code: def progressive_val_score(
      dataset: base.typing.Dataset,
      model,
      metric: metrics.base.Metric,
      moment: str | typing.Callable | None = None,
      delay: str | int | dt.timedelta | typing.Callable | None = None,
      print_every=0,
      show_time=False,
      show_memory=False,
      **print_kwargs,
  ) -> metrics.base.Metric:
      """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
          The stream of observations against which the model will be evaluated.
      model
          The model to evaluate.
      metric
          The metric used to evaluate the model's predictions.
      moment
          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
          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
          Iteration number at which to print the current metric. This only takes into account the
          predictions, and not the training steps.
      show_time
          Whether or not to display the elapsed time.
      show_memory
          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: 90.20%
      [400] ROCAUC: 92.25%
      [600] ROCAUC: 93.23%
      [800] ROCAUC: 94.05%
      [1,000] ROCAUC: 94.79%
      [1,200] ROCAUC: 95.07%
      [1,250] ROCAUC: 95.07%
      ROCAUC: 95.07%
  
      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.update(y, y_pred)
      ...     model.learn_one(x, y)
  
      >>> metric
      ROCAUC: 95.07%
  
      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%
      [1,250] ROCAUC: 96.04%
  
      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')
  
      References
      ----------
      [^1]: [Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation](http://hunch.net/~jl/projects/prediction_bounds/progressive_validation/coltfinal.pdf)
      [^2]: [Grzenda, M., Gomes, H.M. and Bifet, A., 2019. Delayed labelling evaluation for data streams. Data Mining and Knowledge Discovery, pp.1-30](https://link.springer.com/content/pdf/10.1007%2Fs10618-019-00654-y.pdf)
  
      """
  
      checkpoints = iter_progressive_val_score(
          dataset=dataset,
          model=model,
          metric=metric,
          moment=moment,
          delay=delay,
          step=print_every,
          measure_time=show_time,
          measure_memory=show_memory,
      )
  
      active_learning = utils.inspect.isactivelearner(model)
  
      for checkpoint in checkpoints:
          msg = f"[{checkpoint['Step']:,d}] {metric}"
          if active_learning:
              msg += f" – {checkpoint['Samples used']:,d} samples used"
          if show_time:
              H, rem = divmod(checkpoint["Time"].seconds, 3600)
              M, S = divmod(rem, 60)
              msg += f" – {H:02d}:{M:02d}:{S:02d}"
          if show_memory:
              msg += f" – {utils.pretty.humanize_bytes(checkpoint['Memory'])}"
          print(msg, **print_kwargs)
  
      return metric
  
file_path: river/river/evaluate/progressive_validation.py
imports: from __future__ import annotations
  import datetime
  import itertools
  import time
  import typing
  from river import base, metrics, stream, utils
label_desc: Use image node_id 3 for calling a global function with example usage: progressive_val_score(dataset, model, metric, moment, delay, print_every, show_time, show_memory) and returns: metric