tempor.methods.prediction.one_off.classification package¶

Submodules¶

tempor.methods.prediction.one_off.classification.plugin_cde_classifier module
- tempor.methods.prediction.one_off.classification.plugin_cde_classifier.CDEClassifierParams
  - n_units_hidden
  - n_layers_hidden
  - nonlin
    - Type
  - dropout
  - atol
  - rtol
  - interpolation
  - lr
  - weight_decay
  - n_iter
  - batch_size
  - n_iter_print
  - random_state
  - patience
  - clipping_value
  - train_ratio
  - device
  - dataloader_sampler
- tempor.methods.prediction.one_off.classification.plugin_cde_classifier.CDEClassifier
tempor.methods.prediction.one_off.classification.plugin_laplace_classifier module
- tempor.methods.prediction.one_off.classification.plugin_laplace_classifier.LaplaceODEClassifierParams
- tempor.methods.prediction.one_off.classification.plugin_laplace_classifier.LaplaceODEClassifier
tempor.methods.prediction.one_off.classification.plugin_nn_classifier module
- tempor.methods.prediction.one_off.classification.plugin_nn_classifier.NeuralNetClassifierParams
- tempor.methods.prediction.one_off.classification.plugin_nn_classifier.NeuralNetClassifier
tempor.methods.prediction.one_off.classification.plugin_ode_classifier module
- tempor.methods.prediction.one_off.classification.plugin_ode_classifier.ODEClassifierParams
  - n_units_hidden
  - n_layers_hidden
  - nonlin
    - Type
  - dropout
  - atol
  - rtol
  - interpolation
  - lr
  - weight_decay
  - n_iter
  - batch_size
  - n_iter_print
  - random_state
  - patience
  - clipping_value
  - train_ratio
  - device
  - dataloader_sampler
- tempor.methods.prediction.one_off.classification.plugin_ode_classifier.ODEClassifier

Module contents¶

One-off classification estimators.

class tempor.methods.prediction.one_off.classification.BaseOneOffClassifier(**params: Any)[source]¶

Bases: BasePredictor

Base class for one-off classification estimators.

Parameters:¶

**params : Any: Parameters as defined in BasePredictorParams.

fit(data: BaseDataset, *args: Any, **kwargs: Any) → Self[source]¶

Fit the method to the data.

Parameters:¶

data : dataset.BaseDataset¶: The dataset to fit the model to.
*args : Any: Additional arguments to pass to the _fit method.
**kwargs : Any: Additional keyword arguments to pass to the _fit method.

Returns:¶

The fitted model.

Return type:¶

Self

predict(data: PredictiveDataset, *args: Any, **kwargs: Any) → StaticSamplesBase[source]¶

Predicts the target variable for the given data.

Parameters:¶

data : dataset.PredictiveDataset¶: The dataset to predict on.
*args : Any: Additional positional arguments passed to the implementation (_predict).
**kwargs : Any: Additional keyword arguments passed to the implementation (_predict).

Returns:¶

The predictions.

Return type:¶

Any

predict_proba(data: PredictiveDataset, *args: Any, **kwargs: Any) → StaticSamplesBase[source]¶

Predicts the probability of the target variable for the given data.

Parameters:¶

data : dataset.PredictiveDataset¶: The dataset to predict on.
*args : Any: Additional positional arguments passed to the implementation (_predict_proba).
**kwargs : Any: Additional keyword arguments passed to the implementation (_predict_proba).

Returns:¶

The predicted probabilities.

Return type:¶

Any