BoghdadyJR/codesearch_python · Data Studio

text stringlengths 75 104k
def _sanitize_value(x): """ Performs cleaning steps on the data so various type comparisons can be performed correctly. """ if isinstance(x, _six.string_types + _six.integer_types + (float,)): return x elif _HAS_SKLEARN and _sp.issparse(x): return x.todense() elif isinstance(...
def _element_equal(x, y): """ Performs a robust equality test between elements. """ if isinstance(x, _np.ndarray) or isinstance(y, _np.ndarray): try: return (abs(_np.asarray(x) - _np.asarray(y)) < 1e-5).all() except: return False elif isinstance(x, dict): ...
def evaluate_transformer(model, input_data, reference_output, verbose=False): """ Evaluate a transformer specification for testing. Parameters ---------- spec: [str \| MLModel] File from where to load the Model from (OR) a loaded version of MLModel. inpu...
def _get_input_names(spec): """ Returns a list of the names of the inputs to this model. :param spec: The model protobuf specification :return: [str] A list of input feature names """ retval = [feature.name for feature in spec.description.input] return retval
def create(graph, verbose=True): """ Compute the in degree, out degree and total degree of each vertex. Parameters ---------- graph : SGraph The graph on which to compute degree counts. verbose : bool, optional If True, print progress updates. Returns ------- out :...
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]
def _execute(self, code): """Override of litre._execute; sets up variable context before evaluating code """ self.globals['example'] = self.example eval(code, self.globals)
def compile( self , howmany = 1 , pop = -1 , expect_error = False , extension = '.o' , options = ['-c'] , built_handler = lambda built_file: None , source_file = None , source_suffix = '.cpp' # C-style comments by default; handles C++ a...
def load (self, jamfile_location): """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_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( location, self.JAMROOT + self.JAMFILE) if not found: print "er...
def find(self, name, current_location): """Given 'name' which can be project-id or plain directory name, return project module corresponding to that id or directory. Returns nothing of project is not found.""" assert isinstance(name, basestring) assert isinstance(current_location...
def module_name(self, jamfile_location): """Returns the name of module corresponding to 'jamfile-location'. If no module corresponds to location yet, associates default module name with that location.""" assert isinstance(jamfile_location, basestring) module = self.location2modul...
def find_jamfile (self, dir, parent_root=0, no_errors=0): """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 di...
def load_jamfile(self, dir, jamfile_module): """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.""" assert isinstance(dir, basestring) a...
def load_standalone(self, jamfile_module, file): """Loads 'file' as standalone project that has no location associated with it. This is mostly useful for user-config.jam, which should be able to define targets, but although it has some location in filesystem, we do not want any build to...
def initialize(self, module_name, location=None, basename=None, standalone_path=''): """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 wi...
def inherit_attributes(self, project_module, parent_module): """Make 'project-module' inherit attributes of project root and parent module.""" assert isinstance(project_module, basestring) assert isinstance(parent_module, basestring) attributes = self.module2attributes[project_m...
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
def push_current(self, project): """Temporary changes the current project to 'project'. Should be followed by 'pop-current'.""" if __debug__: from .targets import ProjectTarget assert isinstance(project, ProjectTarget) self.saved_current_project.append(self.curren...
def attribute(self, project, attribute): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(attribute, basestring) try: return self.module2attributes[project].get(attribute) ...
def attributeDefault(self, project, attribute, default): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(attribute, basestring) assert isinstance(default, basestring) or default is None ...
def target(self, project_module): """Returns the project target corresponding to the 'project-module'.""" assert isinstance(project_module, basestring) if project_module not in self.module2target: self.module2target[project_module] = \ b2.build.targets.ProjectTarget(p...
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, basestring) assert callable(callable_) self.project_rules_.add_rule(name, callable_)
def __build_python_module_cache(self): """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 ...
def load_module(self, name, extra_path=None): """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 w...
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 isinstance(attribute, basestring) assert isinstance(exact, (int, bool)) if __debug__ and not exact: ...
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: parent = parent[0] print...
def make_wrapper(self, callable_): """Given a free-standing function 'callable', return a new callable that will call 'callable' and report all exceptins, using 'call_and_report_errors'.""" assert callable(callable_) def wrapper(args, *kw): return self.call_and_repo...
def constant(self, name, value): """Declare and set a project global constant. Project global constants are normal variables but should not be changed. They are applied to every child Jamfile.""" assert is_iterable_typed(name, basestring) assert is_iterable_typed(value, basestrin...
def path_constant(self, name, value): """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.""" assert is_iterable...
def conditional(self, condition, requirements): """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 : ...
def create_array_feature_extractor(input_features, output_name, extract_indices, output_type = None): """ Creates a feature extractor from an input array feature, return input_features is a list of one (name, array) tuple. extract_indices is either an integer or a li...
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: context.append(node) i...
def x_build_targets_target( self, node ): ''' 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. ''' target_node = node name = self.get_child_data(target_node,tag='name',...
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 = self.get_child(action_node,tag='name') if name: name = self.get_data(name) ...
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
def print_action(self, test_succeed, action): ''' Print the detailed info of failed or always print tests. ''' #self.info_print(">>> {0}",action.keys()) if not test_succeed or action['info']['always_show_run_output']: output = action['output'].strip() if o...
def _get_weight_param_summary(wp): """Get a summary of _NeuralNetwork_pb2.WeightParams Args: wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display Returns: a str summary for wp """ summary_str = '' if wp.HasField('quantization'): nbits = wp...
def _summarize_network_layer_info(layer): """ Args: layer - an MLModel NeuralNetwork Layer protobuf message Returns: layer_type : str - type of layer layer_name : str - name of the layer layer_inputs : list[str] - a list of strings representing input blobs of the layer layer_outputs : li...
def summarize_neural_network_spec(mlmodel_spec): """ Summarize network into the following structure. Args: mlmodel_spec : mlmodel spec Returns: inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob. outputs : list[(str, str)] - a list of two tuple (name, descript...
def print_network_spec(mlmodel_spec, interface_only=False): """ Print the network information summary. Args: mlmodel_spec : the mlmodel spec interface_only : Shows only the input and output of the network """ inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec) print('...
def _generate_base_svm_classifier_spec(model): """ Takes an SVM classifier produces a starting spec using the parts. that are shared between all SVMs. """ if not(_HAS_SKLEARN): raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.') check_fitted(model, la...
def convert(model, feature_names, target): """Convert a Support Vector Classtion (SVC) model to the protobuf spec. Parameters ---------- model: SVC A trained SVC encoder model. feature_names: [str], optional (default=None) Name of the input columns. target: str, optional (defau...
def make_input_layers(self): """ Extract the ordering of the input layers. """ self.input_layers = [] if hasattr(self.model, 'input_layers'): input_keras_layers = self.model.input_layers[:] self.input_layers = [None] * len(input_keras_layers) f...
def make_output_layers(self): """ Extract the ordering of output layers. """ # TODO # use successors == 0 as the criteria for output layer # will fail when some intermediate layers also generate output. # However, because the possibility of having inserted layers,...
def generate_blob_names(self): """ Generate blob names for each one of the edge. At this time, Keras does not support "fork" operation (a layer with more than 1 blob output). So we just use names of the src layer to identify a blob. We also assume all neural networks are singly...
def _remove_layer(self, layer): """ remove the layer and its input/output edges """ successors = self.get_successors(layer) predecessors = self.get_predecessors(layer) # remove all edges for succ in successors: self._remove_edge(layer, succ) fo...
def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer): """ Insert the new_layer after layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer """ # reminder: new_keras_layer is not part of the original Keras n...
def _insert_layer_between(self, src, snk, new_layer, new_keras_layer): """ Insert the new_layer before layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer """ if snk is None: insert_pos = self.layer_list.inde...
def defuse_activation(self): """ Defuse the fused activation layers in the network. """ idx, nb_layers = 0, len(self.layer_list) while idx < nb_layers: layer = self.layer_list[idx] k_layer = self.keras_layer_map[layer] # unwrap time-distributed...
def _get_1d_interface_edges(self): """ Get edges that represents transition from not 1D to 1D, and 1D to not 1D A 'in_edge e(u,v)' means u operates on non-1D blobs, but v operates on 1D blobs. An 'out_edge e(u,v)' means u operates on 1D blobs, but v operates on non-1D blobs. """ ...
def insert_1d_permute_layers(self): """ Insert permutation layers before a 1D start point or after 1D end point """ idx, nb_layers = 0, len(self.layer_list) in_edges, out_edges = self._get_1d_interface_edges() # Hacky Warning: (1) use a 4-D permute, which is not likely t...
def replace_nodes(root, old, new): ''' Replace the old node with the new one. Old must be an indirect child of root :param root: ast node that contains an indirect reference to old :param old: node to replace :param new: node to replace `old` with ''' rep = Replacer(old, new) ...
def log_component_configuration(component, message): """Report something about component configuration that the user should better know.""" assert isinstance(component, basestring) assert isinstance(message, basestring) __component_logs.setdefault(component, []).append(message)
def create(dataset, transformers): """ Create a Transformer object to transform data for feature engineering. Parameters ---------- dataset : SFrame The dataset to use for training the model. transformers: Transformer \| list[Transformer] An Transformer or a list of Transformer...
def _preprocess_data(audio_data, verbose=True): ''' Preprocess each example, breaking it up into frames. Returns two numpy arrays: preprocessed frame and their indexes ''' from .vggish_input import waveform_to_examples last_progress_update = _time.time() progres...
def _extract_features(self, preprocessed_data, verbose=True): """ Parameters ---------- preprocessed_data : SArray Returns ------- numpy array containing the deep features """ last_progress_update = _time.time() progress_header_printed = F...
def get_deep_features(self, audio_data, verbose): ''' Performs both audio preprocessing and VGGish deep feature extraction. ''' preprocessed_data, row_ids = self._preprocess_data(audio_data, verbose) deep_features = self._extract_features(preprocessed_data, verbose) outp...
def get_spec(self): """ Return the Core ML spec """ if _mac_ver() >= (10, 14): return self.vggish_model.get_spec() else: vggish_model_file = VGGish() coreml_model_path = vggish_model_file.get_model_path(format='coreml') return MLMod...
def remove_trivial(root): ''' Remove redundant statements. The statement `a = 1` will be removed:: a = 1 a = 2 The statement `a = 1` will not be removed because `b` depends on it:: a = 1 b = a + 2 a = 2 :param root: ast node ...
def safe_isinstance(value, types=None, class_names=None): """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 ...
def value_to_jam(value, methods=False): """Makes a token to refer to a Python value inside Jam language code. The token is merely a string that can be passed around in Jam code and eventually passed back. For example, we might want to pass PropertySet instance to a tag function and it might eventually ...
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)
def abbreviate(s): """Apply a set of standard transformations to string to produce an abbreviation no more than 4 characters long. """ if not s: return '' # check the cache if s in abbreviate.abbreviations: return abbreviate.abbreviations[s] # anything less than 4 characters ...
def get_decision(self, child, is_missing = False): """ Get the decision from this node to a child node. Parameters ---------- child: Node A child node of this node. Returns ------- dict: A dictionary that describes how to get from this node t...
def to_dict(self): """ Return the node as a dictionary. Returns ------- dict: All the attributes of this node as a dictionary (minus the left and right). """ out = {} for key in self.__dict__.keys(): if key not in ['left', 'right...
def to_json(self, root_id = 0, output = {}): """ Recursive function to dump this tree as a json blob. Parameters ---------- root_id: Root id of the sub-tree output: Carry over output from the previous sub-trees. Returns ------- dict: A tree in JS...
def get_prediction_score(self, node_id): """ 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 v...
def get_prediction_path(self, node_id, missing_id = []): """ Return the prediction path from this node to the parent node. Parameters ---------- node_id : id of the node to get the prediction path. missing_id : Additional info that contains nodes with missing features...
def create(graph, label_field, threshold=1e-3, weight_field='', self_weight=1.0, undirected=False, max_iterations=None, _single_precision=False, _distributed='auto', verbose=True): """ Given a weighted graph with observed cl...
def _is_not_pickle_safe_gl_model_class(obj_class): """ Check if a Turi create model is pickle safe. The function does it by checking that _CustomModel is the base class. Parameters ---------- obj_class : Class to be checked. Returns ---------- True if the GLC class is a model a...
def _is_not_pickle_safe_gl_class(obj_class): """ Check if class is a Turi create model. The function does it by checking the method resolution order (MRO) of the class and verifies that _Model is the base class. Parameters ---------- obj_class : Class to be checked. Returns ---...
def _get_gl_class_type(obj_class): """ Internal util to get the type of the GLC class. The pickle file stores this name so that it knows how to construct the object on unpickling. Parameters ---------- obj_class : Class which has to be categorized. Returns ---------- A class typ...
def _get_gl_object_from_persistent_id(type_tag, gl_archive_abs_path): """ Internal util to get a GLC object from a persistent ID in the pickle file. Parameters ---------- type_tag : The name of the glc class as saved in the GLC pickler. gl_archive_abs_path: An absolute path to the GLC archive ...
def persistent_id(self, obj): """ Provide a persistent ID for "saving" GLC objects by reference. Return None for all non GLC objects. Parameters ---------- obj: Name of the object whose persistent ID is extracted. Returns -------- None if the ob...
def close(self): """ Close the pickle file, and the zip archive file. The single zip archive file can now be shipped around to be loaded by the unpickler. """ if self.file is None: return # Close the pickle file. self.file.close() self.file = ...
def persistent_load(self, pid): """ Reconstruct a GLC object using the persistent ID. This method should not be used externally. It is required by the unpickler super class. Parameters ---------- pid : The persistent ID used in pickle file to save the GLC object. ...
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 disposed if self.tmp_file and...
def convert(sk_obj, input_features = None, output_feature_names = None): """ Convert scikit-learn pipeline, classifier, or regressor to Core ML format. Parameters ---------- sk_obj: model \| [model] of scikit-learn format. Scikit learn model(s) to convert to a Core ML format. ...
def ParseMessage(descriptor, byte_str): """Generate a new Message instance from this Descriptor and a byte string. Args: descriptor: Protobuf Descriptor object byte_str: Serialized protocol buffer byte string Returns: Newly created protobuf Message object. """ result_class = MakeClass(descriptor...
def MakeClass(descriptor): """Construct a class object for a protobuf described by descriptor. Composite descriptors are handled by defining the new class as a member of the parent class, recursing as deep as necessary. This is the dynamic equivalent to: class Parent(message.Message): __metaclass__ = Ge...
def load_images(url, format='auto', with_path=True, recursive=True, ignore_failure=True, random_order=False): """ Loads images from a directory. JPEG and PNG images are supported. Parameters ---------- url : str The string of the path where all the images are stored. format : {'PNG' \| ...
def _decode(image_data): """ Internal helper function for decoding a single Image or an SArray of Images """ from ...data_structures.sarray import SArray as _SArray from ... import extensions as _extensions if type(image_data) is _SArray: return _extensions.decode_image_sarray(image_data...
def resize(image, width, height, channels=None, decode=False, resample='nearest'): """ Resizes the image or SArray of Images to a specific width, height, and number of channels. Parameters ---------- image : turicreate.Image \| SArray The image or SArray of images to be resiz...
def _convert_1bit_array_to_byte_array(arr): """ Convert bit array to byte array. :param arr: list Bits as a list where each element is an integer of 0 or 1 Returns ------- numpy.array 1D numpy array of type uint8 """ # Padding if necessary while len(arr) < 8 or len(...
def _decompose_bytes_to_bit_arr(arr): """ Unpack bytes to bits :param arr: list Byte Stream, as a list of uint8 values Returns ------- bit_arr: list Decomposed bit stream as a list of 0/1s of length (len(arr) * 8) """ bit_arr = [] for idx in range(len(arr)): ...
def _get_linear_lookup_table_and_weight(nbits, wp): """ Generate a linear lookup table. :param nbits: int Number of bits to represent a quantized weight value :param wp: numpy.array Weight blob to be quantized Returns ------- lookup_table: numpy.array Lookup table ...
def _get_kmeans_lookup_table_and_weight(nbits, w, init='k-means++', tol=1e-2, n_init=1, rand_seed=0): """ Generate K-Means lookup table given a weight parameter field :param nbits: Number of bits for quantization :param w: Weight as numpy array Returns ------- lut: numpy.a...
def _quantize_channelwise_linear(weight, nbits, axis=0): """ Linearly quantize weight blob. :param weight: numpy.array Weight to be quantized. :param nbits: int Number of bits per weight element :param axis: int Axis of the weight blob to compute channel-wise quantization,...
def _quantize_wp(wp, nbits, qm, axis=0, **kwargs): """ Quantize the weight blob :param wp: numpy.array Weight parameters :param nbits: int Number of bits :param qm: Quantization mode :param lut_function: (``callable function``) Python callable representing a look...
def _quantize_wp_field(wp, nbits, qm, shape, axis=0, **kwargs): """ Quantize WeightParam field in Neural Network Protobuf :param wp: MLModel.NeuralNetwork.WeightParam WeightParam field :param nbits: int Number of bits to be quantized :param qm: str Quantization mode :pa...
def compare_models(full_precision_model, quantized_model, sample_data): """ Utility function to compare the performance of a full precision vs quantized model :param full_precision_model: MLModel The full precision model with float32 weights :param quantized_model...
def quantize_weights(full_precision_model, nbits, quantization_mode="linear", sample_data=None, **kwargs): """ Utility function to convert a full precision (float) MLModel to a nbit quantized MLModel (float16). :param f...
def create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, nearest_items=None, similarity_type='jaccard', threshold=0.001, only_top_k=64, verbose=True, target_memory_usage = 8*102...
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): return 'MULTIPLY' elif isinstance(keras_layer, _ke...
def convert_dense(builder, layer, input_names, output_names, keras_layer): """ Convert a dense layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get input and o...
def convert_embedding(builder, layer, input_names, output_names, keras_layer): """Convert a dense layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get input and ou...
def convert_activation(builder, layer, input_names, output_names, keras_layer): """ Convert an activation layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get ...
def convert_advanced_relu(builder, layer, input_names, output_names, keras_layer): """ Convert an ReLU layer with maximum value from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. ...
def convert_convolution(builder, layer, input_names, output_names, keras_layer): """ Convert convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ _check...

def _sanitize_value(x): """ Performs cleaning steps on the data so various type comparisons can be performed correctly. """ if isinstance(x, _six.string_types + _six.integer_types + (float,)): return x elif _HAS_SKLEARN and _sp.issparse(x): return x.todense() elif isinstance(...

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

def evaluate_transformer(model, input_data, reference_output, verbose=False): """ Evaluate a transformer specification for testing. Parameters ---------- spec: [str | MLModel] File from where to load the Model from (OR) a loaded version of MLModel. inpu...

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

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

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]

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

def compile( self , howmany = 1 , pop = -1 , expect_error = False , extension = '.o' , options = ['-c'] , built_handler = lambda built_file: None , source_file = None , source_suffix = '.cpp' # C-style comments by default; handles C++ a...

def load (self, jamfile_location): """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_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( location, self.JAMROOT + self.JAMFILE) if not found: print "er...

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

def module_name(self, jamfile_location): """Returns the name of module corresponding to 'jamfile-location'. If no module corresponds to location yet, associates default module name with that location.""" assert isinstance(jamfile_location, basestring) module = self.location2modul...

def find_jamfile (self, dir, parent_root=0, no_errors=0): """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 di...

def load_jamfile(self, dir, jamfile_module): """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.""" assert isinstance(dir, basestring) a...

def load_standalone(self, jamfile_module, file): """Loads 'file' as standalone project that has no location associated with it. This is mostly useful for user-config.jam, which should be able to define targets, but although it has some location in filesystem, we do not want any build to...

def initialize(self, module_name, location=None, basename=None, standalone_path=''): """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 wi...

def inherit_attributes(self, project_module, parent_module): """Make 'project-module' inherit attributes of project root and parent module.""" assert isinstance(project_module, basestring) assert isinstance(parent_module, basestring) attributes = self.module2attributes[project_m...

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

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

def attribute(self, project, attribute): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(attribute, basestring) try: return self.module2attributes[project].get(attribute) ...

def attributeDefault(self, project, attribute, default): """Returns the value of the specified attribute in the specified jamfile module.""" assert isinstance(project, basestring) assert isinstance(attribute, basestring) assert isinstance(default, basestring) or default is None ...

def target(self, project_module): """Returns the project target corresponding to the 'project-module'.""" assert isinstance(project_module, basestring) if project_module not in self.module2target: self.module2target[project_module] = \ b2.build.targets.ProjectTarget(p...

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, basestring) assert callable(callable_) self.project_rules_.add_rule(name, callable_)

def __build_python_module_cache(self): """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 ...

def load_module(self, name, extra_path=None): """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 w...

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 isinstance(attribute, basestring) assert isinstance(exact, (int, bool)) if __debug__ and not exact: ...

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: parent = parent[0] print...

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

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

def path_constant(self, name, value): """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.""" assert is_iterable...

def conditional(self, condition, requirements): """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 : ...

def create_array_feature_extractor(input_features, output_name, extract_indices, output_type = None): """ Creates a feature extractor from an input array feature, return input_features is a list of one (name, array) tuple. extract_indices is either an integer or a li...

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: context.append(node) i...

def x_build_targets_target( self, node ): ''' 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. ''' target_node = node name = self.get_child_data(target_node,tag='name',...

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 = self.get_child(action_node,tag='name') if name: name = self.get_data(name) ...

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

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

def _get_weight_param_summary(wp): """Get a summary of _NeuralNetwork_pb2.WeightParams Args: wp : _NeuralNetwork_pb2.WeightParams - the _NeuralNetwork_pb2.WeightParams message to display Returns: a str summary for wp """ summary_str = '' if wp.HasField('quantization'): nbits = wp...

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

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

def print_network_spec(mlmodel_spec, interface_only=False): """ Print the network information summary. Args: mlmodel_spec : the mlmodel spec interface_only : Shows only the input and output of the network """ inputs, outputs, layers_info = summarize_neural_network_spec(mlmodel_spec) print('...

def _generate_base_svm_classifier_spec(model): """ Takes an SVM classifier produces a starting spec using the parts. that are shared between all SVMs. """ if not(_HAS_SKLEARN): raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.') check_fitted(model, la...

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

def make_input_layers(self): """ Extract the ordering of the input layers. """ self.input_layers = [] if hasattr(self.model, 'input_layers'): input_keras_layers = self.model.input_layers[:] self.input_layers = [None] * len(input_keras_layers) f...

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

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

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

def _insert_layer_after(self, layer_idx, new_layer, new_keras_layer): """ Insert the new_layer after layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer """ # reminder: new_keras_layer is not part of the original Keras n...

def _insert_layer_between(self, src, snk, new_layer, new_keras_layer): """ Insert the new_layer before layer, whose position is layer_idx. The new layer's parameter is stored in a Keras layer called new_keras_layer """ if snk is None: insert_pos = self.layer_list.inde...

def defuse_activation(self): """ Defuse the fused activation layers in the network. """ idx, nb_layers = 0, len(self.layer_list) while idx < nb_layers: layer = self.layer_list[idx] k_layer = self.keras_layer_map[layer] # unwrap time-distributed...

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

def insert_1d_permute_layers(self): """ Insert permutation layers before a 1D start point or after 1D end point """ idx, nb_layers = 0, len(self.layer_list) in_edges, out_edges = self._get_1d_interface_edges() # Hacky Warning: (1) use a 4-D permute, which is not likely t...

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

def log_component_configuration(component, message): """Report something about component configuration that the user should better know.""" assert isinstance(component, basestring) assert isinstance(message, basestring) __component_logs.setdefault(component, []).append(message)

def create(dataset, transformers): """ Create a Transformer object to transform data for feature engineering. Parameters ---------- dataset : SFrame The dataset to use for training the model. transformers: Transformer | list[Transformer] An Transformer or a list of Transformer...

def _preprocess_data(audio_data, verbose=True): ''' Preprocess each example, breaking it up into frames. Returns two numpy arrays: preprocessed frame and their indexes ''' from .vggish_input import waveform_to_examples last_progress_update = _time.time() progres...

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

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

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

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

def safe_isinstance(value, types=None, class_names=None): """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 ...

def value_to_jam(value, methods=False): """Makes a token to refer to a Python value inside Jam language code. The token is merely a string that can be passed around in Jam code and eventually passed back. For example, we might want to pass PropertySet instance to a tag function and it might eventually ...

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)

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

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

def to_dict(self): """ Return the node as a dictionary. Returns ------- dict: All the attributes of this node as a dictionary (minus the left and right). """ out = {} for key in self.__dict__.keys(): if key not in ['left', 'right...

def to_json(self, root_id = 0, output = {}): """ Recursive function to dump this tree as a json blob. Parameters ---------- root_id: Root id of the sub-tree output: Carry over output from the previous sub-trees. Returns ------- dict: A tree in JS...

def get_prediction_score(self, node_id): """ 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 v...

def get_prediction_path(self, node_id, missing_id = []): """ Return the prediction path from this node to the parent node. Parameters ---------- node_id : id of the node to get the prediction path. missing_id : Additional info that contains nodes with missing features...

def create(graph, label_field, threshold=1e-3, weight_field='', self_weight=1.0, undirected=False, max_iterations=None, _single_precision=False, _distributed='auto', verbose=True): """ Given a weighted graph with observed cl...

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

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

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

def _get_gl_object_from_persistent_id(type_tag, gl_archive_abs_path): """ Internal util to get a GLC object from a persistent ID in the pickle file. Parameters ---------- type_tag : The name of the glc class as saved in the GLC pickler. gl_archive_abs_path: An absolute path to the GLC archive ...

def persistent_id(self, obj): """ Provide a persistent ID for "saving" GLC objects by reference. Return None for all non GLC objects. Parameters ---------- obj: Name of the object whose persistent ID is extracted. Returns -------- None if the ob...

def close(self): """ Close the pickle file, and the zip archive file. The single zip archive file can now be shipped around to be loaded by the unpickler. """ if self.file is None: return # Close the pickle file. self.file.close() self.file = ...

def persistent_load(self, pid): """ Reconstruct a GLC object using the persistent ID. This method should not be used externally. It is required by the unpickler super class. Parameters ---------- pid : The persistent ID used in pickle file to save the GLC object. ...

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 disposed if self.tmp_file and...

def convert(sk_obj, input_features = None, output_feature_names = None): """ Convert scikit-learn pipeline, classifier, or regressor to Core ML format. Parameters ---------- sk_obj: model | [model] of scikit-learn format. Scikit learn model(s) to convert to a Core ML format. ...

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

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

def load_images(url, format='auto', with_path=True, recursive=True, ignore_failure=True, random_order=False): """ Loads images from a directory. JPEG and PNG images are supported. Parameters ---------- url : str The string of the path where all the images are stored. format : {'PNG' | ...

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

def resize(image, width, height, channels=None, decode=False, resample='nearest'): """ Resizes the image or SArray of Images to a specific width, height, and number of channels. Parameters ---------- image : turicreate.Image | SArray The image or SArray of images to be resiz...

def _convert_1bit_array_to_byte_array(arr): """ Convert bit array to byte array. :param arr: list Bits as a list where each element is an integer of 0 or 1 Returns ------- numpy.array 1D numpy array of type uint8 """ # Padding if necessary while len(arr) < 8 or len(...

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

def _get_linear_lookup_table_and_weight(nbits, wp): """ Generate a linear lookup table. :param nbits: int Number of bits to represent a quantized weight value :param wp: numpy.array Weight blob to be quantized Returns ------- lookup_table: numpy.array Lookup table ...

def _get_kmeans_lookup_table_and_weight(nbits, w, init='k-means++', tol=1e-2, n_init=1, rand_seed=0): """ Generate K-Means lookup table given a weight parameter field :param nbits: Number of bits for quantization :param w: Weight as numpy array Returns ------- lut: numpy.a...

def _quantize_channelwise_linear(weight, nbits, axis=0): """ Linearly quantize weight blob. :param weight: numpy.array Weight to be quantized. :param nbits: int Number of bits per weight element :param axis: int Axis of the weight blob to compute channel-wise quantization,...

def _quantize_wp(wp, nbits, qm, axis=0, **kwargs): """ Quantize the weight blob :param wp: numpy.array Weight parameters :param nbits: int Number of bits :param qm: Quantization mode :param lut_function: (``callable function``) Python callable representing a look...

def _quantize_wp_field(wp, nbits, qm, shape, axis=0, **kwargs): """ Quantize WeightParam field in Neural Network Protobuf :param wp: MLModel.NeuralNetwork.WeightParam WeightParam field :param nbits: int Number of bits to be quantized :param qm: str Quantization mode :pa...

def compare_models(full_precision_model, quantized_model, sample_data): """ Utility function to compare the performance of a full precision vs quantized model :param full_precision_model: MLModel The full precision model with float32 weights :param quantized_model...

def quantize_weights(full_precision_model, nbits, quantization_mode="linear", sample_data=None, **kwargs): """ Utility function to convert a full precision (float) MLModel to a nbit quantized MLModel (float16). :param f...

def create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, nearest_items=None, similarity_type='jaccard', threshold=0.001, only_top_k=64, verbose=True, target_memory_usage = 8*102...

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): return 'MULTIPLY' elif isinstance(keras_layer, _ke...

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

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

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

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

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