# skmultiflow.evaluation.EvaluatePrequential¶

class skmultiflow.evaluation.EvaluatePrequential(n_wait=200, max_samples=100000, batch_size=1, pretrain_size=200, max_time=inf, metrics=None, output_file=None, show_plot=False, restart_stream=True, data_points_for_classification=False)[source]

The prequential evaluation method or interleaved test-then-train method.

An alternative to the traditional holdout evaluation, inherited from batch setting problems.

The prequential evaluation is designed specifically for stream settings, in the sense that each sample serves two purposes, and that samples are analysed sequentially, in order of arrival, and become immediately inaccessible.

This method consists of using each sample to test the model, which means to make a predictions, and then the same sample is used to train the model (partial fit). This way the model is always tested on samples that it hasn’t seen yet.

Parameters
• n_wait (int (Default: 200)) – The number of samples to process between each test. Also defines when to update the plot if show_plot=True. Note that setting n_wait too small can significantly slow the evaluation process.

• max_samples (int (Default: 100000)) – The maximum number of samples to process during the evaluation.

• batch_size (int (Default: 1)) – The number of samples to pass at a time to the model(s).

• pretrain_size (int (Default: 200)) – The number of samples to use to train the model before starting the evaluation. Used to enforce a ‘warm’ start.

• max_time (float (Default: float("inf"))) – The maximum duration of the simulation (in seconds).

• metrics (list, optional (Default: ['accuracy', 'kappa'])) –

The list of metrics to track during the evaluation. Also defines the metrics that will be displayed in plots and/or logged into the output file. Valid options are
Classification
’accuracy’
’kappa’
’kappa_t’
’kappa_m’
’true_vs_predicted’
’precision’
’recall’
’f1’
’gmean’
Multi-target Classification
’hamming_score’
’hamming_loss’
’exact_match’
’j_index’
Regression
’mean_square_error’
’mean_absolute_error’
’true_vs_predicted’
Multi-target Regression
’average_mean_squared_error’
’average_mean_absolute_error’
’average_root_mean_square_error’
Experimental
’running_time’
’model_size’

• output_file (string, optional (Default: None)) – File name to save the summary of the evaluation.

• show_plot (bool (Default: False)) – If True, a plot will show the progress of the evaluation. Warning: Plotting can slow down the evaluation process.

• restart_stream (bool, optional (default: True)) – If True, the stream is restarted once the evaluation is complete.

• data_points_for_classification (bool(Default: False)) – If True , the visualization used is a cloud of data points (only works for classification)

Notes

1. This evaluator can process a single learner to track its performance; or multiple learners at a time, to compare different models on the same stream.

2. The metric ‘true_vs_predicted’ is intended to be informative only. It corresponds to evaluations at a specific moment which might not represent the actual learner performance across all instances.

Examples

>>> # The first example demonstrates how to evaluate one model
>>> from skmultiflow.data import SEAGenerator
>>> from skmultiflow.trees import HoeffdingTree
>>> from skmultiflow.evaluation import EvaluatePrequential
>>>
>>> # Set the stream
>>> stream = SEAGenerator(random_state=1)
>>> stream.prepare_for_use()
>>>
>>> # Set the model
>>> ht = HoeffdingTree()
>>>
>>> # Set the evaluator
>>>
>>> evaluator = EvaluatePrequential(max_samples=10000,
>>>                                 max_time=1000,
>>>                                 show_plot=True,
>>>                                 metrics=['accuracy', 'kappa'])
>>>
>>> evaluator.evaluate(stream=stream, model=ht, model_names=['HT'])
>>>
>>> # Run evaluation
>>> evaluator.evaluate(stream=stream, model=ht, model_names=['HT'])

>>> # The second example demonstrates how to compare two models
>>> from skmultiflow.data import SEAGenerator
>>> from skmultiflow.trees import HoeffdingTree
>>> from skmultiflow.bayes import NaiveBayes
>>> from skmultiflow.evaluation import EvaluateHoldout
>>>
>>> # Set the stream
>>> stream = SEAGenerator(random_state=1)
>>> stream.prepare_for_use()
>>>
>>> # Set the models
>>> ht = HoeffdingTree()
>>> nb = NaiveBayes()
>>>
>>> evaluator = EvaluatePrequential(max_samples=10000,
>>>                                 max_time=1000,
>>>                                 show_plot=True,
>>>                                 metrics=['accuracy', 'kappa'])
>>>
>>> # Run evaluation
>>> evaluator.evaluate(stream=stream, model=[ht, nb], model_names=['HT', 'NB'])

>>> # The third example demonstrates how to evaluate one model
>>> # and visualize the predictions using data points.
>>> # Note: You can not in this case compare multiple models
>>> from skmultiflow.data import SEAGenerator
>>> from skmultiflow.trees import HoeffdingTree
>>> from skmultiflow.evaluation import EvaluatePrequential
>>> # Set the stream
>>> stream = SEAGenerator(random_state=1)
>>> stream.prepare_for_use()
>>> # Set the model
>>> ht = HoeffdingTree()
>>> # Set the evaluator
>>> evaluator = EvaluatePrequential(max_samples=200,
>>>                                 n_wait=1,
>>>                                 pretrain_size=1,
>>>                                 max_time=1000,
>>>                                 show_plot=True,
>>>                                 metrics=['accuracy'],
>>>                                 data_points_for_classification=True)
>>> evaluator.evaluate(stream=stream, model=ht, model_names=['HT'])
>>> # Run evaluation
>>> evaluator.evaluate(stream=stream, model=ht, model_names=['HT'])

__init__(n_wait=200, max_samples=100000, batch_size=1, pretrain_size=200, max_time=inf, metrics=None, output_file=None, show_plot=False, restart_stream=True, data_points_for_classification=False)[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__([n_wait, max_samples, batch_size, …]) Initialize self. evaluate(stream, model[, model_names]) Evaluates a model or set of models on samples from a stream. evaluation_summary() get_current_measurements([model_idx]) Get current measurements from the evaluation (measured on last n_wait samples). Collects and returns the information about the configuration of the estimator get_mean_measurements([model_idx]) Get mean measurements from the evaluation. get_measurements([model_idx]) Get measurements from the evaluation. get_params([deep]) Get parameters for this estimator. partial_fit(X, y[, classes, sample_weight]) Partially fit all the models on the given data. Predicts with the estimator(s) being evaluated. Resets the estimator to its initial state. set_params(**params) Set the parameters of this estimator. update_progress_bar(curr, total, steps, time)
evaluate(stream, model, model_names=None)[source]

Evaluates a model or set of models on samples from a stream.

Parameters
• stream (Stream) – The stream from which to draw the samples.

• model (skmultiflow.core.BaseStreamModel or sklearn.base.BaseEstimator or list) – The model or list of models to evaluate.

• model_names (list, optional (Default=None)) – A list with the names of the models.

Returns

The trained model(s).

Return type

StreamModel or list

get_current_measurements(model_idx=None)[source]

Get current measurements from the evaluation (measured on last n_wait samples).

Parameters

model_idx (int, optional (Default=None)) – Indicates the index of the model as defined in evaluate(model). If None, returns a list with the measurements for each model.

Returns

• measurements or list

• Current measurements. If model_idx is None, returns a list with the measurements – for each model.

Raises

IndexError – If the index is invalid.:

get_info()[source]

Collects and returns the information about the configuration of the estimator

Returns

Configuration of the estimator.

Return type

string

get_mean_measurements(model_idx=None)[source]

Get mean measurements from the evaluation.

Parameters

model_idx (int, optional (Default=None)) – Indicates the index of the model as defined in evaluate(model). If None, returns a list with the measurements for each model.

Returns

• measurements or list

• Mean measurements. If model_idx is None, returns a list with the measurements – for each model.

Raises

IndexError – If the index is invalid.:

get_measurements(model_idx=None)[source]

Get measurements from the evaluation.

Parameters

model_idx (int, optional (Default=None)) – Indicates the index of the model as defined in evaluate(model). If None, returns a list with the measurements for each model.

Returns

• tuple (mean, current)

• Mean and Current measurements. If model_idx is None, each member of the tuple – is a a list with the measurements for each model.

Raises

IndexError – If the index is invalid.:

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters

deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

mapping of string to any

partial_fit(X, y, classes=None, sample_weight=None)[source]

Partially fit all the models on the given data.

Parameters
• X (Numpy.ndarray of shape (n_samples, n_features)) – The data upon which the algorithm will create its model.

• y (Array-like) – An array-like containing the classification labels / target values for all samples in X.

• classes (list) – Stores all the classes that may be encountered during the classification task. Not used for regressors.

• sample_weight (Array-like) – Samples weight. If not provided, uniform weights are assumed.

Returns

self

Return type

EvaluatePrequential

predict(X)[source]

Predicts with the estimator(s) being evaluated.

Parameters

X (Numpy.ndarray of shape (n_samples, n_features)) – All the samples we want to predict the label for.

Returns

Model(s) predictions

Return type

list of numpy.ndarray

reset()[source]

Resets the estimator to its initial state.

Returns

Return type

self

set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns

Return type

self