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fix bug in current nlp documentation (#763)

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* fix bug in current nlp documentation

* fixing nlp documentation bug

* fixing nlp documentation bug

* fixing nlp documentation bug

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
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Susan Xueqing Liu 2022-10-15 22:35:49 -04:00 committed by GitHub
parent d3e0d1d852
commit 774773eb5b
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1 changed files with 13 additions and 15 deletions
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flaml/automl.py
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@ -498,7 +498,7 @@ class AutoML(BaseEstimator):
'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2',
'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the follwing signature:
have the following input arguments:
```python
def custom_metric(
@ -2175,7 +2175,7 @@ class AutoML(BaseEstimator):
'f1', 'micro_f1', 'macro_f1', 'log_loss', 'mae', 'mse', 'r2',
'mape'. Default is 'auto'.
If passing a customized metric function, the function needs to
have the following signature:
have the following input arguments:
```python
def custom_metric(
@ -2370,15 +2370,13 @@ class AutoML(BaseEstimator):
```
cv_score_agg_func: customized cross-validation scores aggregate function. Default to average metrics across folds. If specificed, this function needs to
have the following signature:
have the following input arguments:
* val_loss_folds: list of floats, the loss scores of each fold;
* log_metrics_folds: list of dicts/floats, the metrics of each fold to log.
```python
def cv_score_agg_func(val_loss_folds, log_metrics_folds):
return metric_to_minimize, metrics_to_log
```
“val_loss_folds” - list of floats, the loss scores of each fold; “log_metrics_folds” - list of dicts/floats, the metrics of each fold to log.
This function should return the final aggregate result of all folds. A float number of the minimization objective, and a dictionary as the metrics to log or None.
E.g.,
E.g.,
```python
def cv_score_agg_func(val_loss_folds, log_metrics_folds):
@ -2393,16 +2391,16 @@ class AutoML(BaseEstimator):
metrics_to_log += single_fold
if metrics_to_log:
n = len(val_loss_folds)
metrics_to_log = {k: v / n for k, v in metrics_to_log.items()} if isinstance(metrics_to_log, dict) else metrics_to_log / n
metrics_to_log = (
{k: v / n for k, v in metrics_to_log.items()}
if isinstance(metrics_to_log, dict)
else metrics_to_log / n
)
return metric_to_minimize, metrics_to_log
```
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
For TransformersEstimator, available fit_kwargs can be found from
[TrainingArgumentsForAuto](nlp/huggingface/training_args).
e.g.,
skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
For TransformersEstimator, available fit_kwargs can be found from
[TrainingArgumentsForAuto](nlp/huggingface/training_args).
e.g.,