BoghdadyJR/codesearch_python · Data Studio

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def implicit_includes (self, feature, target_type): """ Returns the properties which specify implicit include paths to generated headers. This traverses all targets in this subvariant, and subvariants referred by <implcit-dependecy>properties. For all targets which are of typ...
def cmp_ast(node1, node2): ''' Compare if two nodes are equal. ''' if type(node1) != type(node2): return False if isinstance(node1, (list, tuple)): if len(node1) != len(node2): return False for left, right in zip(node1, node2): if not cmp_ast(left, ...
def create_more_container_files(sourceDir, suffix, maxElements, containers, containers2): """Creates additional files for the individual MPL-containers.""" # Create files for each MPL-container with 20 to 'maxElements' elements # which will be used during generation. for container in containers: ...
def create_input_for_numbered_sequences(headerDir, sourceDir, containers, maxElements): """Creates additional source- and header-files for the numbered sequence MPL-containers.""" # Create additional container-list without "map". containersWithoutMap = containers[:] try: containersWithoutMap.rem...
def adjust_container_limits_for_variadic_sequences(headerDir, containers, maxElements): """Adjusts the limits of variadic sequence MPL-containers.""" for container in containers: headerFile = os.path.join( headerDir, "limits", container + ".hpp" ) regexMatch = r'(define\s+BOOST_MPL_LIMIT_' + c...
def current_boost_dir(): """Returns the (relative) path to the Boost source-directory this file is located in (if any).""" # Path to directory containing this script. path = os.path.dirname( os.path.realpath(__file__) ) # Making sure it is located in "${boost-dir}/libs/mpl/preprocessed". for directo...
def to_positive_multiple_of_10(string): """Converts a string into its encoded positive integer (greater zero) or throws an exception.""" try: value = int(string) except ValueError: msg = '"%r" is not a positive multiple of 10 (greater zero).' % string raise argparse.ArgumentTypeError...
def main(): """The main function.""" # Find the current Boost source-directory in which this script is located. sourceDir = current_boost_dir() if sourceDir == None: sourceDir = "" # Prepare and run cmdline-parser. cmdlineParser = argparse.ArgumentParser(description="A generato...
def add_inner_product(self, name, W, b, input_channels, output_channels, has_bias, input_name, output_name, **kwargs): """ Add an inner product layer to the model. Parameters ---------- name: str The name of this layer W: numpy.array...
def add_convolution(self, name, kernel_channels, output_channels, height, width, stride_height, stride_width, border_mode, groups, W, b, has_bias, is_deconv = False, output_shape = None, input_name = 'data', output_name = 'out', dilation_factors = [1,1], paddi...
def add_resize_bilinear(self, name, input_name, output_name, target_height=1, target_width=1, mode='ALIGN_ENDPOINTS_MODE'): """ Add resize bilinear layer to the model. A layer that resizes the input to a given spatial size using bilinear interpolation. Parameters ...
def add_crop_resize(self, name, input_names, output_name, target_height=1, target_width=1, mode='STRICT_ALIGN_ENDPOINTS_MODE', normalized_roi=False, box_indices_mode='CORNERS_HEIGHT_FIRST', spatial_scale=1.0)...
def _toolkit_serialize_summary_struct(model, sections, section_titles): """ Serialize model summary into a dict with ordered lists of sections and section titles Parameters ---------- model : Model object sections : Ordered list of lists (sections) of tuples (field,value) [ [(fi...
def _add_docstring(format_dict): """ Format a doc-string on the fly. @arg format_dict: A dictionary to format the doc-strings Example: @add_docstring({'context': __doc_string_context}) def predict(x): ''' {context} >> model.predict(data) '...
def _find_only_column_of_type(sframe, target_type, type_name, col_name): """ Finds the only column in `SFrame` with a type specified by `target_type`. If there are zero or more than one such columns, an exception will be raised. The name and type of the target column should be provided as strings fo...
def _find_only_image_column(sframe): """ Finds the only column in `sframe` with a type of turicreate.Image. If there are zero or more than one image columns, an exception will be raised. """ from turicreate import Image return _find_only_column_of_type(sframe, target_type=Image, ...
def _find_only_drawing_column(sframe): """ Finds the only column that can be interpreted as a drawing feature column. A drawing column can be a stroke-based drawing column (with dtype list) or a bitmap-based drawing column (with dtype turicreate.Image) If there are zero or more than one drawing...
def _SGraphFromJsonTree(json_str): """ Convert the Json Tree to SGraph """ g = json.loads(json_str) vertices = [_Vertex(x['id'], dict([(str(k), v) for k, v in _six.iteritems(x) if k != 'id'])) for x in g['vertices']] edges = [...
def _summarize_coefficients(top_coefs, bottom_coefs): """ Return a tuple of sections and section titles. Sections are pretty print of model coefficients Parameters ---------- top_coefs : SFrame of top k coefficients bottom_coefs : SFrame of bottom k coefficients Returns ------- ...
def _toolkit_get_topk_bottomk(values, k=5): """ Returns a tuple of the top k values from the positive and negative values in a SArray Parameters ---------- values : SFrame of model coefficients k: Maximum number of largest positive and k lowest negative numbers to return Returns -...
def __extract_model_summary_value(model, value): """ Extract a model summary field value """ field_value = None if isinstance(value, _precomputed_field): field_value = value.field else: field_value = model._get(value) if isinstance(field_value, float): try: ...
def _make_repr_table_from_sframe(X): """ Serializes an SFrame to a list of strings, that, when printed, creates a well-formatted table. """ assert isinstance(X, _SFrame) column_names = X.column_names() out_data = [ [None]*len(column_names) for i in range(X.num_rows())] column_sizes = [le...
def _toolkit_repr_print(model, fields, section_titles, width = None): """ Display a toolkit repr according to some simple rules. Parameters ---------- model : Turi Create model fields: List of lists of tuples Each tuple should be (display_name, field_name), where field_name can ...
def _map_unity_proxy_to_object(value): """ Map returning value, if it is unity SFrame, SArray, map it """ vtype = type(value) if vtype in _proxy_map: return _proxy_map[vtype](value) elif vtype == list: return [_map_unity_proxy_to_object(v) for v in value] elif vtype == dict: ...
def _toolkits_select_columns(dataset, columns): """ Same as select columns but redirect runtime error to ToolkitError. """ try: return dataset.select_columns(columns) except RuntimeError: missing_features = list(set(columns).difference(set(dataset.column_names()))) raise Tool...
def _raise_error_if_column_exists(dataset, column_name = 'dataset', dataset_variable_name = 'dataset', column_name_error_message_name = 'column_name'): """ Check if a column exists in an SFrame with error message. """ err_msg = 'The SFrame {0} must...
def _check_categorical_option_type(option_name, option_value, possible_values): """ Check whether or not the requested option is one of the allowed values. """ err_msg = '{0} is not a valid option for {1}. '.format(option_value, option_name) err_msg += ' Expected one of: '.format(possible_values) ...
def _raise_error_if_not_sarray(dataset, variable_name="SArray"): """ Check if the input is an SArray. Provide a proper error message otherwise. """ err_msg = "Input %s is not an SArray." if not isinstance(dataset, _SArray): raise ToolkitError(err_msg % variable_name)
def _raise_error_if_not_sframe(dataset, variable_name="SFrame"): """ Check if the input is an SFrame. Provide a proper error message otherwise. """ err_msg = "Input %s is not an SFrame. If it is a Pandas DataFrame," err_msg += " you may use the to_sframe() function to convert it to an SFrame." ...
def _raise_error_if_sframe_empty(dataset, variable_name="SFrame"): """ Check if the input is empty. """ err_msg = "Input %s either has no rows or no columns. A non-empty SFrame " err_msg += "is required." if dataset.num_rows() == 0 or dataset.num_columns() == 0: raise ToolkitError(err_m...
def _raise_error_evaluation_metric_is_valid(metric, allowed_metrics): """ Check if the input is an SFrame. Provide a proper error message otherwise. """ err_msg = "Evaluation metric '%s' not recognized. The supported evaluation" err_msg += " metrics are (%s)." if metric not in allowed_metr...
def _numeric_param_check_range(variable_name, variable_value, range_bottom, range_top): """ Checks if numeric parameter is within given range """ err_msg = "%s must be between %i and %i" if variable_value < range_bottom or variable_value > range_top: raise ToolkitError(err_msg % (variable_n...
def _validate_data(dataset, target, features=None, validation_set='auto'): """ Validate and canonicalize training and validation data. Parameters ---------- dataset : SFrame Dataset for training the model. target : string Name of the column containing the target variable. ...
def _validate_row_label(dataset, label=None, default_label='__id'): """ Validate a row label column. If the row label is not specified, a column is created with row numbers, named with the string in the `default_label` parameter. Parameters ---------- dataset : SFrame Input dataset....
def _mac_ver(): """ Returns Mac version as a tuple of integers, making it easy to do proper version comparisons. On non-Macs, it returns an empty tuple. """ import platform import sys if sys.platform == 'darwin': ver_str = platform.mac_ver()[0] return tuple([int(v) for v in v...
def _print_neural_compute_device(cuda_gpus, use_mps, cuda_mem_req=None, has_mps_impl=True): """ Print a message making it clear to the user what compute resource is used in neural network training. """ num_cuda_gpus = len(cuda_gpus) if num_cuda_gpus >= 1: gpu_names = ', '.join(gpu['name'...
def _GetMessageFromFactory(factory, full_name): """Get a proto class from the MessageFactory by name. Args: factory: a MessageFactory instance. full_name: str, the fully qualified name of the proto type. Returns: A class, for the type identified by full_name. Raises: KeyError, if the proto is n...
def MakeSimpleProtoClass(fields, full_name=None, pool=None): """Create a Protobuf class whose fields are basic types. Note: this doesn't validate field names! Args: fields: dict of {name: field_type} mappings for each field in the proto. If this is an OrderedDict the order will be maintained, otherw...
def _MakeFileDescriptorProto(proto_file_name, full_name, field_items): """Populate FileDescriptorProto for MessageFactory's DescriptorPool.""" package, name = full_name.rsplit('.', 1) file_proto = descriptor_pb2.FileDescriptorProto() file_proto.name = os.path.join(package.replace('.', '/'), proto_file_name) f...
def convert(model, input_name, output_features): """Convert a decision tree model to protobuf format. Parameters ---------- decision_tree : DecisionTreeClassifier A trained scikit-learn tree model. input_name: str Name of the input columns. output_name: str Name of the...
def _get_model_metadata(model_class, metadata, version=None): """ Returns user-defined metadata, making sure information all models should have is also available, as a dictionary """ from turicreate import __version__ info = { 'turicreate_version': __version__, 'type': model_cla...
def _set_model_metadata(mlmodel, model_class, metadata, version=None): """ Sets user-defined metadata, making sure information all models should have is also available """ info = _get_model_metadata(model_class, metadata, version) mlmodel.user_defined_metadata.update(info)
def _ToCamelCase(name): """Converts name to camel-case and returns it.""" capitalize_next = False result = [] for c in name: if c == '_': if result: capitalize_next = True elif capitalize_next: result.append(c.upper()) capitalize_next = False else: result += c # L...
def _ToJsonName(name): """Converts name to Json name and returns it.""" capitalize_next = False result = [] for c in name: if c == '_': capitalize_next = True elif capitalize_next: result.append(c.upper()) capitalize_next = False else: result += c return ''.join(result)
def _SetOptions(self, options, options_class_name): """Sets the descriptor's options This function is used in generated proto2 files to update descriptor options. It must not be used outside proto2. """ self._options = options self._options_class_name = options_class_name # Does this descr...
def GetOptions(self): """Retrieves descriptor options. This method returns the options set or creates the default options for the descriptor. """ if self._options: return self._options from google.protobuf import descriptor_pb2 try: options_class = getattr(descriptor_pb2, self._...
def CopyToProto(self, proto): """Copies this to the matching proto in descriptor_pb2. Args: proto: An empty proto instance from descriptor_pb2. Raises: Error: If self couldnt be serialized, due to to few constructor arguments. """ if (self.file is not None and self._serialized_...
def EnumValueName(self, enum, value): """Returns the string name of an enum value. This is just a small helper method to simplify a common operation. Args: enum: string name of the Enum. value: int, value of the enum. Returns: string name of the enum value. Raises: KeyErr...
def resolve_reference(target_reference, project): """ Given a target_reference, made in context of 'project', returns the AbstractTarget instance that is referred to, as well as properties explicitly specified for this reference. """ # Separate target name from properties override assert isinsta...
def generate_from_reference(target_reference, project, property_set_): """ Attempts to generate the target given by target reference, which can refer both to a main target or to a file. Returns a list consisting of - usage requirements - generated virtual targets, if any target_reference: Targe...
def main_target_alternative (self, target): """ Registers the specified target as a main target alternatives. Returns 'target'. """ assert isinstance(target, AbstractTarget) target.project ().add_alternative (target) return target
def main_target_sources (self, sources, main_target_name, no_renaming=0): """Return the list of sources to use, if main target rule is invoked with 'sources'. If there are any objects in 'sources', they are treated as main target instances, and the name of such targets are adjusted to be...
def main_target_requirements(self, specification, project): """Returns the requirement to use when declaring a main target, which are obtained by - translating all specified property paths, and - refining project requirements with the one specified for the target 'specificat...
def main_target_usage_requirements (self, specification, project): """ Returns the use requirement to use when declaraing a main target, which are obtained by - translating all specified property paths, and - adding project's usage requirements specification: Use...
def main_target_default_build (self, specification, project): """ Return the default build value to use when declaring a main target, which is obtained by using specified value if not empty and parent's default build attribute otherwise. specification: Default build explicit...
def start_building (self, main_target_instance): """ Helper rules to detect cycles in main target references. """ assert isinstance(main_target_instance, MainTarget) if id(main_target_instance) in self.targets_being_built_: names = [] for t in self.targets_being_b...
def create_typed_target (self, type, project, name, sources, requirements, default_build, usage_requirements): """ Creates a TypedTarget with the specified properties. The 'name', 'sources', 'requirements', 'default_build' and 'usage_requirements' are assumed to be in the form specified ...
def generate (self, ps): """ Generates all possible targets contained in this project. """ assert isinstance(ps, property_set.PropertySet) self.manager_.targets().log( "Building project '%s' with '%s'" % (self.name (), str(ps))) self.manager_.targets().increase_indent...
def targets_to_build (self): """ Computes and returns a list of AbstractTarget instances which must be built when this project is built. """ result = [] if not self.built_main_targets_: self.build_main_targets () # Collect all main targets here, except f...
def mark_targets_as_explicit (self, target_names): """Add 'target' to the list of targets in this project that should be build only by explicit request.""" # Record the name of the target, not instance, since this # rule is called before main target instaces are created. assert ...
def add_alternative (self, target_instance): """ Add new target alternative. """ assert isinstance(target_instance, AbstractTarget) if self.built_main_targets_: raise IllegalOperation ("add-alternative called when main targets are already created for project '%s'" % self.full...
def has_main_target (self, name): """Tells if a main target with the specified name exists.""" assert isinstance(name, basestring) if not self.built_main_targets_: self.build_main_targets() return name in self.main_target_
def create_main_target (self, name): """ Returns a 'MainTarget' class instance corresponding to the 'name'. """ assert isinstance(name, basestring) if not self.built_main_targets_: self.build_main_targets () return self.main_targets_.get (name, None)
def find_really(self, id): """ Find and return the target with the specified id, treated relative to self. """ assert isinstance(id, basestring) result = None current_location = self.get ('location') __re_split_project_target = re.compile (r'(.)//(.)') ...
def add_constant(self, name, value, path=0): """Adds a new constant for this project. The constant will be available for use in Jamfile module for this project. If 'path' is true, the constant will be interpreted relatively to the location of project. """ assert ...
def add_alternative (self, target): """ Add a new alternative for this target. """ assert isinstance(target, BasicTarget) d = target.default_build () if self.alternatives_ and self.default_build_ != d: get_manager().errors()("default build must be identical in all al...
def __select_alternatives (self, property_set_, debug): """ Returns the best viable alternative for this property_set See the documentation for selection rules. # TODO: shouldn't this be 'alternative' (singular)? """ # When selecting alternatives we have to consider defau...
def generate (self, ps): """ Select an alternative for this main target, by finding all alternatives which requirements are satisfied by 'properties' and picking the one with longest requirements set. Returns the result of calling 'generate' on that alternative. """ ...
def __generate_really (self, prop_set): """ Generates the main target with the given property set and returns a list which first element is property_set object containing usage_requirements of generated target and with generated virtual target in other elements. It's possible...
def sources (self): """ Returns the list of AbstractTargets which are used as sources. The extra properties specified for sources are not represented. The only used of this rule at the moment is the '--dump-tests' feature of the test system. """ if self.source...
def common_properties (self, build_request, requirements): """ Given build request and requirements, return properties common to dependency build request and target build properties. """ # For optimization, we add free unconditional requirements directly, # withou...
def match (self, property_set_, debug): """ Returns the alternative condition for this alternative, if the condition is satisfied by 'property_set'. """ # The condition is composed of all base non-conditional properties. # It's not clear if we should expand 'self.requirements...
def generate_dependency_properties(self, properties, ps): """ Takes a target reference, which might be either target id or a dependency property, and generates that target using 'property_set' as build request. Returns a tuple (result, usage_requirements). """ ...
def generate (self, ps): """ Determines final build properties, generates sources, and calls 'construct'. This method should not be overridden. """ assert isinstance(ps, property_set.PropertySet) self.manager_.errors().push_user_context( "Generating target " +...
def compute_usage_requirements (self, subvariant): """ Given the set of generated targets, and refined build properties, determines and sets appripriate usage requirements on those targets. """ assert isinstance(subvariant, virtual_target.Subvariant) rproperties =...
def create_subvariant (self, root_targets, all_targets, build_request, sources, rproperties, usage_requirements): """Creates a new subvariant-dg instances for 'targets' - 'root-targets' the virtual targets will be returned to dependents - '...
def variant (name, parents_or_properties, explicit_properties = []): """ Declares a new variant. First determines explicit properties for this variant, by refining parents' explicit properties with the passed explicit properties. The result is remembered and will be used if this vari...
def register_globals (): """ Registers all features and variants declared by this module. """ # This feature is used to determine which OS we're on. # In future, this may become <target-os> and <host-os> # TODO: check this. Compatibility with bjam names? Subfeature for version? os = sys.platfor...
def lib(names, sources=[], requirements=[], default_build=[], usage_requirements=[]): """The implementation of the 'lib' rule. Beyond standard syntax that rule allows simplified: 'lib a b c ;'.""" assert is_iterable_typed(names, basestring) assert is_iterable_typed(sources, basestring) assert is_ite...
def adjust_properties (self, prop_set): """ For all virtual targets for the same dependency graph as self, i.e. which belong to the same main target, add their directories to include path. """ assert isinstance(prop_set, property_set.PropertySet) s = self.targets ...
def create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, random_seed=0, verbose=True): """ Create a model that makes recommendations using item popularity. When no target column is provided, the popularity is ...
def get_params(self, deep=False): """Get parameter.s""" params = super(XGBModel, self).get_params(deep=deep) if params['missing'] is np.nan: params['missing'] = None # sklearn doesn't handle nan. see #4725 if not params.get('eval_metric', True): del params['eval_...
def get_xgb_params(self): """Get xgboost type parameters.""" xgb_params = self.get_params() xgb_params['silent'] = 1 if self.silent else 0 if self.nthread <= 0: xgb_params.pop('nthread', None) return xgb_params
def fit(self, X, y, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True): # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init """ Fit the gradient boosting model Parameters ---------- X : array_like ...
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True): # pylint: disable = attribute-defined-outside-init,arguments-differ """ Fit gradient boosting classifier Parameters ---------- X : array_like ...
def add_grist (features): """ Transform a string by bracketing it with "<>". If already bracketed, does nothing. features: one string or a sequence of strings return: the gristed string, if features is a string, or a sequence of gristed strings, if features is a sequence """ assert is_iterab...
def replace_grist (features, new_grist): """ Replaces the grist of a string by a new one. Returns the string with the new grist. """ assert is_iterable_typed(features, basestring) or isinstance(features, basestring) assert isinstance(new_grist, basestring) # this function is used a lot in th...
def get_value (property): """ Gets the value of a property, that is, the part following the grist, if any. """ assert is_iterable_typed(property, basestring) or isinstance(property, basestring) return replace_grist (property, '')
def get_grist (value): """ Returns the grist of a string. If value is a sequence, does it for every value and returns the result as a sequence. """ assert is_iterable_typed(value, basestring) or isinstance(value, basestring) def get_grist_one (name): split = __re_grist_and_value.match (n...
def ungrist (value): """ Returns the value without grist. If value is a sequence, does it for every value and returns the result as a sequence. """ assert is_iterable_typed(value, basestring) or isinstance(value, basestring) def ungrist_one (value): stripped = __re_grist_content.match (v...
def replace_suffix (name, new_suffix): """ Replaces the suffix of name by new_suffix. If no suffix exists, the new one is added. """ assert isinstance(name, basestring) assert isinstance(new_suffix, basestring) split = os.path.splitext (name) return split [0] + new_suffix
def split_action_id (id): """ Splits an id in the toolset and specific rule parts. E.g. 'gcc.compile.c++' returns ('gcc', 'compile.c++') """ assert isinstance(id, basestring) split = id.split ('.', 1) toolset = split [0] name = '' if len (split) > 1: name = split [1] retu...
def on_windows (): """ Returns true if running on windows, whether in cygwin or not. """ if bjam.variable("NT"): return True elif bjam.variable("UNIX"): uname = bjam.variable("JAMUNAME") if uname and uname[0].startswith("CYGWIN"): return True return False
def _validate_dataset(dataset): """ Validate the main Kmeans dataset. Parameters ---------- dataset: SFrame Input dataset. """ if not (isinstance(dataset, _SFrame)): raise TypeError("Input 'dataset' must be an SFrame.") if dataset.num_rows() == 0 or dataset.num_columns(...
def _validate_initial_centers(initial_centers): """ Validate the initial centers. Parameters ---------- initial_centers : SFrame Initial cluster center locations, in SFrame form. """ if not (isinstance(initial_centers, _SFrame)): raise TypeError("Input 'initial_centers' must...
def _validate_num_clusters(num_clusters, initial_centers, num_rows): """ Validate the combination of the `num_clusters` and `initial_centers` parameters in the Kmeans model create function. If the combination is valid, determine and return the correct number of clusters. Parameters ---------- ...
def _validate_features(features, column_type_map, valid_types, label): """ Identify the subset of desired `features` that are valid for the Kmeans model. A warning is emitted for each feature that is excluded. Parameters ---------- features : list[str] Desired feature names. column...
def create(dataset, num_clusters=None, features=None, label=None, initial_centers=None, max_iterations=10, batch_size=None, verbose=True): """ Create a k-means clustering model. The KmeansModel object contains the computed cluster centers and the cluster assignment for each instance in...
def predict(self, dataset, output_type='cluster_id', verbose=True): """ Return predicted cluster label for instances in the new 'dataset'. K-means predictions are made by assigning each new instance to the closest cluster center. Parameters ---------- dataset : S...
def _get(self, field): """ Return the value of a given field. +-----------------------+----------------------------------------------+ \| Field \| Description \| +=======================+==============================================...

def implicit_includes (self, feature, target_type): """ Returns the properties which specify implicit include paths to generated headers. This traverses all targets in this subvariant, and subvariants referred by <implcit-dependecy>properties. For all targets which are of typ...

def cmp_ast(node1, node2): ''' Compare if two nodes are equal. ''' if type(node1) != type(node2): return False if isinstance(node1, (list, tuple)): if len(node1) != len(node2): return False for left, right in zip(node1, node2): if not cmp_ast(left, ...

def create_more_container_files(sourceDir, suffix, maxElements, containers, containers2): """Creates additional files for the individual MPL-containers.""" # Create files for each MPL-container with 20 to 'maxElements' elements # which will be used during generation. for container in containers: ...

def create_input_for_numbered_sequences(headerDir, sourceDir, containers, maxElements): """Creates additional source- and header-files for the numbered sequence MPL-containers.""" # Create additional container-list without "map". containersWithoutMap = containers[:] try: containersWithoutMap.rem...

def adjust_container_limits_for_variadic_sequences(headerDir, containers, maxElements): """Adjusts the limits of variadic sequence MPL-containers.""" for container in containers: headerFile = os.path.join( headerDir, "limits", container + ".hpp" ) regexMatch = r'(define\s+BOOST_MPL_LIMIT_' + c...

def current_boost_dir(): """Returns the (relative) path to the Boost source-directory this file is located in (if any).""" # Path to directory containing this script. path = os.path.dirname( os.path.realpath(__file__) ) # Making sure it is located in "${boost-dir}/libs/mpl/preprocessed". for directo...

def to_positive_multiple_of_10(string): """Converts a string into its encoded positive integer (greater zero) or throws an exception.""" try: value = int(string) except ValueError: msg = '"%r" is not a positive multiple of 10 (greater zero).' % string raise argparse.ArgumentTypeError...

def main(): """The main function.""" # Find the current Boost source-directory in which this script is located. sourceDir = current_boost_dir() if sourceDir == None: sourceDir = "" # Prepare and run cmdline-parser. cmdlineParser = argparse.ArgumentParser(description="A generato...

def add_inner_product(self, name, W, b, input_channels, output_channels, has_bias, input_name, output_name, **kwargs): """ Add an inner product layer to the model. Parameters ---------- name: str The name of this layer W: numpy.array...

def add_convolution(self, name, kernel_channels, output_channels, height, width, stride_height, stride_width, border_mode, groups, W, b, has_bias, is_deconv = False, output_shape = None, input_name = 'data', output_name = 'out', dilation_factors = [1,1], paddi...

def add_resize_bilinear(self, name, input_name, output_name, target_height=1, target_width=1, mode='ALIGN_ENDPOINTS_MODE'): """ Add resize bilinear layer to the model. A layer that resizes the input to a given spatial size using bilinear interpolation. Parameters ...

def add_crop_resize(self, name, input_names, output_name, target_height=1, target_width=1, mode='STRICT_ALIGN_ENDPOINTS_MODE', normalized_roi=False, box_indices_mode='CORNERS_HEIGHT_FIRST', spatial_scale=1.0)...

def _toolkit_serialize_summary_struct(model, sections, section_titles): """ Serialize model summary into a dict with ordered lists of sections and section titles Parameters ---------- model : Model object sections : Ordered list of lists (sections) of tuples (field,value) [ [(fi...

def _add_docstring(format_dict): """ Format a doc-string on the fly. @arg format_dict: A dictionary to format the doc-strings Example: @add_docstring({'context': __doc_string_context}) def predict(x): ''' {context} >> model.predict(data) '...

def _find_only_column_of_type(sframe, target_type, type_name, col_name): """ Finds the only column in `SFrame` with a type specified by `target_type`. If there are zero or more than one such columns, an exception will be raised. The name and type of the target column should be provided as strings fo...

def _find_only_image_column(sframe): """ Finds the only column in `sframe` with a type of turicreate.Image. If there are zero or more than one image columns, an exception will be raised. """ from turicreate import Image return _find_only_column_of_type(sframe, target_type=Image, ...

def _find_only_drawing_column(sframe): """ Finds the only column that can be interpreted as a drawing feature column. A drawing column can be a stroke-based drawing column (with dtype list) or a bitmap-based drawing column (with dtype turicreate.Image) If there are zero or more than one drawing...

def _SGraphFromJsonTree(json_str): """ Convert the Json Tree to SGraph """ g = json.loads(json_str) vertices = [_Vertex(x['id'], dict([(str(k), v) for k, v in _six.iteritems(x) if k != 'id'])) for x in g['vertices']] edges = [...

def _summarize_coefficients(top_coefs, bottom_coefs): """ Return a tuple of sections and section titles. Sections are pretty print of model coefficients Parameters ---------- top_coefs : SFrame of top k coefficients bottom_coefs : SFrame of bottom k coefficients Returns ------- ...

def _toolkit_get_topk_bottomk(values, k=5): """ Returns a tuple of the top k values from the positive and negative values in a SArray Parameters ---------- values : SFrame of model coefficients k: Maximum number of largest positive and k lowest negative numbers to return Returns -...

def __extract_model_summary_value(model, value): """ Extract a model summary field value """ field_value = None if isinstance(value, _precomputed_field): field_value = value.field else: field_value = model._get(value) if isinstance(field_value, float): try: ...

def _make_repr_table_from_sframe(X): """ Serializes an SFrame to a list of strings, that, when printed, creates a well-formatted table. """ assert isinstance(X, _SFrame) column_names = X.column_names() out_data = [ [None]*len(column_names) for i in range(X.num_rows())] column_sizes = [le...

def _toolkit_repr_print(model, fields, section_titles, width = None): """ Display a toolkit repr according to some simple rules. Parameters ---------- model : Turi Create model fields: List of lists of tuples Each tuple should be (display_name, field_name), where field_name can ...

def _map_unity_proxy_to_object(value): """ Map returning value, if it is unity SFrame, SArray, map it """ vtype = type(value) if vtype in _proxy_map: return _proxy_map[vtype](value) elif vtype == list: return [_map_unity_proxy_to_object(v) for v in value] elif vtype == dict: ...

def _toolkits_select_columns(dataset, columns): """ Same as select columns but redirect runtime error to ToolkitError. """ try: return dataset.select_columns(columns) except RuntimeError: missing_features = list(set(columns).difference(set(dataset.column_names()))) raise Tool...

def _raise_error_if_column_exists(dataset, column_name = 'dataset', dataset_variable_name = 'dataset', column_name_error_message_name = 'column_name'): """ Check if a column exists in an SFrame with error message. """ err_msg = 'The SFrame {0} must...

def _check_categorical_option_type(option_name, option_value, possible_values): """ Check whether or not the requested option is one of the allowed values. """ err_msg = '{0} is not a valid option for {1}. '.format(option_value, option_name) err_msg += ' Expected one of: '.format(possible_values) ...

def _raise_error_if_not_sarray(dataset, variable_name="SArray"): """ Check if the input is an SArray. Provide a proper error message otherwise. """ err_msg = "Input %s is not an SArray." if not isinstance(dataset, _SArray): raise ToolkitError(err_msg % variable_name)

def _raise_error_if_not_sframe(dataset, variable_name="SFrame"): """ Check if the input is an SFrame. Provide a proper error message otherwise. """ err_msg = "Input %s is not an SFrame. If it is a Pandas DataFrame," err_msg += " you may use the to_sframe() function to convert it to an SFrame." ...

def _raise_error_if_sframe_empty(dataset, variable_name="SFrame"): """ Check if the input is empty. """ err_msg = "Input %s either has no rows or no columns. A non-empty SFrame " err_msg += "is required." if dataset.num_rows() == 0 or dataset.num_columns() == 0: raise ToolkitError(err_m...

def _raise_error_evaluation_metric_is_valid(metric, allowed_metrics): """ Check if the input is an SFrame. Provide a proper error message otherwise. """ err_msg = "Evaluation metric '%s' not recognized. The supported evaluation" err_msg += " metrics are (%s)." if metric not in allowed_metr...

def _numeric_param_check_range(variable_name, variable_value, range_bottom, range_top): """ Checks if numeric parameter is within given range """ err_msg = "%s must be between %i and %i" if variable_value < range_bottom or variable_value > range_top: raise ToolkitError(err_msg % (variable_n...

def _validate_data(dataset, target, features=None, validation_set='auto'): """ Validate and canonicalize training and validation data. Parameters ---------- dataset : SFrame Dataset for training the model. target : string Name of the column containing the target variable. ...

def _validate_row_label(dataset, label=None, default_label='__id'): """ Validate a row label column. If the row label is not specified, a column is created with row numbers, named with the string in the `default_label` parameter. Parameters ---------- dataset : SFrame Input dataset....

def _mac_ver(): """ Returns Mac version as a tuple of integers, making it easy to do proper version comparisons. On non-Macs, it returns an empty tuple. """ import platform import sys if sys.platform == 'darwin': ver_str = platform.mac_ver()[0] return tuple([int(v) for v in v...

def _print_neural_compute_device(cuda_gpus, use_mps, cuda_mem_req=None, has_mps_impl=True): """ Print a message making it clear to the user what compute resource is used in neural network training. """ num_cuda_gpus = len(cuda_gpus) if num_cuda_gpus >= 1: gpu_names = ', '.join(gpu['name'...

def _GetMessageFromFactory(factory, full_name): """Get a proto class from the MessageFactory by name. Args: factory: a MessageFactory instance. full_name: str, the fully qualified name of the proto type. Returns: A class, for the type identified by full_name. Raises: KeyError, if the proto is n...

def MakeSimpleProtoClass(fields, full_name=None, pool=None): """Create a Protobuf class whose fields are basic types. Note: this doesn't validate field names! Args: fields: dict of {name: field_type} mappings for each field in the proto. If this is an OrderedDict the order will be maintained, otherw...

def _MakeFileDescriptorProto(proto_file_name, full_name, field_items): """Populate FileDescriptorProto for MessageFactory's DescriptorPool.""" package, name = full_name.rsplit('.', 1) file_proto = descriptor_pb2.FileDescriptorProto() file_proto.name = os.path.join(package.replace('.', '/'), proto_file_name) f...

def convert(model, input_name, output_features): """Convert a decision tree model to protobuf format. Parameters ---------- decision_tree : DecisionTreeClassifier A trained scikit-learn tree model. input_name: str Name of the input columns. output_name: str Name of the...

def _get_model_metadata(model_class, metadata, version=None): """ Returns user-defined metadata, making sure information all models should have is also available, as a dictionary """ from turicreate import __version__ info = { 'turicreate_version': __version__, 'type': model_cla...

def _set_model_metadata(mlmodel, model_class, metadata, version=None): """ Sets user-defined metadata, making sure information all models should have is also available """ info = _get_model_metadata(model_class, metadata, version) mlmodel.user_defined_metadata.update(info)

def _ToCamelCase(name): """Converts name to camel-case and returns it.""" capitalize_next = False result = [] for c in name: if c == '_': if result: capitalize_next = True elif capitalize_next: result.append(c.upper()) capitalize_next = False else: result += c # L...

def _ToJsonName(name): """Converts name to Json name and returns it.""" capitalize_next = False result = [] for c in name: if c == '_': capitalize_next = True elif capitalize_next: result.append(c.upper()) capitalize_next = False else: result += c return ''.join(result)

def _SetOptions(self, options, options_class_name): """Sets the descriptor's options This function is used in generated proto2 files to update descriptor options. It must not be used outside proto2. """ self._options = options self._options_class_name = options_class_name # Does this descr...

def GetOptions(self): """Retrieves descriptor options. This method returns the options set or creates the default options for the descriptor. """ if self._options: return self._options from google.protobuf import descriptor_pb2 try: options_class = getattr(descriptor_pb2, self._...

def CopyToProto(self, proto): """Copies this to the matching proto in descriptor_pb2. Args: proto: An empty proto instance from descriptor_pb2. Raises: Error: If self couldnt be serialized, due to to few constructor arguments. """ if (self.file is not None and self._serialized_...

def EnumValueName(self, enum, value): """Returns the string name of an enum value. This is just a small helper method to simplify a common operation. Args: enum: string name of the Enum. value: int, value of the enum. Returns: string name of the enum value. Raises: KeyErr...

def resolve_reference(target_reference, project): """ Given a target_reference, made in context of 'project', returns the AbstractTarget instance that is referred to, as well as properties explicitly specified for this reference. """ # Separate target name from properties override assert isinsta...

def generate_from_reference(target_reference, project, property_set_): """ Attempts to generate the target given by target reference, which can refer both to a main target or to a file. Returns a list consisting of - usage requirements - generated virtual targets, if any target_reference: Targe...

def main_target_alternative (self, target): """ Registers the specified target as a main target alternatives. Returns 'target'. """ assert isinstance(target, AbstractTarget) target.project ().add_alternative (target) return target

def main_target_sources (self, sources, main_target_name, no_renaming=0): """Return the list of sources to use, if main target rule is invoked with 'sources'. If there are any objects in 'sources', they are treated as main target instances, and the name of such targets are adjusted to be...

def main_target_requirements(self, specification, project): """Returns the requirement to use when declaring a main target, which are obtained by - translating all specified property paths, and - refining project requirements with the one specified for the target 'specificat...

def main_target_usage_requirements (self, specification, project): """ Returns the use requirement to use when declaraing a main target, which are obtained by - translating all specified property paths, and - adding project's usage requirements specification: Use...

def main_target_default_build (self, specification, project): """ Return the default build value to use when declaring a main target, which is obtained by using specified value if not empty and parent's default build attribute otherwise. specification: Default build explicit...

def start_building (self, main_target_instance): """ Helper rules to detect cycles in main target references. """ assert isinstance(main_target_instance, MainTarget) if id(main_target_instance) in self.targets_being_built_: names = [] for t in self.targets_being_b...

def create_typed_target (self, type, project, name, sources, requirements, default_build, usage_requirements): """ Creates a TypedTarget with the specified properties. The 'name', 'sources', 'requirements', 'default_build' and 'usage_requirements' are assumed to be in the form specified ...

def generate (self, ps): """ Generates all possible targets contained in this project. """ assert isinstance(ps, property_set.PropertySet) self.manager_.targets().log( "Building project '%s' with '%s'" % (self.name (), str(ps))) self.manager_.targets().increase_indent...

def targets_to_build (self): """ Computes and returns a list of AbstractTarget instances which must be built when this project is built. """ result = [] if not self.built_main_targets_: self.build_main_targets () # Collect all main targets here, except f...

def mark_targets_as_explicit (self, target_names): """Add 'target' to the list of targets in this project that should be build only by explicit request.""" # Record the name of the target, not instance, since this # rule is called before main target instaces are created. assert ...

def add_alternative (self, target_instance): """ Add new target alternative. """ assert isinstance(target_instance, AbstractTarget) if self.built_main_targets_: raise IllegalOperation ("add-alternative called when main targets are already created for project '%s'" % self.full...

def has_main_target (self, name): """Tells if a main target with the specified name exists.""" assert isinstance(name, basestring) if not self.built_main_targets_: self.build_main_targets() return name in self.main_target_

def create_main_target (self, name): """ Returns a 'MainTarget' class instance corresponding to the 'name'. """ assert isinstance(name, basestring) if not self.built_main_targets_: self.build_main_targets () return self.main_targets_.get (name, None)

def find_really(self, id): """ Find and return the target with the specified id, treated relative to self. """ assert isinstance(id, basestring) result = None current_location = self.get ('location') __re_split_project_target = re.compile (r'(.*)//(.*)') ...

def add_constant(self, name, value, path=0): """Adds a new constant for this project. The constant will be available for use in Jamfile module for this project. If 'path' is true, the constant will be interpreted relatively to the location of project. """ assert ...

def add_alternative (self, target): """ Add a new alternative for this target. """ assert isinstance(target, BasicTarget) d = target.default_build () if self.alternatives_ and self.default_build_ != d: get_manager().errors()("default build must be identical in all al...

def __select_alternatives (self, property_set_, debug): """ Returns the best viable alternative for this property_set See the documentation for selection rules. # TODO: shouldn't this be 'alternative' (singular)? """ # When selecting alternatives we have to consider defau...

def generate (self, ps): """ Select an alternative for this main target, by finding all alternatives which requirements are satisfied by 'properties' and picking the one with longest requirements set. Returns the result of calling 'generate' on that alternative. """ ...

def __generate_really (self, prop_set): """ Generates the main target with the given property set and returns a list which first element is property_set object containing usage_requirements of generated target and with generated virtual target in other elements. It's possible...

def sources (self): """ Returns the list of AbstractTargets which are used as sources. The extra properties specified for sources are not represented. The only used of this rule at the moment is the '--dump-tests' feature of the test system. """ if self.source...

def common_properties (self, build_request, requirements): """ Given build request and requirements, return properties common to dependency build request and target build properties. """ # For optimization, we add free unconditional requirements directly, # withou...

def match (self, property_set_, debug): """ Returns the alternative condition for this alternative, if the condition is satisfied by 'property_set'. """ # The condition is composed of all base non-conditional properties. # It's not clear if we should expand 'self.requirements...

def generate_dependency_properties(self, properties, ps): """ Takes a target reference, which might be either target id or a dependency property, and generates that target using 'property_set' as build request. Returns a tuple (result, usage_requirements). """ ...

def generate (self, ps): """ Determines final build properties, generates sources, and calls 'construct'. This method should not be overridden. """ assert isinstance(ps, property_set.PropertySet) self.manager_.errors().push_user_context( "Generating target " +...

def compute_usage_requirements (self, subvariant): """ Given the set of generated targets, and refined build properties, determines and sets appripriate usage requirements on those targets. """ assert isinstance(subvariant, virtual_target.Subvariant) rproperties =...

def create_subvariant (self, root_targets, all_targets, build_request, sources, rproperties, usage_requirements): """Creates a new subvariant-dg instances for 'targets' - 'root-targets' the virtual targets will be returned to dependents - '...

def variant (name, parents_or_properties, explicit_properties = []): """ Declares a new variant. First determines explicit properties for this variant, by refining parents' explicit properties with the passed explicit properties. The result is remembered and will be used if this vari...

def register_globals (): """ Registers all features and variants declared by this module. """ # This feature is used to determine which OS we're on. # In future, this may become <target-os> and <host-os> # TODO: check this. Compatibility with bjam names? Subfeature for version? os = sys.platfor...

def lib(names, sources=[], requirements=[], default_build=[], usage_requirements=[]): """The implementation of the 'lib' rule. Beyond standard syntax that rule allows simplified: 'lib a b c ;'.""" assert is_iterable_typed(names, basestring) assert is_iterable_typed(sources, basestring) assert is_ite...

def adjust_properties (self, prop_set): """ For all virtual targets for the same dependency graph as self, i.e. which belong to the same main target, add their directories to include path. """ assert isinstance(prop_set, property_set.PropertySet) s = self.targets ...

def create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, random_seed=0, verbose=True): """ Create a model that makes recommendations using item popularity. When no target column is provided, the popularity is ...

def get_params(self, deep=False): """Get parameter.s""" params = super(XGBModel, self).get_params(deep=deep) if params['missing'] is np.nan: params['missing'] = None # sklearn doesn't handle nan. see #4725 if not params.get('eval_metric', True): del params['eval_...

def get_xgb_params(self): """Get xgboost type parameters.""" xgb_params = self.get_params() xgb_params['silent'] = 1 if self.silent else 0 if self.nthread <= 0: xgb_params.pop('nthread', None) return xgb_params

def fit(self, X, y, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True): # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init """ Fit the gradient boosting model Parameters ---------- X : array_like ...

def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True): # pylint: disable = attribute-defined-outside-init,arguments-differ """ Fit gradient boosting classifier Parameters ---------- X : array_like ...

def add_grist (features): """ Transform a string by bracketing it with "<>". If already bracketed, does nothing. features: one string or a sequence of strings return: the gristed string, if features is a string, or a sequence of gristed strings, if features is a sequence """ assert is_iterab...

def replace_grist (features, new_grist): """ Replaces the grist of a string by a new one. Returns the string with the new grist. """ assert is_iterable_typed(features, basestring) or isinstance(features, basestring) assert isinstance(new_grist, basestring) # this function is used a lot in th...

def get_value (property): """ Gets the value of a property, that is, the part following the grist, if any. """ assert is_iterable_typed(property, basestring) or isinstance(property, basestring) return replace_grist (property, '')

def get_grist (value): """ Returns the grist of a string. If value is a sequence, does it for every value and returns the result as a sequence. """ assert is_iterable_typed(value, basestring) or isinstance(value, basestring) def get_grist_one (name): split = __re_grist_and_value.match (n...

def ungrist (value): """ Returns the value without grist. If value is a sequence, does it for every value and returns the result as a sequence. """ assert is_iterable_typed(value, basestring) or isinstance(value, basestring) def ungrist_one (value): stripped = __re_grist_content.match (v...

def replace_suffix (name, new_suffix): """ Replaces the suffix of name by new_suffix. If no suffix exists, the new one is added. """ assert isinstance(name, basestring) assert isinstance(new_suffix, basestring) split = os.path.splitext (name) return split [0] + new_suffix

def split_action_id (id): """ Splits an id in the toolset and specific rule parts. E.g. 'gcc.compile.c++' returns ('gcc', 'compile.c++') """ assert isinstance(id, basestring) split = id.split ('.', 1) toolset = split [0] name = '' if len (split) > 1: name = split [1] retu...

def on_windows (): """ Returns true if running on windows, whether in cygwin or not. """ if bjam.variable("NT"): return True elif bjam.variable("UNIX"): uname = bjam.variable("JAMUNAME") if uname and uname[0].startswith("CYGWIN"): return True return False

def _validate_dataset(dataset): """ Validate the main Kmeans dataset. Parameters ---------- dataset: SFrame Input dataset. """ if not (isinstance(dataset, _SFrame)): raise TypeError("Input 'dataset' must be an SFrame.") if dataset.num_rows() == 0 or dataset.num_columns(...

def _validate_initial_centers(initial_centers): """ Validate the initial centers. Parameters ---------- initial_centers : SFrame Initial cluster center locations, in SFrame form. """ if not (isinstance(initial_centers, _SFrame)): raise TypeError("Input 'initial_centers' must...

def _validate_num_clusters(num_clusters, initial_centers, num_rows): """ Validate the combination of the `num_clusters` and `initial_centers` parameters in the Kmeans model create function. If the combination is valid, determine and return the correct number of clusters. Parameters ---------- ...

def _validate_features(features, column_type_map, valid_types, label): """ Identify the subset of desired `features` that are valid for the Kmeans model. A warning is emitted for each feature that is excluded. Parameters ---------- features : list[str] Desired feature names. column...

def create(dataset, num_clusters=None, features=None, label=None, initial_centers=None, max_iterations=10, batch_size=None, verbose=True): """ Create a k-means clustering model. The KmeansModel object contains the computed cluster centers and the cluster assignment for each instance in...

def predict(self, dataset, output_type='cluster_id', verbose=True): """ Return predicted cluster label for instances in the new 'dataset'. K-means predictions are made by assigning each new instance to the closest cluster center. Parameters ---------- dataset : S...

def _get(self, field): """ Return the value of a given field. +-----------------------+----------------------------------------------+ | Field | Description | +=======================+==============================================...