pcntoolkit.regression_model.test_model#

Classes#

TestModel

Test model for regression model testing.

Module Contents#

class TestModel(name: str, success_ratio: float = 1.0)#

Bases: pcntoolkit.regression_model.regression_model.RegressionModel

Test model for regression model testing.

Initialize the test model.

Args:

name: The name of the model. success_ratio: The ratio of successful fits.

backward(X: xarray.DataArray, be: xarray.DataArray, Z: xarray.DataArray) → xarray.DataArray#

Compute points in feature space for given z-scores

Parameters:

  • X (xr.DataArray containing covariates)

  • be (xr.DataArray containing batch effects)

  • Y (xr.DataArray containing covariates)

Returns:

Data with Y values derived from Z-scores

Return type:

xr.DataArray

elemwise_logp(X: xarray.DataArray, be: xarray.DataArray, Y: xarray.DataArray) → xarray.DataArray#

Compute the log-probability of the data under the model.

fit(X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) → None#

Fit the model to the data.

Parameters:

  • X (xr.DataArray containing covariates)

  • be (xr.DataArray containing batch effects)

  • be_maps (dictionary of dictionaries mapping batch effect to indices)

  • Y (xr.DataArray containing covariates)

Return type:

Nothing

forward(X: xarray.DataArray, be: xarray.DataArray, Y: xarray.DataArray) → xarray.DataArray#

Compute Z-scores for provided Y values

Parameters:

  • X (xr.DataArray containing covariates)

  • be (xr.DataArray containing batch effects)

  • Y (xr.DataArray containing covariates)

Returns:

Data with Z-scores derived from Y values

Return type:

xr.DataArray

classmethod from_args(name: str, args: dict) → pcntoolkit.regression_model.regression_model.RegressionModel#

Create model instance from arguments dictionary.

Used for instantiating models from the command line.

Parameters:

  • name (str) – Unique identifier for the model instance

  • args (dict) – Dictionary of model parameters and configuration

Returns:

New instance of the regression model

Return type:

RegressionModel

Raises:

NotImplementedError – Must be implemented by concrete subclasses

classmethod from_dict(my_dict: dict, path: str) → pcntoolkit.regression_model.regression_model.RegressionModel#

Create model instance from dictionary representation.

Used for loading models from disk.

Parameters:

  • dct (dict) – Dictionary containing model parameters and configuration

  • path (str) – Path to load any associated files

Returns:

New instance of the regression model

Return type:

RegressionModel

Raises:

NotImplementedError – Must be implemented by concrete subclasses

to_dict(path: str | None = None) → dict#

Convert model instance to dictionary representation.

Used for saving models to disk.

Parameters:

path (str | None, optional) – Path to save any associated files, by default None

Returns:

Dictionary containing model parameters and configuration

Return type:

dict

transfer(X: xarray.DataArray, be: xarray.DataArray, be_maps: dict[str, dict[str, int]], Y: xarray.DataArray) → pcntoolkit.regression_model.regression_model.RegressionModel#

Transfer the model to a new dataset.

Parameters:

  • X (xr.DataArray containing covariates)

  • be (xr.DataArray containing batch effects)

  • be_maps (dictionary of dictionaries mapping batch effect to indices)

  • Y (xr.DataArray containing covariates)

Returns:

New instance of the regression model, transfered to the new dataset

Return type:

RegressionModel

property has_batch_effect: bool#

Check if model includes batch effects.

Returns:

True if model includes batch effects, False otherwise

Return type:

bool

success_ratio = 1.0#