snorkel.labeling.model.label_model.LabelModel

class snorkel.labeling.model.label_model.LabelModel(cardinality=2, **kwargs)

Bases: torch.nn.modules.module.Module, snorkel.labeling.model.base_labeler.BaseLabeler

A model for learning the LF accuracies and combining their output labels.

This class learns a model of the labeling functions’ conditional probabilities of outputting the true (unobserved) label Y, P(lf | Y), and uses this learned model to re-weight and combine their output labels.

This class is based on the approach in [Training Complex Models with Multi-Task Weak Supervision](https://arxiv.org/abs/1810.02840), published in AAAI‘19. In this approach, we compute the inverse generalized covariance matrix of the junction tree of a given LF dependency graph, and perform a matrix completion-style approach with respect to these empirical statistics. The result is an estimate of the conditional LF probabilities, P(lf | Y), which are then set as the parameters of the label model used to re-weight and combine the labels output by the LFs.

Currently this class uses a conditionally independent label model, in which the LFs are assumed to be conditionally independent given Y.

Examples

>>> label_model = LabelModel()
>>> label_model = LabelModel(cardinality=3)
>>> label_model = LabelModel(cardinality=3, device='cpu')
>>> label_model = LabelModel(cardinality=3)

Parameters

• cardinality (int) – Number of classes, by default 2

• **kwargs – Arguments for changing config defaults

Raises

ValueError – If config device set to cuda but only cpu is available

cardinality

Number of classes, by default 2

config

Training configuration

seed

Random seed

__init__(cardinality=2, **kwargs)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Return type

None

Methods

__init__([cardinality])	Initializes internal Module state, shared by both nn.Module and ScriptModule.
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.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
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(L_train[, Y_dev, class_balance])	Train label model.
float()	Casts all floating point parameters and buffers to float datatype.
forward(*input)	rtype None
get_conditional_probs()	Return the estimated conditional probabilities table.
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)	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)	Return label probabilities P(Y | lambda).
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor[, persistent])	Adds a 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.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
save(destination)	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()	rtype ~T
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

T_destination
dump_patches

fit(L_train, Y_dev=None, class_balance=None, **kwargs)

Train label model.

Train label model to estimate mu, the parameters used to combine LFs.

Parameters

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

• Y_dev (Optional[ndarray]) – Gold labels for dev set for estimating class_balance, by default None

• class_balance (Optional[List[float]]) – Each class’s percentage of the population, by default None

• **kwargs –

  Arguments for changing train config defaults.

  n_epochs

  The number of epochs to train (where each epoch is a single optimization step), default is 100

  lr

  Base learning rate (will also be affected by lr_scheduler choice and settings), default is 0.01

  l2

  Centered L2 regularization strength, default is 0.0

  optimizer

  Which optimizer to use (one of [“sgd”, “adam”, “adamax”]), default is “sgd”

  optimizer_config

  Settings for the optimizer

  lr_scheduler

  Which lr_scheduler to use (one of [“constant”, “linear”, “exponential”, “step”]), default is “constant”

  lr_scheduler_config

  Settings for the LRScheduler

  prec_init

  LF precision initializations / priors, default is 0.7

  seed

  A random seed to initialize the random number generator with

  log_freq

  Report loss every this many epochs (steps), default is 10

  mu_eps

  Restrict the learned conditional probabilities to [mu_eps, 1-mu_eps], default is None

Raises

Exception – If loss in NaN

Examples

>>> L = np.array([[0, 0, -1], [-1, 0, 1], [1, -1, 0]])
>>> Y_dev = [0, 1, 0]
>>> label_model = LabelModel(verbose=False)
>>> label_model.fit(L)
>>> label_model.fit(L, Y_dev=Y_dev, seed=2020, lr=0.05)
>>> label_model.fit(L, class_balance=[0.7, 0.3], n_epochs=200, l2=0.4)

Return type

None

get_conditional_probs()

Return the estimated conditional probabilities table.

Return the estimated conditional probabilites table cprobs, where cprobs is an (m, k+1, k)-dim np.ndarray with:

cprobs[i, j, k] = P(lf_i = j-1 | Y = k)

where m is the number of LFs, k is the cardinality, and cprobs includes the conditional abstain probabilities P(lf_i = -1 | Y = y).

Returns

An [m, k + 1, k] np.ndarray conditional probabilities table.

Return type

np.ndarray

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])

load(source)

Load existing label model.

Parameters

source (str) – Filename to load model from

Example

Load parameters saved in saved_label_model

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

Return type

None

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

Return predicted labels, with ties broken according to policy.

Policies to break ties include:

• “abstain”: return an abstain vote (-1)

• “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)

Return label probabilities P(Y | lambda).

Parameters

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

Returns

An [n,k] array of probabilistic labels

Return type

np.ndarray

Example

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

save(destination)

Save label model.

Parameters

destination (str) – Filename for saving model

Example

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

Return type

None

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

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 data points in L

• metrics (Optional[List[str]]) – A list of metric names. Possbile metrics are - accuracy, coverage, precision, recall, f1, f1_micro, f1_macro, fbeta, matthews_corrcoef, roc_auc. See sklearn.metrics for details on the metrics.

• tie_break_policy (str) – Policy to break ties when converting probabilistic labels to predictions. Same as predict() method above.

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}