mixle.utils.automatic.factories module

Estimator builders for automatically-typed data.

Builds estimators for mixle.stats. By default the plain maximum-likelihood estimators are produced; pass use_bstats=True to build the Bayesian path, which attaches the conjugate default prior for each family so estimation performs the closed-form conjugate / MAP update.

get_optional_estimator(est, missing_value=None, use_bstats=False)

Parameters:

• est (ParameterEstimator)

• missing_value (Any | None)

• use_bstats (bool)

get_length_estimator(len_dict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Length model for sequences.

Observed lengths are often bounded protocol/domain facts, not Poisson counts, so use an integer categorical model while the support is small. Fall back to a Poisson only when length support is broad enough to look count-like.

Parameters:

• len_dict (dict[int, int])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_sequence_estimator(est, len_dict=None, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Parameters:

• est (ParameterEstimator)

• len_dict (dict[int, int] | None)

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_set_estimator(member_dict, num_sets, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Bernoulli set model with membership probabilities from observed sets.

Parameters:

• member_dict (dict[Any, int])

• num_sets (int)

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_ignored_estimator(use_bstats=False)

Parameters:

use_bstats (bool)

Return type:

ParameterEstimator

get_composite_estimator(ests, use_bstats=False)

Parameters:

• ests (Sequence[ParameterEstimator])

• use_bstats (bool)

Return type:

ParameterEstimator

get_dict_record_estimator(keys, ests)

Parameters:

• keys (Sequence[Any])

• ests (Sequence[ParameterEstimator])

Return type:

ParameterEstimator

get_categorical_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Parameters:

• vdict (dict[T, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_integer_categorical_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Parameters:

• vdict (dict[int, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_poisson_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Parameters:

• vdict (dict[int, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_gaussian_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Parameters:

• vdict (dict[floating | float, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_lognormal_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Return a LogGaussian (log-normal) estimator fit to the log of strictly-positive values.

Parameters:

• vdict (dict[floating | float, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_gamma_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Return a Gamma estimator initialized from the method-of-moments fit of positive values.

Parameters:

• vdict (dict[floating | float, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_student_t_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False)

Return a fixed-df Student-t estimator; df is set from the excess kurtosis of the data.

Parameters:

• vdict (dict[floating | float, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

Return type:

ParameterEstimator

get_gaussian_mixture_estimator(vdict, pseudo_count=None, emp_suff_stat=True, use_bstats=False, n_components=2)

Return a K-component Gaussian mixture estimator (robust init) for multimodal numeric data.

Parameters:

• vdict (dict[floating | float, float])

• pseudo_count (float | None)

• emp_suff_stat (bool)

• use_bstats (bool)

• n_components (int)

Return type:

ParameterEstimator

get_multivariate_gaussian_estimator(dim, use_bstats=False)

Parameters:

• dim (int)

• use_bstats (bool)

Return type:

ParameterEstimator

get_dpm_mixture(data, rng=None, max_components=20, max_its=100, delta=1.0e-6, pseudo_count=1.0, print_iter=1, out=None)

Fit a Dirichlet process mixture to automatically-typed data.

Component estimators are constructed with get_estimator(use_bstats=True) (one independent conjugate-prior instance per stick), and the truncated stick-breaking posterior is fit with variational inference via mixle.inference.estimation.fit.

Parameters:

• data – Sequence of observations of any auto-detectable type.

• rng (Optional[RandomState]) – Source of randomness for initialization.

• max_components (int) – Truncation level of the stick-breaking representation.

• max_its (int) – Maximum number of variational iterations.

• delta (float) – Stop when the ELBO improves by less than delta.

• pseudo_count (Optional[float]) – Prior strength for the component priors.

• print_iter (int) – Progress print frequency.

• out – Output stream for iteration logging (defaults to sys.stdout).

Returns:

DirichletProcessMixtureDistribution fit to the data.