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value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
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train | tracker_print | 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. | src/external/xgboost/subtree/rabit/wrapper/rabit.py | 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 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)
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train | allreduce | Perform allreduce, return the result.
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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_fun(data)
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"""Perform allreduce, return the result.
Parameters
----------
data: numpy array
Input data.
op: int
Reduction operators, can be MIN, MAX, SUM, BITOR
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data: numpy array
Input data.
op: int
Reduction operators, can be MIN, MAX, SUM, BITOR
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train | _load_model | Internal function used by the module,
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Arguments:
ptr: ctypes.POINTER(ctypes._char)
pointer to the memory region of buffer
length: int
the length of buffer | src/external/xgboost/subtree/rabit/wrapper/rabit.py | def _load_model(ptr, length):
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Internal function used by the module,
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Arguments:
ptr: ctypes.POINTER(ctypes._char)
pointer to the memory region of buffer
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the length of buffer
"""
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Internal function used by the module,
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pointer to the memory region of buffer
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the length of buffer
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train | load_checkpoint | Load latest check point.
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with_local: bool, optional
whether the checkpoint contains local model
Returns
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tuple : tuple
if with_local: return (version, gobal_model, local_model)
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Parameters
----------
with_local: bool, optional
whether the checkpoint contains local model
Returns
-------
tuple : tuple
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Parameters
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with_local: bool, optional
whether the checkpoint contains local model
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tuple : tuple
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train | checkpoint | Checkpoint the model.
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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 model/state when calling this function,
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This means we finished a stage of execution.
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Parameters
----------
global_model: anytype that can be pickled
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"""Checkpoint the model.
This means we finished a stage of execution.
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Parameters
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global_model: anytype that can be pickled
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train | stack_annotations | 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 formatted as the
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Converts object detection annotations (ground truth or predictions) to
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Parameters
----------
annotations_sarray: SArray
An `SArray` with unstacked predictions, exactly form... | def stack_annotations(annotations_sarray):
"""
Converts object detection annotations (ground truth or predictions) to
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train | unstack_annotations | Converts object detection annotations (ground truth or predictions) to
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Parameters
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annotations_sframe: SFrame
An `SFrame` with stacked predictions, produced by the
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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 unstack_annotations(annotations_sframe, num_rows=None):
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Converts object detection annotations (ground truth or predictions) to
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train | create | Create a RankingFactorizationRecommender that learns latent factors for each
user and item and uses them to make rating predictions.
Parameters
----------
observation_data : SFrame
The dataset to use for training the model. It must contain a column of
user ids and a column of item ids. ... | src/unity/python/turicreate/toolkits/recommender/ranking_factorization_recommender.py | def create(observation_data,
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num_factors=32,
regularization=1e-9,
linear_regularization=1e-9,
side_data_factorization=True,
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train | preprocess | splits _sources/reference.rst into separate files | src/external/coremltools_wrap/coremltools/mlmodel/docs/preprocess.py | 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")
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os.remove("./_sources/reference.rst")
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train | PackTag | 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. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/wire_format.py | def PackTag(field_number, wire_type):
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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.
"""
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field_number: Expected to be an integer in the range [1, 1 << 29)
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train | _VarUInt64ByteSizeNoTag | Returns the number of bytes required to serialize a single varint
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uint64 must be unsigned. | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/wire_format.py | def _VarUInt64ByteSizeNoTag(uint64):
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train | _seconds_as_string | Returns seconds as a human-friendly string, e.g. '1d 4h 47m 41s' | src/unity/python/turicreate/toolkits/style_transfer/_utils.py | def _seconds_as_string(seconds):
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unit_strings = []
cur = max(int(seconds), 1)
for suffix, size in TIME_UNITS:
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train | _get_converter_module | Returns the module holding the conversion functions for a
particular model). | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_converter_internal.py | def _get_converter_module(sk_obj):
"""
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"""
try:
cv_idx = _converter_lookup[sk_obj.__class__]
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train | _convert_sklearn_model | Converts a generic sklearn pipeline, transformer, classifier, or regressor
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"""
Converts a generic sklearn pipeline, transformer, classifier, or regressor
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Converts a generic sklearn pipeline, transformer, classifier, or regressor
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train | TreeEnsembleBase.set_post_evaluation_transform | r"""
Set the post processing transform applied after the prediction value
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value: str
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Set the post processing transform applied after the prediction value
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Set the post processing transform applied after the prediction value
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train | TreeEnsembleBase.add_branch_node | Add a branch node to the tree ensemble.
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tree_id: int
ID of the tree to add the node to.
node_id: int
ID of the node within the tree.
feature_index: int
Index of the feature in the input being split on.
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"""
Add a branch node to the tree ensemble.
Parameters
----------
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train | TreeEnsembleBase.add_leaf_node | Add a leaf node to the tree ensemble.
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tree_id: int
ID of the tree to add the node to.
node_id: int
ID of the node within the tree.
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Add a leaf node to the tree ensemble.
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tree_id: int
ID of the tree to add the node to.
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ID of the node within the tree.
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Add a leaf node to the tree ensemble.
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tree_id: int
ID of the tree to add the node to.
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train | create | Creates a new 'PropertySet' instance for the given raw properties,
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train | create_with_validation | Creates new 'PropertySet' instances after checking
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train | create_from_user_input | Creates a property-set from the input given by the user, in the
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train | refine_from_user_input | Refines requirements with requirements provided by the user.
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- specification -- string list of requirements provided by the use
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train | PropertySet.base | Returns properties that are neither incidental nor free. | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def base (self):
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train | PropertySet.free | Returns free properties which are not dependency properties. | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def free (self):
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train | PropertySet.dependency | Returns dependency properties. | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def dependency (self):
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train | PropertySet.non_dependency | Returns properties that are not dependencies. | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def non_dependency (self):
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train | PropertySet.incidental | Returns incidental properties. | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def incidental (self):
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train | PropertySet.refine | Refines this set's properties using the requirements passed as an argument. | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def refine (self, requirements):
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train | PropertySet.get_properties | Returns all contained properties associated with 'feature | deps/src/boost_1_68_0/tools/build/src/build/property_set.py | def get_properties(self, feature):
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train | _create | A unified interface for training recommender models. Based on simple
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trained model can be used to predict ratings and make recommendations.
To use specific options of a desired model, use the ``create`` function
of the correspond... | src/unity/python/turicreate/toolkits/recommender/util.py | def _create(observation_data,
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ranking=True,
verbose=True):
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A unified interface for training recommender models. Based on simple
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train | compare_models | Compare the prediction or recommendation performance of recommender models
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Models that are trained to predict ratings are compared separately from
models that are trained without target ratings. The ratings prediction
models are compared on root-mean-squared error, and the re... | src/unity/python/turicreate/toolkits/recommender/util.py | def compare_models(dataset, models, model_names=None, user_sample=1.0,
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target=None,
exclude_known_for_precision_recall=True,
make_plot=False,
verbose=True,
**kwargs):
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train | precision_recall_by_user | 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 number of relevant items. Recall represents the ratio of relevant,
retrieved items to the number of relevant items.
Let :math:`p_k` be a vecto... | src/unity/python/turicreate/toolkits/recommender/util.py | 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 precision_recall_by_user(observed_user_items,
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train | random_split_by_user | Create a recommender-friendly train-test split of the provided data set.
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total number of users in `dataset`. Then, for each of the chosen test users,
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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 random_split_by_user(dataset,
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train | _Recommender._list_fields | Get the current settings of the model. The keys depend on the type of
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-------
out : list
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Get the current settings of the model. The keys depend on the type of
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Returns
-------
out : list
A list of fields that can be queried using the ``get`` method.
"""
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Get the current settings of the model. The keys depend on the type of
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out : list
A list of fields that can be queried using the ``get`` method.
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Returns
-------
sections : list (of list of tuples)
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"""
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Returns
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sections : list (of lis... | def _get_summary_struct(self):
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train | _Recommender._set_current_options | Set current options for a model.
Parameters
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options : dict
A dictionary of the desired option settings. The key should be the name
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"""
Set current options for a model.
Parameters
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options : dict
A dictionary of the desired option settings. The key should be the name
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Set current options for a model.
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train | _Recommender.__prepare_dataset_parameter | Processes the dataset parameter for type correctness.
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"""
Processes the dataset parameter for type correctness.
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train | _Recommender._get_data_schema | Returns a dictionary of (column : type) for the data used in the
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Returns a dictionary of (column : type) for the data used in the
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if not hasattr(self, "_data_schema"):
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train | _Recommender.predict | Return a score prediction for the user ids and item ids in the provided
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dataset : SFrame
Dataset in the same form used for training.
new_observation_data : SFrame, optional
``new_observation_data`` gives additional observa... | src/unity/python/turicreate/toolkits/recommender/util.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 predict(self, dataset,
new_observation_data=None, new_user_data=None, new_item_data=None):
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Return a score prediction for the user ids and item ids in the provided
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train | _Recommender.get_similar_items | Get the k most similar items for each item in items.
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return the most similar items according to the user-chosen
similarity; the factorization_recommender will r... | src/unity/python/turicreate/toolkits/recommender/util.py | 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_items(self, items=None, k=10, verbose=False):
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Get the k most similar items for each item in items.
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train | _Recommender.get_similar_users | Get the k most similar users for each entry in `users`.
Each type of recommender has its own model for the similarity
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return the nearest users based on the cosine similarity
between latent user factors. (This method is not ... | src/unity/python/turicreate/toolkits/recommender/util.py | 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
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train | _Recommender.recommend | Recommend the ``k`` highest scored items for each user.
Parameters
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users : SArray, SFrame, or list, optional
Users or observation queries for which to make recommendations.
For list, SArray, and single-column inputs, this is simply a set
of user I... | src/unity/python/turicreate/toolkits/recommender/util.py | 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(self, users=None, k=10, exclude=None, items=None,
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train | _Recommender.recommend_from_interactions | Recommend the ``k`` highest scored items based on the
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----------
observed_items : SArray, SFrame, or list
A list/SArray of items to use to make recommendations, or
an SFrame of items and optionally ratings and/or... | src/unity/python/turicreate/toolkits/recommender/util.py | def recommend_from_interactions(
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new_user_data=None, new_item_data=None,
exclude_known=True, diversity=0, random_seed=None,
verbose=True):
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Recommend the ``k`` highest scored items based on the
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exclude_known=True, diversity=0, random_seed=None,
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train | _Recommender.evaluate_precision_recall | Compute a model's precision and recall scores for a particular dataset.
Parameters
----------
dataset : SFrame
An SFrame in the same format as the one used during training.
This will be compared to the model's recommendations, which exclude
the (user, item) p... | src/unity/python/turicreate/toolkits/recommender/util.py | 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.
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Compute a model's precision and recall scores for a particular dataset.
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train | _Recommender.evaluate_rmse | 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
The name of the target rating column in `dataset`.
Ret... | src/unity/python/turicreate/toolkits/recommender/util.py | 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_rmse(self, dataset, target):
"""
Evaluate the prediction error for each user-item pair in the given data
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Parameters
----------
dataset : SFrame
An SFrame in the same format as the one used during training.
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train | _Recommender.evaluate | r"""
Evaluate the model's ability to make rating predictions or
recommendations.
If the model is trained to predict a particular target, the
default metric used for model comparison is root-mean-squared error
(RMSE). Suppose :math:`y` and :math:`\widehat{y}` are vectors of lengt... | src/unity/python/turicreate/toolkits/recommender/util.py | 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.
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Evaluate the model's ability to make rating predictions or
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train | _Recommender._get_popularity_baseline | Returns a new popularity model matching the data set this model was
trained with. Can be used for comparison purposes. | src/unity/python/turicreate/toolkits/recommender/util.py | 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_popularity_baseline(self):
"""
Returns a new popularity model matching the data set this model was
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"""
response = self.__proxy__.get_popularity_baseline()
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train | _Recommender._get_item_intersection_info | For a collection of item -> item pairs, returns information about the
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Parameters
----------
item_pairs : 2-column SFrame of two item columns, or a list of
(item_1, item_2) tuples.
Returns
-------
out : SFrame
A ... | src/unity/python/turicreate/toolkits/recommender/util.py | 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 _get_item_intersection_info(self, item_pairs):
"""
For a collection of item -> item pairs, returns information about the
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----------
item_pairs : 2-column SFrame of two item columns, or a list of
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train | _Recommender.export_coreml | 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') | src/unity/python/turicreate/toolkits/recommender/util.py | 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 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')
"""
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train | RandomForestRegression.evaluate | 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 the same as that used in training.
metric : str, optional
Name of the ev... | src/unity/python/turicreate/toolkits/regression/random_forest_regression.py | def evaluate(self, dataset, metric='auto', missing_value_action='auto'):
"""
Evaluate the model on the given dataset.
Parameters
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Dataset in the same format used for training. The columns names and
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Evaluate the model on the given dataset.
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train | RandomForestRegression.predict | Predict the target column of the given dataset.
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Parameters
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dataset : SFrame
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Predict the target column of the given dataset.
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Predict the target column of the given dataset.
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train | create_feature_vectorizer | Creates a feature vectorizer from input features, return the spec for
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Parameters
----------
input_features: [list of 2-tuples]
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"""
Creates a feature vectorizer from input features, return the spec for
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train | utils.query_boost_version | Read in the Boost version from a given boost_root. | deps/src/boost_1_68_0/libs/predef/tools/ci/common.py | def query_boost_version(boost_root):
'''
Read in the Boost version from a given boost_root.
'''
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'''
Read in the Boost version from a given boost_root.
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boost_version = None
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train | utils.git_clone | 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. | deps/src/boost_1_68_0/libs/predef/tools/ci/common.py | def git_clone(sub_repo, branch, commit = None, cwd = None, no_submodules = False):
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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:
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train | ci_travis.install_toolset | Installs specific toolset on CI system. | deps/src/boost_1_68_0/libs/predef/tools/ci/common.py | 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:
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Installs specific toolset on CI system.
'''
info = toolset_info[toolset]
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os.chdir(self.work_dir)
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train | create | Create a :class:`~turicreate.svm_classifier.SVMClassifier` to predict the class of a binary
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convergence_threshold = _DEFAULT_SOLVER_OPTIONS['convergence_threshold'],
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train | _get_layer_converter_fn | Get the right converter function for Keras | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_keras2_converter.py | 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 _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:
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train | _load_keras_model | 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 file)
custom_objects:
A dictionary of layers or other custom classes
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"""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 _load_keras_model(model_network_path, model_weight_path, custom_objects=None):
"""Load a keras model from disk
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----------
model_network_path: str
Path where the model network path is (json file)
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train | Plot.show | 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').
Examples
--------
... | src/unity/python/turicreate/visualization/_plot.py | 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 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
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train | Plot.save | 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 plot will
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"""
A method for saving the Plot object in a vega representation
Parameters
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filepath: string
The destination filepath where the plot object must be saved as.
The extension of this filepath determines what format the pl... | def save(self, filepath):
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train | mthread_submit | 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 arguments to launch each job
this usually includes th... | src/external/xgboost/subtree/rabit/tracker/rabit_demo.py | def mthread_submit(nslave, worker_args, worker_envs):
"""
customized submit script, that submit nslave jobs, each must contain args as parameter
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Parameters
nslave number of slave process to start up
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nslave number of slave process to start up
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train | _get_value | Get the right value from the scikit-tree | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_tree_ensemble.py | def _get_value(scikit_value, mode = 'regressor', scaling = 1.0, n_classes = 2, tree_index = 0):
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"""
# Regression
if mode == 'regressor':
return scikit_value[0] * scaling
# Binary classification
if n_classes == 2:
# Decision tree
... | 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
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if n_classes == 2:
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train | _recurse | Traverse through the tree and append to the tree spec. | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_tree_ensemble.py | def _recurse(coreml_tree, scikit_tree, tree_id, node_id, scaling = 1.0, mode = 'regressor',
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"""Traverse through the tree and append to the tree spec.
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train | convert_tree_ensemble | Convert a generic tree regressor model to the protobuf spec.
This currently supports:
* Decision tree regression
* Gradient boosted tree regression
* Random forest regression
* Decision tree classifier.
* Gradient boosted tree classifier.
* Random forest classifier.
-------... | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_tree_ensemble.py | 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 convert_tree_ensemble(model, input_features,
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train | _vgg16_data_prep | Takes images scaled to [0, 1] and returns them appropriately scaled and
mean-subtracted for VGG-16 | src/unity/python/turicreate/toolkits/style_transfer/style_transfer.py | 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 _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))) | [
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train | create | Create a :class:`StyleTransfer` model.
Parameters
----------
style_dataset: SFrame
Input style images. The columns named by the ``style_feature`` parameters will
be extracted for training the model.
content_dataset : SFrame
Input content images. The columns named by the ``conte... | src/unity/python/turicreate/toolkits/style_transfer/style_transfer.py | def create(style_dataset, content_dataset, style_feature=None,
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verbose=True, batch_size = 6, **kwargs):
"""
Create a :class:`StyleTransfer` model.
Parameters
----------
style_dataset: SFrame
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Create a :class:`StyleTransfer` model.
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train | StyleTransfer._canonize_content_input | Takes input and returns tuple of the input in canonical form (SFrame)
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train | StyleTransfer.stylize | 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 used
during training.
style : int or list, optional
The selected styl... | src/unity/python/turicreate/toolkits/style_transfer/style_transfer.py | 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 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
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train | StyleTransfer.export_coreml | 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
----------
path : string
A string to the path for saving the Core ML model.
image_shape: tu... | src/unity/python/turicreate/toolkits/style_transfer/style_transfer.py | def export_coreml(self, path, image_shape=(256, 256),
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"""
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 export_coreml(self, path, image_shape=(256, 256),
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"""
Save the model in Core ML format. The Core ML model takes an image of
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train | StyleTransfer.get_styles | Returns SFrame of style images used for training the model
Parameters
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style: int or list, optional
The selected style or list of styles to return. If `None`, all
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See Also
--------
stylize
Examples
... | src/unity/python/turicreate/toolkits/style_transfer/style_transfer.py | 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
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See Also
... | def get_styles(self, style=None):
"""
Returns SFrame of style images used for training the model
Parameters
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style: int or list, optional
The selected style or list of styles to return. If `None`, all
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train | convert | Convert an MXNet model to the protobuf spec.
Parameters
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model: MXNet model
A trained MXNet neural network model.
input_shape: list of tuples
A list of (name, shape) tuples, defining the input names and their
shapes. The list also serves to define the desired order of... | src/unity/python/turicreate/toolkits/_mxnet/_mxnet_to_coreml/_mxnet_converter.py | def convert(model, input_shape, class_labels=None, mode=None,
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"""Convert an MXNet model to the protobuf spec.
Parameters
----------
model: MXNet model
A trained MXNet neural network model.
input_shape: list of tuples
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Parameters
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model: MXNet model
A trained MXNet neural network model.
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train | load_model | Load a libsvm model from a path on disk.
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model_path: str
Path on disk where the libsvm model representation is.
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* Epsilon-SVR
* NU-SVR
Parameters
----------
model_path: str
Path on disk where the libsvm model representation is.
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train | add_enumerated_multiarray_shapes | Annotate an input or output multiArray feature in a Neural Network spec to
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The MLModel spec containing the feature
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The name of the image feature for which to add shape information.
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Annotate an input or output multiArray feature in a Neural Network spec to
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The MLModel spec containing the feature
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Annotate an input or output multiArray feature in a Neural Network spec to
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train | add_enumerated_image_sizes | Annotate an input or output image feature in a Neural Network spec to
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Annotate an input or output image feature in a Neural Network spec to
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train | update_image_size_range | Annotate an input or output Image feature in a Neural Network spec to
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The MLModel spec containing the feature
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Annotate an input or output Image feature in a Neural Network spec to
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The MLModel spec containing the feature
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Annotate an input or output MLMultiArray feature in a Neural Network spec
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train | get_allowed_shape_ranges | For a given model specification, returns a dictionary with a shape range object for each input feature name. | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | def get_allowed_shape_ranges(spec):
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train | can_allow_multiple_input_shapes | Examines a model specification and determines if it can compute results for more than one output shape.
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train | NeuralNetworkMultiArrayShapeRange.isFlexible | Returns true if any one of the channel, height, or width ranges of this shape allow more than one input value. | src/external/coremltools_wrap/coremltools/coremltools/models/neural_network/flexible_shape_utils.py | def isFlexible(self):
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train | define_macro | Generate a macro definition or undefinition | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def define_macro(out_f, (name, args, body), undefine=False, check=True):
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out_f.write(
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train | filename | Generate the filename | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def filename(out_dir, name, undefine=False):
"""Generate the filename"""
if undefine:
prefix = 'undef_'
else:
prefix = ''
return os.path.join(out_dir, '{0}{1}.hpp'.format(prefix, name.lower())) | def filename(out_dir, name, undefine=False):
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train | length_limits | Generates the length limits | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def length_limits(max_length_limit, length_limit_step):
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string_len = len(str(max_length_limit))
return [
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train | generate_take | Generate the take function | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def generate_take(out_f, steps, line_prefix):
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'{0}{{\n'
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train | generate_make_string | Generate the make_string template | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def generate_make_string(out_f, max_step):
"""Generate the make_string template"""
steps = [2 ** n for n in xrange(int(math.log(max_step, 2)), -1, -1)]
with Namespace(
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generate_take(out_f, steps, nsp.prefix(... | def generate_make_string(out_f, max_step):
"""Generate the make_string template"""
steps = [2 ** n for n in xrange(int(math.log(max_step, 2)), -1, -1)]
with Namespace(
out_f,
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train | generate_string | Generate string.hpp | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def generate_string(out_dir, limits):
"""Generate string.hpp"""
max_limit = max((int(v) for v in limits))
with open(filename(out_dir, 'string'), 'wb') as out_f:
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max_limit = max((int(v) for v in limits))
with open(filename(out_dir, 'string'), 'wb') as out_f:
with IncludeGuard(out_f):
out_f.write(
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train | existing_path | Throws when the path does not exist | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def existing_path(value):
"""Throws when the path does not exist"""
if os.path.exists(value):
return value
else:
raise argparse.ArgumentTypeError("Path {0} not found".format(value)) | def existing_path(value):
"""Throws when the path does not exist"""
if os.path.exists(value):
return value
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raise argparse.ArgumentTypeError("Path {0} not found".format(value)) | [
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train | main | The main function of the script | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def main():
"""The main function of the script"""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
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required=False,
type=existing_path,
help='The path to the include/boost directory of Metaparse'
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parser.add_argument(
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"""The main function of the script"""
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
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type=existing_path,
help='The path to the include/boost directory of Metaparse'
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train | Namespace.begin | Generate the beginning part | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def begin(self):
"""Generate the beginning part"""
self.out_f.write('\n')
for depth, name in enumerate(self.names):
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train | Namespace.end | Generate the closing part | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def end(self):
"""Generate the closing part"""
for depth in xrange(len(self.names) - 1, -1, -1):
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train | IncludeGuard.begin | Generate the beginning part | deps/src/boost_1_68_0/libs/metaparse/tools/string_headers.py | def begin(self):
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name = 'BOOST_METAPARSE_V1_CPP11_IMPL_STRING_HPP'
self.out_f.write('#ifndef {0}\n#define {0}\n'.format(name))
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train | get_deep_features | Calculates the deep features used by the Sound Classifier.
Internally the Sound Classifier calculates deep features for both model
creation and predictions. If the same data will be used multiple times,
calculating the deep features just once will result in a significant speed
up.
Parameters
-... | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | def get_deep_features(audio_data, verbose=True):
'''
Calculates the deep features used by the Sound Classifier.
Internally the Sound Classifier calculates deep features for both model
creation and predictions. If the same data will be used multiple times,
calculating the deep features just once wil... | def get_deep_features(audio_data, verbose=True):
'''
Calculates the deep features used by the Sound Classifier.
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train | create | Creates a :class:`SoundClassifier` model.
Parameters
----------
dataset : SFrame
Input data. The column named by the 'feature' parameter will be
extracted for modeling.
target : string or int
Name of the column containing the target variable. The values in this
column m... | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | def create(dataset, target, feature, max_iterations=10,
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'''
Creates a :class:`SoundClassifier` model.
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----------
dataset : SFrame
Input data. The column named by the 'feature... | def create(dataset, target, feature, max_iterations=10,
custom_layer_sizes=[100, 100], verbose=True,
validation_set='auto', batch_size=64):
'''
Creates a :class:`SoundClassifier` model.
Parameters
----------
dataset : SFrame
Input data. The column named by the 'feature... | [
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] | apple/turicreate | python | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py#L78-L303 | [
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train | SoundClassifier._load_version | A function to load a previously saved SoundClassifier instance. | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | def _load_version(cls, state, version):
"""
A function to load a previously saved SoundClassifier instance.
"""
from ._audio_feature_extractor import _get_feature_extractor
from .._mxnet import _mxnet_utils
state['_feature_extractor'] = _get_feature_extractor(state['feat... | def _load_version(cls, state, version):
"""
A function to load a previously saved SoundClassifier instance.
"""
from ._audio_feature_extractor import _get_feature_extractor
from .._mxnet import _mxnet_utils
state['_feature_extractor'] = _get_feature_extractor(state['feat... | [
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train | SoundClassifier.classify | Return the classification for each examples in the ``dataset``.
The output SFrame contains predicted class labels and its probability.
Parameters
----------
dataset : SFrame | SArray | dict
The audio data to be classified.
If dataset is an SFrame, it must have a ... | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | def classify(self, dataset, verbose=True, batch_size=64):
"""
Return the classification for each examples in the ``dataset``.
The output SFrame contains predicted class labels and its probability.
Parameters
----------
dataset : SFrame | SArray | dict
The aud... | def classify(self, dataset, verbose=True, batch_size=64):
"""
Return the classification for each examples in the ``dataset``.
The output SFrame contains predicted class labels and its probability.
Parameters
----------
dataset : SFrame | SArray | dict
The aud... | [
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train | SoundClassifier.evaluate | Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset to use for evaluation, must include a column with the same
name as the features used for model training. Additional colu... | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset to use for evaluation, must include a c... | def evaluate(self, dataset, metric='auto', verbose=True, batch_size=64):
"""
Evaluate the model by making predictions of target values and comparing
these to actual values.
Parameters
----------
dataset : SFrame
Dataset to use for evaluation, must include a c... | [
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train | SoundClassifier.export_coreml | Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('./myModel.mlmodel') | src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py | def export_coreml(self, filename):
"""
Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('./myModel.mlmodel')
"""
import coremltools
from coremltools.proto.FeatureTypes_pb2 import A... | def export_coreml(self, filename):
"""
Save the model in Core ML format.
See Also
--------
save
Examples
--------
>>> model.export_coreml('./myModel.mlmodel')
"""
import coremltools
from coremltools.proto.FeatureTypes_pb2 import A... | [
"Save",
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"model",
"in",
"Core",
"ML",
"format",
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] | apple/turicreate | python | https://github.com/apple/turicreate/blob/74514c3f99e25b46f22c6e02977fe3da69221c2e/src/unity/python/turicreate/toolkits/sound_classifier/sound_classifier.py#L602-L721 | [
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