API

Import ncem as:

import ncem

Estimator classes: estimators

Estimator classes from ncem for advanced use.

estimators.Estimator()

Estimator class for models.

estimators.EstimatorGraph()

EstimatorGraph class for spatial models.

estimators.EstimatorNoGraph()

EstimatorNoGraph class for baseline models.

estimators.EstimatorCVAE([use_type_cond, ...])

Estimator class for conditional variational autoencoder models.

estimators.EstimatorCVAEncem([cond_type, ...])

Estimator class for conditional variational autoencoder NCEM models.

estimators.EstimatorED([use_type_cond, ...])

Estimator class for encoder-decoder models.

estimators.EstimatorEDncem([cond_type, ...])

Estimator class for encoder-decoder NCEM models.

estimators.EstimatorInteractions([log_transform])

Estimator class for interactions models.

estimators.EstimatorLinear([log_transform])

Estimator class for linear models.

Model classes: models

Model classes from ncem for advanced use.

Classes that wrap tensorflow models.

models.ModelCVAE(input_shapes[, latent_dim, ...])

Model class for conditional variational autoencoder.

models.ModelCVAEncem(input_shapes[, ...])

Model class for NCEM conditional variational autoencoder with graph layer IND (MAX) or GCN.

models.ModelED(input_shapes[, latent_dim, ...])

Model class for non-spatial encoder-decoder.

models.ModelEDncem(input_shapes[, ...])

Model class for NCEM encoder-decoder with graph layer IND (MAX) or GCN.

models.ModelInteractions(input_shapes[, ...])

Model class for interaction model, baseline and spatial model.

models.ModelLinear(input_shapes[, l2_coef, ...])

Model class for linear model, baseline and spatial model.

Train: train

The interface for training ncem compatible models.

Trainer classes

Classes that wrap estimator classes to use in grid search training.

train.TrainModelCVAE()

train.TrainModelCVAEncem()

train.TrainModelED()

train.TrainModelEDncem()

train.TrainModelInteractions()

train.TrainModelLinear()

Grid search summaries

Classes to pool evaluation metrics across fits in a grid search.

train.GridSearchContainer(source_path, ...)

GridSearchContainer class.