ConvModel

class ConvModel(x: Panel, y: Panel, model_type: str = None, conv_layers: int = 1, conv_filters: int = 32, kernel_size: int = 3, dense_layers: int = 1, dense_units: int = 32, activation: str = 'relu', loss: str = None, optimizer: str = None, metrics: list[str] = None, last_activation: str = None)[source]

Bases: BaseModel

Convolutional model.

Methods Summary

`build`()	Build the model.
`compile`(**kwargs)	Compile the model.
`fit`(**kwargs)	Fit the model.
`get_auc`()	Get the AUC score.
`predict`([data])	Predict.
`predict_proba`([data])	Predict probabilities.
`residuals`()	Residuals.
`score`([on])	Score the model.
`set_arrays`()	Set the arrays.

Methods Documentation

build() → None[source]: Build the model.

compile(**kwargs) → None

Compile the model.

Parameters: **kwargs – Additional arguments to pass to the compile method.

fit(**kwargs) → None

Fit the model.

Parameters: **kwargs – Additional arguments to pass to the fit method.

get_auc() → float: Get the AUC score.

predict(data: Optional[Panel] = None, **kwargs) → Panel

Predict.

Parameters

data (Panel) – Panel of data to predict.
**kwargs – Additional arguments to pass to the predict method.

Returns

Panel of predicted values.

predict_proba(data: Optional[Panel] = None, **kwargs) → Panel

Predict probabilities.

Parameters

data (Panel) – Panel of data to predict.
**kwargs – Additional arguments to pass to the predict method.

Returns

Panel of predicted probabilities.

residuals() → Panel

Residuals.

Returns: Panel of residuals.

score(on: list[str] | str = None, **kwargs) → pd.DataFrame

Score the model.

Parameters

on (list[str] or str) – Columns to score on.
**kwargs – Additional arguments to pass to the score method.

Returns

Panel of scores.

set_arrays() → None: Set the arrays.