a single Image or an SArray of Images """ from ...data_structures.sarray import SArray as _SArray from ... import extensions as _extensions if type(imag...
Resizes the image or SArray of Images to a specific width, height, and number of channels. Parameters ---------- image : turicreate.Image \| SArray The image or SArray of images to be resized. width : int The width the image is resized to. height : int The height the ima...
Convert bit array to byte array. :param arr: list Bits as a list where each element is an integer of 0 or 1 Returns ------- numpy.array 1D numpy array of type uint8 def _convert_1bit_array_to_byte_array(arr): """ Convert bit array to byte array. :param arr: list B...
Unpack bytes to bits :param arr: list Byte Stream, as a list of uint8 values Returns ------- bit_arr: list Decomposed bit stream as a list of 0/1s of length (len(arr) * 8) def _decompose_bytes_to_bit_arr(arr): """ Unpack bytes to bits :param arr: list Byte Stream,...
Generate a linear lookup table. :param nbits: int Number of bits to represent a quantized weight value :param wp: numpy.array Weight blob to be quantized Returns ------- lookup_table: numpy.array Lookup table of shape (2^nbits, ) qw: numpy.array Decomposed bit ...
Generate K-Means lookup table given a weight parameter field :param nbits: Number of bits for quantization :param w: Weight as numpy array Returns ------- lut: numpy.array Lookup table, numpy array of shape (1 << nbits, ); wq: numpy.array Quantized weight of ty...
Linearly quantize weight blob. :param weight: numpy.array Weight to be quantized. :param nbits: int Number of bits per weight element :param axis: int Axis of the weight blob to compute channel-wise quantization, can be 0 or 1 Returns ------- quantized_weight: numpy.a...
Quantize the weight blob :param wp: numpy.array Weight parameters :param nbits: int Number of bits :param qm: Quantization mode :param lut_function: (``callable function``) Python callable representing a look-up table Returns ------- scale: numpy.array ...
Quantize WeightParam field in Neural Network Protobuf :param wp: MLModel.NeuralNetwork.WeightParam WeightParam field :param nbits: int Number of bits to be quantized :param qm: str Quantization mode :param shape: tuple Tensor shape held by wp :param axis: int ...
Utility function to compare the performance of a full precision vs quantized model :param full_precision_model: MLModel The full precision model with float32 weights :param quantized_model: MLModel Quantized version of the model with quantized weights :param sample_data: str \| [dict] ...
Utility function to convert a full precision (float) MLModel to a nbit quantized MLModel (float16). :param full_precision_model: MLModel Model which will be converted to half precision. Currently conversion for only neural network models is supported. If a pipeline model is passed in th...
Create a recommender that uses item-item similarities based on users in common. Parameters ---------- observation_data : SFrame The dataset to use for training the model. It must contain a column of user ids and a column of item ids. Each row represents an observed interaction b...
Get the keras layer name from the activation name. def _get_elementwise_name_from_keras_layer(keras_layer): """ Get the keras layer name from the activation name. """ if isinstance(keras_layer, _keras.layers.Add): return 'ADD' elif isinstance(keras_layer, _keras.layers.Multiply): re...
Convert a dense layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_dense(builder, layer, input_names, output_names, keras_layer): """ Convert a dense layer from k...
Convert a dense layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_embedding(builder, layer, input_names, output_names, keras_layer): """Convert a dense layer from ke...
Convert an activation layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_activation(builder, layer, input_names, output_names, keras_layer): """ Convert an activa...
Convert an ReLU layer with maximum value from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer): """ C...
Convert convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_convolution(builder, layer, input_names, output_names, keras_layer): """ Convert convolutio...

Performs cleaning steps on the data so various type comparisons can be performed correctly. def _sanitize_value(x): """ Performs cleaning steps on the data so various type comparisons can be performed correctly. """ if isinstance(x, _six.string_types + _six.integer_types + (float,)): re...

Performs a robust equality test between elements. def _element_equal(x, y): """ Performs a robust equality test between elements. """ if isinstance(x, _np.ndarray) or isinstance(y, _np.ndarray): try: return (abs(_np.asarray(x) - _np.asarray(y)) < 1e-5).all() except: ...

Evaluate a transformer specification for testing. Parameters ---------- spec: [str | MLModel] File from where to load the Model from (OR) a loaded version of MLModel. input_data: list[dict] Test data on which to evaluate the models. reference_output: list[dict] Exp...

Returns a list of the names of the inputs to this model. :param spec: The model protobuf specification :return: [str] A list of input feature names def _get_input_names(spec): """ Returns a list of the names of the inputs to this model. :param spec: The model protobuf specification :return: [st...

Compute the in degree, out degree and total degree of each vertex. Parameters ---------- graph : SGraph The graph on which to compute degree counts. verbose : bool, optional If True, print progress updates. Returns ------- out : DegreeCountingModel Examples ------...

replace the index'th emphasized text with s def replace_emphasis(self, s, index = 0): """replace the index'th emphasized text with s""" e = self.emphasized[index] self.body[e[0]:e[1]] = [s] del self.emphasized[index]

Override of litre._execute; sets up variable context before evaluating code def _execute(self, code): """Override of litre._execute; sets up variable context before evaluating code """ self.globals['example'] = self.example eval(code, self.globals)

Compile examples on the stack, whose topmost item is the last example seen but not yet handled so far. :howmany: How many of the topmost examples on the stack to compile. You can pass a number, or 'all' to indicate that all examples should be compiled. :pop: How many of t...

Loads jamfile at the given location. After loading, project global file and jamfile needed by the loaded one will be loaded recursively. If the jamfile at that location is loaded already, does nothing. Returns the project module for the Jamfile. def load (self, jamfile_location): """Loa...

Loads parent of Jamfile at 'location'. Issues an error if nothing is found. def load_parent(self, location): """Loads parent of Jamfile at 'location'. Issues an error if nothing is found.""" assert isinstance(location, basestring) found = b2.util.path.glob_in_parents( ...

Given 'name' which can be project-id or plain directory name, return project module corresponding to that id or directory. Returns nothing of project is not found. def find(self, name, current_location): """Given 'name' which can be project-id or plain directory name, return project mod...

Returns the name of module corresponding to 'jamfile-location'. If no module corresponds to location yet, associates default module name with that location. def module_name(self, jamfile_location): """Returns the name of module corresponding to 'jamfile-location'. If no module correspon...

Find the Jamfile at the given location. This returns the exact names of all the Jamfiles in the given directory. The optional parent-root argument causes this to search not the given directory but the ones above it up to the directory given in it. def find_jamfile (self, dir, parent_root=0, no_...

Load a Jamfile at the given directory. Returns nothing. Will attempt to load the file as indicated by the JAMFILE patterns. Effect of calling this rule twice with the same 'dir' is underfined. def load_jamfile(self, dir, jamfile_module): """Load a Jamfile at the given directory. Returns nothing...

Loads 'file' as standalone project that has no location associated with it. This is mostly useful for user-config.jam, which should be able to define targets, but although it has some location in filesystem, we do not want any build to happen in user's HOME, for example. The ca...

Initialize the module for a project. module-name is the name of the project module. location is the location (directory) of the project to initialize. If not specified, standalone project will be initialized standalone_path is the path to the source-location. ...

Make 'project-module' inherit attributes of project root and parent module. def inherit_attributes(self, project_module, parent_module): """Make 'project-module' inherit attributes of project root and parent module.""" assert isinstance(project_module, basestring) assert isinsta...

Associate the given id with the given project module. def register_id(self, id, module): """Associate the given id with the given project module.""" assert isinstance(id, basestring) assert isinstance(module, basestring) self.id2module[id] = module

Temporary changes the current project to 'project'. Should be followed by 'pop-current'. def push_current(self, project): """Temporary changes the current project to 'project'. Should be followed by 'pop-current'.""" if __debug__: from .targets import ProjectTarget ...

Returns the value of the specified attribute in the specified jamfile module. def attribute(self, project, attribute): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(attribute, basestri...

Returns the value of the specified attribute in the specified jamfile module. def attributeDefault(self, project, attribute, default): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(att...

Returns the project target corresponding to the 'project-module'. def target(self, project_module): """Returns the project target corresponding to the 'project-module'.""" assert isinstance(project_module, basestring) if project_module not in self.module2target: self.module2target[p...

Makes rule 'name' available to all subsequently loaded Jamfiles. Calling that rule wil relay to 'callable'. def add_rule(self, name, callable_): """Makes rule 'name' available to all subsequently loaded Jamfiles. Calling that rule wil relay to 'callable'.""" assert isinstance(name, ba...

Recursively walks through the b2/src subdirectories and creates an index of base module name to package name. The index is stored within self.__python_module_cache and allows for an O(1) module lookup. For example, given the base module name `toolset`, self.__python_module_cache...

Load a Python module that should be useable from Jamfiles. There are generally two types of modules Jamfiles might want to use: - Core Boost.Build. Those are imported using plain names, e.g. 'toolset', so this function checks if we have module named b2.package.module already. ...

Set the named attribute from the specification given by the user. The value actually set may be different. def set(self, attribute, specification, exact=False): """Set the named attribute from the specification given by the user. The value actually set may be different.""" assert isinst...

Prints the project attributes. def dump(self): """Prints the project attributes.""" id = self.get("id") if not id: id = "(none)" else: id = id[0] parent = self.get("parent") if not parent: parent = "(none)" else: p...

Given a free-standing function 'callable', return a new callable that will call 'callable' and report all exceptins, using 'call_and_report_errors'. def make_wrapper(self, callable_): """Given a free-standing function 'callable', return a new callable that will call 'callable' and repor...

Declare and set a project global constant. Project global constants are normal variables but should not be changed. They are applied to every child Jamfile. def constant(self, name, value): """Declare and set a project global constant. Project global constants are normal variables but s...

Declare and set a project global constant, whose value is a path. The path is adjusted to be relative to the invocation directory. The given value path is taken to be either absolute, or relative to this project root. def path_constant(self, name, value): """Declare and set a project gl...

Calculates conditional requirements for multiple requirements at once. This is a shorthand to be reduce duplication and to keep an inline declarative syntax. For example: lib x : x.cpp : [ conditional <toolset>gcc <variant>debug : <define>DEBUG_EXCEPTION <define>DEBUG_TRACE ...

Creates a feature extractor from an input array feature, return input_features is a list of one (name, array) tuple. extract_indices is either an integer or a list. If it's an integer, the output type is by default a double (but may also be an integer). If a list, the output type is an array. def cr...

Add a single build XML output file to our data. def add_input(self, input): ''' Add a single build XML output file to our data. ''' events = xml.dom.pulldom.parse(input) context = [] for (event,node) in events: if event == xml.dom.pulldom.START_ELEMENT: ...

Process the target dependency DAG into an ancestry tree so we can look up which top-level library and test targets specific build actions correspond to. def x_build_targets_target( self, node ): ''' Process the target dependency DAG into an ancestry tree so we can look up which top-leve...

Given a build action log, process into the corresponding test log and specific test log sub-part. def x_build_action( self, node ): ''' Given a build action log, process into the corresponding test log and specific test log sub-part. ''' action_node = node name =...

The time-stamp goes to the corresponding attribute in the result. def x_build_timestamp( self, node ): ''' The time-stamp goes to the corresponding attribute in the result. ''' self.timestamps.append(self.get_data(node).strip()) return None

Print the detailed info of failed or always print tests. def print_action(self, test_succeed, action): ''' Print the detailed info of failed or always print tests. ''' #self.info_print(">>> {0}",action.keys()) if not test_succeed or action['info']['always_show_run_output']: ...

Get a summary of _NeuralNetwork_pb2.WeightParams Args: wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display Returns: a str summary for wp def _get_weight_param_summary(wp): """Get a summary of _NeuralNetwork_pb2.WeightParams Args: wp : _NeuralNetwork...

Args: layer - an MLModel NeuralNetwork Layer protobuf message Returns: layer_type : str - type of layer layer_name : str - name of the layer layer_inputs : list[str] - a list of strings representing input blobs of the layer layer_outputs : list[str] - a list of strings representing output blobs ...

Summarize network into the following structure. Args: mlmodel_spec : mlmodel spec Returns: inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob. outputs : list[(str, str)] - a list of two tuple (name, descriptor) for each output blob layers : list[(str, list[str...

Print the network information summary. Args: mlmodel_spec : the mlmodel spec interface_only : Shows only the input and output of the network def print_network_spec(mlmodel_spec, interface_only=False): """ Print the network information summary. Args: mlmodel_spec : the mlmodel spec interface...

Takes an SVM classifier produces a starting spec using the parts. that are shared between all SVMs. def _generate_base_svm_classifier_spec(model): """ Takes an SVM classifier produces a starting spec using the parts. that are shared between all SVMs. """ if not(_HAS_SKLEARN): raise Ru...

Convert a Support Vector Classtion (SVC) model to the protobuf spec. Parameters ---------- model: SVC A trained SVC encoder model. feature_names: [str], optional (default=None) Name of the input columns. target: str, optional (default=None) Name of the output column. R...

Extract the ordering of the input layers. def make_input_layers(self): """ Extract the ordering of the input layers. """ self.input_layers = [] if hasattr(self.model, 'input_layers'): input_keras_layers = self.model.input_layers[:] self.input_layers = [No...

Extract the ordering of output layers. def make_output_layers(self): """ Extract the ordering of output layers. """ # TODO # use successors == 0 as the criteria for output layer # will fail when some intermediate layers also generate output. # However, because th...

Generate blob names for each one of the edge. At this time, Keras does not support "fork" operation (a layer with more than 1 blob output). So we just use names of the src layer to identify a blob. We also assume all neural networks are singly-connected graphs - which should be the case. def ...

remove the layer and its input/output edges def _remove_layer(self, layer): """ remove the layer and its input/output edges """ successors = self.get_successors(layer) predecessors = self.get_predecessors(layer) # remove all edges for succ in successors: ...

Insert the new_layer after layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer): """ Insert the new_layer after layer, whose position is layer_idx. The new layer's ...

Insert the new_layer before layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer def _insert_layer_between(self, src, snk, new_layer, new_keras_layer): """ Insert the new_layer before layer, whose position is layer_idx. The new layer's ...

Defuse the fused activation layers in the network. 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_la...

Get edges that represents transition from not 1D to 1D, and 1D to not 1D A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs. An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs. def _get_1d_interface_edges(self): """ Get edges t...

Insert permutation layers before a 1D start point or after 1D end point def insert_1d_permute_layers(self): """ Insert permutation layers before a 1D start point or after 1D end point """ idx, nb_layers = 0, len(self.layer_list) in_edges, out_edges = self._get_1d_interface_edges...

Replace the old node with the new one. Old must be an indirect child of root :param root: ast node that contains an indirect reference to old :param old: node to replace :param new: node to replace `old` with def replace_nodes(root, old, new): ''' Replace the old node with the new one. ...

Report something about component configuration that the user should better know. def log_component_configuration(component, message): """Report something about component configuration that the user should better know.""" assert isinstance(component, basestring) assert isinstance(message, basestring) __...

Create a Transformer object to transform data for feature engineering. Parameters ---------- dataset : SFrame The dataset to use for training the model. transformers: Transformer | list[Transformer] An Transformer or a list of Transformers. See Also -------- turicreate.to...

Preprocess each example, breaking it up into frames. Returns two numpy arrays: preprocessed frame and their indexes def _preprocess_data(audio_data, verbose=True): ''' Preprocess each example, breaking it up into frames. Returns two numpy arrays: preprocessed frame and their indexes ...

Parameters ---------- preprocessed_data : SArray Returns ------- numpy array containing the deep features def _extract_features(self, preprocessed_data, verbose=True): """ Parameters ---------- preprocessed_data : SArray Returns ...

Performs both audio preprocessing and VGGish deep feature extraction. def get_deep_features(self, audio_data, verbose): ''' Performs both audio preprocessing and VGGish deep feature extraction. ''' preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose) deep_feat...

Return the Core ML spec def get_spec(self): """ Return the Core ML spec """ if _mac_ver() >= (10, 14): return self.vggish_model.get_spec() else: vggish_model_file = VGGish() coreml_model_path = vggish_model_file.get_model_path(format='coreml')...

Remove redundant statements. The statement `a = 1` will be removed:: a = 1 a = 2 The statement `a = 1` will not be removed because `b` depends on it:: a = 1 b = a + 2 a = 2 :param root: ast node def remove_trivial(root): ''' R...

To prevent circular imports, this extends isinstance() by checking also if `value` has a particular class name (or inherits from a particular class name). This check is safe in that an AttributeError is not raised in case `value` doesn't have a __class__ attribute. def safe_isinstance(value, types=None, cl...

Makes a token to refer to a Python value inside Jam language code. The token is merely a string that can be passed around in Jam code and eventually passed back. For example, we might want to pass PropertySet instance to a tag function and it might eventually call back to virtual_target.add_suffix_and_...

Abbreviates each part of string that is delimited by a '-'. def abbreviate_dashed(s): """Abbreviates each part of string that is delimited by a '-'.""" r = [] for part in s.split('-'): r.append(abbreviate(part)) return '-'.join(r)

Apply a set of standard transformations to string to produce an abbreviation no more than 4 characters long. def abbreviate(s): """Apply a set of standard transformations to string to produce an abbreviation no more than 4 characters long. """ if not s: return '' # check the cache i...

Get the decision from this node to a child node. Parameters ---------- child: Node A child node of this node. Returns ------- dict: A dictionary that describes how to get from this node to the child node. def get_decision(self, child, is_missing = F...

Return the node as a dictionary. Returns ------- dict: All the attributes of this node as a dictionary (minus the left and right). def to_dict(self): """ Return the node as a dictionary. Returns ------- dict: All the attributes of this nod...

Recursive function to dump this tree as a json blob. Parameters ---------- root_id: Root id of the sub-tree output: Carry over output from the previous sub-trees. Returns ------- dict: A tree in JSON format. Starts at the root node and recursively repres...

Return the prediction score (if leaf node) or None if its an intermediate node. Parameters ---------- node_id: id of the node to get the prediction value. Returns ------- float or None: returns float value of prediction if leaf node and None if not. ...

Return the prediction path from this node to the parent node. Parameters ---------- node_id : id of the node to get the prediction path. missing_id : Additional info that contains nodes with missing features. Returns ------- list: The list of decisions (top t...

Given a weighted graph with observed class labels of a subset of vertices, infer the label probability for the unobserved vertices using the "label propagation" algorithm. The algorithm iteratively updates the label probability of current vertex as a weighted sum of label probability of self and the ne...

Check if a Turi create model is pickle safe. The function does it by checking that _CustomModel is the base class. Parameters ---------- obj_class : Class to be checked. Returns ---------- True if the GLC class is a model and is pickle safe. def _is_not_pickle_safe_gl_model_class(obj_...

Check if class is a Turi create model. The function does it by checking the method resolution order (MRO) of the class and verifies that _Model is the base class. Parameters ---------- obj_class : Class to be checked. Returns ---------- True if the class is a GLC Model. def _is_no...

Internal util to get the type of the GLC class. The pickle file stores this name so that it knows how to construct the object on unpickling. Parameters ---------- obj_class : Class which has to be categorized. Returns ---------- A class type for the pickle file to save. def _get_gl_cla...

Internal util to get a GLC object from a persistent ID in the pickle file. Parameters ---------- type_tag : The name of the glc class as saved in the GLC pickler. gl_archive_abs_path: An absolute path to the GLC archive where the object was saved. Returns ---------- ...

Provide a persistent ID for "saving" GLC objects by reference. Return None for all non GLC objects. Parameters ---------- obj: Name of the object whose persistent ID is extracted. Returns -------- None if the object is not a GLC object. (ClassName, relative pat...

Close the pickle file, and the zip archive file. The single zip archive file can now be shipped around to be loaded by the unpickler. def close(self): """ Close the pickle file, and the zip archive file. The single zip archive file can now be shipped around to be loaded by the unpickler...

Reconstruct a GLC object using the persistent ID. This method should not be used externally. It is required by the unpickler super class. Parameters ---------- pid : The persistent ID used in pickle file to save the GLC object. Returns ---------- The GLC o...

Clean up files that were created. def close(self): """ Clean up files that were created. """ if self.file: self.file.close() self.file = None # If temp_file is a folder, we do not remove it because we may # still need it after the unpickler is di...

Convert scikit-learn pipeline, classifier, or regressor to Core ML format. Parameters ---------- sk_obj: model | [model] of scikit-learn format. Scikit learn model(s) to convert to a Core ML format. The input model may be a single scikit learn model, a scikit learn pipeline model, ...

Generate a new Message instance from this Descriptor and a byte string. Args: descriptor: Protobuf Descriptor object byte_str: Serialized protocol buffer byte string Returns: Newly created protobuf Message object. def ParseMessage(descriptor, byte_str): """Generate a new Message instance from this ...

Construct a class object for a protobuf described by descriptor. Composite descriptors are handled by defining the new class as a member of the parent class, recursing as deep as necessary. This is the dynamic equivalent to: class Parent(message.Message): __metaclass__ = GeneratedProtocolMessageType D...

Loads images from a directory. JPEG and PNG images are supported. Parameters ---------- url : str The string of the path where all the images are stored. format : {'PNG' | 'JPG' | 'auto'}, optional The format of the images in the directory. The default 'auto' parameter value tr...

Internal helper function for decoding a single Image or an SArray of Images def _decode(image_data): """ Internal helper function for decoding a single Image or an SArray of Images """ from ...data_structures.sarray import SArray as _SArray from ... import extensions as _extensions if type(imag...

Resizes the image or SArray of Images to a specific width, height, and number of channels. Parameters ---------- image : turicreate.Image | SArray The image or SArray of images to be resized. width : int The width the image is resized to. height : int The height the ima...

Convert bit array to byte array. :param arr: list Bits as a list where each element is an integer of 0 or 1 Returns ------- numpy.array 1D numpy array of type uint8 def _convert_1bit_array_to_byte_array(arr): """ Convert bit array to byte array. :param arr: list B...

Unpack bytes to bits :param arr: list Byte Stream, as a list of uint8 values Returns ------- bit_arr: list Decomposed bit stream as a list of 0/1s of length (len(arr) * 8) def _decompose_bytes_to_bit_arr(arr): """ Unpack bytes to bits :param arr: list Byte Stream,...

Generate a linear lookup table. :param nbits: int Number of bits to represent a quantized weight value :param wp: numpy.array Weight blob to be quantized Returns ------- lookup_table: numpy.array Lookup table of shape (2^nbits, ) qw: numpy.array Decomposed bit ...

Generate K-Means lookup table given a weight parameter field :param nbits: Number of bits for quantization :param w: Weight as numpy array Returns ------- lut: numpy.array Lookup table, numpy array of shape (1 << nbits, ); wq: numpy.array Quantized weight of ty...

Linearly quantize weight blob. :param weight: numpy.array Weight to be quantized. :param nbits: int Number of bits per weight element :param axis: int Axis of the weight blob to compute channel-wise quantization, can be 0 or 1 Returns ------- quantized_weight: numpy.a...

Quantize the weight blob :param wp: numpy.array Weight parameters :param nbits: int Number of bits :param qm: Quantization mode :param lut_function: (``callable function``) Python callable representing a look-up table Returns ------- scale: numpy.array ...

Quantize WeightParam field in Neural Network Protobuf :param wp: MLModel.NeuralNetwork.WeightParam WeightParam field :param nbits: int Number of bits to be quantized :param qm: str Quantization mode :param shape: tuple Tensor shape held by wp :param axis: int ...

Utility function to compare the performance of a full precision vs quantized model :param full_precision_model: MLModel The full precision model with float32 weights :param quantized_model: MLModel Quantized version of the model with quantized weights :param sample_data: str | [dict] ...

Utility function to convert a full precision (float) MLModel to a nbit quantized MLModel (float16). :param full_precision_model: MLModel Model which will be converted to half precision. Currently conversion for only neural network models is supported. If a pipeline model is passed in th...

Create a recommender that uses item-item similarities based on users in common. Parameters ---------- observation_data : SFrame The dataset to use for training the model. It must contain a column of user ids and a column of item ids. Each row represents an observed interaction b...

Get the keras layer name from the activation name. def _get_elementwise_name_from_keras_layer(keras_layer): """ Get the keras layer name from the activation name. """ if isinstance(keras_layer, _keras.layers.Add): return 'ADD' elif isinstance(keras_layer, _keras.layers.Multiply): re...

Convert a dense layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_dense(builder, layer, input_names, output_names, keras_layer): """ Convert a dense layer from k...

Convert a dense layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_embedding(builder, layer, input_names, output_names, keras_layer): """Convert a dense layer from ke...

Convert an activation layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_activation(builder, layer, input_names, output_names, keras_layer): """ Convert an activa...

Convert an ReLU layer with maximum value from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer): """ C...

Convert convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. def convert_convolution(builder, layer, input_names, output_names, keras_layer): """ Convert convolutio...