AutoPredict() automatically handles the process of finding the best models from a completed Scan() experiment, evaluates those models, and uses the winning model to make predictions on input data.
scan_object = talos.autom8.AutoPredict(scan_object, x_val=x, y_val=y, x_pred=x)NOTE: the input data must be in same format as 'x' that was used in Scan().
Also, x_val and y_val should not have been exposed to the model during the
Scan() experiment.
AutoPredict() will add four new properties to Scan():
preds_model contains the winning Keras model (function)
preds_parameters contains the hyperparameters for the selected model
preds_probabilities contains the prediction probabilities for x_pred
predict_classes contains the predicted classes for x_pred.
| Argument | Input | Description |
|---|---|---|
scan_object |
class object | the class object returned from Scan() |
x_val |
array or list of arrays | validation data features |
y_val |
array or list of arrays | validation data labels |
y_pred |
array or list of arrays | prediction data features |
task |
string | 'binary', 'multi_class', 'multi_label', or 'continuous' |
metric |
None | the metric against which the validation is performed |
n_models |
int | number of promising models to be included in the evaluation process |
folds |
None | number of folds to be used for cross-validation |
shuffle |
None | if data is shuffled before splitting |
asc |
None | should be True if metric is a loss |