BoghdadyJR/codesearch_python · Data Studio

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def save_partial(self, obj): """Partial objects do not serialize correctly in python2.x -- this fixes the bugs""" self.save_reduce(_genpartial, (obj.func, obj.args, obj.keywords))
def save_file(self, obj): """Save a file""" try: import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute except ImportError: import io as pystringIO if not hasattr(obj, 'name') or not hasattr(obj, 'mode'): raise pickle.Pi...
def save_ufunc(self, obj): """Hack function for saving numpy ufunc objects""" name = obj.__name__ numpy_tst_mods = ['numpy', 'scipy.special'] for tst_mod_name in numpy_tst_mods: tst_mod = sys.modules.get(tst_mod_name, None) if tst_mod and name in tst_mod.__dict__:...
def _ExtractSymbols(desc_proto, package): """Pulls out all the symbols from a descriptor proto. Args: desc_proto: The proto to extract symbols from. package: The package containing the descriptor type. Yields: The fully qualified name found in the descriptor. """ message_name = '.'.join((packag...
def Add(self, file_desc_proto): """Adds the FileDescriptorProto and its types to this database. Args: file_desc_proto: The FileDescriptorProto to add. Raises: DescriptorDatabaseConflictingDefinitionError: if an attempt is made to add a proto with the same name but different definition t...
def convert(model, input_features, output_features): """Convert a normalizer model to the protobuf spec. Parameters ---------- model: Normalizer A Normalizer. input_features: str Name of the input column. output_features: str Name of the output column. Returns ...
def mpi_submit(nslave, worker_args, worker_envs): """ customized submit script, that submit nslave jobs, each must contain args as parameter note this can be a lambda function containing additional parameters in input Parameters nslave number of slave process to start up args arg...
def consume(self): ''' Consume byte-code ''' generic_consume = getattr(self, 'generic_consume', None) for instr in disassembler(self.code): method_name = 'consume_%s' % (instr.opname) method = getattr(self, method_name, generic_consume) ...
def create(dataset, target, features=None, max_iterations=10, validation_set='auto', verbose=True, class_weights=None, random_seed=None, metric='auto', **kwargs): """ Create a (binary or multi-class) classifier model of type :class...
def classify(self, dataset, missing_value_action='auto'): """ Return a classification, for each example in the ``dataset``, using the trained random forest model. The output SFrame contains predictions as class labels (0 or 1) and probabilities associated with the the example. P...
def _get_layer_converter_fn(layer): """Get the right converter function for Keras """ layer_type = type(layer) if layer_type in _KERAS_LAYER_REGISTRY: return _KERAS_LAYER_REGISTRY[layer_type] else: raise TypeError("Keras layer of type %s is not supported." % type(layer))
def convertToSpec(model, input_names = None, output_names = None, image_input_names = None, input_name_shape_dict = {}, is_bgr = False, red_bias = 0.0, green_bias = 0.0, blue_b...
def convert(model, input_names = None, output_names = None, image_input_names = None, input_name_shape_dict = {}, is_bgr = False, red_bias = 0.0, green_bias = 0.0, blue_bias = ...
def convert(model, feature_names, target): """Convert a boosted tree model to protobuf format. Parameters ---------- decision_tree : RandomForestRegressor A trained scikit-learn tree model. feature_names: [str] Name of the input columns. target: str Name of the output ...
def create(dataset, features=None, distance=None, radius=1., min_core_neighbors=10, verbose=True): """ Create a DBSCAN clustering model. The DBSCAN method partitions the input dataset into three types of points, based on the estimated probability density at each point. - Core points ...
def find_lib_path(): """Load find the path to xgboost dynamic library files. Returns ------- lib_path: list(string) List of all found library path to xgboost """ curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) # make pythonpack hack: copy this directory one...
def check_expected_type(model, expected_type): """Check if a model is of the right type. Raise error if not. Parameters ---------- model: model Any scikit-learn model expected_type: Type Expected type of the scikit-learn. """ if (model.__class__.__name__ != expected_type.__...
def convert(model, input_names='input', target_name='target', probability='classProbability', input_length='auto'): """ Convert a LIBSVM model to Core ML format. Parameters ---------- model: a libsvm model (C-SVC, nu-SVC, epsilon-SVR, or nu-SVR) or string path to a saved model....
def append(self, value): """Appends an item to the list. Similar to list.append().""" self._values.append(self._type_checker.CheckValue(value)) if not self._message_listener.dirty: self._message_listener.Modified()
def insert(self, key, value): """Inserts the item at the specified position. Similar to list.insert().""" self._values.insert(key, self._type_checker.CheckValue(value)) if not self._message_listener.dirty: self._message_listener.Modified()
def extend(self, elem_seq): """Extends by appending the given iterable. Similar to list.extend().""" if elem_seq is None: return try: elem_seq_iter = iter(elem_seq) except TypeError: if not elem_seq: # silently ignore falsy inputs :-/. # TODO(ptucker): Deprecate this b...
def MergeFrom(self, other): """Appends the contents of another repeated field of the same type to this one. We do not check the types of the individual fields. """ self._values.extend(other._values) self._message_listener.Modified()
def remove(self, elem): """Removes an item from the list. Similar to list.remove().""" self._values.remove(elem) self._message_listener.Modified()
def pop(self, key=-1): """Removes and returns an item at a given index. Similar to list.pop().""" value = self._values[key] self.__delitem__(key) return value
def add(self, kwargs): """Adds a new element at the end of the list and returns it. Keyword arguments may be used to initialize the element. """ new_element = self._message_descriptor._concrete_class(kwargs) new_element._SetListener(self._message_listener) self._values.append(new_element) ...
def extend(self, elem_seq): """Extends by appending the given sequence of elements of the same type as this one, copying each individual message. """ message_class = self._message_descriptor._concrete_class listener = self._message_listener values = self._values for message in elem_seq: ...
def difference (b, a): """ Returns the elements of B that are not in A. """ a = set(a) result = [] for item in b: if item not in a: result.append(item) return result
def intersection (set1, set2): """ Removes from set1 any items which don't appear in set2 and returns the result. """ assert is_iterable(set1) assert is_iterable(set2) result = [] for v in set1: if v in set2: result.append (v) return result
def contains (small, large): """ Returns true iff all elements of 'small' exist in 'large'. """ small = to_seq (small) large = to_seq (large) for s in small: if not s in large: return False return True
def equal (a, b): """ Returns True iff 'a' contains the same elements as 'b', irrespective of their order. # TODO: Python 2.4 has a proper set class. """ assert is_iterable(a) assert is_iterable(b) return contains (a, b) and contains (b, a)
def annotate(data, image_column=None, annotation_column='annotations'): """ Annotate your images loaded in either an SFrame or SArray Format The annotate util is a GUI assisted application used to create labels in SArray Image data. Specifying a column, with dtype Image, in an SFrame ...
def recover_annotation(): """ Recover the last annotated SFrame. If you annotate an SFrame and forget to assign it to a variable, this function allows you to recover the last annotated SFrame. Returns ------- out : SFrame A new SFrame th...
def convert(model, input_features, output_features): """Convert a DictVectorizer model to the protobuf spec. Parameters ---------- model: DictVectorizer A fitted DictVectorizer model. input_features: str Name of the input column. output_features: str Name of the output...
def print_callback(val): """ Internal function. This function is called via a call back returning from IPC to Cython to Python. It tries to perform incremental printing to IPython Notebook or Jupyter Notebook and when all else fails, just prints locally. """ success = False try: ...
def run(toolkit_name, options, verbose=True, show_progress=False): """ Internal function to execute toolkit on the turicreate server. Parameters ---------- toolkit_name : string The name of the toolkit. options : dict A map containing the required input for the toolkit function...
def _RoundTowardZero(value, divider): """Truncates the remainder part after division.""" # For some languanges, the sign of the remainder is implementation # dependent if any of the operands is negative. Here we enforce # "rounded toward zero" semantics. For example, for (-5) / 2 an # implementation may give ...
def _IsValidPath(message_descriptor, path): """Checks whether the path is valid for Message Descriptor.""" parts = path.split('.') last = parts.pop() for name in parts: field = message_descriptor.fields_by_name[name] if (field is None or field.label == FieldDescriptor.LABEL_REPEATED or f...
def _CheckFieldMaskMessage(message): """Raises ValueError if message is not a FieldMask.""" message_descriptor = message.DESCRIPTOR if (message_descriptor.name != 'FieldMask' or message_descriptor.file.name != 'google/protobuf/field_mask.proto'): raise ValueError('Message {0} is not a FieldMask.'.format...
def _SnakeCaseToCamelCase(path_name): """Converts a path name from snake_case to camelCase.""" result = [] after_underscore = False for c in path_name: if c.isupper(): raise Error('Fail to print FieldMask to Json string: Path name ' '{0} must not contain uppercase letters.'.format(pa...
def _CamelCaseToSnakeCase(path_name): """Converts a field name from camelCase to snake_case.""" result = [] for c in path_name: if c == '_': raise ParseError('Fail to parse FieldMask: Path name ' '{0} must not contain "_"s.'.format(path_name)) if c.isupper(): result += '...
def _MergeMessage( node, source, destination, replace_message, replace_repeated): """Merge all fields specified by a sub-tree from source to destination.""" source_descriptor = source.DESCRIPTOR for name in node: child = node[name] field = source_descriptor.fields_by_name[name] if field is None: ...
def _AddFieldPaths(node, prefix, field_mask): """Adds the field paths descended from node to field_mask.""" if not node: field_mask.paths.append(prefix) return for name in sorted(node): if prefix: child_path = prefix + '.' + name else: child_path = name _AddFieldPaths(node[name], c...
def Pack(self, msg, type_url_prefix='type.googleapis.com/'): """Packs the specified message into current Any message.""" if len(type_url_prefix) < 1 or type_url_prefix[-1] != '/': self.type_url = '%s/%s' % (type_url_prefix, msg.DESCRIPTOR.full_name) else: self.type_url = '%s%s' % (type_url_prefi...
def Unpack(self, msg): """Unpacks the current Any message into specified message.""" descriptor = msg.DESCRIPTOR if not self.Is(descriptor): return False msg.ParseFromString(self.value) return True
def ToJsonString(self): """Converts Timestamp to RFC 3339 date string format. Returns: A string converted from timestamp. The string is always Z-normalized and uses 3, 6 or 9 fractional digits as required to represent the exact time. Example of the return format: '1972-01-01T10:00:20.021Z' ...
def FromJsonString(self, value): """Parse a RFC 3339 date string format to Timestamp. Args: value: A date string. Any fractional digits (or none) and any offset are accepted as long as they fit into nano-seconds precision. Example of accepted format: '1972-01-01T10:00:20.021-05:00' ...
def FromNanoseconds(self, nanos): """Converts nanoseconds since epoch to Timestamp.""" self.seconds = nanos // _NANOS_PER_SECOND self.nanos = nanos % _NANOS_PER_SECOND
def FromMicroseconds(self, micros): """Converts microseconds since epoch to Timestamp.""" self.seconds = micros // _MICROS_PER_SECOND self.nanos = (micros % _MICROS_PER_SECOND) * _NANOS_PER_MICROSECOND
def FromMilliseconds(self, millis): """Converts milliseconds since epoch to Timestamp.""" self.seconds = millis // _MILLIS_PER_SECOND self.nanos = (millis % _MILLIS_PER_SECOND) * _NANOS_PER_MILLISECOND
def ToDatetime(self): """Converts Timestamp to datetime.""" return datetime.utcfromtimestamp( self.seconds + self.nanos / float(_NANOS_PER_SECOND))
def FromDatetime(self, dt): """Converts datetime to Timestamp.""" td = dt - datetime(1970, 1, 1) self.seconds = td.seconds + td.days * _SECONDS_PER_DAY self.nanos = td.microseconds * _NANOS_PER_MICROSECOND
def ToMicroseconds(self): """Converts a Duration to microseconds.""" micros = _RoundTowardZero(self.nanos, _NANOS_PER_MICROSECOND) return self.seconds * _MICROS_PER_SECOND + micros
def ToMilliseconds(self): """Converts a Duration to milliseconds.""" millis = _RoundTowardZero(self.nanos, _NANOS_PER_MILLISECOND) return self.seconds * _MILLIS_PER_SECOND + millis
def FromMicroseconds(self, micros): """Converts microseconds to Duration.""" self._NormalizeDuration( micros // _MICROS_PER_SECOND, (micros % _MICROS_PER_SECOND) * _NANOS_PER_MICROSECOND)
def FromMilliseconds(self, millis): """Converts milliseconds to Duration.""" self._NormalizeDuration( millis // _MILLIS_PER_SECOND, (millis % _MILLIS_PER_SECOND) * _NANOS_PER_MILLISECOND)
def ToTimedelta(self): """Converts Duration to timedelta.""" return timedelta( seconds=self.seconds, microseconds=_RoundTowardZero( self.nanos, _NANOS_PER_MICROSECOND))
def FromTimedelta(self, td): """Convertd timedelta to Duration.""" self._NormalizeDuration(td.seconds + td.days * _SECONDS_PER_DAY, td.microseconds * _NANOS_PER_MICROSECOND)
def _NormalizeDuration(self, seconds, nanos): """Set Duration by seconds and nonas.""" # Force nanos to be negative if the duration is negative. if seconds < 0 and nanos > 0: seconds += 1 nanos -= _NANOS_PER_SECOND self.seconds = seconds self.nanos = nanos
def ToJsonString(self): """Converts FieldMask to string according to proto3 JSON spec.""" camelcase_paths = [] for path in self.paths: camelcase_paths.append(_SnakeCaseToCamelCase(path)) return ','.join(camelcase_paths)
def IsValidForDescriptor(self, message_descriptor): """Checks whether the FieldMask is valid for Message Descriptor.""" for path in self.paths: if not _IsValidPath(message_descriptor, path): return False return True
def AllFieldsFromDescriptor(self, message_descriptor): """Gets all direct fields of Message Descriptor to FieldMask.""" self.Clear() for field in message_descriptor.fields: self.paths.append(field.name)
def Union(self, mask1, mask2): """Merges mask1 and mask2 into this FieldMask.""" _CheckFieldMaskMessage(mask1) _CheckFieldMaskMessage(mask2) tree = _FieldMaskTree(mask1) tree.MergeFromFieldMask(mask2) tree.ToFieldMask(self)
def Intersect(self, mask1, mask2): """Intersects mask1 and mask2 into this FieldMask.""" _CheckFieldMaskMessage(mask1) _CheckFieldMaskMessage(mask2) tree = _FieldMaskTree(mask1) intersection = _FieldMaskTree() for path in mask2.paths: tree.IntersectPath(path, intersection) intersection...
def MergeMessage( self, source, destination, replace_message_field=False, replace_repeated_field=False): """Merges fields specified in FieldMask from source to destination. Args: source: Source message. destination: The destination message to be merged into. replace_message_field:...
def AddPath(self, path): """Adds a field path into the tree. If the field path to add is a sub-path of an existing field path in the tree (i.e., a leaf node), it means the tree already matches the given path so nothing will be added to the tree. If the path matches an existing non-leaf node in the ...
def IntersectPath(self, path, intersection): """Calculates the intersection part of a field path with this tree. Args: path: The field path to calculates. intersection: The out tree to record the intersection part. """ node = self._root for name in path.split('.'): if name not in ...
def AddLeafNodes(self, prefix, node): """Adds leaf nodes begin with prefix to this tree.""" if not node: self.AddPath(prefix) for name in node: child_path = prefix + '.' + name self.AddLeafNodes(child_path, node[name])
def MergeMessage( self, source, destination, replace_message, replace_repeated): """Merge all fields specified by this tree from source to destination.""" _MergeMessage( self._root, source, destination, replace_message, replace_repeated)
def configure (command = None, condition = None, options = None): """ Configures a new resource compilation command specific to a condition, usually a toolset selection condition. The possible options are: * <rc-type>(rc\|windres) - Indicates the type of options the command ...
def convert(model, feature_names, target): """Convert a Nu Support Vector Regression (NuSVR) model to the protobuf spec. Parameters ---------- model: NuSVR A trained NuSVR encoder model. feature_names: [str] Name of the input columns. target: str Name of the output colu...
def create(dataset, target, features=None, l2_penalty=1e-2, l1_penalty=0.0, solver='auto', feature_rescaling=True, convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'], step_size = _DEFAULT_SOLVER_OPTIONS['step_size'], lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'...
def export_coreml(self, filename): """ Export the model in Core ML format. Parameters ---------- filename: str A valid filename where the model can be saved. Examples -------- >>> model.export_coreml("MyModel.mlmodel") """ from ...
def predict(self, dataset, missing_value_action='auto'): """ Return target value predictions for ``dataset``, using the trained linear regression model. This method can be used to get fitted values for the model by inputting the training dataset. Parameters ---------- ...
def evaluate(self, dataset, metric='auto', missing_value_action='auto'): r"""Evaluate the model by making target value predictions and comparing to actual values. Two metrics are used to evaluate linear regression models. The first is root-mean-squared error (RMSE) while the second is ...
def frame(data, window_length, hop_length): """Convert array into a sequence of successive possibly overlapping frames. An n-dimensional array of shape (num_samples, ...) is converted into an (n+1)-D array of shape (num_frames, window_length, ...), where each frame starts hop_length points after the preceding ...
def periodic_hann(window_length): """Calculate a "periodic" Hann window. The classic Hann window is defined as a raised cosine that starts and ends on zero, and where every value appears twice, except the middle point for an odd-length window. Matlab calls this a "symmetric" window and np.hanning() returns ...
def stft_magnitude(signal, fft_length, hop_length=None, window_length=None): """Calculate the short-time Fourier transform magnitude. Args: signal: 1D np.array of the input time-domain signal. fft_length: Size of the FFT to apply. hop_length: Advance (in samples) b...
def spectrogram_to_mel_matrix(num_mel_bins=20, num_spectrogram_bins=129, audio_sample_rate=8000, lower_edge_hertz=125.0, upper_edge_hertz=3800.0): """Return a matrix that can post-multiply spectrogr...
def log_mel_spectrogram(data, audio_sample_rate=8000, log_offset=0.0, window_length_secs=0.025, hop_length_secs=0.010, **kwargs): """Convert waveform to a log magnitude mel-frequency spectrogram. ...
def generate_random_sframe(num_rows, column_codes, random_seed = 0): """ Creates a random SFrame with `num_rows` rows and randomly generated column types determined by `column_codes`. The output SFrame is deterministic based on `random_seed`. `column_types` is a string with each character denoti...
def generate_random_regression_sframe(num_rows, column_codes, random_seed = 0, target_noise_level = 0.25): """ Creates a random SFrame with `num_rows` rows and randomly generated column types determined by `column_codes`. The output SFrame is deterministic based on `random_seed`. In addition, a ta...
def generate_random_classification_sframe(num_rows, column_codes, num_classes, misclassification_spread = 0.25, num_extra_class_bins = None, random_seed = 0): """ Creates a random SFrame...
def infer_shapes(nn_spec, input_spec, input_shape_dict = None): """ Input: spec : mlmodel spec input_shape_dict: dictionary of string --> tuple string: input name tuple: input shape as a 5 length tuple in order (Seq, Batch, C, H, W) If inp...
def convert(libsvm_model, feature_names, target, input_length, probability): """Convert a svm model to the protobuf spec. This currently supports: * C-SVC * nu-SVC * Epsilon-SVR * nu-SVR Parameters ---------- model_path: libsvm_model Libsvm representation of the mode...
def create(dataset, session_id, target, features=None, prediction_window=100, validation_set='auto', max_iterations=10, batch_size=32, verbose=True): """ Create an :class:`ActivityClassifier` model. Parameters ---------- dataset : SFrame Input data which consists of `sessions` of...
def _encode_target(data, target, mapping=None): """ Encode targets to integers in [0, num_classes - 1] """ if mapping is None: mapping = {t: i for i, t in enumerate(sorted(data[target].unique()))} data[target] = data[target].apply(lambda t: mapping[t]) return data, mapping
def export_coreml(self, filename): """ Export the model in Core ML format. Parameters ---------- filename: str A valid filename where the model can be saved. Examples -------- >>> model.export_coreml("MyModel.mlmodel") """ impor...
def predict(self, dataset, output_type='class', output_frequency='per_row'): """ Return predictions for ``dataset``, using the trained activity classifier. Predictions can be generated as class labels, or as a probability vector with probabilities for each class. The activity cl...
def evaluate(self, dataset, metric='auto'): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFrame Dataset of new observations. Must include columns with the same ...
def classify(self, dataset, output_frequency='per_row'): """ Return a classification, for each ``prediction_window`` examples in the ``dataset``, using the trained activity classification model. The output SFrame contains predictions as both class labels as well as probabilities ...
def predict_topk(self, dataset, output_type='probability', k=3, output_frequency='per_row'): """ Return top-k predictions for the ``dataset``, using the trained model. Predictions are returned as an SFrame with three columns: `prediction_id`, `class`, and `probability`, or `rank`, depen...
def count_characters(root, out): """Count the occurrances of the different characters in the files""" if os.path.isfile(root): with open(root, 'rb') as in_f: for line in in_f: for char in line: if char not in out: out[char] = 0 ...
def main(): """The main function of the script""" desc = 'Generate character statistics from a source tree' parser = argparse.ArgumentParser(description=desc) parser.add_argument( '--src', dest='src', required=True, help='The root of the source tree' ) parser.add_...
def save_spec(spec, filename): """ Save a protobuf model specification to file. Parameters ---------- spec: Model_pb Protobuf representation of the model filename: str File path where the spec gets saved. Examples -------- .. sourcecode:: python >>> corem...
def load_spec(filename): """ Load a protobuf model specification from file Parameters ---------- filename: str Location on disk (a valid filepath) from which the file is loaded as a protobuf spec. Returns ------- model_spec: Model_pb Protobuf representation of t...
def _get_nn_layers(spec): """ Returns a list of neural network layers if the model contains any. Parameters ---------- spec: Model_pb A model protobuf specification. Returns ------- [NN layer] list of all layers (including layers from elements of a pipeline """ ...
def evaluate_regressor(model, data, target="target", verbose=False): """ Evaluate a CoreML regression model and compare against predictions from the original framework (for testing correctness of conversion) Parameters ---------- filename: [str \| MLModel] File path from which to load th...
def evaluate_classifier(model, data, target='target', verbose=False): """ Evaluate a CoreML classifier model and compare against predictions from the original framework (for testing correctness of conversion). Use this evaluation for models that don't deal with probabilities. Parameters -------...
def evaluate_classifier_with_probabilities(model, data, probabilities='probabilities', verbose = False): """ Evaluate a classifier specification for testing. Parameters ---------- filename: [str \| Model] F...
def rename_feature(spec, current_name, new_name, rename_inputs=True, rename_outputs=True): """ Rename a feature in the specification. Parameters ---------- spec: Model_pb The specification containing the feature to rename. current_name: str Current name of th...

def save_partial(self, obj): """Partial objects do not serialize correctly in python2.x -- this fixes the bugs""" self.save_reduce(_genpartial, (obj.func, obj.args, obj.keywords))

def save_file(self, obj): """Save a file""" try: import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute except ImportError: import io as pystringIO if not hasattr(obj, 'name') or not hasattr(obj, 'mode'): raise pickle.Pi...

def save_ufunc(self, obj): """Hack function for saving numpy ufunc objects""" name = obj.__name__ numpy_tst_mods = ['numpy', 'scipy.special'] for tst_mod_name in numpy_tst_mods: tst_mod = sys.modules.get(tst_mod_name, None) if tst_mod and name in tst_mod.__dict__:...

def _ExtractSymbols(desc_proto, package): """Pulls out all the symbols from a descriptor proto. Args: desc_proto: The proto to extract symbols from. package: The package containing the descriptor type. Yields: The fully qualified name found in the descriptor. """ message_name = '.'.join((packag...

def Add(self, file_desc_proto): """Adds the FileDescriptorProto and its types to this database. Args: file_desc_proto: The FileDescriptorProto to add. Raises: DescriptorDatabaseConflictingDefinitionError: if an attempt is made to add a proto with the same name but different definition t...

def convert(model, input_features, output_features): """Convert a normalizer model to the protobuf spec. Parameters ---------- model: Normalizer A Normalizer. input_features: str Name of the input column. output_features: str Name of the output column. Returns ...

def mpi_submit(nslave, worker_args, worker_envs): """ customized submit script, that submit nslave jobs, each must contain args as parameter note this can be a lambda function containing additional parameters in input Parameters nslave number of slave process to start up args arg...

def consume(self): ''' Consume byte-code ''' generic_consume = getattr(self, 'generic_consume', None) for instr in disassembler(self.code): method_name = 'consume_%s' % (instr.opname) method = getattr(self, method_name, generic_consume) ...

def create(dataset, target, features=None, max_iterations=10, validation_set='auto', verbose=True, class_weights=None, random_seed=None, metric='auto', **kwargs): """ Create a (binary or multi-class) classifier model of type :class...

def classify(self, dataset, missing_value_action='auto'): """ Return a classification, for each example in the ``dataset``, using the trained random forest model. The output SFrame contains predictions as class labels (0 or 1) and probabilities associated with the the example. P...

def _get_layer_converter_fn(layer): """Get the right converter function for Keras """ layer_type = type(layer) if layer_type in _KERAS_LAYER_REGISTRY: return _KERAS_LAYER_REGISTRY[layer_type] else: raise TypeError("Keras layer of type %s is not supported." % type(layer))

def convertToSpec(model, input_names = None, output_names = None, image_input_names = None, input_name_shape_dict = {}, is_bgr = False, red_bias = 0.0, green_bias = 0.0, blue_b...

def convert(model, input_names = None, output_names = None, image_input_names = None, input_name_shape_dict = {}, is_bgr = False, red_bias = 0.0, green_bias = 0.0, blue_bias = ...

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

def create(dataset, features=None, distance=None, radius=1., min_core_neighbors=10, verbose=True): """ Create a DBSCAN clustering model. The DBSCAN method partitions the input dataset into three types of points, based on the estimated probability density at each point. - **Core** points ...

def find_lib_path(): """Load find the path to xgboost dynamic library files. Returns ------- lib_path: list(string) List of all found library path to xgboost """ curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__))) # make pythonpack hack: copy this directory one...

def check_expected_type(model, expected_type): """Check if a model is of the right type. Raise error if not. Parameters ---------- model: model Any scikit-learn model expected_type: Type Expected type of the scikit-learn. """ if (model.__class__.__name__ != expected_type.__...

def convert(model, input_names='input', target_name='target', probability='classProbability', input_length='auto'): """ Convert a LIBSVM model to Core ML format. Parameters ---------- model: a libsvm model (C-SVC, nu-SVC, epsilon-SVR, or nu-SVR) or string path to a saved model....

def append(self, value): """Appends an item to the list. Similar to list.append().""" self._values.append(self._type_checker.CheckValue(value)) if not self._message_listener.dirty: self._message_listener.Modified()

def insert(self, key, value): """Inserts the item at the specified position. Similar to list.insert().""" self._values.insert(key, self._type_checker.CheckValue(value)) if not self._message_listener.dirty: self._message_listener.Modified()

def extend(self, elem_seq): """Extends by appending the given iterable. Similar to list.extend().""" if elem_seq is None: return try: elem_seq_iter = iter(elem_seq) except TypeError: if not elem_seq: # silently ignore falsy inputs :-/. # TODO(ptucker): Deprecate this b...

def MergeFrom(self, other): """Appends the contents of another repeated field of the same type to this one. We do not check the types of the individual fields. """ self._values.extend(other._values) self._message_listener.Modified()

def remove(self, elem): """Removes an item from the list. Similar to list.remove().""" self._values.remove(elem) self._message_listener.Modified()

def pop(self, key=-1): """Removes and returns an item at a given index. Similar to list.pop().""" value = self._values[key] self.__delitem__(key) return value

def add(self, **kwargs): """Adds a new element at the end of the list and returns it. Keyword arguments may be used to initialize the element. """ new_element = self._message_descriptor._concrete_class(**kwargs) new_element._SetListener(self._message_listener) self._values.append(new_element) ...

def extend(self, elem_seq): """Extends by appending the given sequence of elements of the same type as this one, copying each individual message. """ message_class = self._message_descriptor._concrete_class listener = self._message_listener values = self._values for message in elem_seq: ...

def difference (b, a): """ Returns the elements of B that are not in A. """ a = set(a) result = [] for item in b: if item not in a: result.append(item) return result

def intersection (set1, set2): """ Removes from set1 any items which don't appear in set2 and returns the result. """ assert is_iterable(set1) assert is_iterable(set2) result = [] for v in set1: if v in set2: result.append (v) return result

def contains (small, large): """ Returns true iff all elements of 'small' exist in 'large'. """ small = to_seq (small) large = to_seq (large) for s in small: if not s in large: return False return True

def equal (a, b): """ Returns True iff 'a' contains the same elements as 'b', irrespective of their order. # TODO: Python 2.4 has a proper set class. """ assert is_iterable(a) assert is_iterable(b) return contains (a, b) and contains (b, a)

def annotate(data, image_column=None, annotation_column='annotations'): """ Annotate your images loaded in either an SFrame or SArray Format The annotate util is a GUI assisted application used to create labels in SArray Image data. Specifying a column, with dtype Image, in an SFrame ...

def recover_annotation(): """ Recover the last annotated SFrame. If you annotate an SFrame and forget to assign it to a variable, this function allows you to recover the last annotated SFrame. Returns ------- out : SFrame A new SFrame th...

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

def print_callback(val): """ Internal function. This function is called via a call back returning from IPC to Cython to Python. It tries to perform incremental printing to IPython Notebook or Jupyter Notebook and when all else fails, just prints locally. """ success = False try: ...

def run(toolkit_name, options, verbose=True, show_progress=False): """ Internal function to execute toolkit on the turicreate server. Parameters ---------- toolkit_name : string The name of the toolkit. options : dict A map containing the required input for the toolkit function...

def _RoundTowardZero(value, divider): """Truncates the remainder part after division.""" # For some languanges, the sign of the remainder is implementation # dependent if any of the operands is negative. Here we enforce # "rounded toward zero" semantics. For example, for (-5) / 2 an # implementation may give ...

def _IsValidPath(message_descriptor, path): """Checks whether the path is valid for Message Descriptor.""" parts = path.split('.') last = parts.pop() for name in parts: field = message_descriptor.fields_by_name[name] if (field is None or field.label == FieldDescriptor.LABEL_REPEATED or f...

def _CheckFieldMaskMessage(message): """Raises ValueError if message is not a FieldMask.""" message_descriptor = message.DESCRIPTOR if (message_descriptor.name != 'FieldMask' or message_descriptor.file.name != 'google/protobuf/field_mask.proto'): raise ValueError('Message {0} is not a FieldMask.'.format...

def _SnakeCaseToCamelCase(path_name): """Converts a path name from snake_case to camelCase.""" result = [] after_underscore = False for c in path_name: if c.isupper(): raise Error('Fail to print FieldMask to Json string: Path name ' '{0} must not contain uppercase letters.'.format(pa...

def _CamelCaseToSnakeCase(path_name): """Converts a field name from camelCase to snake_case.""" result = [] for c in path_name: if c == '_': raise ParseError('Fail to parse FieldMask: Path name ' '{0} must not contain "_"s.'.format(path_name)) if c.isupper(): result += '...

def _MergeMessage( node, source, destination, replace_message, replace_repeated): """Merge all fields specified by a sub-tree from source to destination.""" source_descriptor = source.DESCRIPTOR for name in node: child = node[name] field = source_descriptor.fields_by_name[name] if field is None: ...

def _AddFieldPaths(node, prefix, field_mask): """Adds the field paths descended from node to field_mask.""" if not node: field_mask.paths.append(prefix) return for name in sorted(node): if prefix: child_path = prefix + '.' + name else: child_path = name _AddFieldPaths(node[name], c...

def Pack(self, msg, type_url_prefix='type.googleapis.com/'): """Packs the specified message into current Any message.""" if len(type_url_prefix) < 1 or type_url_prefix[-1] != '/': self.type_url = '%s/%s' % (type_url_prefix, msg.DESCRIPTOR.full_name) else: self.type_url = '%s%s' % (type_url_prefi...

def Unpack(self, msg): """Unpacks the current Any message into specified message.""" descriptor = msg.DESCRIPTOR if not self.Is(descriptor): return False msg.ParseFromString(self.value) return True

def ToJsonString(self): """Converts Timestamp to RFC 3339 date string format. Returns: A string converted from timestamp. The string is always Z-normalized and uses 3, 6 or 9 fractional digits as required to represent the exact time. Example of the return format: '1972-01-01T10:00:20.021Z' ...

def FromJsonString(self, value): """Parse a RFC 3339 date string format to Timestamp. Args: value: A date string. Any fractional digits (or none) and any offset are accepted as long as they fit into nano-seconds precision. Example of accepted format: '1972-01-01T10:00:20.021-05:00' ...

def FromNanoseconds(self, nanos): """Converts nanoseconds since epoch to Timestamp.""" self.seconds = nanos // _NANOS_PER_SECOND self.nanos = nanos % _NANOS_PER_SECOND

def FromMicroseconds(self, micros): """Converts microseconds since epoch to Timestamp.""" self.seconds = micros // _MICROS_PER_SECOND self.nanos = (micros % _MICROS_PER_SECOND) * _NANOS_PER_MICROSECOND

def FromMilliseconds(self, millis): """Converts milliseconds since epoch to Timestamp.""" self.seconds = millis // _MILLIS_PER_SECOND self.nanos = (millis % _MILLIS_PER_SECOND) * _NANOS_PER_MILLISECOND

def ToDatetime(self): """Converts Timestamp to datetime.""" return datetime.utcfromtimestamp( self.seconds + self.nanos / float(_NANOS_PER_SECOND))

def FromDatetime(self, dt): """Converts datetime to Timestamp.""" td = dt - datetime(1970, 1, 1) self.seconds = td.seconds + td.days * _SECONDS_PER_DAY self.nanos = td.microseconds * _NANOS_PER_MICROSECOND

def ToMicroseconds(self): """Converts a Duration to microseconds.""" micros = _RoundTowardZero(self.nanos, _NANOS_PER_MICROSECOND) return self.seconds * _MICROS_PER_SECOND + micros

def ToMilliseconds(self): """Converts a Duration to milliseconds.""" millis = _RoundTowardZero(self.nanos, _NANOS_PER_MILLISECOND) return self.seconds * _MILLIS_PER_SECOND + millis

def FromMicroseconds(self, micros): """Converts microseconds to Duration.""" self._NormalizeDuration( micros // _MICROS_PER_SECOND, (micros % _MICROS_PER_SECOND) * _NANOS_PER_MICROSECOND)

def FromMilliseconds(self, millis): """Converts milliseconds to Duration.""" self._NormalizeDuration( millis // _MILLIS_PER_SECOND, (millis % _MILLIS_PER_SECOND) * _NANOS_PER_MILLISECOND)

def ToTimedelta(self): """Converts Duration to timedelta.""" return timedelta( seconds=self.seconds, microseconds=_RoundTowardZero( self.nanos, _NANOS_PER_MICROSECOND))

def FromTimedelta(self, td): """Convertd timedelta to Duration.""" self._NormalizeDuration(td.seconds + td.days * _SECONDS_PER_DAY, td.microseconds * _NANOS_PER_MICROSECOND)

def _NormalizeDuration(self, seconds, nanos): """Set Duration by seconds and nonas.""" # Force nanos to be negative if the duration is negative. if seconds < 0 and nanos > 0: seconds += 1 nanos -= _NANOS_PER_SECOND self.seconds = seconds self.nanos = nanos

def ToJsonString(self): """Converts FieldMask to string according to proto3 JSON spec.""" camelcase_paths = [] for path in self.paths: camelcase_paths.append(_SnakeCaseToCamelCase(path)) return ','.join(camelcase_paths)

def IsValidForDescriptor(self, message_descriptor): """Checks whether the FieldMask is valid for Message Descriptor.""" for path in self.paths: if not _IsValidPath(message_descriptor, path): return False return True

def AllFieldsFromDescriptor(self, message_descriptor): """Gets all direct fields of Message Descriptor to FieldMask.""" self.Clear() for field in message_descriptor.fields: self.paths.append(field.name)

def Union(self, mask1, mask2): """Merges mask1 and mask2 into this FieldMask.""" _CheckFieldMaskMessage(mask1) _CheckFieldMaskMessage(mask2) tree = _FieldMaskTree(mask1) tree.MergeFromFieldMask(mask2) tree.ToFieldMask(self)

def Intersect(self, mask1, mask2): """Intersects mask1 and mask2 into this FieldMask.""" _CheckFieldMaskMessage(mask1) _CheckFieldMaskMessage(mask2) tree = _FieldMaskTree(mask1) intersection = _FieldMaskTree() for path in mask2.paths: tree.IntersectPath(path, intersection) intersection...

def MergeMessage( self, source, destination, replace_message_field=False, replace_repeated_field=False): """Merges fields specified in FieldMask from source to destination. Args: source: Source message. destination: The destination message to be merged into. replace_message_field:...

def AddPath(self, path): """Adds a field path into the tree. If the field path to add is a sub-path of an existing field path in the tree (i.e., a leaf node), it means the tree already matches the given path so nothing will be added to the tree. If the path matches an existing non-leaf node in the ...

def IntersectPath(self, path, intersection): """Calculates the intersection part of a field path with this tree. Args: path: The field path to calculates. intersection: The out tree to record the intersection part. """ node = self._root for name in path.split('.'): if name not in ...

def AddLeafNodes(self, prefix, node): """Adds leaf nodes begin with prefix to this tree.""" if not node: self.AddPath(prefix) for name in node: child_path = prefix + '.' + name self.AddLeafNodes(child_path, node[name])

def MergeMessage( self, source, destination, replace_message, replace_repeated): """Merge all fields specified by this tree from source to destination.""" _MergeMessage( self._root, source, destination, replace_message, replace_repeated)

def configure (command = None, condition = None, options = None): """ Configures a new resource compilation command specific to a condition, usually a toolset selection condition. The possible options are: * <rc-type>(rc|windres) - Indicates the type of options the command ...

def convert(model, feature_names, target): """Convert a Nu Support Vector Regression (NuSVR) model to the protobuf spec. Parameters ---------- model: NuSVR A trained NuSVR encoder model. feature_names: [str] Name of the input columns. target: str Name of the output colu...

def create(dataset, target, features=None, l2_penalty=1e-2, l1_penalty=0.0, solver='auto', feature_rescaling=True, convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'], step_size = _DEFAULT_SOLVER_OPTIONS['step_size'], lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'...

def export_coreml(self, filename): """ Export the model in Core ML format. Parameters ---------- filename: str A valid filename where the model can be saved. Examples -------- >>> model.export_coreml("MyModel.mlmodel") """ from ...

def predict(self, dataset, missing_value_action='auto'): """ Return target value predictions for ``dataset``, using the trained linear regression model. This method can be used to get fitted values for the model by inputting the training dataset. Parameters ---------- ...

def evaluate(self, dataset, metric='auto', missing_value_action='auto'): r"""Evaluate the model by making target value predictions and comparing to actual values. Two metrics are used to evaluate linear regression models. The first is root-mean-squared error (RMSE) while the second is ...

def frame(data, window_length, hop_length): """Convert array into a sequence of successive possibly overlapping frames. An n-dimensional array of shape (num_samples, ...) is converted into an (n+1)-D array of shape (num_frames, window_length, ...), where each frame starts hop_length points after the preceding ...

def periodic_hann(window_length): """Calculate a "periodic" Hann window. The classic Hann window is defined as a raised cosine that starts and ends on zero, and where every value appears twice, except the middle point for an odd-length window. Matlab calls this a "symmetric" window and np.hanning() returns ...

def stft_magnitude(signal, fft_length, hop_length=None, window_length=None): """Calculate the short-time Fourier transform magnitude. Args: signal: 1D np.array of the input time-domain signal. fft_length: Size of the FFT to apply. hop_length: Advance (in samples) b...

def spectrogram_to_mel_matrix(num_mel_bins=20, num_spectrogram_bins=129, audio_sample_rate=8000, lower_edge_hertz=125.0, upper_edge_hertz=3800.0): """Return a matrix that can post-multiply spectrogr...

def log_mel_spectrogram(data, audio_sample_rate=8000, log_offset=0.0, window_length_secs=0.025, hop_length_secs=0.010, **kwargs): """Convert waveform to a log magnitude mel-frequency spectrogram. ...

def generate_random_sframe(num_rows, column_codes, random_seed = 0): """ Creates a random SFrame with `num_rows` rows and randomly generated column types determined by `column_codes`. The output SFrame is deterministic based on `random_seed`. `column_types` is a string with each character denoti...

def generate_random_regression_sframe(num_rows, column_codes, random_seed = 0, target_noise_level = 0.25): """ Creates a random SFrame with `num_rows` rows and randomly generated column types determined by `column_codes`. The output SFrame is deterministic based on `random_seed`. In addition, a ta...

def generate_random_classification_sframe(num_rows, column_codes, num_classes, misclassification_spread = 0.25, num_extra_class_bins = None, random_seed = 0): """ Creates a random SFrame...

def infer_shapes(nn_spec, input_spec, input_shape_dict = None): """ Input: spec : mlmodel spec input_shape_dict: dictionary of string --> tuple string: input name tuple: input shape as a 5 length tuple in order (Seq, Batch, C, H, W) If inp...

def convert(libsvm_model, feature_names, target, input_length, probability): """Convert a svm model to the protobuf spec. This currently supports: * C-SVC * nu-SVC * Epsilon-SVR * nu-SVR Parameters ---------- model_path: libsvm_model Libsvm representation of the mode...

def create(dataset, session_id, target, features=None, prediction_window=100, validation_set='auto', max_iterations=10, batch_size=32, verbose=True): """ Create an :class:`ActivityClassifier` model. Parameters ---------- dataset : SFrame Input data which consists of `sessions` of...

def _encode_target(data, target, mapping=None): """ Encode targets to integers in [0, num_classes - 1] """ if mapping is None: mapping = {t: i for i, t in enumerate(sorted(data[target].unique()))} data[target] = data[target].apply(lambda t: mapping[t]) return data, mapping

def export_coreml(self, filename): """ Export the model in Core ML format. Parameters ---------- filename: str A valid filename where the model can be saved. Examples -------- >>> model.export_coreml("MyModel.mlmodel") """ impor...

def predict(self, dataset, output_type='class', output_frequency='per_row'): """ Return predictions for ``dataset``, using the trained activity classifier. Predictions can be generated as class labels, or as a probability vector with probabilities for each class. The activity cl...

def evaluate(self, dataset, metric='auto'): """ Evaluate the model by making predictions of target values and comparing these to actual values. Parameters ---------- dataset : SFrame Dataset of new observations. Must include columns with the same ...

def classify(self, dataset, output_frequency='per_row'): """ Return a classification, for each ``prediction_window`` examples in the ``dataset``, using the trained activity classification model. The output SFrame contains predictions as both class labels as well as probabilities ...

def predict_topk(self, dataset, output_type='probability', k=3, output_frequency='per_row'): """ Return top-k predictions for the ``dataset``, using the trained model. Predictions are returned as an SFrame with three columns: `prediction_id`, `class`, and `probability`, or `rank`, depen...

def count_characters(root, out): """Count the occurrances of the different characters in the files""" if os.path.isfile(root): with open(root, 'rb') as in_f: for line in in_f: for char in line: if char not in out: out[char] = 0 ...

def main(): """The main function of the script""" desc = 'Generate character statistics from a source tree' parser = argparse.ArgumentParser(description=desc) parser.add_argument( '--src', dest='src', required=True, help='The root of the source tree' ) parser.add_...

def save_spec(spec, filename): """ Save a protobuf model specification to file. Parameters ---------- spec: Model_pb Protobuf representation of the model filename: str File path where the spec gets saved. Examples -------- .. sourcecode:: python >>> corem...

def load_spec(filename): """ Load a protobuf model specification from file Parameters ---------- filename: str Location on disk (a valid filepath) from which the file is loaded as a protobuf spec. Returns ------- model_spec: Model_pb Protobuf representation of t...

def _get_nn_layers(spec): """ Returns a list of neural network layers if the model contains any. Parameters ---------- spec: Model_pb A model protobuf specification. Returns ------- [NN layer] list of all layers (including layers from elements of a pipeline """ ...

def evaluate_regressor(model, data, target="target", verbose=False): """ Evaluate a CoreML regression model and compare against predictions from the original framework (for testing correctness of conversion) Parameters ---------- filename: [str | MLModel] File path from which to load th...

def evaluate_classifier(model, data, target='target', verbose=False): """ Evaluate a CoreML classifier model and compare against predictions from the original framework (for testing correctness of conversion). Use this evaluation for models that don't deal with probabilities. Parameters -------...

def evaluate_classifier_with_probabilities(model, data, probabilities='probabilities', verbose = False): """ Evaluate a classifier specification for testing. Parameters ---------- filename: [str | Model] F...

def rename_feature(spec, current_name, new_name, rename_inputs=True, rename_outputs=True): """ Rename a feature in the specification. Parameters ---------- spec: Model_pb The specification containing the feature to rename. current_name: str Current name of th...