snorkel.labeling.RandomVoter

class snorkel.labeling.RandomVoter(cardinality=2, **kwargs)

Bases: snorkel.labeling.model.baselines.BaselineVoter

Random vote label model.

Example

>>> L = np.array([[0, 0, -1], [-1, 0, 1], [1, -1, 0]])
>>> random_voter = RandomVoter()
>>> predictions = random_voter.predict_proba(L)

__init__(cardinality=2, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Return type

None

Methods

__init__([cardinality])	Initialize self.
add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
fit(*args, **kwargs)	Train majority class model.
float()	Casts all floating point parameters and buffers to float datatype.
forward(*input)	Defines the computation performed at every call.
get_weights()	Return the vector of learned LF weights for combining LFs.
half()	Casts all floating point parameters and buffers to half datatype.
load(source, **kwargs)	Load existing label model.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
predict(L[, return_probs, tie_break_policy])	Return predicted labels, with ties broken according to policy.
predict_proba(L)	Assign random votes to the data points.
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor)	Adds a persistent buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_parameter(name, param)	Adds a parameter to the module.
save(destination, **kwargs)	Save label model.
score(L, Y[, metrics, tie_break_policy])	Calculate one or more scores from user-specified and/or user-defined metrics.
share_memory()
state_dict([destination, prefix, keep_vars])	Returns a dictionary containing a whole state of the module.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
zero_grad()	Sets gradients of all model parameters to zero.

Attributes

dump_patches

fit(*args, **kwargs)

Train majority class model.

Set class balance for majority class label model.

Parameters

balance – A [k] array of class probabilities

Return type

None

get_weights()

Return the vector of learned LF weights for combining LFs.

Returns

[m,1] vector of learned LF weights for combining LFs.

Return type

np.ndarray

Example

>>> L = np.array([[1, 1, 1], [1, 1, -1], [-1, 0, 0], [0, 0, 0]])
>>> label_model = LabelModel(verbose=False)
>>> label_model.fit(L, seed=123)
>>> np.around(label_model.get_weights(), 2)  # doctest: +SKIP
array([0.99, 0.99, 0.99])

static load(source, **kwargs)

Load existing label model.

Parameters

• source (str) – File location from where to load model

• **kwargs – Arguments for torch.load

Returns

LabelModel with appropriate loaded parameters

Return type

LabelModel

Example

Load parameters saved in saved_label_model

>>> label_model.load('./saved_label_model')  # doctest: +SKIP

predict(L, return_probs=False, tie_break_policy='random')

Return predicted labels, with ties broken according to policy.

Policies to break ties include: “abstain”: return an abstain vote (0) “true-random”: randomly choose among the tied options “random”: randomly choose among tied option using deterministic hash

NOTE: if tie_break_policy=”true-random”, repeated runs may have slightly different results due to difference in broken ties

Parameters

• L (ndarray) – An [n,m] matrix with values in {-1,0,1,…,k-1}

• return_probs (Optional[bool]) – Whether to return probs along with preds

• tie_break_policy (str) – Policy to break ties when converting probabilistic labels to predictions

Return type

Union[ndarray, Tuple[ndarray, ndarray]]

Returns

• np.ndarray – An [n,1] array of integer labels

• (np.ndarray, np.ndarray) – An [n,1] array of integer labels and an [n,k] array of probabilistic labels

Example

>>> L = np.array([[0, 0, -1], [1, 1, -1], [0, 0, -1]])
>>> label_model = LabelModel(verbose=False)
>>> label_model.fit(L)
>>> label_model.predict(L)
array([0, 1, 0])

predict_proba(L)

Assign random votes to the data points.

Parameters

L (ndarray) – An [n, m] matrix of labels

Returns

A [n, k] array of probabilistic labels

Return type

np.ndarray

Example

>>> L = np.array([[0, 0, -1], [-1, 0, 1], [1, -1, 0]])
>>> random_voter = RandomVoter()
>>> predictions = random_voter.predict_proba(L)

save(destination, **kwargs)

Save label model.

Parameters

• destination (str) – File location for saving model

• **kwargs – Arguments for torch.save

Example

>>> label_model.save('./saved_label_model')  # doctest: +SKIP

Return type

None

score(L, Y, metrics=['accuracy'], tie_break_policy='random')

Calculate one or more scores from user-specified and/or user-defined metrics.

Parameters

• L (ndarray) – An [n,m] matrix with values in {-1,0,1,…,k-1}

• Y (ndarray) – Gold labels associated with datapoints in L

• metrics (Optional[List[str]]) – A list of metric names

• tie_break_policy (str) – Policy to break ties when converting probabilistic labels to predictions

Returns

A dictionary mapping metric names to metric scores

Return type

Dict[str, float]

Example

>>> L = np.array([[1, 1, -1], [0, 0, -1], [1, 1, -1]])
>>> label_model = LabelModel(verbose=False)
>>> label_model.fit(L)
>>> label_model.score(L, Y=np.array([1, 1, 1]))
{'accuracy': 0.6666666666666666}
>>> label_model.score(L, Y=np.array([1, 1, 1]), metrics=["f1"])
{'f1': 0.8}