remove distributing_samples.py and ensembles.py examples from master

Same experimental/stable split as experimental_non_aggregative; these
stay available on devel.

TODO.txt

@@ -15,7 +15,6 @@ scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum."
 - This functionality should be accessible via sampling protocols and evaluation functions
 
 - [TODO] document confidence in manuals
-- [TODO] Test the return_type="index" in protocols and finish the "distributing_samples.py" example
 - [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
 - [TODO] add HistNetQ
 - [TODO] add CDE-iteration and Bayes-CDE methods

examples/distributing_samples.py

@@ -1,38 +0,0 @@
-"""
-Imagine we want to generate many samples out of a collection, that we want to distribute for others to run their
-own experiments in the very same test samples. One naive solution would come down to applying a given protocol to
-our collection (say the artificial prevalence protocol on the 'academic-success' UCI dataset), store all those samples
-on disk and make them available online. Distributing many such samples is undesirable.
-In this example, we generate the indexes that allow anyone to regenerate the samples out of the original collection.
-"""
-
-import quapy as qp
-from quapy.method.aggregative import PACC
-from quapy.protocol import UPP
-
-data = qp.datasets.fetch_UCIMulticlassDataset('academic-success')
-train, test = data.train_test
-
-# let us train a quantifier to check whether we can actually replicate the results
-quantifier = PACC()
-quantifier.fit(train)
-
-# let us simulate our experimental results
-protocol = UPP(test, sample_size=100, repeats=100, random_state=0)
-our_mae = qp.evaluation.evaluate(quantifier, protocol=protocol, error_metric='mae')
-
-print(f'We have obtained a MAE={our_mae:.3f}')
-
-# let us distribute the indexes; we specify that we want the indexes, not the samples
-protocol = UPP(test, sample_size=100, repeats=100, random_state=0, return_type='index')
-indexes = protocol.samples_parameters()
-
-# Imagine we distribute the indexes; now we show how to replicate our experiments.
-from quapy.protocol import ProtocolFromIndex
-data = qp.datasets.fetch_UCIMulticlassDataset('academic-success')
-train, test = data.train_test
-protocol = ProtocolFromIndex(data=test, indexes=indexes)
-their_mae = qp.evaluation.evaluate(quantifier, protocol=protocol, error_metric='mae')
-
-print(f'Another lab obtains a MAE={our_mae:.3f}')
-

examples/ensembles.py

@@ -1,56 +0,0 @@
-from sklearn.exceptions import ConvergenceWarning
-from sklearn.linear_model import LogisticRegression
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.neighbors import KNeighborsClassifier
-from statsmodels.sandbox.distributions.genpareto import quant
-
-import quapy as qp
-from quapy.protocol import UPP
-from quapy.method.aggregative import PACC, DMy, EMQ, KDEyML
-from quapy.method.meta import SCMQ, MCMQ, MCSQ
-import warnings
-warnings.filterwarnings("ignore", category=DeprecationWarning)
-warnings.filterwarnings("ignore", category=ConvergenceWarning)
-
-qp.environ["SAMPLE_SIZE"]=100
-
-def train_and_test_model(quantifier, train, test):
-    quantifier.fit(train)
-    report = qp.evaluation.evaluation_report(quantifier, UPP(test), error_metrics=['mae', 'mrae'])
-    print(quantifier.__class__.__name__)
-    print(report.mean(numeric_only=True))
-
-
-quantifiers = [
-    PACC(),
-    DMy(),
-    EMQ(),
-    KDEyML()
-]
-
-classifier = LogisticRegression()
-
-dataset_name = qp.datasets.UCI_MULTICLASS_DATASETS[0]
-data = qp.datasets.fetch_UCIMulticlassDataset(dataset_name)
-train, test = data.train_test
-
-scmq = SCMQ(classifier, quantifiers)
-
-train_and_test_model(scmq, train, test)
-
-# for quantifier in quantifiers:
-#     train_and_test_model(quantifier, train, test)
-
-classifiers = [
-    LogisticRegression(),
-    KNeighborsClassifier(),
-    # MultinomialNB()
-]
-
-mcmq = MCMQ(classifiers, quantifiers)
-
-train_and_test_model(mcmq, train, test)
-
-mcsq = MCSQ(classifiers, PACC())
-
-train_and_test_model(mcsq, train, test)