API

Import batchglm’s high-level API as:

import batchglm.api as glm

Fitting models

All models are collected in the train and model module. Each model consists of at least:

1) models.glm_nb.Model class which basicially describes the model 3) train.xxxxx.Estimator class which takes a Model object and fits the corresponding model onto it.

where xxxxxx is the backend desired, like tf2, numpy or statsmodel.

For example, here is a short snippet to give a sense of how the API might work:

from batchglm.models.glm_nb import Model as NBModel
from batchglm.train.numpy.glm_nb import Estimator as NBEstimator
from batchglm.utils.input import InputDataGLM

input_data = InputDataGLM(data=data_matrix, design_loc=_design_loc, design_scale=_design_scale, as_dask=as_dask)
model = NBModel(input_data=input_data)
estimator = NBEstimator(model=model, init_location="standard", init_scale="standard")
estimator.initialize()
estimator.train_sequence(training_strategy="DEFAULT")
# Now you can perform statistical tests, for example, on parameters like model.theta_location.

Currently implemented models:

Negative Binomial

Planned or Incomplete Models:

Beta

Normal

Poisson

Data Utilities

We also provide some data utilities for working with things like design and constraint matrices.