Task
- class lightautoml.tasks.base.Task(name, loss=None, loss_params=None, metric=None, metric_params=None, greater_is_better=None)[source]
Bases:
objectSpecify task (binary classification, multiclass classification, regression), metrics, losses.
- __init__(name, loss=None, loss_params=None, metric=None, metric_params=None, greater_is_better=None)[source]
- Parameters
name (
str) – Task name.loss (
Union[dict,str,None]) – Objective function or dict of functions.loss_params (
Optional[Dict]) – Additional loss parameters, if dict there is no presence check for loss_params.metric (
Union[str,Callable,None]) – String name or callable.metric_params (
Optional[Dict]) – Additional metric parameters.greater_is_better (
Optional[bool]) – Whether or not higher value is better.
Note
There is 3 different task types:
‘binary’ - for binary classification.
‘reg’ - for regression.
‘multiclass’ - for multiclass classification.
Avaliable losses for binary task:
‘logloss’ - (uses by default) Standard logistic loss.
Avaliable losses for regression task:
‘mse’ - (uses by default) Mean Squared Error.
‘mae’ - Mean Absolute Error.
‘mape’ - Mean Absolute Percentage Error.
‘rmsle’ - Root Mean Squared Log Error.
‘huber’ - Huber loss, reqired params:
a- threshold between MAE and MSE losses.‘fair’ - Fair loss, required params:
c- sets smoothness.‘quantile’ - Quantile loss, required params:
q- sets quantile.
Avaliable losses for multi-classification task:
‘crossentropy’ - (uses by default) Standard crossentropy function.
‘f1’ - Optimizes F1-Macro Score, now avaliable for LightGBM and NN models. Here we implicitly assume that the prediction lies not in the set
{0, 1}, but in the interval[0, 1].
Available metrics for binary task:
‘auc’ - (uses by default) ROC-AUC score.
‘accuracy’ - Accuracy score (uses argmax prediction).
‘logloss’ - Standard logistic loss.
Avaliable metrics for regression task:
‘mse’ - (uses by default) Mean Squared Error.
‘mae’ - Mean Absolute Error.
‘mape’ - Mean Absolute Percentage Error.
‘rmsle’ - Root Mean Squared Log Error.
‘huber’ - Huber loss, reqired params:
a- threshold between MAE and MSE losses.‘fair’ - Fair loss, required params:
c- sets smoothness.‘quantile’ - Quantile loss, required params:
q- sets quantile.
Avaliable metrics for multi-classification task:
‘crossentropy’ - (uses by default) Standard cross-entropy loss.
‘auc’ - ROC-AUC of each class against the rest.
‘auc_mu’ - AUC-Mu. Multi-class extension of standard AUC for binary classification. In short, mean of n_classes * (n_classes - 1) / 2 binary AUCs. More info on http://proceedings.mlr.press/v97/kleiman19a/kleiman19a.pdf
Example
>>> task = Task('binary', metric='auc')