esat package#

Subpackages#

Submodules#

esat.configs module#

esat.metrics module#

Collection of metric functions which are used throughout the code base.

esat.metrics.calculate_Q(residuals, uncertainty)#
esat.metrics.q_factor(V, U, W, H)#
esat.metrics.q_loss(V, U, W, H, uncertainty: bool = True)#
esat.metrics.qr_loss(V, U, W, H, alpha=4.0)#

esat.utils module#

Collection of utility functions used throughout the code base.

esat.utils.calculate_factor_correlation(factor1, factor2)#
esat.utils.compare_all_factors(matrix1, matrix2)#
esat.utils.memory_estimate(n_features, n_samples, factors, cores: int = None)#

Estimate the memory usage of the algorithm.

Parameters:
  • n_features – Number of features.

  • n_samples – Number of samples.

  • factors – Number of factors.

Returns:

Estimated memory usage in bytes.

Return type:

int

esat.utils.min_timestep(data: DataFrame)#

Find the minimum timestep in a dataframe.

Parameters:

data – Dataframe to be searched.

Returns:

Minimum timestep.

Return type:

int

esat.utils.np_encoder(object)#

Convert any numpy type to a generic type for json serialization.

Parameters:

object – Object to be converted.

Returns:

Generic object or an unchanged object if not a numpy type

Return type:

object

esat.estimator module#

Module contents#