from copy import deepcopy
from typing import Union
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score, make_scorer, accuracy_score
from sklearn.model_selection import GridSearchCV, cross_val_predict
from tqdm import tqdm
import quapy as qp
from quapy import functional as F
from quapy.data import LabelledCollection
from quapy.evaluation import evaluate
from quapy.model_selection import GridSearchQ
try:
from . import neural
except ModuleNotFoundError:
neural = None
from .base import BaseQuantifier
from quapy.method.aggregative import CC, ACC, PACC, HDy, EMQ
if neural:
QuaNet = neural.QuaNetTrainer
else:
QuaNet = "QuaNet is not available due to missing torch package"
class Ensemble(BaseQuantifier):
VALID_POLICIES = {'ave', 'ptr', 'ds'} | qp.error.QUANTIFICATION_ERROR_NAMES
"""
Methods from the articles:
Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
Information Fusion, 34, 87-100.
and
Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
Dynamic ensemble selection for quantification tasks.
Information Fusion, 45, 1-15.
"""
def __init__(self,
quantifier: BaseQuantifier,
size=50,
red_size=25,
min_pos=5,
policy='ave',
max_sample_size=None,
val_split=None,
n_jobs=1,
verbose=False):
assert policy in Ensemble.VALID_POLICIES, \
f'unknown policy={policy}; valid are {Ensemble.VALID_POLICIES}'
assert max_sample_size is None or max_sample_size > 0, \
'wrong value for max_sample_size; set it to a positive number or None'
self.base_quantifier = quantifier
self.size = size
self.min_pos = min_pos
self.red_size = red_size
self.policy = policy
self.val_split = val_split
self.n_jobs = n_jobs
self.post_proba_fn = None
self.verbose = verbose
self.max_sample_size = max_sample_size
def sout(self, msg):
if self.verbose:
print('[Ensemble]' + msg)
def fit(self, data: qp.data.LabelledCollection, val_split: Union[qp.data.LabelledCollection, float] = None):
self.sout('Fit')
if self.policy == 'ds' and not data.binary:
raise ValueError(f'ds policy is only defined for binary quantification, but this dataset is not binary')
if val_split is None:
val_split = self.val_split
# randomly chooses the prevalences for each member of the ensemble (preventing classes with less than
# min_pos positive examples)
sample_size = len(data) if self.max_sample_size is None else min(self.max_sample_size, len(data))
prevs = [_draw_simplex(ndim=data.n_classes, min_val=self.min_pos / sample_size) for _ in range(self.size)]
posteriors = None
if self.policy == 'ds':
# precompute the training posterior probabilities
posteriors, self.post_proba_fn = self.ds_policy_get_posteriors(data)
is_static_policy = (self.policy in qp.error.QUANTIFICATION_ERROR_NAMES)
args = (
(self.base_quantifier, data, val_split, prev, posteriors, is_static_policy, self.verbose, sample_size)
for prev in prevs
)
self.ensemble = qp.util.parallel(
_delayed_new_instance,
tqdm(args, desc='fitting ensamble', total=self.size) if self.verbose else args,
n_jobs=self.n_jobs)
# static selection policy (the name of a quantification-oriented error function to minimize)
if self.policy in qp.error.QUANTIFICATION_ERROR_NAMES:
self.accuracy_policy(error_name=self.policy)
self.sout('Fit [Done]')
return self
def quantify(self, instances):
predictions = np.asarray(
qp.util.parallel(_delayed_quantify, ((Qi, instances) for Qi in self.ensemble), n_jobs=self.n_jobs)
)
if self.policy == 'ptr':
predictions = self.ptr_policy(predictions)
elif self.policy == 'ds':
predictions = self.ds_policy(predictions, instances)
predictions = np.mean(predictions, axis=0)
return F.normalize_prevalence(predictions)
def set_params(self, **parameters):
raise NotImplementedError(f'{self.__class__.__name__} should not be used within GridSearchQ; '
f'instead, use Ensemble(GridSearchQ(q),...), with q a Quantifier (recommended), '
f'or Ensemble(Q(GridSearchCV(l))) with Q a quantifier class that has a learner '
f'l optimized for classification (not recommended).')
def get_params(self, deep=True):
raise NotImplementedError()
def accuracy_policy(self, error_name):
"""
Selects the red_size best performant quantifiers in a static way (i.e., dropping all non-selected instances).
For each model in the ensemble, the performance is measured in terms of _error_name_ on the quantification of
the samples used for training the rest of the models in the ensemble.
"""
error = qp.error.from_name(error_name)
tests = [m[3] for m in self.ensemble]
scores = []
for i, model in enumerate(self.ensemble):
scores.append(evaluate(model[0], tests[:i] + tests[i + 1:], error, self.n_jobs))
order = np.argsort(scores)
self.ensemble = _select_k(self.ensemble, order, k=self.red_size)
def ptr_policy(self, predictions):
"""
Selects the predictions made by models that have been trained on samples with a prevalence that is most similar
to a first approximation of the test prevalence as made by all models in the ensemble.
"""
test_prev_estim = predictions.mean(axis=0)
tr_prevs = [m[1] for m in self.ensemble]
ptr_differences = [qp.error.mse(ptr_i, test_prev_estim) for ptr_i in tr_prevs]
order = np.argsort(ptr_differences)
return _select_k(predictions, order, k=self.red_size)
def ds_policy_get_posteriors(self, data: LabelledCollection):
"""
In the original article, this procedure is not described in a sufficient level of detail. The paper only says
that the distribution of posterior probabilities from training and test examples is compared by means of the
Hellinger Distance. However, how these posterior probabilities are generated is not specified. In the article,
a Logistic Regressor (LR) is used as the classifier device and that could be used for this purpose. However, in
general, a Quantifier is not necessarily an instance of Aggreggative Probabilistic Quantifiers, and so, that the
quantifier builds on top of a probabilistic classifier cannot be given for granted. Additionally, it would not
be correct to generate the posterior probabilities for training documents that have concurred in training the
classifier that generates them.
This function thus generates the posterior probabilities for all training documents in a cross-validation way,
using a LR with hyperparameters that have previously been optimized via grid search in 5FCV.
:return P,f, where P is a ndarray containing the posterior probabilities of the training data, generated via
cross-validation and using an optimized LR, and the function to be used in order to generate posterior
probabilities for test instances.
"""
X, y = data.Xy
lr_base = LogisticRegression(class_weight='balanced', max_iter=1000)
optim = GridSearchCV(
lr_base, param_grid={'C': np.logspace(-4, 4, 9)}, cv=5, n_jobs=self.n_jobs, refit=True
).fit(X, y)
posteriors = cross_val_predict(
optim.best_estimator_, X, y, cv=5, n_jobs=self.n_jobs, method='predict_proba'
)
posteriors_generator = optim.best_estimator_.predict_proba
return posteriors, posteriors_generator
def ds_policy(self, predictions, test):
test_posteriors = self.post_proba_fn(test)
test_distribution = get_probability_distribution(test_posteriors)
tr_distributions = [m[2] for m in self.ensemble]
dist = [F.HellingerDistance(tr_dist_i, test_distribution) for tr_dist_i in tr_distributions]
order = np.argsort(dist)
return _select_k(predictions, order, k=self.red_size)
@property
def classes_(self):
return self.base_quantifier.classes_
@property
def binary(self):
return self.base_quantifier.binary
@property
def aggregative(self):
return False
@property
def probabilistic(self):
return False
def get_probability_distribution(posterior_probabilities, bins=8):
assert posterior_probabilities.shape[1] == 2, 'the posterior probabilities do not seem to be for a binary problem'
posterior_probabilities = posterior_probabilities[:, 1] # take the positive posteriors only
distribution, _ = np.histogram(posterior_probabilities, bins=bins, range=(0, 1), density=True)
return distribution
def _select_k(elements, order, k):
return [elements[idx] for idx in order[:k]]
def _delayed_new_instance(args):
base_quantifier, data, val_split, prev, posteriors, keep_samples, verbose, sample_size = args
if verbose:
print(f'\tfit-start for prev {F.strprev(prev)}, sample_size={sample_size}')
model = deepcopy(base_quantifier)
if val_split is not None:
if isinstance(val_split, float):
assert 0 < val_split < 1, 'val_split should be in (0,1)'
data, val_split = data.split_stratified(train_prop=1 - val_split)
sample_index = data.sampling_index(sample_size, *prev)
sample = data.sampling_from_index(sample_index)
if val_split is not None:
model.fit(sample, val_split=val_split)
else:
model.fit(sample)
tr_prevalence = sample.prevalence()
tr_distribution = get_probability_distribution(posteriors[sample_index]) if (posteriors is not None) else None
if verbose:
print(f'\t\--fit-ended for prev {F.strprev(prev)}')
return (model, tr_prevalence, tr_distribution, sample if keep_samples else None)
def _delayed_quantify(args):
quantifier, instances = args
return quantifier[0].quantify(instances)
def _draw_simplex(ndim, min_val, max_trials=100):
"""
returns a uniform sampling from the ndim-dimensional simplex but guarantees that all dimensions
are >= min_class_prev (for min_val>0, this makes the sampling not truly uniform)
:param ndim: number of dimensions of the simplex
:param min_val: minimum class prevalence allowed. If less than 1/ndim a ValueError will be throw since
there is no possible solution.
:return: a sample from the ndim-dimensional simplex that is uniform in S(ndim)-R where S(ndim) is the simplex
and R is the simplex subset containing dimensions lower than min_val
"""
if min_val >= 1 / ndim:
raise ValueError(f'no sample can be draw from the {ndim}-dimensional simplex so that '
f'all its values are >={min_val} (try with a larger value for min_pos)')
trials = 0
while True:
u = F.uniform_simplex_sampling(ndim)
if all(u >= min_val):
return u
trials += 1
if trials >= max_trials:
raise ValueError(f'it looks like finding a random simplex with all its dimensions being'
f'>= {min_val} is unlikely (it failed after {max_trials} trials)')
def _instantiate_ensemble(learner, base_quantifier_class, param_grid, optim, param_model_sel, **kwargs):
if optim is None:
base_quantifier = base_quantifier_class(learner)
elif optim in qp.error.CLASSIFICATION_ERROR:
if optim == qp.error.f1e:
scoring = make_scorer(f1_score)
elif optim == qp.error.acce:
scoring = make_scorer(accuracy_score)
learner = GridSearchCV(learner, param_grid, scoring=scoring)
base_quantifier = base_quantifier_class(learner)
else:
base_quantifier = GridSearchQ(base_quantifier_class(learner),
param_grid=param_grid,
**param_model_sel,
error=optim)
return Ensemble(base_quantifier, **kwargs)
def _check_error(error):
if error is None:
return None
if error in qp.error.QUANTIFICATION_ERROR or error in qp.error.CLASSIFICATION_ERROR:
return error
elif isinstance(error, str):
return qp.error.from_name(error)
else:
raise ValueError(f'unexpected error type; must either be a callable function or a str representing\n'
f'the name of an error function in {qp.error.ERROR_NAMES}')
def ensembleFactory(learner, base_quantifier_class, param_grid=None, optim=None, param_model_sel: dict = None,
**kwargs):
if optim is not None:
if param_grid is None:
raise ValueError(f'param_grid is None but optim was requested.')
if param_model_sel is None:
raise ValueError(f'param_model_sel is None but optim was requested.')
error = _check_error(optim)
return _instantiate_ensemble(learner, base_quantifier_class, param_grid, error, param_model_sel, **kwargs)
def ECC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
return ensembleFactory(learner, CC, param_grid, optim, param_mod_sel, **kwargs)
def EACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
return ensembleFactory(learner, ACC, param_grid, optim, param_mod_sel, **kwargs)
def EPACC(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
return ensembleFactory(learner, PACC, param_grid, optim, param_mod_sel, **kwargs)
def EHDy(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
return ensembleFactory(learner, HDy, param_grid, optim, param_mod_sel, **kwargs)
def EEMQ(learner, param_grid=None, optim=None, param_mod_sel=None, **kwargs):
return ensembleFactory(learner, EMQ, param_grid, optim, param_mod_sel, **kwargs)