# iter_progressive_val_score¶

Evaluates the performance of a model on a streaming dataset and yields results.

This does exactly the same as evaluate.progressive_val_score. The only difference is that this function returns an iterator, yielding results at every step. This can be useful if you want to have control over what you do with the results. For instance, you might want to plot the results.

## Parameters¶

• dataset (Iterable[Tuple[dict, Any]])

The stream of observations against which the model will be evaluated.

• model

The model to evaluate.

• metric (river.metrics.base.Metric)

The metric used to evaluate the model's predictions.

• moment (Union[str, Callable]) – defaults to None

The attribute used for measuring time. If a callable is passed, then it is expected to take as input a dict of features. If None, then the observations are implicitly timestamped in the order in which they arrive.

• delay (Union[str, int, datetime.timedelta, Callable]) – defaults to None

The amount to wait before revealing the target associated with each observation to the model. This value is expected to be able to sum with the moment value. For instance, if moment is a datetime.date, then delay is expected to be a datetime.timedelta. If a callable is passed, then it is expected to take as input a dict of features and the target. If a str is passed, then it will be used to access the relevant field from the features. If None is passed, then no delay will be used, which leads to doing standard online validation.

• step – defaults to 1

Iteration number at which to yield results. This only takes into account the predictions, and not the training steps.

• measure_time – defaults to False

Whether or not to measure the elapsed time.

• measure_memory – defaults to False

Whether or not to measure the memory usage of the model.

## Examples¶

Take the following model:

>>> from river import linear_model
>>> from river import preprocessing

>>> model = (
...     preprocessing.StandardScaler() |
...     linear_model.LogisticRegression()
... )


We can evaluate it on the Phishing dataset as so:

>>> from river import datasets
>>> from river import evaluate
>>> from river import metrics

>>> steps = evaluate.iter_progressive_val_score(
...     model=model,
...     dataset=datasets.Phishing(),
...     metric=metrics.ROCAUC(),
...     step=200
... )

>>> for step in steps:
...     print(step)
{'ROCAUC': ROCAUC: 89.80%, 'Step': 200}
{'ROCAUC': ROCAUC: 92.09%, 'Step': 400}
{'ROCAUC': ROCAUC: 93.13%, 'Step': 600}
{'ROCAUC': ROCAUC: 93.99%, 'Step': 800}
{'ROCAUC': ROCAUC: 94.74%, 'Step': 1000}
{'ROCAUC': ROCAUC: 95.03%, 'Step': 1200}