forked from moreo/QuaPy
evaluation updated
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@ -2,7 +2,7 @@ import quapy as qp
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from quapy.method.aggregative import newELM
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from quapy.method.base import newOneVsAll
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import USimplexPP
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from quapy.protocol import UPP
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"""
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In this example, we will show hoy to define a quantifier based on explicit loss minimization (ELM).
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@ -57,7 +57,7 @@ param_grid = {
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'binary_quantifier__classifier__C': [0.01, 1, 100], # classifier-dependent hyperparameter
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}
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print('starting model selection')
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model_selection = GridSearchQ(quantifier, param_grid, protocol=USimplexPP(val), verbose=True, refit=False)
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model_selection = GridSearchQ(quantifier, param_grid, protocol=UPP(val), verbose=True, refit=False)
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quantifier = model_selection.fit(train_modsel).best_model()
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print('training on the whole training set')
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@ -65,7 +65,7 @@ train, test = qp.datasets.fetch_twitter('hcr', for_model_selection=False, pickle
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quantifier.fit(train)
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# evaluation
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mae = qp.evaluation.evaluate(quantifier, protocol=USimplexPP(test), error_metric='mae')
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mae = qp.evaluation.evaluate(quantifier, protocol=UPP(test), error_metric='mae')
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print(f'MAE = {mae:.4f}')
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@ -2,7 +2,7 @@ import quapy as qp
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from quapy.method.aggregative import MS2
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from quapy.method.base import newOneVsAll
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from quapy.model_selection import GridSearchQ
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from quapy.protocol import USimplexPP
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from quapy.protocol import UPP
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from sklearn.linear_model import LogisticRegression
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import numpy as np
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@ -29,7 +29,7 @@ print(f'the quantifier is an instance of {quantifier.__class__.__name__}')
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train_modsel, val = qp.datasets.fetch_twitter('hcr', for_model_selection=True, pickle=True).train_test
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"""
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model selection: for this example, we are relying on the USimplexPP protocol, i.e., a variant of the
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model selection: for this example, we are relying on the UPP protocol, i.e., a variant of the
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artificial-prevalence protocol that generates random samples (100 in this case) for randomly picked priors
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from the unit simplex. The priors are sampled using the Kraemer algorithm. Note this is in contrast to the
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standard APP protocol, that instead explores a prefixed grid of prevalence values.
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@ -39,7 +39,7 @@ param_grid = {
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'binary_quantifier__classifier__class_weight': ['balanced', None] # classifier-dependent hyperparameter
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}
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print('starting model selection')
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model_selection = GridSearchQ(quantifier, param_grid, protocol=USimplexPP(val), verbose=True, refit=False)
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model_selection = GridSearchQ(quantifier, param_grid, protocol=UPP(val), verbose=True, refit=False)
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quantifier = model_selection.fit(train_modsel).best_model()
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print('training on the whole training set')
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@ -47,7 +47,7 @@ train, test = qp.datasets.fetch_twitter('hcr', for_model_selection=False, pickle
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quantifier.fit(train)
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# evaluation
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mae = qp.evaluation.evaluate(quantifier, protocol=USimplexPP(test), error_metric='mae')
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mae = qp.evaluation.evaluate(quantifier, protocol=UPP(test), error_metric='mae')
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print(f'MAE = {mae:.4f}')
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@ -3,7 +3,7 @@ Change Log 0.1.7
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- Protocols are now abstracted as instances of AbstractProtocol. There is a new class extending AbstractProtocol called
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AbstractStochasticSeededProtocol, which implements a seeding policy to allow replicate the series of samplings.
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There are some examples of protocols, APP, NPP, USimplexPP, DomainMixer (experimental).
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There are some examples of protocols, APP, NPP, UPP, DomainMixer (experimental).
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The idea is to start the sampling by simply calling the __call__ method.
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This change has a great impact in the framework, since many functions in qp.evaluation, qp.model_selection,
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and sampling functions in LabelledCollection relied of the old functions. E.g., the functionality of
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@ -211,11 +211,13 @@ def __check_eps(eps=None):
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CLASSIFICATION_ERROR = {f1e, acce}
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QUANTIFICATION_ERROR = {mae, mrae, mse, mkld, mnkld}
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QUANTIFICATION_ERROR_SINGLE = {ae, rae, se, kld, nkld}
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QUANTIFICATION_ERROR_SMOOTH = {kld, nkld, rae, mkld, mnkld, mrae}
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CLASSIFICATION_ERROR_NAMES = {func.__name__ for func in CLASSIFICATION_ERROR}
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QUANTIFICATION_ERROR_NAMES = {func.__name__ for func in QUANTIFICATION_ERROR}
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QUANTIFICATION_ERROR_SINGLE_NAMES = {func.__name__ for func in QUANTIFICATION_ERROR_SINGLE}
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QUANTIFICATION_ERROR_SMOOTH_NAMES = {func.__name__ for func in QUANTIFICATION_ERROR_SMOOTH}
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ERROR_NAMES = CLASSIFICATION_ERROR_NAMES | QUANTIFICATION_ERROR_NAMES
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ERROR_NAMES = CLASSIFICATION_ERROR_NAMES | QUANTIFICATION_ERROR_NAMES | QUANTIFICATION_ERROR_SINGLE_NAMES
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f1_error = f1e
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acc_error = acce
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@ -7,7 +7,34 @@ from quapy.method.base import BaseQuantifier
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import pandas as pd
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def prediction(model: BaseQuantifier, protocol: AbstractProtocol, aggr_speedup='auto', verbose=False):
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def prediction(
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model: BaseQuantifier,
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protocol: AbstractProtocol,
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aggr_speedup: Union[str, bool] = 'auto',
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verbose=False):
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"""
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Uses a quantification model to generate predictions for the samples generated via a specific protocol.
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This function is central to all evaluation processes, and is endowed with an optimization to speed-up the
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prediction of protocols that generate samples from a large collection. The optimization applies to aggregative
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quantifiers only, and to OnLabelledCollection protocols, and comes down to generating the classification
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predictions once and for all, and then generating samples over the classification predictions (instead of over
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the raw instances), so that the classifier prediction is never called again. This behaviour is obtained by
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setting `aggr_speedup` to 'auto' or True, and is only carried out if the overall process is convenient in terms
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of computations (e.g., if the number of classification predictions needed for the original collection exceed the
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number of classification predictions needed for all samples, then the optimization is not undertaken).
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:param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`
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:param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of
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:class:`quapy.protocol.OnLabelledCollection`, then the aggregation speed-up can be run. This is the protocol
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in charge of generating the samples for which the model has to issue class prevalence predictions.
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:param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of
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instances in the original collection on which the protocol acts is larger than the number of instances
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in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is
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convenient or not. Set to False to deactivate.
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:param verbose: boolean, show or not information in stdout
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:return: a tuple `(true_prevs, estim_prevs)` in which each element in the tuple is an array of shape
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`(n_samples, n_classes)` containing the true, or predicted, prevalence values for each sample
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"""
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assert aggr_speedup in [False, True, 'auto', 'force'], 'invalid value for aggr_speedup'
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sout = lambda x: print(x) if verbose else None
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@ -54,8 +81,29 @@ def __prediction_helper(quantification_fn, protocol: AbstractProtocol, verbose=F
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def evaluation_report(model: BaseQuantifier,
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protocol: AbstractProtocol,
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error_metrics: Iterable[Union[str,Callable]] = 'mae',
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aggr_speedup='auto',
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aggr_speedup: Union[str, bool] = 'auto',
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verbose=False):
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"""
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Generates a report (a pandas' DataFrame) containing information of the evaluation of the model as according
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to a specific protocol and in terms of one or more evaluation metrics (errors).
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:param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`
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:param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of
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:class:`quapy.protocol.OnLabelledCollection`, then the aggregation speed-up can be run. This is the protocol
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in charge of generating the samples in which the model is evaluated.
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:param error_metrics: a string, or list of strings, representing the name(s) of an error function in `qp.error`
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(e.g., 'mae', the default value), or a callable function, or a list of callable functions, implementing
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the error function itself.
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:param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of
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instances in the original collection on which the protocol acts is larger than the number of instances
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in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is
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convenient or not. Set to False to deactivate.
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:param verbose: boolean, show or not information in stdout
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:return: a pandas' DataFrame containing the columns 'true-prev' (the true prevalence of each sample),
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'estim-prev' (the prevalence estimated by the model for each sample), and as many columns as error metrics
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have been indicated, each displaying the score in terms of that metric for every sample.
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"""
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true_prevs, estim_prevs = prediction(model, protocol, aggr_speedup=aggr_speedup, verbose=verbose)
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return _prevalence_report(true_prevs, estim_prevs, error_metrics)
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@ -85,8 +133,27 @@ def evaluate(
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model: BaseQuantifier,
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protocol: AbstractProtocol,
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error_metric: Union[str, Callable],
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aggr_speedup='auto',
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aggr_speedup: Union[str, bool] = 'auto',
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verbose=False):
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"""
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Evaluates a quantification model according to a specific sample generation protocol and in terms of one
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evaluation metric (error).
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:param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`
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:param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of
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:class:`quapy.protocol.OnLabelledCollection`, then the aggregation speed-up can be run. This is the protocol
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in charge of generating the samples in which the model is evaluated.
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:param error_metric: a string representing the name(s) of an error function in `qp.error`
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(e.g., 'mae'), or a callable function implementing the error function itself.
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:param aggr_speedup: whether or not to apply the speed-up. Set to "force" for applying it even if the number of
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instances in the original collection on which the protocol acts is larger than the number of instances
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in the samples to be generated. Set to True or "auto" (default) for letting QuaPy decide whether it is
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convenient or not. Set to False to deactivate.
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:param verbose: boolean, show or not information in stdout
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:return: if the error metric is not averaged (e.g., 'ae', 'rae'), returns an array of shape `(n_samples,)` with
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the error scores for each sample; if the error metric is averaged (e.g., 'mae', 'mrae') then returns
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a single float
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"""
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if isinstance(error_metric, str):
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error_metric = qp.error.from_name(error_metric)
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@ -96,9 +163,21 @@ def evaluate(
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def evaluate_on_samples(
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model: BaseQuantifier,
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samples: [qp.data.LabelledCollection],
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samples: Iterable[qp.data.LabelledCollection],
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error_metric: Union[str, Callable],
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verbose=False):
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"""
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Evaluates a quantification model on a given set of samples and in terms of one evaluation metric (error).
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:param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`
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:param samples: a list of samples on which the quantifier is to be evaluated
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:param error_metric: a string representing the name(s) of an error function in `qp.error`
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(e.g., 'mae'), or a callable function implementing the error function itself.
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:param verbose: boolean, show or not information in stdout
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:return: if the error metric is not averaged (e.g., 'ae', 'rae'), returns an array of shape `(n_samples,)` with
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the error scores for each sample; if the error metric is averaged (e.g., 'mae', 'mrae') then returns
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a single float
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"""
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return evaluate(model, IterateProtocol(samples), error_metric, aggr_speedup=False, verbose=verbose)
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@ -6,7 +6,7 @@ import torch
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from torch.nn import MSELoss
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from torch.nn.functional import relu
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from protocol import USimplexPP
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from protocol import UPP
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from quapy.method.aggregative import *
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from quapy.util import EarlyStop
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@ -218,7 +218,7 @@ class QuaNetTrainer(BaseQuantifier):
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self.quanet.train(mode=train)
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losses = []
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mae_errors = []
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sampler = USimplexPP(
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sampler = UPP(
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data,
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sample_size=self.sample_size,
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repeats=iterations,
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@ -327,7 +327,7 @@ class NPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
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return self.repeats
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class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
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class UPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol):
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"""
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A variant of :class:`APP` that, instead of using a grid of equidistant prevalence values,
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relies on the Kraemer algorithm for sampling unit (k-1)-simplex uniformly at random, with
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@ -348,7 +348,7 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
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def __init__(self, data: LabelledCollection, sample_size=None, repeats=100, random_state=0,
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return_type='sample_prev'):
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super(USimplexPP, self).__init__(random_state)
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super(UPP, self).__init__(random_state)
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self.data = data
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self.sample_size = qp._get_sample_size(sample_size)
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self.repeats = repeats
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@ -357,9 +357,9 @@ class USimplexPP(AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol)
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def samples_parameters(self):
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"""
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Return all the necessary parameters to replicate the samples as according to the USimplexPP protocol.
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Return all the necessary parameters to replicate the samples as according to the UPP protocol.
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:return: a list of indexes that realize the USimplexPP sampling
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:return: a list of indexes that realize the UPP sampling
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"""
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indexes = []
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for prevs in F.uniform_simplex_sampling(n_classes=self.data.n_classes, size=self.repeats):
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@ -474,3 +474,8 @@ class DomainMixer(AbstractStochasticSeededProtocol):
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return self.repeats * len(self.mixture_points)
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# aliases
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ArtificialPrevalenceProtocol = APP
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NaturalPrevalenceProtocol = NPP
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UniformPrevalenceProtocol = UPP
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@ -1,8 +1,14 @@
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import unittest
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import numpy as np
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import quapy as qp
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from sklearn.linear_model import LogisticRegression
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from time import time
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from quapy.method.aggregative import EMQ
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from error import QUANTIFICATION_ERROR_SINGLE, QUANTIFICATION_ERROR, QUANTIFICATION_ERROR_NAMES, \
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QUANTIFICATION_ERROR_SINGLE_NAMES
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from quapy.method.aggregative import EMQ, PCC
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from quapy.method.base import BaseQuantifier
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@ -48,6 +54,31 @@ class EvalTestCase(unittest.TestCase):
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self.assertEqual(tend_no_optim>(tend_optim/2), True)
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def test_evaluation_output(self):
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data = qp.datasets.fetch_reviews('hp', tfidf=True, min_df=10, pickle=True)
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train, test = data.training, data.test
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qp.environ['SAMPLE_SIZE']=100
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protocol = qp.protocol.APP(test, random_state=0)
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q = PCC(LogisticRegression()).fit(train)
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single_errors = list(QUANTIFICATION_ERROR_SINGLE_NAMES)
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averaged_errors = ['m'+e for e in single_errors]
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single_errors = single_errors + [qp.error.from_name(e) for e in single_errors]
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averaged_errors = averaged_errors + [qp.error.from_name(e) for e in averaged_errors]
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for error_metric, averaged_error_metric in zip(single_errors, averaged_errors):
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score = qp.evaluation.evaluate(q, protocol, error_metric=averaged_error_metric)
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self.assertTrue(isinstance(score, float))
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scores = qp.evaluation.evaluate(q, protocol, error_metric=error_metric)
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self.assertTrue(isinstance(scores, np.ndarray))
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self.assertEqual(scores.mean(), score)
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if __name__ == '__main__':
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unittest.main()
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@ -1,7 +1,7 @@
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import unittest
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import numpy as np
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from quapy.data import LabelledCollection
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from quapy.protocol import APP, NPP, USimplexPP, DomainMixer, AbstractStochasticSeededProtocol
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from quapy.protocol import APP, NPP, UPP, DomainMixer, AbstractStochasticSeededProtocol
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def mock_labelled_collection(prefix=''):
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@ -102,14 +102,14 @@ class TestProtocols(unittest.TestCase):
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def test_kraemer_replicate(self):
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data = mock_labelled_collection()
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p = USimplexPP(data, sample_size=5, repeats=10, random_state=42)
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p = UPP(data, sample_size=5, repeats=10, random_state=42)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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self.assertEqual(samples1, samples2)
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p = USimplexPP(data, sample_size=5, repeats=10) # <- random_state is by default set to 0
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p = UPP(data, sample_size=5, repeats=10) # <- random_state is by default set to 0
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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@ -118,7 +118,7 @@ class TestProtocols(unittest.TestCase):
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def test_kraemer_not_replicate(self):
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data = mock_labelled_collection()
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p = USimplexPP(data, sample_size=5, repeats=10, random_state=None)
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p = UPP(data, sample_size=5, repeats=10, random_state=None)
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samples1 = samples_to_str(p)
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samples2 = samples_to_str(p)
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