mixle.models.grammar module

Grammar-learning experiment helpers.

class GrammarLearningResult(model, history, validation_history=None)

Bases: FitResult[HeterogeneousPCFGDistribution]

Fitted PCFG plus training and optional validation log-likelihood history.

Parameters:

• model (ModelT)

• history (list[float])

• validation_history (list[float] | None)

class PCFGParseNode(label, span, log_prob, rule_index, rule_type, children=(), value=None)

Bases: object

Node in a Viterbi parse tree.

Parameters:

• label (Any)

• span (tuple[int, int])

• log_prob (float)

• rule_index (int)

• rule_type (str)

• children (tuple[PCFGParseNode, ...])

• value (Any)

label: Any

span: tuple[int, int]

log_prob: float

rule_index: int

rule_type: str

children: tuple[PCFGParseNode, ...] = ()

value: Any = None

leaves()

Return terminal observations under this node.

Return type:

list[Any]

fit_induced_pcfg(data, terminal_estimators, max_nonterminals, initial_model=None, vdata=None, max_its=10, init_p=1.0, seed=None, terminal_rule_mass=0.5, rule_pseudo_count=1.0e-3, prune_threshold=0.0, min_rule_prob=0.0, start='S', name=None)

Fit an induced heterogeneous PCFG and track train/validation likelihoods.

Parameters:

• data (Sequence[Sequence[Any]])

• terminal_estimators (Sequence[ParameterEstimator])

• max_nonterminals (int)

• initial_model (HeterogeneousPCFGDistribution | None)

• vdata (Sequence[Sequence[Any]] | None)

• max_its (int)

• init_p (float)

• seed (int | None)

• terminal_rule_mass (float)

• rule_pseudo_count (float | None)

• prune_threshold (float)

• min_rule_prob (float)

• start (Any)

• name (str | None)

Return type:

GrammarLearningResult

pcfg_log_likelihood(model, data)

Return total PCFG log likelihood on raw sequences.

Parameters:

• model (HeterogeneousPCFGDistribution)

• data (Sequence[Sequence[Any]])

Return type:

float

viterbi_parse(model, sequence)

Return the maximum-probability CKY parse under a heterogeneous PCFG.

Parameters:

• model (HeterogeneousPCFGDistribution)

• sequence (Sequence[Any])

Return type:

PCFGParseNode

grammar_rule_table(model)

Return a flat, inspectable rule table for learned PCFGs.

Parameters:

model (HeterogeneousPCFGDistribution)

Return type:

list[dict[str, Any]]