BoghdadyJR/codesearch_python · Data Studio

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def convert_convolution1d(builder, layer, input_names, output_names, keras_layer): """ Convert convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get...
def convert_separable_convolution(builder, layer, input_names, output_names, keras_layer): """ Convert separable convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object....
def convert_batchnorm(builder, layer, input_names, output_names, keras_layer): """ Convert a Batch Normalization layer. Parameters keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get input and output names i...
def convert_flatten(builder, layer, input_names, output_names, keras_layer): """ Convert a flatten layer from keras to coreml. ---------- Parameters keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ input_name, ou...
def convert_merge(builder, layer, input_names, output_names, keras_layer): """ Convert concat layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get input and ou...
def convert_pooling(builder, layer, input_names, output_names, keras_layer): """ Convert pooling layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ _check_data_for...
def convert_padding(builder, layer, input_names, output_names, keras_layer): """ Convert padding layer from keras to coreml. Keras only supports zero padding at this time. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural n...
def convert_cropping(builder, layer, input_names, output_names, keras_layer): """ Convert padding layer from keras to coreml. Keras only supports zero padding at this time. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural ...
def convert_upsample(builder, layer, input_names, output_names, keras_layer): """ Convert convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ _check_dat...
def convert_permute(builder, layer, input_names, output_names, keras_layer): """ Convert a softmax layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ input_name, o...
def convert_simple_rnn(builder, layer, input_names, output_names, keras_layer): """ Convert an SimpleRNN layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get i...
def convert_lstm(builder, layer, input_names, output_names, keras_layer): """ Convert an LSTM layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ hidden_size = ker...
def convert_gru(builder, layer, input_names, output_names, keras_layer): """ Convert a GRU layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ hidden_size = keras_...
def convert_bidirectional(builder, layer, input_names, output_names, keras_layer): """ Convert a bidirectional layer from keras to coreml. Currently assumes the units are LSTMs. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A n...
def SLICE_0(self, instr): 'obj[:]' value = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=None, step=None, upper=None, kw) subscr = _ast.Subscript(value=value, slice=slice, ctx=_ast.Load(), kw) self.ast_stack.append(subscr...
def STORE_SLICE_1(self, instr): 'obj[lower:] = expr' lower = self.ast_stack.pop() value = self.ast_stack.pop() expr = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=lower, step=None, upper=None, **kw) subscr = _ast.Subs...
def STORE_SLICE_3(self, instr): 'obj[lower:upper] = expr' upper = self.ast_stack.pop() lower = self.ast_stack.pop() value = self.ast_stack.pop() expr = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=lower, step...
def DELETE_SLICE_0(self, instr): 'obj[:] = expr' value = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=None, step=None, upper=None, kw) subscr = _ast.Subscript(value=value, slice=slice, ctx=_ast.Del(), kw) delete = _ast.D...
def create(item_data, item_id, observation_data = None, user_id = None, target = None, weights = 'auto', similarity_metrics = 'auto', item_data_transform = 'auto', max_item_neighborhood_size = 64, verbose=True): """Create a content-based recommender...
def lhs(node): ''' Return a set of symbols in `node` that are assigned. :param node: ast node :returns: set of strings. ''' gen = ConditionalSymbolVisitor() if isinstance(node, (list, tuple)): gen.visit_list(node) else: gen.visit(node) return gen.lhs
def conditional_lhs(node): ''' Group outputs into conditional and stable :param node: ast node :returns: tuple of (conditional, stable) ''' gen = ConditionalSymbolVisitor() gen.visit(node) return gen.cond_lhs, gen.stable_lhs
def conditional_symbols(node): ''' Group lhs and rhs into conditional, stable and undefined :param node: ast node :returns: tuple of (conditional_lhs, stable_lhs),(conditional_rhs, stable_rhs), undefined ''' gen = ConditionalSymbolVisitor() gen.visit(node) lhs = gen.cond_lhs,...
def _loadlib(lib='standard'): """Load rabit library.""" global _LIB if _LIB is not None: warnings.warn('rabit.int call was ignored because it has'\ ' already been initialized', level=2) return if lib == 'standard': _LIB = ctypes.cdll.LoadLibrary(WRAPPER_...
def init(args=None, lib='standard'): """Intialize the rabit module, call this once before using anything. Parameters ---------- args: list of str, optional The list of arguments used to initialized the rabit usually you need to pass in sys.argv. Defaults to sys.argv when it is N...
def tracker_print(msg): """Print message to the tracker. This function can be used to communicate the information of the progress to the tracker Parameters ---------- msg : str The message to be printed to tracker. """ if not isinstance(msg, str): msg = str(msg) _LI...
def allreduce(data, op, prepare_fun=None): """Perform allreduce, return the result. Parameters ---------- data: numpy array Input data. op: int Reduction operators, can be MIN, MAX, SUM, BITOR prepare_fun: function Lazy preprocessing function, if it is not None, prepare_...
def _load_model(ptr, length): """ Internal function used by the module, unpickle a model from a buffer specified by ptr, length Arguments: ptr: ctypes.POINTER(ctypes._char) pointer to the memory region of buffer length: int the length of buffer """ data = ...
def load_checkpoint(with_local=False): """Load latest check point. Parameters ---------- with_local: bool, optional whether the checkpoint contains local model Returns ------- tuple : tuple if with_local: return (version, gobal_model, local_model) else return (versi...
def checkpoint(global_model, local_model=None): """Checkpoint the model. This means we finished a stage of execution. Every time we call check point, there is a version number which will increase by one. Parameters ---------- global_model: anytype that can be pickled globally shared mo...
def stack_annotations(annotations_sarray): """ Converts object detection annotations (ground truth or predictions) to stacked format (an `SFrame` where each row is one object instance). Parameters ---------- annotations_sarray: SArray An `SArray` with unstacked predictions, exactly form...
def unstack_annotations(annotations_sframe, num_rows=None): """ Converts object detection annotations (ground truth or predictions) to unstacked format (an `SArray` where each element is a list of object instances). Parameters ---------- annotations_sframe: SFrame An `SFrame` with s...
def create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, num_factors=32, regularization=1e-9, linear_regularization=1e-9, side_data_factorization=True, ranking_regularization=0.25, ...
def preprocess(): "splits _sources/reference.rst into separate files" text = open("./_sources/reference.rst", "r").read() os.remove("./_sources/reference.rst") if not os.path.exists("./_sources/reference"): os.makedirs("./_sources/reference") def pairwise(iterable): "s -> (s0, s1)...
def PackTag(field_number, wire_type): """Returns an unsigned 32-bit integer that encodes the field number and wire type information in standard protocol message wire format. Args: field_number: Expected to be an integer in the range [1, 1 << 29) wire_type: One of the WIRETYPE_* constants. """ if not ...
def _VarUInt64ByteSizeNoTag(uint64): """Returns the number of bytes required to serialize a single varint using boundary value comparisons. (unrolled loop optimization -WPierce) uint64 must be unsigned. """ if uint64 <= 0x7f: return 1 if uint64 <= 0x3fff: return 2 if uint64 <= 0x1fffff: return 3 if uint...
def _seconds_as_string(seconds): """ Returns seconds as a human-friendly string, e.g. '1d 4h 47m 41s' """ TIME_UNITS = [('s', 60), ('m', 60), ('h', 24), ('d', None)] unit_strings = [] cur = max(int(seconds), 1) for suffix, size in TIME_UNITS: if size is not None: cur, res...
def _get_converter_module(sk_obj): """ Returns the module holding the conversion functions for a particular model). """ try: cv_idx = _converter_lookup[sk_obj.__class__] except KeyError: raise ValueError( "Transformer '%s' not supported; supported transformers are...
def _convert_sklearn_model(input_sk_obj, input_features = None, output_feature_names = None, class_labels = None): """ Converts a generic sklearn pipeline, transformer, classifier, or regressor into an coreML specification. """ if not(HAS_SKLEARN): raise RuntimeErr...
def set_default_prediction_value(self, values): """ Set the default prediction value(s). The values given here form the base prediction value that the values at activated leaves are added to. If values is a scalar, then the output of the tree must also be 1 dimensional; otherwi...
def set_post_evaluation_transform(self, value): r""" Set the post processing transform applied after the prediction value from the tree ensemble. Parameters ---------- value: str A value denoting the transform applied. Possible values are: - "...
def add_branch_node(self, tree_id, node_id, feature_index, feature_value, branch_mode, true_child_id, false_child_id, relative_hit_rate = None, missing_value_tracks_true_child = False): """ Add a branch node to the tree ensemble. Parameters ---------- tre...
def add_leaf_node(self, tree_id, node_id, values, relative_hit_rate = None): """ Add a leaf node to the tree ensemble. Parameters ---------- tree_id: int ID of the tree to add the node to. node_id: int ID of the node within the tree. val...
def create (raw_properties = []): """ Creates a new 'PropertySet' instance for the given raw properties, or returns an already existing one. """ assert (is_iterable_typed(raw_properties, property.Property) or is_iterable_typed(raw_properties, basestring)) # FIXME: propagate to caller...
def create_with_validation (raw_properties): """ Creates new 'PropertySet' instances after checking that all properties are valid and converting implicit properties into gristed form. """ assert is_iterable_typed(raw_properties, basestring) properties = [property.create_from_string(s) fo...
def create_from_user_input(raw_properties, jamfile_module, location): """Creates a property-set from the input given by the user, in the context of 'jamfile-module' at 'location'""" assert is_iterable_typed(raw_properties, basestring) assert isinstance(jamfile_module, basestring) assert isinstance(l...
def refine_from_user_input(parent_requirements, specification, jamfile_module, location): """Refines requirements with requirements provided by the user. Specially handles "-<property>value" syntax in specification to remove given requirements. - parent-requirements -- prope...
def base (self): """ Returns properties that are neither incidental nor free. """ result = [p for p in self.lazy_properties if not(p.feature.incidental or p.feature.free)] result.extend(self.base_) return result
def free (self): """ Returns free properties which are not dependency properties. """ result = [p for p in self.lazy_properties if not p.feature.incidental and p.feature.free] result.extend(self.free_) return result
def dependency (self): """ Returns dependency properties. """ result = [p for p in self.lazy_properties if p.feature.dependency] result.extend(self.dependency_) return self.dependency_
def non_dependency (self): """ Returns properties that are not dependencies. """ result = [p for p in self.lazy_properties if not p.feature.dependency] result.extend(self.non_dependency_) return result
def incidental (self): """ Returns incidental properties. """ result = [p for p in self.lazy_properties if p.feature.incidental] result.extend(self.incidental_) return result
def refine (self, requirements): """ Refines this set's properties using the requirements passed as an argument. """ assert isinstance(requirements, PropertySet) if requirements not in self.refined_: r = property.refine(self.all_, requirements.all_) self.refined_...
def target_path (self): """ Computes the target path that should be used for target with these properties. Returns a tuple of - the computed path - if the path is relative to build directory, a value of 'true'. """ if not self.t...
def add (self, ps): """ Creates a new property set containing the properties in this one, plus the ones of the property set passed as argument. """ assert isinstance(ps, PropertySet) if ps not in self.added_: self.added_[ps] = create(self.all_ + ps.all()) ...
def get (self, feature): """ Returns all values of 'feature'. """ if type(feature) == type([]): feature = feature[0] if not isinstance(feature, b2.build.feature.Feature): feature = b2.build.feature.get(feature) assert isinstance(feature, b2.build.feature.F...
def get_properties(self, feature): """Returns all contained properties associated with 'feature'""" if not isinstance(feature, b2.build.feature.Feature): feature = b2.build.feature.get(feature) assert isinstance(feature, b2.build.feature.Feature) result = [] for p in...
def _create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, ranking=True, verbose=True): """ A unified interface for training recommender models. Based on simple characteristics of the data, a type of model is s...
def compare_models(dataset, models, model_names=None, user_sample=1.0, metric='auto', target=None, exclude_known_for_precision_recall=True, make_plot=False, verbose=True, **kwargs): """ Compare the ...
def precision_recall_by_user(observed_user_items, recommendations, cutoffs=[10]): """ Compute precision and recall at a given cutoff for each user. In information retrieval terms, precision represents the ratio of relevant, retrieved items to the...
def random_split_by_user(dataset, user_id='user_id', item_id='item_id', max_num_users=1000, item_test_proportion=.2, random_seed=0): """Create a recommender-friendly train-test split of the p...
def _list_fields(self): """ Get the current settings of the model. The keys depend on the type of model. Returns ------- out : list A list of fields that can be queried using the ``get`` method. """ response = self.__proxy__.list_fields() ...
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 _set_current_options(self, options): """ Set current options for a model. Parameters ---------- options : dict A dictionary of the desired option settings. The key should be the name of the option and each value is the desired value of the option. ...
def __prepare_dataset_parameter(self, dataset): """ Processes the dataset parameter for type correctness. Returns it as an SFrame. """ # Translate the dataset argument into the proper type if not isinstance(dataset, _SFrame): def raise_dataset_type_exception(...
def _get_data_schema(self): """ Returns a dictionary of (column : type) for the data used in the model. """ if not hasattr(self, "_data_schema"): response = self.__proxy__.get_data_schema() self._data_schema = {k : _turicreate._cython.cy_flexible_type.py...
def predict(self, dataset, new_observation_data=None, new_user_data=None, new_item_data=None): """ Return a score prediction for the user ids and item ids in the provided data set. Parameters ---------- dataset : SFrame Dataset in the same for...
def get_similar_items(self, items=None, k=10, verbose=False): """ Get the k most similar items for each item in items. Each type of recommender has its own model for the similarity between items. For example, the item_similarity_recommender will return the most similar items acc...
def get_similar_users(self, users=None, k=10): """Get the k most similar users for each entry in `users`. Each type of recommender has its own model for the similarity between users. For example, the factorization_recommender will return the nearest users based on the cosine similarity ...
def recommend(self, users=None, k=10, exclude=None, items=None, new_observation_data=None, new_user_data=None, new_item_data=None, exclude_known=True, diversity=0, random_seed=None, verbose=True): """ Recommend the ``k`` highest scored items for each...
def recommend_from_interactions( self, observed_items, k=10, exclude=None, items=None, new_user_data=None, new_item_data=None, exclude_known=True, diversity=0, random_seed=None, verbose=True): """ Recommend the ``k`` highest scored items based on the ...
def evaluate_precision_recall(self, dataset, cutoffs=list(range(1,11,1))+list(range(11,50,5)), skip_set=None, exclude_known=True, verbose=True, **kwargs): """ Compute a model's precision and recall scores for a particular dataset. ...
def evaluate_rmse(self, dataset, target): """ Evaluate the prediction error for each user-item pair in the given data set. Parameters ---------- dataset : SFrame An SFrame in the same format as the one used during training. target : str T...
def evaluate(self, dataset, metric='auto', exclude_known_for_precision_recall=True, target=None, verbose=True, **kwargs): r""" Evaluate the model's ability to make rating predictions or recommendations. If the model is trained to predic...
def _get_popularity_baseline(self): """ Returns a new popularity model matching the data set this model was trained with. Can be used for comparison purposes. """ response = self.__proxy__.get_popularity_baseline() from .popularity_recommender import PopularityRecommen...
def _get_item_intersection_info(self, item_pairs): """ For a collection of item -> item pairs, returns information about the users in that intersection. Parameters ---------- item_pairs : 2-column SFrame of two item columns, or a list of (item_1, item_2) tupl...
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') """ print...
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 ...
def predict(self, dataset, missing_value_action='auto'): """ Predict the target column of the given dataset. The target column is provided during :func:`~turicreate.random_forest_regression.create`. If the target column is in the `dataset` it will be ignored. Parameters...
def create_feature_vectorizer(input_features, output_feature_name, known_size_map = {}): """ Creates a feature vectorizer from input features, return the spec for a feature vectorizer that puts everything into a single array of length equal to the total size of all the inpu...
def query_boost_version(boost_root): ''' Read in the Boost version from a given boost_root. ''' boost_version = None if os.path.exists(os.path.join(boost_root,'Jamroot')): with codecs.open(os.path.join(boost_root,'Jamroot'), 'r', 'utf-8') as f: for lin...
def git_clone(sub_repo, branch, commit = None, cwd = None, no_submodules = False): ''' This clone mimicks the way Travis-CI clones a project's repo. So far Travis-CI is the most limiting in the sense of only fetching partial history of the repo. ''' if not cwd: ...
def install_toolset(self, toolset): ''' Installs specific toolset on CI system. ''' info = toolset_info[toolset] if sys.platform.startswith('linux'): os.chdir(self.work_dir) if 'ppa' in info: for ppa in info['ppa']: util...
def create(dataset, target, features=None, penalty=1.0, solver='auto', feature_rescaling=True, convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'], lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'], max_iterations = _DEFAULT_SOLVER_OPTIONS['max_iterations'], ...
def classify(self, dataset, missing_value_action='auto'): """ Return a classification, for each example in the ``dataset``, using the trained SVM model. The output SFrame contains predictions as class labels (0 or 1) associated with the the example. Parameters ----------...
def _get_layer_converter_fn(layer, add_custom_layers = False): """Get the right converter function for Keras """ layer_type = type(layer) if layer_type in _KERAS_LAYER_REGISTRY: convert_func = _KERAS_LAYER_REGISTRY[layer_type] if convert_func is _layers2.convert_activation: a...
def _load_keras_model(model_network_path, model_weight_path, custom_objects=None): """Load a keras model from disk Parameters ---------- model_network_path: str Path where the model network path is (json file) model_weight_path: str Path where the model network weights are (hd5 fil...
def show(self): """ A method for displaying the Plot object Notes ----- - The plot will render either inline in a Jupyter Notebook, or in a native GUI window, depending on the value provided in `turicreate.visualization.set_target` (defaults to 'auto'). ...
def save(self, filepath): """ A method for saving the Plot object in a vega representation Parameters ---------- filepath: string The destination filepath where the plot object must be saved as. The extension of this filepath determines what format the pl...
def mthread_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...
def _get_value(scikit_value, mode = 'regressor', scaling = 1.0, n_classes = 2, tree_index = 0): """ Get the right value from the scikit-tree """ # Regression if mode == 'regressor': return scikit_value[0] * scaling # Binary classification if n_classes == 2: # Decision tree ...
def _recurse(coreml_tree, scikit_tree, tree_id, node_id, scaling = 1.0, mode = 'regressor', n_classes = 2, tree_index = 0): """Traverse through the tree and append to the tree spec. """ if not(HAS_SKLEARN): raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disab...
def convert_tree_ensemble(model, input_features, output_features = ('predicted_class', float), mode = 'regressor', base_prediction = None, class_labels = None, post_evaluation_transform = No...
def _vgg16_data_prep(batch): """ Takes images scaled to [0, 1] and returns them appropriately scaled and mean-subtracted for VGG-16 """ from mxnet import nd mean = nd.array([123.68, 116.779, 103.939], ctx=batch.context) return nd.broadcast_sub(255 * batch, mean.reshape((-1, 1, 1)))
def create(style_dataset, content_dataset, style_feature=None, content_feature=None, max_iterations=None, model='resnet-16', verbose=True, batch_size = 6, **kwargs): """ Create a :class:`StyleTransfer` model. Parameters ---------- style_dataset: SFrame Input style images. Th...
def _canonize_content_input(self, dataset, single_style): """ Takes input and returns tuple of the input in canonical form (SFrame) along with an unpack callback function that can be applied to prediction results to "undo" the canonization. """ unpack = lambda x: x ...
def stylize(self, images, style=None, verbose=True, max_size=800, batch_size = 4): """ Stylize an SFrame of Images given a style index or a list of styles. Parameters ---------- images : SFrame \| Image A dataset that has the same content image column that was...
def export_coreml(self, path, image_shape=(256, 256), include_flexible_shape=True): """ Save the model in Core ML format. The Core ML model takes an image of fixed size, and a style index inputs and produces an output of an image of fixed size Parameters -------...
def get_styles(self, style=None): """ Returns SFrame of style images used for training the model Parameters ---------- style: int or list, optional The selected style or list of styles to return. If `None`, all styles will be returned See Also ...
def convert(model, input_shape, class_labels=None, mode=None, preprocessor_args=None, builder=None, verbose=True): """Convert an MXNet model to the protobuf spec. Parameters ---------- model: MXNet model A trained MXNet neural network model. input_shape: list of tuples ...
def load_model(model_path): """Load a libsvm model from a path on disk. This currently supports: * C-SVC * NU-SVC * Epsilon-SVR * NU-SVR Parameters ---------- model_path: str Path on disk where the libsvm model representation is. Returns ------- model: ...

def convert_convolution1d(builder, layer, input_names, output_names, keras_layer): """ Convert convolution layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get...

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

def convert_batchnorm(builder, layer, input_names, output_names, keras_layer): """ Convert a Batch Normalization layer. Parameters keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ # Get input and output names i...

def convert_flatten(builder, layer, input_names, output_names, keras_layer): """ Convert a flatten layer from keras to coreml. ---------- Parameters keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ input_name, ou...

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

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

def convert_padding(builder, layer, input_names, output_names, keras_layer): """ Convert padding layer from keras to coreml. Keras only supports zero padding at this time. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural n...

def convert_cropping(builder, layer, input_names, output_names, keras_layer): """ Convert padding layer from keras to coreml. Keras only supports zero padding at this time. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural ...

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

def convert_permute(builder, layer, input_names, output_names, keras_layer): """ Convert a softmax layer from keras to coreml. Parameters keras_layer: layer ---------- A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ input_name, o...

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

def convert_lstm(builder, layer, input_names, output_names, keras_layer): """ Convert an LSTM layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ hidden_size = ker...

def convert_gru(builder, layer, input_names, output_names, keras_layer): """ Convert a GRU layer from keras to coreml. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A neural network builder object. """ hidden_size = keras_...

def convert_bidirectional(builder, layer, input_names, output_names, keras_layer): """ Convert a bidirectional layer from keras to coreml. Currently assumes the units are LSTMs. Parameters ---------- keras_layer: layer A keras layer object. builder: NeuralNetworkBuilder A n...

def SLICE_0(self, instr): 'obj[:]' value = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=None, step=None, upper=None, **kw) subscr = _ast.Subscript(value=value, slice=slice, ctx=_ast.Load(), **kw) self.ast_stack.append(subscr...

def STORE_SLICE_1(self, instr): 'obj[lower:] = expr' lower = self.ast_stack.pop() value = self.ast_stack.pop() expr = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=lower, step=None, upper=None, **kw) subscr = _ast.Subs...

def STORE_SLICE_3(self, instr): 'obj[lower:upper] = expr' upper = self.ast_stack.pop() lower = self.ast_stack.pop() value = self.ast_stack.pop() expr = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=lower, step...

def DELETE_SLICE_0(self, instr): 'obj[:] = expr' value = self.ast_stack.pop() kw = dict(lineno=instr.lineno, col_offset=0) slice = _ast.Slice(lower=None, step=None, upper=None, **kw) subscr = _ast.Subscript(value=value, slice=slice, ctx=_ast.Del(), **kw) delete = _ast.D...

def create(item_data, item_id, observation_data = None, user_id = None, target = None, weights = 'auto', similarity_metrics = 'auto', item_data_transform = 'auto', max_item_neighborhood_size = 64, verbose=True): """Create a content-based recommender...

def lhs(node): ''' Return a set of symbols in `node` that are assigned. :param node: ast node :returns: set of strings. ''' gen = ConditionalSymbolVisitor() if isinstance(node, (list, tuple)): gen.visit_list(node) else: gen.visit(node) return gen.lhs

def conditional_lhs(node): ''' Group outputs into conditional and stable :param node: ast node :returns: tuple of (conditional, stable) ''' gen = ConditionalSymbolVisitor() gen.visit(node) return gen.cond_lhs, gen.stable_lhs

def conditional_symbols(node): ''' Group lhs and rhs into conditional, stable and undefined :param node: ast node :returns: tuple of (conditional_lhs, stable_lhs),(conditional_rhs, stable_rhs), undefined ''' gen = ConditionalSymbolVisitor() gen.visit(node) lhs = gen.cond_lhs,...

def _loadlib(lib='standard'): """Load rabit library.""" global _LIB if _LIB is not None: warnings.warn('rabit.int call was ignored because it has'\ ' already been initialized', level=2) return if lib == 'standard': _LIB = ctypes.cdll.LoadLibrary(WRAPPER_...

def init(args=None, lib='standard'): """Intialize the rabit module, call this once before using anything. Parameters ---------- args: list of str, optional The list of arguments used to initialized the rabit usually you need to pass in sys.argv. Defaults to sys.argv when it is N...

def tracker_print(msg): """Print message to the tracker. This function can be used to communicate the information of the progress to the tracker Parameters ---------- msg : str The message to be printed to tracker. """ if not isinstance(msg, str): msg = str(msg) _LI...

def allreduce(data, op, prepare_fun=None): """Perform allreduce, return the result. Parameters ---------- data: numpy array Input data. op: int Reduction operators, can be MIN, MAX, SUM, BITOR prepare_fun: function Lazy preprocessing function, if it is not None, prepare_...

def _load_model(ptr, length): """ Internal function used by the module, unpickle a model from a buffer specified by ptr, length Arguments: ptr: ctypes.POINTER(ctypes._char) pointer to the memory region of buffer length: int the length of buffer """ data = ...

def load_checkpoint(with_local=False): """Load latest check point. Parameters ---------- with_local: bool, optional whether the checkpoint contains local model Returns ------- tuple : tuple if with_local: return (version, gobal_model, local_model) else return (versi...

def checkpoint(global_model, local_model=None): """Checkpoint the model. This means we finished a stage of execution. Every time we call check point, there is a version number which will increase by one. Parameters ---------- global_model: anytype that can be pickled globally shared mo...

def stack_annotations(annotations_sarray): """ Converts object detection annotations (ground truth or predictions) to stacked format (an `SFrame` where each row is one object instance). Parameters ---------- annotations_sarray: SArray An `SArray` with unstacked predictions, exactly form...

def unstack_annotations(annotations_sframe, num_rows=None): """ Converts object detection annotations (ground truth or predictions) to unstacked format (an `SArray` where each element is a list of object instances). Parameters ---------- annotations_sframe: SFrame An `SFrame` with s...

def create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, num_factors=32, regularization=1e-9, linear_regularization=1e-9, side_data_factorization=True, ranking_regularization=0.25, ...

def preprocess(): "splits _sources/reference.rst into separate files" text = open("./_sources/reference.rst", "r").read() os.remove("./_sources/reference.rst") if not os.path.exists("./_sources/reference"): os.makedirs("./_sources/reference") def pairwise(iterable): "s -> (s0, s1)...

def PackTag(field_number, wire_type): """Returns an unsigned 32-bit integer that encodes the field number and wire type information in standard protocol message wire format. Args: field_number: Expected to be an integer in the range [1, 1 << 29) wire_type: One of the WIRETYPE_* constants. """ if not ...

def _VarUInt64ByteSizeNoTag(uint64): """Returns the number of bytes required to serialize a single varint using boundary value comparisons. (unrolled loop optimization -WPierce) uint64 must be unsigned. """ if uint64 <= 0x7f: return 1 if uint64 <= 0x3fff: return 2 if uint64 <= 0x1fffff: return 3 if uint...

def _seconds_as_string(seconds): """ Returns seconds as a human-friendly string, e.g. '1d 4h 47m 41s' """ TIME_UNITS = [('s', 60), ('m', 60), ('h', 24), ('d', None)] unit_strings = [] cur = max(int(seconds), 1) for suffix, size in TIME_UNITS: if size is not None: cur, res...

def _get_converter_module(sk_obj): """ Returns the module holding the conversion functions for a particular model). """ try: cv_idx = _converter_lookup[sk_obj.__class__] except KeyError: raise ValueError( "Transformer '%s' not supported; supported transformers are...

def _convert_sklearn_model(input_sk_obj, input_features = None, output_feature_names = None, class_labels = None): """ Converts a generic sklearn pipeline, transformer, classifier, or regressor into an coreML specification. """ if not(HAS_SKLEARN): raise RuntimeErr...

def set_default_prediction_value(self, values): """ Set the default prediction value(s). The values given here form the base prediction value that the values at activated leaves are added to. If values is a scalar, then the output of the tree must also be 1 dimensional; otherwi...

def set_post_evaluation_transform(self, value): r""" Set the post processing transform applied after the prediction value from the tree ensemble. Parameters ---------- value: str A value denoting the transform applied. Possible values are: - "...

def add_branch_node(self, tree_id, node_id, feature_index, feature_value, branch_mode, true_child_id, false_child_id, relative_hit_rate = None, missing_value_tracks_true_child = False): """ Add a branch node to the tree ensemble. Parameters ---------- tre...

def add_leaf_node(self, tree_id, node_id, values, relative_hit_rate = None): """ Add a leaf node to the tree ensemble. Parameters ---------- tree_id: int ID of the tree to add the node to. node_id: int ID of the node within the tree. val...

def create (raw_properties = []): """ Creates a new 'PropertySet' instance for the given raw properties, or returns an already existing one. """ assert (is_iterable_typed(raw_properties, property.Property) or is_iterable_typed(raw_properties, basestring)) # FIXME: propagate to caller...

def create_with_validation (raw_properties): """ Creates new 'PropertySet' instances after checking that all properties are valid and converting implicit properties into gristed form. """ assert is_iterable_typed(raw_properties, basestring) properties = [property.create_from_string(s) fo...

def create_from_user_input(raw_properties, jamfile_module, location): """Creates a property-set from the input given by the user, in the context of 'jamfile-module' at 'location'""" assert is_iterable_typed(raw_properties, basestring) assert isinstance(jamfile_module, basestring) assert isinstance(l...

def refine_from_user_input(parent_requirements, specification, jamfile_module, location): """Refines requirements with requirements provided by the user. Specially handles "-<property>value" syntax in specification to remove given requirements. - parent-requirements -- prope...

def base (self): """ Returns properties that are neither incidental nor free. """ result = [p for p in self.lazy_properties if not(p.feature.incidental or p.feature.free)] result.extend(self.base_) return result

def free (self): """ Returns free properties which are not dependency properties. """ result = [p for p in self.lazy_properties if not p.feature.incidental and p.feature.free] result.extend(self.free_) return result

def dependency (self): """ Returns dependency properties. """ result = [p for p in self.lazy_properties if p.feature.dependency] result.extend(self.dependency_) return self.dependency_

def non_dependency (self): """ Returns properties that are not dependencies. """ result = [p for p in self.lazy_properties if not p.feature.dependency] result.extend(self.non_dependency_) return result

def incidental (self): """ Returns incidental properties. """ result = [p for p in self.lazy_properties if p.feature.incidental] result.extend(self.incidental_) return result

def refine (self, requirements): """ Refines this set's properties using the requirements passed as an argument. """ assert isinstance(requirements, PropertySet) if requirements not in self.refined_: r = property.refine(self.all_, requirements.all_) self.refined_...

def target_path (self): """ Computes the target path that should be used for target with these properties. Returns a tuple of - the computed path - if the path is relative to build directory, a value of 'true'. """ if not self.t...

def add (self, ps): """ Creates a new property set containing the properties in this one, plus the ones of the property set passed as argument. """ assert isinstance(ps, PropertySet) if ps not in self.added_: self.added_[ps] = create(self.all_ + ps.all()) ...

def get (self, feature): """ Returns all values of 'feature'. """ if type(feature) == type([]): feature = feature[0] if not isinstance(feature, b2.build.feature.Feature): feature = b2.build.feature.get(feature) assert isinstance(feature, b2.build.feature.F...

def get_properties(self, feature): """Returns all contained properties associated with 'feature'""" if not isinstance(feature, b2.build.feature.Feature): feature = b2.build.feature.get(feature) assert isinstance(feature, b2.build.feature.Feature) result = [] for p in...

def _create(observation_data, user_id='user_id', item_id='item_id', target=None, user_data=None, item_data=None, ranking=True, verbose=True): """ A unified interface for training recommender models. Based on simple characteristics of the data, a type of model is s...

def compare_models(dataset, models, model_names=None, user_sample=1.0, metric='auto', target=None, exclude_known_for_precision_recall=True, make_plot=False, verbose=True, **kwargs): """ Compare the ...

def precision_recall_by_user(observed_user_items, recommendations, cutoffs=[10]): """ Compute precision and recall at a given cutoff for each user. In information retrieval terms, precision represents the ratio of relevant, retrieved items to the...

def random_split_by_user(dataset, user_id='user_id', item_id='item_id', max_num_users=1000, item_test_proportion=.2, random_seed=0): """Create a recommender-friendly train-test split of the p...

def _list_fields(self): """ Get the current settings of the model. The keys depend on the type of model. Returns ------- out : list A list of fields that can be queried using the ``get`` method. """ response = self.__proxy__.list_fields() ...

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 _set_current_options(self, options): """ Set current options for a model. Parameters ---------- options : dict A dictionary of the desired option settings. The key should be the name of the option and each value is the desired value of the option. ...

def __prepare_dataset_parameter(self, dataset): """ Processes the dataset parameter for type correctness. Returns it as an SFrame. """ # Translate the dataset argument into the proper type if not isinstance(dataset, _SFrame): def raise_dataset_type_exception(...

def _get_data_schema(self): """ Returns a dictionary of (column : type) for the data used in the model. """ if not hasattr(self, "_data_schema"): response = self.__proxy__.get_data_schema() self._data_schema = {k : _turicreate._cython.cy_flexible_type.py...

def predict(self, dataset, new_observation_data=None, new_user_data=None, new_item_data=None): """ Return a score prediction for the user ids and item ids in the provided data set. Parameters ---------- dataset : SFrame Dataset in the same for...

def get_similar_items(self, items=None, k=10, verbose=False): """ Get the k most similar items for each item in items. Each type of recommender has its own model for the similarity between items. For example, the item_similarity_recommender will return the most similar items acc...

def get_similar_users(self, users=None, k=10): """Get the k most similar users for each entry in `users`. Each type of recommender has its own model for the similarity between users. For example, the factorization_recommender will return the nearest users based on the cosine similarity ...

def recommend(self, users=None, k=10, exclude=None, items=None, new_observation_data=None, new_user_data=None, new_item_data=None, exclude_known=True, diversity=0, random_seed=None, verbose=True): """ Recommend the ``k`` highest scored items for each...

def recommend_from_interactions( self, observed_items, k=10, exclude=None, items=None, new_user_data=None, new_item_data=None, exclude_known=True, diversity=0, random_seed=None, verbose=True): """ Recommend the ``k`` highest scored items based on the ...

def evaluate_precision_recall(self, dataset, cutoffs=list(range(1,11,1))+list(range(11,50,5)), skip_set=None, exclude_known=True, verbose=True, **kwargs): """ Compute a model's precision and recall scores for a particular dataset. ...

def evaluate_rmse(self, dataset, target): """ Evaluate the prediction error for each user-item pair in the given data set. Parameters ---------- dataset : SFrame An SFrame in the same format as the one used during training. target : str T...

def evaluate(self, dataset, metric='auto', exclude_known_for_precision_recall=True, target=None, verbose=True, **kwargs): r""" Evaluate the model's ability to make rating predictions or recommendations. If the model is trained to predic...

def _get_popularity_baseline(self): """ Returns a new popularity model matching the data set this model was trained with. Can be used for comparison purposes. """ response = self.__proxy__.get_popularity_baseline() from .popularity_recommender import PopularityRecommen...

def _get_item_intersection_info(self, item_pairs): """ For a collection of item -> item pairs, returns information about the users in that intersection. Parameters ---------- item_pairs : 2-column SFrame of two item columns, or a list of (item_1, item_2) tupl...

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') """ print...

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 ...

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

def create_feature_vectorizer(input_features, output_feature_name, known_size_map = {}): """ Creates a feature vectorizer from input features, return the spec for a feature vectorizer that puts everything into a single array of length equal to the total size of all the inpu...

def query_boost_version(boost_root): ''' Read in the Boost version from a given boost_root. ''' boost_version = None if os.path.exists(os.path.join(boost_root,'Jamroot')): with codecs.open(os.path.join(boost_root,'Jamroot'), 'r', 'utf-8') as f: for lin...

def git_clone(sub_repo, branch, commit = None, cwd = None, no_submodules = False): ''' This clone mimicks the way Travis-CI clones a project's repo. So far Travis-CI is the most limiting in the sense of only fetching partial history of the repo. ''' if not cwd: ...

def install_toolset(self, toolset): ''' Installs specific toolset on CI system. ''' info = toolset_info[toolset] if sys.platform.startswith('linux'): os.chdir(self.work_dir) if 'ppa' in info: for ppa in info['ppa']: util...

def create(dataset, target, features=None, penalty=1.0, solver='auto', feature_rescaling=True, convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'], lbfgs_memory_level = _DEFAULT_SOLVER_OPTIONS['lbfgs_memory_level'], max_iterations = _DEFAULT_SOLVER_OPTIONS['max_iterations'], ...

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

def _get_layer_converter_fn(layer, add_custom_layers = False): """Get the right converter function for Keras """ layer_type = type(layer) if layer_type in _KERAS_LAYER_REGISTRY: convert_func = _KERAS_LAYER_REGISTRY[layer_type] if convert_func is _layers2.convert_activation: a...

def _load_keras_model(model_network_path, model_weight_path, custom_objects=None): """Load a keras model from disk Parameters ---------- model_network_path: str Path where the model network path is (json file) model_weight_path: str Path where the model network weights are (hd5 fil...

def show(self): """ A method for displaying the Plot object Notes ----- - The plot will render either inline in a Jupyter Notebook, or in a native GUI window, depending on the value provided in `turicreate.visualization.set_target` (defaults to 'auto'). ...

def save(self, filepath): """ A method for saving the Plot object in a vega representation Parameters ---------- filepath: string The destination filepath where the plot object must be saved as. The extension of this filepath determines what format the pl...

def mthread_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...

def _get_value(scikit_value, mode = 'regressor', scaling = 1.0, n_classes = 2, tree_index = 0): """ Get the right value from the scikit-tree """ # Regression if mode == 'regressor': return scikit_value[0] * scaling # Binary classification if n_classes == 2: # Decision tree ...

def _recurse(coreml_tree, scikit_tree, tree_id, node_id, scaling = 1.0, mode = 'regressor', n_classes = 2, tree_index = 0): """Traverse through the tree and append to the tree spec. """ if not(HAS_SKLEARN): raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disab...

def convert_tree_ensemble(model, input_features, output_features = ('predicted_class', float), mode = 'regressor', base_prediction = None, class_labels = None, post_evaluation_transform = No...

def _vgg16_data_prep(batch): """ Takes images scaled to [0, 1] and returns them appropriately scaled and mean-subtracted for VGG-16 """ from mxnet import nd mean = nd.array([123.68, 116.779, 103.939], ctx=batch.context) return nd.broadcast_sub(255 * batch, mean.reshape((-1, 1, 1)))

def create(style_dataset, content_dataset, style_feature=None, content_feature=None, max_iterations=None, model='resnet-16', verbose=True, batch_size = 6, **kwargs): """ Create a :class:`StyleTransfer` model. Parameters ---------- style_dataset: SFrame Input style images. Th...

def _canonize_content_input(self, dataset, single_style): """ Takes input and returns tuple of the input in canonical form (SFrame) along with an unpack callback function that can be applied to prediction results to "undo" the canonization. """ unpack = lambda x: x ...

def stylize(self, images, style=None, verbose=True, max_size=800, batch_size = 4): """ Stylize an SFrame of Images given a style index or a list of styles. Parameters ---------- images : SFrame | Image A dataset that has the same content image column that was...

def export_coreml(self, path, image_shape=(256, 256), include_flexible_shape=True): """ Save the model in Core ML format. The Core ML model takes an image of fixed size, and a style index inputs and produces an output of an image of fixed size Parameters -------...

def get_styles(self, style=None): """ Returns SFrame of style images used for training the model Parameters ---------- style: int or list, optional The selected style or list of styles to return. If `None`, all styles will be returned See Also ...

def convert(model, input_shape, class_labels=None, mode=None, preprocessor_args=None, builder=None, verbose=True): """Convert an MXNet model to the protobuf spec. Parameters ---------- model: MXNet model A trained MXNet neural network model. input_shape: list of tuples ...

def load_model(model_path): """Load a libsvm model from a path on disk. This currently supports: * C-SVC * NU-SVC * Epsilon-SVR * NU-SVR Parameters ---------- model_path: str Path on disk where the libsvm model representation is. Returns ------- model: ...