# progressive_val_score¶

Evaluates the performance of a model on a streaming dataset.

This method is the canonical way to evaluate a model's performance. When used correctly, it allows you to exactly assess how a model would have performed in a production scenario.

dataset is converted into a stream of questions and answers. At each step the model is either asked to predict an observation, or is either updated. The target is only revealed to the model after a certain amount of time, which is determined by the delay parameter. Note that under the hood this uses the stream.simulate_qa function to go through the data in arrival order.

By default, there is no delay, which means that the samples are processed one after the other. When there is no delay, this function essentially performs progressive validation. When there is a delay, then we refer to it as delayed progressive validation.

It is recommended to use this method when you want to determine a model's performance on a dataset. In particular, it is advised to use the delay parameter in order to get a reliable assessment. Indeed, in a production scenario, it is often the case that ground truths are made available after a certain amount of time. By using this method, you can reproduce this scenario and therefore truthfully assess what would have been the performance of a model on a given dataset.

## 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.

• print_every – defaults to 0

Iteration number at which to print the current metric. This only takes into account the predictions, and not the training steps.

• show_time – defaults to False

Whether or not to display the elapsed time.

• show_memory – defaults to False

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

• print_kwargs

Extra keyword arguments are passed to the print function. For instance, this allows providing a file argument, which indicates where to output progress.

## 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

>>> evaluate.progressive_val_score(
...     model=model,
...     dataset=datasets.Phishing(),
...     metric=metrics.ROCAUC(),
...     print_every=200
... )
[200] ROCAUC: 89.80%
[400] ROCAUC: 92.09%
[600] ROCAUC: 93.13%
[800] ROCAUC: 93.99%
[1,000] ROCAUC: 94.74%
[1,200] ROCAUC: 95.03%
ROCAUC: 95.04%


We haven't specified a delay, therefore this is strictly equivalent to the following piece of code:

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

>>> metric = metrics.ROCAUC()

>>> for x, y in datasets.Phishing():
...     y_pred = model.predict_proba_one(x)
...     metric = metric.update(y, y_pred)
...     model = model.learn_one(x, y)

>>> metric
ROCAUC: 95.04%


When print_every is specified, the current state is printed at regular intervals. Under the hood, Python's print method is being used. You can pass extra keyword arguments to modify its behavior. For instance, you may use the file argument if you want to log the progress to a file of your choice.

>>> with open('progress.log', 'w') as f:
...     metric = evaluate.progressive_val_score(
...         model=model,
...         dataset=datasets.Phishing(),
...         metric=metrics.ROCAUC(),
...         print_every=200,
...         file=f
...     )

>>> with open('progress.log') as f:
...     for line in f.read().splitlines():
...         print(line)
[200] ROCAUC: 94.00%
[400] ROCAUC: 94.70%
[600] ROCAUC: 95.17%
[800] ROCAUC: 95.42%
[1,000] ROCAUC: 95.82%
[1,200] ROCAUC: 96.00%


Note that the performance is slightly better than above because we haven't used a fresh copy of the model. Instead, we've reused the existing model which has already done a full pass on the data.

>>> import os; os.remove('progress.log')