BoghdadyJR/codesearch_python · Data Studio

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def create_classification_with_model_selector(dataset, target, model_selector, features=None, validation_set='auto', verbose=True): """ Create a :class:`~turicreate.toolkits.SupervisedLearningModel`, This is generic function that allows you to create any model that implements SupervisedLearningMode...
def predict(self, dataset, missing_value_action='auto', output_type='', options={}, **kwargs): """ Return predictions for ``dataset``, using the trained supervised_learning model. Predictions are generated as class labels (0 or 1). Parameters ---------- ...
def evaluate(self, dataset, metric="auto", missing_value_action='auto', with_predictions=False, options={}, **kwargs): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFr...
def classify(self, dataset, missing_value_action='auto'): """ Return predictions for ``dataset``, using the trained supervised_learning model. Predictions are generated as class labels (0 or 1). Parameters ---------- dataset: SFrame Dataset of new obs...
def evaluate(self, dataset, metric='auto', missing_value_action='auto'): """ Evaluate the model on the given dataset. Parameters ---------- dataset : SFrame Dataset in the same format used for training. The columns names and types of the dataset must be t...
def predict(self, dataset, missing_value_action='auto'): """ Predict the target column of the given dataset. The target column is provided during :func:`~turicreate.boosted_trees_regression.create`. If the target column is in the `dataset` it will be ignored. Parameters...
def print_code(co, lasti= -1, level=0): """Disassemble a code object.""" code = co.co_code for constant in co.co_consts: print( '\| \|' * level, end=' ') print( 'constant:', constant) labels = findlabels(code) linestarts = dict(findlinestarts(co)) n = len...
def convert(model, feature_names, target): """Convert a decision tree model to protobuf format. Parameters ---------- decision_tree : DecisionTreeRegressor A trained scikit-learn tree model. feature_names: [str] Name of the input columns. target: str Name of the output...
def _check_prob_and_prob_vector(predictions): """ Check that the predictionsa are either probabilities of prob-vectors. """ from .._deps import numpy ptype = predictions.dtype import array if ptype not in [float, numpy.ndarray, array.array, int]: err_msg = "Input `predictions` must...
def _supervised_evaluation_error_checking(targets, predictions): """ Perform basic error checking for the evaluation metrics. Check types and sizes of the inputs. """ _raise_error_if_not_sarray(targets, "targets") _raise_error_if_not_sarray(predictions, "predictions") if (len(targets) != len...
def log_loss(targets, predictions, index_map=None): r""" Compute the logloss for the given targets and the given predicted probabilities. This quantity is defined to be the negative of the sum of the log probability of each observation, normalized by the number of observations: .. math:: ...
def max_error(targets, predictions): r""" Compute the maximum absolute deviation between two SArrays. Parameters ---------- targets : SArray[float or int] An Sarray of ground truth target values. predictions : SArray[float or int] The prediction that corresponds to each target ...
def rmse(targets, predictions): r""" Compute the root mean squared error between two SArrays. Parameters ---------- targets : SArray[float or int] An Sarray of ground truth target values. predictions : SArray[float or int] The prediction that corresponds to each target value. ...
def confusion_matrix(targets, predictions): r""" Compute the confusion matrix for classifier predictions. Parameters ---------- targets : SArray Ground truth class labels (cannot be of type float). predictions : SArray The prediction that corresponds to each target value. ...
def accuracy(targets, predictions, average='micro'): r""" Compute the accuracy score; which measures the fraction of predictions made by the classifier that are exactly correct. The score lies in the range [0,1] with 0 being the worst and 1 being the best. Parameters ---------- targets : SA...
def fbeta_score(targets, predictions, beta=1.0, average='macro'): r""" Compute the F-beta score. The F-beta score is the weighted harmonic mean of precision and recall. The score lies in the range [0,1] with 1 being ideal and 0 being the worst. The `beta` value is the weight given to `precision` vs...
def f1_score(targets, predictions, average='macro'): r""" Compute the F1 score (sometimes known as the balanced F-score or F-measure). The F1 score is commonly interpreted as the average of precision and recall. The score lies in the range [0,1] with 1 being ideal and 0 being the worst. The F1 ...
def precision(targets, predictions, average='macro'): r""" Compute the precision score for classification tasks. The precision score quantifies the ability of a classifier to not label a `negative` example as `positive`. The precision score can be interpreted as the probability that a `positive` pr...
def auc(targets, predictions, average='macro', index_map=None): r""" Compute the area under the ROC curve for the given targets and predictions. Parameters ---------- targets : SArray An SArray containing the observed values. For binary classification, the alpha-numerically first ca...
def get_library_meta(self): ''' Fetches the meta data for the current library. The data could be in the superlib meta data file. If we can't find the data None is returned. ''' parent_dir = os.path.dirname(self.library_dir) if self.test_file_exists(os.path.join(self.libra...
def convert(model, feature_names = None, target = 'target', force_32bit_float = True): """ Convert a trained XGBoost model to Core ML format. Parameters ---------- decision_tree : Booster A trained XGboost tree model. feature_names: [str] \| str Names of input features that will...
def dumps(obj): """ Dumps a serializable object to JSON. This API maps to the Python built-in json dumps method, with a few differences: * The return value is always valid JSON according to RFC 7159. * The input can be any of the following types: - SFrame - SArray - SGraph ...
def draw_strokes(stroke_based_drawings): """ Visualizes drawings (ground truth or predictions) by returning images to represent the stroke-based data from the user. Parameters ---------- stroke_based_drawings: SArray or list An `SArray` of type `list`. Each element in the SArray ...
def fit(self, data): """ Fit a transformer using the SFrame `data`. Parameters ---------- data : SFrame The data used to fit the transformer. Returns ------- self (A fitted version of the object) See Also -------- tra...
def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of lis...
def extract_features(self, dataset, missing_value_action='auto'): """ For each example in the dataset, extract the leaf indices of each tree as features. For multiclass classification, each leaf index contains #num_class numbers. The returned feature vectors can be used...
def _extract_features_with_missing(self, dataset, tree_id = 0, missing_value_action = 'auto'): """ Extract features along with all the missing features associated with a dataset. Parameters ---------- dataset: bool Dataset on which to make predict...
def _dump_to_text(self, with_stats): """ Dump the models into a list of strings. Each string is a text representation of a tree. Parameters ---------- with_stats : bool If true, include node statistics in the output. Returns ------- o...
def _dump_to_json(self, with_stats): """ Dump the models into a list of strings. Each string is a text representation of a tree. Parameters ---------- with_stats : bool If true, include node statistics in the output. Returns ------- o...
def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of lis...
def convert(model, input_features, output_features): """Convert a boosted tree model to protobuf format. Parameters ---------- decision_tree : GradientBoostingRegressor A trained scikit-learn tree model. input_feature: [str] Name of the input columns. output_features: str ...
def _sort_topk_votes(x, k): """ Sort a dictionary of classes and corresponding vote totals according to the votes, then truncate to the highest 'k' classes. """ y = sorted(x.items(), key=lambda x: x[1], reverse=True)[:k] return [{'class': i[0], 'votes': i[1]} for i in y]
def _construct_auto_distance(features, column_types): """ Construct a composite distance function for a set of features, based on the types of those features. NOTE: This function is very similar to `:func:_nearest_neighbors.choose_auto_distance`. The function is separate because the auto-distan...
def create(dataset, target, features=None, distance=None, verbose=True): """ Create a :class:`~turicreate.nearest_neighbor_classifier.NearestNeighborClassifier` model. This model predicts the class of a query instance by finding the most common class among the query's nearest neighbors. .. warn...
def _load_version(cls, state, version): """ A function to load a previously saved NearestNeighborClassifier model. Parameters ---------- unpickler : GLUnpickler A GLUnpickler file handler. version : int Version number maintained by the class writ...
def classify(self, dataset, max_neighbors=10, radius=None, verbose=True): """ Return the predicted class for each observation in dataset. This prediction is made based on the closest neighbors stored in the nearest neighbors classifier model. Parameters ---------- ...
def predict(self, dataset, max_neighbors=10, radius=None, output_type='class', verbose=True): """ Return predicted class labels for instances in dataset. This model makes predictions based on the closest neighbors stored in the nearest neighbors classifier model. ...
def predict_topk(self, dataset, max_neighbors=10, radius=None, k=3, verbose=False): """ Return top-k most likely predictions for each observation in ``dataset``. Predictions are returned as an SFrame with three columns: `row_id`, `class`, and `probability`. ...
def evaluate(self, dataset, metric='auto', max_neighbors=10, radius=None): """ Evaluate the model's predictive accuracy. This is done by predicting the target class for instances in a new dataset and comparing to known target values. Parameters ---------- dataset...
def _compact_class_repr(obj): """ A compact version of __repr__ for each of the steps. """ dict_str_list = [] post_repr_string = "" # If features are present, then shorten it. init_func = obj.__init__ if _sys.version_info.major == 2: init_func = init_...
def _preprocess(self, data): """ Internal function to perform fit_transform() on all but last step. """ transformed_data = _copy(data) for name, step in self._transformers[:-1]: transformed_data = step.fit_transform(transformed_data) if type(transformed_da...
def fit(self, data): """ Fits a transformer using the SFrame `data`. Parameters ---------- data : SFrame The data used to fit the transformer. Returns ------- self (A fitted object) See Also -------- transform, fit_tr...
def fit_transform(self, data): """ First fit a transformer using the SFrame `data` and then return a transformed version of `data`. Parameters ---------- data : SFrame The data used to fit the transformer. The same data is then also transformed. ...
def transform(self, data): """ Transform the SFrame `data` using a fitted model. Parameters ---------- data : SFrame The data to be transformed. Returns ------- A transformed SFrame. Returns ------- out: SFrame ...
def _load_version(cls, unpickler, version): """ An function to load an object with a specific version of the class. Parameters ---------- pickler : file A GLUnpickler file handle. version : int A version number as maintained by the class writer. ...
def create(graph, reset_probability=0.15, threshold=1e-2, max_iterations=20, _single_precision=False, _distributed='auto', verbose=True): """ Compute the PageRank for each vertex in the graph. Return a model object with total PageRank as well as the Pag...
def init(version = None, command = None, options = None): """ Initializes the gcc toolset for the given version. If necessary, command may be used to specify where the compiler is located. The parameter 'options' is a space-delimited list of options, each one specified as <option-nam...
def init_link_flags(toolset, linker, condition): """ Now, the vendor specific flags. The parameter linker can be either gnu, darwin, osf, hpux or sun. """ toolset_link = toolset + '.link' if linker == 'gnu': # Strip the binary when no debugging is needed. We use --strip-all flag ...
def add_dependency (self, targets, sources): """Adds a dependency from 'targets' to 'sources' Both 'targets' and 'sources' can be either list of target names, or a single target name. """ if isinstance (targets, str): targets = [targets] if isinstance (source...
def get_target_variable(self, targets, variable): """Gets the value of `variable` on set on the first target in `targets`. Args: targets (str or list): one or more targets to get the variable from. variable (str): the name of the variable Returns: the value...
def set_target_variable (self, targets, variable, value, append=0): """ Sets a target variable. The 'variable' will be available to bjam when it decides where to generate targets, and will also be available to updating rule for that 'taret'. """ if isinstance (targets, s...
def set_update_action (self, action_name, targets, sources, properties=None): """ Binds a target to the corresponding update action. If target needs to be updated, the action registered with action_name will be used. The 'action_name' must be previously registered by ...
def register_action (self, action_name, command='', bound_list = [], flags = [], function = None): """Creates a new build engine action. Creates on bjam side an action named 'action_name', with 'command' as the command to be executed, 'bound_variables' naming th...
def register_bjam_action (self, action_name, function=None): """Informs self that 'action_name' is declared in bjam. From this point, 'action_name' is a valid argument to the set_update_action method. The action_name should be callable in the global module of bjam. """ ...
def pixel_data(self): """ Returns the pixel data stored in the Image object. Returns ------- out : numpy.array The pixel data of the Image object. It returns a multi-dimensional numpy array, where the shape of the array represents the shape of ...
def show(self): """ Displays the image. Requires PIL/Pillow. Alternatively, you can create an :class:`turicreate.SArray` of this image and use py:func:`turicreate.SArray.show()` See Also -------- turicreate.image_analysis.resize Examples -------...
def predict(self, data, useCPUOnly=False, **kwargs): """ Return predictions for the model. The kwargs gets passed into the model as a dictionary. Parameters ---------- data : dict[str, value] Dictionary of data to make predictions from where the keys are ...
def visualize_spec(self, port=None, input_shape_dict=None): """ Visualize the model. Parameters ---------- port : int if server is to be hosted on specific localhost port input_shape_dict : dict The shapes are calculat...
def _construct_auto_distance(feature_names, column_names, column_types, sample): """ Construct composite distance parameters based on selected features and their types. """ ## Make a dictionary from the column_names and column_types col_type_dict = {k: v for k, v in zip(column_names, column_typ...
def create(dataset, label=None, features=None, distance=None, method='auto', verbose=True, **kwargs): """ Create a nearest neighbor model, which can be searched efficiently and quickly for the nearest neighbors of a query observation. If the `method` argument is specified as `auto`, the type ...
def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of lis...
def _list_fields(self): """ List the fields stored in the model, including data, model, and training options. Each field can be queried with the ``get`` method. Returns ------- out : list List of fields queryable with the ``get`` method. """ o...
def _get(self, field): """ Return the value of a given field. The list of all queryable fields is detailed below, and can be obtained with the :func:`~turicreate.nearest_neighbors.NearestNeighborsModel._list_fields` method. +-----------------------+----------------------...
def _training_stats(self): """ Return a dictionary of statistics collected during creation of the model. These statistics are also available with the ``get`` method and are described in more detail in that method's documentation. Returns ------- out : dict ...
def query(self, dataset, label=None, k=5, radius=None, verbose=True): """ For each row of the input 'dataset', retrieve the nearest neighbors from the model's stored data. In general, the query dataset does not need to be the same as the reference data stored in the model, but if ...
def similarity_graph(self, k=5, radius=None, include_self_edges=False, output_type='SGraph', verbose=True): """ Construct the similarity graph on the reference dataset, which is already stored in the model. This is conceptually very similar to running `query` wit...
def random_split_by_session(dataset, session_id, fraction=0.9, seed=None): """ Randomly split an SFrame into two SFrames based on the `session_id` such that one split contains data for a `fraction` of the sessions while the second split contains all data for the rest of the sessions. Parameters ...
def read_msbuild_xml(path, values={}): """Reads the MS Build XML file at the path and returns its contents. Keyword arguments: values -- The map to append the contents to (default {}) """ # Attempt to read the file contents try: document = parse(path) except Exception as e: ...
def read_msbuild_json(path, values=[]): """Reads the MS Build JSON file at the path and returns its contents. Keyword arguments: values -- The list to append the contents to (default []) """ if not os.path.exists(path): logging.info('Could not find MS Build JSON file at %s', path) r...
def main(): """Script entrypoint.""" # Parse the arguments parser = argparse.ArgumentParser( description='Convert MSBuild XML to JSON format') parser.add_argument( '-t', '--toolchain', help='The name of the toolchain', required=True) parser.add_argument( '-o', '--output', he...
def __merge_json_values(current, previous): """Merges the values between the current and previous run of the script.""" for value in current: name = value['name'] # Find the previous value previous_value = __find_and_remove_value(previous, value) if previous_value is not None: ...
def __find_and_remove_value(list, compare): """Finds the value in the list that corresponds with the value of compare.""" # next throws if there are no matches try: found = next(value for value in list if value['name'] == compare['name'] and value['switch'] == ...
def __convert(root, tag, values, func): """Converts the tag type found in the root and converts them using the func and appends them to the values. """ elements = root.getElementsByTagName(tag) for element in elements: converted = func(element) # Append to the list __append...
def __convert_enum(node): """Converts an EnumProperty node to JSON format.""" name = __get_attribute(node, 'Name') logging.debug('Found EnumProperty named %s', name) converted_values = [] for value in node.getElementsByTagName('EnumValue'): converted = __convert_node(value) conver...
def __convert_bool(node): """Converts an BoolProperty node to JSON format.""" converted = __convert_node(node, default_value='true') # Check for a switch for reversing the value reverse_switch = __get_attribute(node, 'ReverseSwitch') if reverse_switch: converted_reverse = copy.deepcopy(con...
def __convert_string_list(node): """Converts a StringListProperty node to JSON format.""" converted = __convert_node(node) # Determine flags for the string list flags = vsflags(VSFlags.UserValue) # Check for a separator to determine if it is semicolon appendable # If not present assume the val...
def __convert_string(node): """Converts a StringProperty node to JSON format.""" converted = __convert_node(node, default_flags=vsflags(VSFlags.UserValue)) return __check_for_flag(converted)
def __convert_node(node, default_value='', default_flags=vsflags()): """Converts a XML node to a JSON equivalent.""" name = __get_attribute(node, 'Name') logging.debug('Found %s named %s', node.tagName, name) converted = {} converted['name'] = name converted['switch'] = __get_attribute(node, 'S...
def __with_argument(node, value): """Modifies the flags in value if the node contains an Argument.""" arguments = node.getElementsByTagName('Argument') if arguments: logging.debug('Found argument within %s', value['name']) value['flags'] = vsflags(VSFlags.UserValueIgnored, VSFlags.Continue)
def __preprocess_arguments(root): """Preprocesses occurrences of Argument within the root. Argument XML values reference other values within the document by name. The referenced value does not contain a switch. This function will add the switch associated with the argument. """ # Set the flags ...
def __get_attribute(node, name, default_value=''): """Retrieves the attribute of the given name from the node. If not present then the default_value is used. """ if node.hasAttribute(name): return node.attributes[name].value.strip() else: return default_value
def __get_path(path): """Gets the path to the file.""" if not os.path.isabs(path): path = os.path.join(os.getcwd(), path) return os.path.normpath(path)
def __output_path(toolchain, rule, output_dir): """Gets the output path for a file given the toolchain, rule and output_dir""" filename = '%s_%s.json' % (toolchain, rule) return os.path.join(output_dir, filename)
def __write_json_file(path, values): """Writes a JSON file at the path with the values provided.""" # Sort the keys to ensure ordering sort_order = ['name', 'switch', 'comment', 'value', 'flags'] sorted_values = [ OrderedDict( sorted( value.items(), key=lambda value: ...
def __append_list(append_to, value): """Appends the value to the list.""" if value is not None: if isinstance(value, list): append_to.extend(value) else: append_to.append(value)
def decompile_func(func): ''' Decompile a function into ast.FunctionDef node. :param func: python function (can not be a built-in) :return: ast.FunctionDef instance. ''' code = func.__code__ # For python 3 # defaults = func.func_defaults if sys.version_info.major < 3 else func....
def compile_func(ast_node, filename, globals, **defaults): ''' Compile a function from an ast.FunctionDef instance. :param ast_node: ast.FunctionDef instance :param filename: path where function source can be found. :param globals: will be used as func_globals :return: A python functi...
def decompile_pyc(bin_pyc, output=sys.stdout): ''' decompile apython pyc or pyo binary file. :param bin_pyc: input file objects :param output: output file objects ''' from turicreate.meta.asttools import python_source bin = bin_pyc.read() code = marshal.loads(bin[8:])...
def ParseInput(self, a_file): """Consumes input extracting definitions. Args: a_file: The file like stream to parse. Raises: PDDMError if there are any issues. """ input_lines = a_file.read().splitlines() self.ParseLines(input_lines)
def ParseLines(self, input_lines): """Parses list of lines. Args: input_lines: A list of strings of input to parse (no newlines on the strings). Raises: PDDMError if there are any issues. """ current_macro = None for line in input_lines: if line.startswith(...
def Expand(self, macro_ref_str): """Expands the macro reference. Args: macro_ref_str: String of a macro reference (i.e. foo(a, b)). Returns: The text from the expansion. Raises: PDDMError if there are any issues. """ match = _MACRO_RE.match(macro_ref_str) if match is Non...
def ProcessContent(self, strip_expansion=False): """Processes the file contents.""" self._ParseFile() if strip_expansion: # Without a collection the expansions become blank, removing them. collection = None else: collection = MacroCollection() for section in self._sections: s...
def convert(model, input_features, output_features): """Convert a _imputer model to the protobuf spec. Parameters ---------- model: Imputer A trained Imputer model. input_features: str Name of the input column. output_features: str Name of the output column. Retur...
def reset (): """ Clear the module state. This is mainly for testing purposes. """ global __all_attributes, __all_features, __implicit_features, __composite_properties global __subfeature_from_value, __all_top_features, __free_features global __all_subfeatures # sets the default value of False ...
def feature (name, values, attributes = []): """ Declares a new feature with the given name, values, and attributes. name: the feature name values: a sequence of the allowable values - may be extended later with feature.extend attributes: a sequence of the feature's attributes (e.g. implicit...
def set_default (feature, value): """ Sets the default value of the given feature, overriding any previous default. feature: the name of the feature value: the default value to assign """ f = __all_features[feature] bad_attribute = None if f.free: bad_attribute = "free" ...
def defaults(features): """ Returns the default property values for the given features. """ assert is_iterable_typed(features, Feature) # FIXME: should merge feature and property modules. from . import property result = [] for f in features: if not f.free and not f.optional and f.de...
def valid (names): """ Returns true iff all elements of names are valid features. """ if isinstance(names, str): names = [names] assert is_iterable_typed(names, basestring) return all(name in __all_features for name in names)
def values (feature): """ Return the values of the given feature. """ assert isinstance(feature, basestring) validate_feature (feature) return __all_features[feature].values
def is_implicit_value (value_string): """ Returns true iff 'value_string' is a value_string of an implicit feature. """ assert isinstance(value_string, basestring) if value_string in __implicit_features: return __implicit_features[value_string] v = value_string.split('-') if v[0] n...

def create_classification_with_model_selector(dataset, target, model_selector, features=None, validation_set='auto', verbose=True): """ Create a :class:`~turicreate.toolkits.SupervisedLearningModel`, This is generic function that allows you to create any model that implements SupervisedLearningMode...

def predict(self, dataset, missing_value_action='auto', output_type='', options={}, **kwargs): """ Return predictions for ``dataset``, using the trained supervised_learning model. Predictions are generated as class labels (0 or 1). Parameters ---------- ...

def evaluate(self, dataset, metric="auto", missing_value_action='auto', with_predictions=False, options={}, **kwargs): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFr...

def classify(self, dataset, missing_value_action='auto'): """ Return predictions for ``dataset``, using the trained supervised_learning model. Predictions are generated as class labels (0 or 1). Parameters ---------- dataset: SFrame Dataset of new obs...

def evaluate(self, dataset, metric='auto', missing_value_action='auto'): """ Evaluate the model on the given dataset. Parameters ---------- dataset : SFrame Dataset in the same format used for training. The columns names and types of the dataset must be t...

def predict(self, dataset, missing_value_action='auto'): """ Predict the target column of the given dataset. The target column is provided during :func:`~turicreate.boosted_trees_regression.create`. If the target column is in the `dataset` it will be ignored. Parameters...

def print_code(co, lasti= -1, level=0): """Disassemble a code object.""" code = co.co_code for constant in co.co_consts: print( '| |' * level, end=' ') print( 'constant:', constant) labels = findlabels(code) linestarts = dict(findlinestarts(co)) n = len...

def convert(model, feature_names, target): """Convert a decision tree model to protobuf format. Parameters ---------- decision_tree : DecisionTreeRegressor A trained scikit-learn tree model. feature_names: [str] Name of the input columns. target: str Name of the output...

def _check_prob_and_prob_vector(predictions): """ Check that the predictionsa are either probabilities of prob-vectors. """ from .._deps import numpy ptype = predictions.dtype import array if ptype not in [float, numpy.ndarray, array.array, int]: err_msg = "Input `predictions` must...

def _supervised_evaluation_error_checking(targets, predictions): """ Perform basic error checking for the evaluation metrics. Check types and sizes of the inputs. """ _raise_error_if_not_sarray(targets, "targets") _raise_error_if_not_sarray(predictions, "predictions") if (len(targets) != len...

def log_loss(targets, predictions, index_map=None): r""" Compute the logloss for the given targets and the given predicted probabilities. This quantity is defined to be the negative of the sum of the log probability of each observation, normalized by the number of observations: .. math:: ...

def max_error(targets, predictions): r""" Compute the maximum absolute deviation between two SArrays. Parameters ---------- targets : SArray[float or int] An Sarray of ground truth target values. predictions : SArray[float or int] The prediction that corresponds to each target ...

def rmse(targets, predictions): r""" Compute the root mean squared error between two SArrays. Parameters ---------- targets : SArray[float or int] An Sarray of ground truth target values. predictions : SArray[float or int] The prediction that corresponds to each target value. ...

def confusion_matrix(targets, predictions): r""" Compute the confusion matrix for classifier predictions. Parameters ---------- targets : SArray Ground truth class labels (cannot be of type float). predictions : SArray The prediction that corresponds to each target value. ...

def accuracy(targets, predictions, average='micro'): r""" Compute the accuracy score; which measures the fraction of predictions made by the classifier that are exactly correct. The score lies in the range [0,1] with 0 being the worst and 1 being the best. Parameters ---------- targets : SA...

def fbeta_score(targets, predictions, beta=1.0, average='macro'): r""" Compute the F-beta score. The F-beta score is the weighted harmonic mean of precision and recall. The score lies in the range [0,1] with 1 being ideal and 0 being the worst. The `beta` value is the weight given to `precision` vs...

def f1_score(targets, predictions, average='macro'): r""" Compute the F1 score (sometimes known as the balanced F-score or F-measure). The F1 score is commonly interpreted as the average of precision and recall. The score lies in the range [0,1] with 1 being ideal and 0 being the worst. The F1 ...

def precision(targets, predictions, average='macro'): r""" Compute the precision score for classification tasks. The precision score quantifies the ability of a classifier to not label a `negative` example as `positive`. The precision score can be interpreted as the probability that a `positive` pr...

def auc(targets, predictions, average='macro', index_map=None): r""" Compute the area under the ROC curve for the given targets and predictions. Parameters ---------- targets : SArray An SArray containing the observed values. For binary classification, the alpha-numerically first ca...

def get_library_meta(self): ''' Fetches the meta data for the current library. The data could be in the superlib meta data file. If we can't find the data None is returned. ''' parent_dir = os.path.dirname(self.library_dir) if self.test_file_exists(os.path.join(self.libra...

def convert(model, feature_names = None, target = 'target', force_32bit_float = True): """ Convert a trained XGBoost model to Core ML format. Parameters ---------- decision_tree : Booster A trained XGboost tree model. feature_names: [str] | str Names of input features that will...

def dumps(obj): """ Dumps a serializable object to JSON. This API maps to the Python built-in json dumps method, with a few differences: * The return value is always valid JSON according to RFC 7159. * The input can be any of the following types: - SFrame - SArray - SGraph ...

def draw_strokes(stroke_based_drawings): """ Visualizes drawings (ground truth or predictions) by returning images to represent the stroke-based data from the user. Parameters ---------- stroke_based_drawings: SArray or list An `SArray` of type `list`. Each element in the SArray ...

def fit(self, data): """ Fit a transformer using the SFrame `data`. Parameters ---------- data : SFrame The data used to fit the transformer. Returns ------- self (A fitted version of the object) See Also -------- tra...

def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of lis...

def extract_features(self, dataset, missing_value_action='auto'): """ For each example in the dataset, extract the leaf indices of each tree as features. For multiclass classification, each leaf index contains #num_class numbers. The returned feature vectors can be used...

def _extract_features_with_missing(self, dataset, tree_id = 0, missing_value_action = 'auto'): """ Extract features along with all the missing features associated with a dataset. Parameters ---------- dataset: bool Dataset on which to make predict...

def _dump_to_text(self, with_stats): """ Dump the models into a list of strings. Each string is a text representation of a tree. Parameters ---------- with_stats : bool If true, include node statistics in the output. Returns ------- o...

def _dump_to_json(self, with_stats): """ Dump the models into a list of strings. Each string is a text representation of a tree. Parameters ---------- with_stats : bool If true, include node statistics in the output. Returns ------- o...

def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of lis...

def convert(model, input_features, output_features): """Convert a boosted tree model to protobuf format. Parameters ---------- decision_tree : GradientBoostingRegressor A trained scikit-learn tree model. input_feature: [str] Name of the input columns. output_features: str ...

def _sort_topk_votes(x, k): """ Sort a dictionary of classes and corresponding vote totals according to the votes, then truncate to the highest 'k' classes. """ y = sorted(x.items(), key=lambda x: x[1], reverse=True)[:k] return [{'class': i[0], 'votes': i[1]} for i in y]

def _construct_auto_distance(features, column_types): """ Construct a composite distance function for a set of features, based on the types of those features. NOTE: This function is very similar to `:func:_nearest_neighbors.choose_auto_distance`. The function is separate because the auto-distan...

def create(dataset, target, features=None, distance=None, verbose=True): """ Create a :class:`~turicreate.nearest_neighbor_classifier.NearestNeighborClassifier` model. This model predicts the class of a query instance by finding the most common class among the query's nearest neighbors. .. warn...

def _load_version(cls, state, version): """ A function to load a previously saved NearestNeighborClassifier model. Parameters ---------- unpickler : GLUnpickler A GLUnpickler file handler. version : int Version number maintained by the class writ...

def classify(self, dataset, max_neighbors=10, radius=None, verbose=True): """ Return the predicted class for each observation in *dataset*. This prediction is made based on the closest neighbors stored in the nearest neighbors classifier model. Parameters ---------- ...

def predict(self, dataset, max_neighbors=10, radius=None, output_type='class', verbose=True): """ Return predicted class labels for instances in *dataset*. This model makes predictions based on the closest neighbors stored in the nearest neighbors classifier model. ...

def predict_topk(self, dataset, max_neighbors=10, radius=None, k=3, verbose=False): """ Return top-k most likely predictions for each observation in ``dataset``. Predictions are returned as an SFrame with three columns: `row_id`, `class`, and `probability`. ...

def evaluate(self, dataset, metric='auto', max_neighbors=10, radius=None): """ Evaluate the model's predictive accuracy. This is done by predicting the target class for instances in a new dataset and comparing to known target values. Parameters ---------- dataset...

def _compact_class_repr(obj): """ A compact version of __repr__ for each of the steps. """ dict_str_list = [] post_repr_string = "" # If features are present, then shorten it. init_func = obj.__init__ if _sys.version_info.major == 2: init_func = init_...

def _preprocess(self, data): """ Internal function to perform fit_transform() on all but last step. """ transformed_data = _copy(data) for name, step in self._transformers[:-1]: transformed_data = step.fit_transform(transformed_data) if type(transformed_da...

def fit(self, data): """ Fits a transformer using the SFrame `data`. Parameters ---------- data : SFrame The data used to fit the transformer. Returns ------- self (A fitted object) See Also -------- transform, fit_tr...

def fit_transform(self, data): """ First fit a transformer using the SFrame `data` and then return a transformed version of `data`. Parameters ---------- data : SFrame The data used to fit the transformer. The same data is then also transformed. ...

def transform(self, data): """ Transform the SFrame `data` using a fitted model. Parameters ---------- data : SFrame The data to be transformed. Returns ------- A transformed SFrame. Returns ------- out: SFrame ...

def _load_version(cls, unpickler, version): """ An function to load an object with a specific version of the class. Parameters ---------- pickler : file A GLUnpickler file handle. version : int A version number as maintained by the class writer. ...

def create(graph, reset_probability=0.15, threshold=1e-2, max_iterations=20, _single_precision=False, _distributed='auto', verbose=True): """ Compute the PageRank for each vertex in the graph. Return a model object with total PageRank as well as the Pag...

def init(version = None, command = None, options = None): """ Initializes the gcc toolset for the given version. If necessary, command may be used to specify where the compiler is located. The parameter 'options' is a space-delimited list of options, each one specified as <option-nam...

def init_link_flags(toolset, linker, condition): """ Now, the vendor specific flags. The parameter linker can be either gnu, darwin, osf, hpux or sun. """ toolset_link = toolset + '.link' if linker == 'gnu': # Strip the binary when no debugging is needed. We use --strip-all flag ...

def add_dependency (self, targets, sources): """Adds a dependency from 'targets' to 'sources' Both 'targets' and 'sources' can be either list of target names, or a single target name. """ if isinstance (targets, str): targets = [targets] if isinstance (source...

def get_target_variable(self, targets, variable): """Gets the value of `variable` on set on the first target in `targets`. Args: targets (str or list): one or more targets to get the variable from. variable (str): the name of the variable Returns: the value...

def set_target_variable (self, targets, variable, value, append=0): """ Sets a target variable. The 'variable' will be available to bjam when it decides where to generate targets, and will also be available to updating rule for that 'taret'. """ if isinstance (targets, s...

def set_update_action (self, action_name, targets, sources, properties=None): """ Binds a target to the corresponding update action. If target needs to be updated, the action registered with action_name will be used. The 'action_name' must be previously registered by ...

def register_action (self, action_name, command='', bound_list = [], flags = [], function = None): """Creates a new build engine action. Creates on bjam side an action named 'action_name', with 'command' as the command to be executed, 'bound_variables' naming th...

def register_bjam_action (self, action_name, function=None): """Informs self that 'action_name' is declared in bjam. From this point, 'action_name' is a valid argument to the set_update_action method. The action_name should be callable in the global module of bjam. """ ...

def pixel_data(self): """ Returns the pixel data stored in the Image object. Returns ------- out : numpy.array The pixel data of the Image object. It returns a multi-dimensional numpy array, where the shape of the array represents the shape of ...

def show(self): """ Displays the image. Requires PIL/Pillow. Alternatively, you can create an :class:`turicreate.SArray` of this image and use py:func:`turicreate.SArray.show()` See Also -------- turicreate.image_analysis.resize Examples -------...

def predict(self, data, useCPUOnly=False, **kwargs): """ Return predictions for the model. The kwargs gets passed into the model as a dictionary. Parameters ---------- data : dict[str, value] Dictionary of data to make predictions from where the keys are ...

def visualize_spec(self, port=None, input_shape_dict=None): """ Visualize the model. Parameters ---------- port : int if server is to be hosted on specific localhost port input_shape_dict : dict The shapes are calculat...

def _construct_auto_distance(feature_names, column_names, column_types, sample): """ Construct composite distance parameters based on selected features and their types. """ ## Make a dictionary from the column_names and column_types col_type_dict = {k: v for k, v in zip(column_names, column_typ...

def create(dataset, label=None, features=None, distance=None, method='auto', verbose=True, **kwargs): """ Create a nearest neighbor model, which can be searched efficiently and quickly for the nearest neighbors of a query observation. If the `method` argument is specified as `auto`, the type ...

def _get_summary_struct(self): """ Returns a structured description of the model, including (where relevant) the schema of the training data, description of the training data, training statistics, and model hyperparameters. Returns ------- sections : list (of lis...

def _list_fields(self): """ List the fields stored in the model, including data, model, and training options. Each field can be queried with the ``get`` method. Returns ------- out : list List of fields queryable with the ``get`` method. """ o...

def _get(self, field): """ Return the value of a given field. The list of all queryable fields is detailed below, and can be obtained with the :func:`~turicreate.nearest_neighbors.NearestNeighborsModel._list_fields` method. +-----------------------+----------------------...

def _training_stats(self): """ Return a dictionary of statistics collected during creation of the model. These statistics are also available with the ``get`` method and are described in more detail in that method's documentation. Returns ------- out : dict ...

def query(self, dataset, label=None, k=5, radius=None, verbose=True): """ For each row of the input 'dataset', retrieve the nearest neighbors from the model's stored data. In general, the query dataset does not need to be the same as the reference data stored in the model, but if ...

def similarity_graph(self, k=5, radius=None, include_self_edges=False, output_type='SGraph', verbose=True): """ Construct the similarity graph on the reference dataset, which is already stored in the model. This is conceptually very similar to running `query` wit...

def random_split_by_session(dataset, session_id, fraction=0.9, seed=None): """ Randomly split an SFrame into two SFrames based on the `session_id` such that one split contains data for a `fraction` of the sessions while the second split contains all data for the rest of the sessions. Parameters ...

def read_msbuild_xml(path, values={}): """Reads the MS Build XML file at the path and returns its contents. Keyword arguments: values -- The map to append the contents to (default {}) """ # Attempt to read the file contents try: document = parse(path) except Exception as e: ...

def read_msbuild_json(path, values=[]): """Reads the MS Build JSON file at the path and returns its contents. Keyword arguments: values -- The list to append the contents to (default []) """ if not os.path.exists(path): logging.info('Could not find MS Build JSON file at %s', path) r...

def main(): """Script entrypoint.""" # Parse the arguments parser = argparse.ArgumentParser( description='Convert MSBuild XML to JSON format') parser.add_argument( '-t', '--toolchain', help='The name of the toolchain', required=True) parser.add_argument( '-o', '--output', he...

def __merge_json_values(current, previous): """Merges the values between the current and previous run of the script.""" for value in current: name = value['name'] # Find the previous value previous_value = __find_and_remove_value(previous, value) if previous_value is not None: ...

def __find_and_remove_value(list, compare): """Finds the value in the list that corresponds with the value of compare.""" # next throws if there are no matches try: found = next(value for value in list if value['name'] == compare['name'] and value['switch'] == ...

def __convert(root, tag, values, func): """Converts the tag type found in the root and converts them using the func and appends them to the values. """ elements = root.getElementsByTagName(tag) for element in elements: converted = func(element) # Append to the list __append...

def __convert_enum(node): """Converts an EnumProperty node to JSON format.""" name = __get_attribute(node, 'Name') logging.debug('Found EnumProperty named %s', name) converted_values = [] for value in node.getElementsByTagName('EnumValue'): converted = __convert_node(value) conver...

def __convert_bool(node): """Converts an BoolProperty node to JSON format.""" converted = __convert_node(node, default_value='true') # Check for a switch for reversing the value reverse_switch = __get_attribute(node, 'ReverseSwitch') if reverse_switch: converted_reverse = copy.deepcopy(con...

def __convert_string_list(node): """Converts a StringListProperty node to JSON format.""" converted = __convert_node(node) # Determine flags for the string list flags = vsflags(VSFlags.UserValue) # Check for a separator to determine if it is semicolon appendable # If not present assume the val...

def __convert_string(node): """Converts a StringProperty node to JSON format.""" converted = __convert_node(node, default_flags=vsflags(VSFlags.UserValue)) return __check_for_flag(converted)

def __convert_node(node, default_value='', default_flags=vsflags()): """Converts a XML node to a JSON equivalent.""" name = __get_attribute(node, 'Name') logging.debug('Found %s named %s', node.tagName, name) converted = {} converted['name'] = name converted['switch'] = __get_attribute(node, 'S...

def __with_argument(node, value): """Modifies the flags in value if the node contains an Argument.""" arguments = node.getElementsByTagName('Argument') if arguments: logging.debug('Found argument within %s', value['name']) value['flags'] = vsflags(VSFlags.UserValueIgnored, VSFlags.Continue)

def __preprocess_arguments(root): """Preprocesses occurrences of Argument within the root. Argument XML values reference other values within the document by name. The referenced value does not contain a switch. This function will add the switch associated with the argument. """ # Set the flags ...

def __get_attribute(node, name, default_value=''): """Retrieves the attribute of the given name from the node. If not present then the default_value is used. """ if node.hasAttribute(name): return node.attributes[name].value.strip() else: return default_value

def __get_path(path): """Gets the path to the file.""" if not os.path.isabs(path): path = os.path.join(os.getcwd(), path) return os.path.normpath(path)

def __output_path(toolchain, rule, output_dir): """Gets the output path for a file given the toolchain, rule and output_dir""" filename = '%s_%s.json' % (toolchain, rule) return os.path.join(output_dir, filename)

def __write_json_file(path, values): """Writes a JSON file at the path with the values provided.""" # Sort the keys to ensure ordering sort_order = ['name', 'switch', 'comment', 'value', 'flags'] sorted_values = [ OrderedDict( sorted( value.items(), key=lambda value: ...

def __append_list(append_to, value): """Appends the value to the list.""" if value is not None: if isinstance(value, list): append_to.extend(value) else: append_to.append(value)

def decompile_func(func): ''' Decompile a function into ast.FunctionDef node. :param func: python function (can not be a built-in) :return: ast.FunctionDef instance. ''' code = func.__code__ # For python 3 # defaults = func.func_defaults if sys.version_info.major < 3 else func....

def compile_func(ast_node, filename, globals, **defaults): ''' Compile a function from an ast.FunctionDef instance. :param ast_node: ast.FunctionDef instance :param filename: path where function source can be found. :param globals: will be used as func_globals :return: A python functi...

def decompile_pyc(bin_pyc, output=sys.stdout): ''' decompile apython pyc or pyo binary file. :param bin_pyc: input file objects :param output: output file objects ''' from turicreate.meta.asttools import python_source bin = bin_pyc.read() code = marshal.loads(bin[8:])...

def ParseInput(self, a_file): """Consumes input extracting definitions. Args: a_file: The file like stream to parse. Raises: PDDMError if there are any issues. """ input_lines = a_file.read().splitlines() self.ParseLines(input_lines)

def ParseLines(self, input_lines): """Parses list of lines. Args: input_lines: A list of strings of input to parse (no newlines on the strings). Raises: PDDMError if there are any issues. """ current_macro = None for line in input_lines: if line.startswith(...

def Expand(self, macro_ref_str): """Expands the macro reference. Args: macro_ref_str: String of a macro reference (i.e. foo(a, b)). Returns: The text from the expansion. Raises: PDDMError if there are any issues. """ match = _MACRO_RE.match(macro_ref_str) if match is Non...

def ProcessContent(self, strip_expansion=False): """Processes the file contents.""" self._ParseFile() if strip_expansion: # Without a collection the expansions become blank, removing them. collection = None else: collection = MacroCollection() for section in self._sections: s...

def convert(model, input_features, output_features): """Convert a _imputer model to the protobuf spec. Parameters ---------- model: Imputer A trained Imputer model. input_features: str Name of the input column. output_features: str Name of the output column. Retur...

def reset (): """ Clear the module state. This is mainly for testing purposes. """ global __all_attributes, __all_features, __implicit_features, __composite_properties global __subfeature_from_value, __all_top_features, __free_features global __all_subfeatures # sets the default value of False ...

def feature (name, values, attributes = []): """ Declares a new feature with the given name, values, and attributes. name: the feature name values: a sequence of the allowable values - may be extended later with feature.extend attributes: a sequence of the feature's attributes (e.g. implicit...

def set_default (feature, value): """ Sets the default value of the given feature, overriding any previous default. feature: the name of the feature value: the default value to assign """ f = __all_features[feature] bad_attribute = None if f.free: bad_attribute = "free" ...

def defaults(features): """ Returns the default property values for the given features. """ assert is_iterable_typed(features, Feature) # FIXME: should merge feature and property modules. from . import property result = [] for f in features: if not f.free and not f.optional and f.de...

def valid (names): """ Returns true iff all elements of names are valid features. """ if isinstance(names, str): names = [names] assert is_iterable_typed(names, basestring) return all(name in __all_features for name in names)

def values (feature): """ Return the values of the given feature. """ assert isinstance(feature, basestring) validate_feature (feature) return __all_features[feature].values

def is_implicit_value (value_string): """ Returns true iff 'value_string' is a value_string of an implicit feature. """ assert isinstance(value_string, basestring) if value_string in __implicit_features: return __implicit_features[value_string] v = value_string.split('-') if v[0] n...