testing quapy via replicating Tweet Quantification experiments

TweetSentQuant/experiments.py

@@ -0,0 +1,136 @@
+from sklearn.linear_model import LogisticRegression
+import quapy as qp
+import quapy.functional as F
+import numpy as np
+import os
+import pickle
+import itertools
+from joblib import Parallel, delayed
+import multiprocessing
+
+
+n_jobs = multiprocessing.cpu_count()
+
+
+def quantification_models():
+    def newLR():
+        return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
+    __C_range = np.logspace(-4, 5, 10)
+    lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
+    yield 'cc', qp.method.aggregative.CC(newLR()), lr_params
+    yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
+    yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
+    yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
+
+
+def evaluate_experiment(true_prevalences, estim_prevalences):
+    print('\nEvaluation Metrics:\n'+'='*22)
+    for eval_measure in [qp.error.mae, qp.error.mrae]:
+        err = eval_measure(true_prevalences, estim_prevalences)
+        print(f'\t{eval_measure.__name__}={err:.4f}')
+    print()
+
+
+def evaluate_method_point_test(true_prev, estim_prev):
+    print('\nPoint-Test evaluation:\n' + '=' * 22)
+    print(f'true-prev={F.strprev(true_prev)}, estim-prev={F.strprev(estim_prev)}')
+    for eval_measure in [qp.error.mae, qp.error.mrae]:
+        err = eval_measure(true_prev, estim_prev)
+        print(f'\t{eval_measure.__name__}={err:.4f}')
+
+
+def result_path(dataset_name, model_name, optim_loss):
+    return f'./results/{dataset_name}-{model_name}-{optim_loss}.pkl'
+
+
+def is_already_computed(dataset_name, model_name, optim_loss):
+    if dataset_name=='semeval':
+        check_datasets = ['semeval13', 'semeval14', 'semeval15']
+    else:
+        check_datasets = [dataset_name]
+    return all(os.path.exists(result_path(name, model_name, optim_loss)) for name in check_datasets)
+
+
+def save_results(dataset_name, model_name, optim_loss, *results):
+    rpath = result_path(dataset_name, model_name, optim_loss)
+    qp.util.create_parent_dir(rpath)
+    with open(rpath, 'wb') as foo:
+        pickle.dump(tuple(results), foo, pickle.HIGHEST_PROTOCOL)
+
+
+def run(experiment):
+
+    sample_size = 100
+    qp.environ['SAMPLE_SIZE'] = sample_size
+
+    optim_loss, dataset_name, (model_name, model, hyperparams) = experiment
+
+    if is_already_computed(dataset_name, model_name, optim_loss=optim_loss):
+        print(f'result for dataset={dataset_name} model={model_name} loss={optim_loss} already computed.')
+        return
+
+    benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
+
+    # model selection (hyperparameter optimization for a quantification-oriented loss)
+    model_selection = qp.model_selection.GridSearchQ(
+        model,
+        param_grid=hyperparams,
+        sample_size=sample_size,
+        n_prevpoints=21,
+        n_repetitions=5,
+        error='mae',
+        refit=False,
+        verbose=True
+    )
+    model_selection.fit(benchmark_devel.training, benchmark_devel.test)
+    model = model_selection.best_model()
+
+    # model evaluation
+    test_names = [dataset_name] if dataset_name != 'semeval' else ['semeval13', 'semeval14', 'semeval15']
+    for test_no, test_name in enumerate(test_names):
+        benchmark_eval = qp.datasets.fetch_twitter(test_name, for_model_selection=False, min_df=5, pickle=True)
+        if test_no == 0:
+            # fits the model only the first time
+            model.fit(benchmark_eval.training)
+
+        true_prevalences, estim_prevalences = qp.evaluation.artificial_sampling_prediction(
+            model,
+            test=benchmark_eval.test,
+            sample_size=sample_size,
+            n_prevpoints=21,
+            n_repetitions=25
+        )
+        test_estim_prevalence = model.quantify(benchmark_eval.test.instances)
+        test_true_prevalence = benchmark_eval.test.prevalence()
+
+        evaluate_experiment(true_prevalences, estim_prevalences)
+        evaluate_method_point_test(test_true_prevalence, test_estim_prevalence)
+        save_results(test_name, model_name, optim_loss,
+                     true_prevalences, estim_prevalences,
+                     benchmark_eval.training.prevalence(), test_true_prevalence, test_estim_prevalence,
+                     model_selection.best_params_)
+
+
+if __name__ == '__main__':
+
+    np.random.seed(0)
+
+    optim_losses = ['mae', 'mrae']
+    datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN
+    models = quantification_models()
+
+    results = Parallel(n_jobs=n_jobs)(
+        delayed(run)(experiment) for experiment in itertools.product(optim_losses, datasets, models)
+    )
+
+
+# QUANTIFIER_ALIASES = {
+#     'emq': lambda learner: ExpectationMaximizationQuantifier(learner),
+#     'svmq': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='q'),
+#     'svmkld': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='kld'),
+#     'svmnkld': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='nkld'),
+#     'svmmae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mae'),
+#     'svmmrae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mrae'),
+#     'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(),
+# }
+#

TweetSentQuant/tables.py

@@ -0,0 +1,187 @@
+import quapy as qp
+from os import makedirs
+# from evaluate import evaluate_directory, statistical_significance, get_ranks_from_Gao_Sebastiani
+import sys, os
+import pickle
+from experiments import result_path
+
+tables_path = './tables'
+MAXTONE = 50  # sets the intensity of the maximum color reached by the worst (red) and best (green) results
+
+makedirs(tables_path, exist_ok=True)
+
+sample_size = 100
+qp.environ['SAMPLE_SIZE'] = sample_size
+
+
+
+# results_dict = evaluate_directory('results/*.pkl', evaluation_measures)
+# stats = {
+#     dataset : {
+#         'mae': statistical_significance(f'results/{dataset}-*-mae-run?.pkl', ae),
+#         'mrae': statistical_significance(f'results/{dataset}-*-mrae-run?.pkl', rae),
+#     } for dataset in datasets
+# }
+
+nice = {
+    'mae':'AE',
+    'mrae':'RAE',
+    'svmkld': 'SVM(KLD)',
+    'svmnkld': 'SVM(NKLD)',
+    'svmq': 'SVM(Q)',
+    'svmae': 'SVM(AE)',
+    'svmnae': 'SVM(NAE)',
+    'svmmae': 'SVM(AE)',
+    'svmmrae': 'SVM(RAE)',
+    'quanet': 'QuaNet',
+    'hdy': 'HDy',
+    'dys': 'DyS',
+    'svmperf':'',
+    'sanders': 'Sanders',
+    'semeval13': 'SemEval13',
+    'semeval14': 'SemEval14',
+    'semeval15': 'SemEval15',
+    'semeval16': 'SemEval16'
+}
+#     }
+# }
+
+
+def nicerm(key):
+    return '\mathrm{'+nice[key]+'}'
+
+def color_from_rel_rank(rel_rank, maxtone=100):
+    rel_rank = rel_rank*2-1
+    if rel_rank < 0:
+        color = 'red'
+        tone = maxtone*(-rel_rank)
+    else:
+        color = 'green'
+        tone = maxtone*rel_rank
+    return '\cellcolor{' + color + f'!{int(tone)}' + '}'
+
+def color_from_abs_rank(abs_rank, n_methods, maxtone=100):
+    rel_rank = 1.-(abs_rank-1.)/(n_methods-1)
+    return color_from_rel_rank(rel_rank, maxtone)
+
+
+def save_table(path, table):
+    print(f'saving results in {path}')
+    with open(path, 'wt') as foo:
+        foo.write(table)
+
+
+# Tables evaluation scores for AE and RAE (two tables)
+# ----------------------------------------------------
+
+datasets = qp.datasets.TWITTER_SENTIMENT_DATASETS_TEST
+evaluation_measures = [qp.error.mae, qp.error.mrae]
+gao_seb_methods = ['cc', 'acc', 'pcc', 'pacc', 'emq', 'svmq', 'svmkld', 'svmnkld']
+
+results_dict = {}
+stats={}
+def getscore(dataset, method, loss):
+    path = result_path(dataset, method, loss)
+    if os.path.exists(path):
+        true_prevs, estim_prevs, _, _, _, _ = pickle.load(open(path, 'rb'))
+        err = getattr(qp.error, loss)
+        return err(true_prevs, estim_prevs)
+    return None
+
+
+for i, eval_func in enumerate(evaluation_measures):
+    eval_name = eval_func.__name__
+    added_methods = ['svm' + eval_name]  # , 'quanet', 'dys']
+    methods = gao_seb_methods + added_methods
+    nold_methods = len(gao_seb_methods)
+    nnew_methods = len(added_methods)
+
+    tabular = """
+    \\begin{tabularx}{\\textwidth}{|c||""" + ('Y|'*len(gao_seb_methods))+ '|' + ('Y|'*len(added_methods)) + """} \hline
+      & \multicolumn{"""+str(nold_methods)+"""}{c||}{Methods tested in~\cite{Gao:2016uq}} & \multicolumn{"""+str(nnew_methods)+"""}{c||}{} \\\\ \hline
+    """
+
+    for method in methods:
+        tabular += ' & \side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} '
+    tabular += '\\\\\hline\n'
+
+    for dataset in datasets:
+        tabular += nice.get(dataset, dataset.upper()) + ' '
+        for method in methods:
+            #simplify...
+            score = getscore(dataset, method, eval_name)
+            if score:
+                tabular += f' & {score:.3f} '
+            else:
+                tabular += ' & --- '
+        tabular += '\\\\\hline\n'
+    tabular += "\end{tabularx}"
+
+    save_table(f'./tables/tab_results_{eval_name}.new.tex', tabular)
+
+sys.exit(0)
+
+# gao_seb_ranks, gao_seb_results = get_ranks_from_Gao_Sebastiani()
+
+# Tables ranks for AE and RAE (two tables)
+# ----------------------------------------------------
+# for i, eval_func in enumerate(evaluation_measures):
+#     eval_name = eval_func.__name__
+#     methods = ['cc', 'acc', 'pcc', 'pacc', 'emq', 'svmq', 'svmkld', 'svmnkld']
+#     table = """
+#     \\begin{table}[h]
+#     """
+#     if i == 0:
+#         caption = """
+#           \caption{Rank positions of the quantification methods in the AE
+#           experiments, and (between parentheses) the rank positions
+#           obtained in the evaluation of~\cite{Gao:2016uq}.}
+#         """
+#     else:
+#         caption = "\caption{Same as Table~\\ref{tab:maeranks}, but with " + nice[eval_name] + " instead of AE.}"
+#     table += caption + """
+#             \\begin{center}
+#             \\resizebox{\\textwidth}{!}{
+#         """
+#     tabular = """
+#         \\begin{tabularx}{\\textwidth}{|c||Y|Y|Y|Y|Y|Y|Y|Y|} \hline
+#           & \multicolumn{8}{c|}{Methods tested in~\cite{Gao:2016uq}}  \\\\ \hline
+#     """
+#
+#     for method in methods:
+#         tabular += ' & \side{' + nice.get(method, method.upper()) +'$^{' + nicerm(eval_name) + '}$} '
+#     tabular += '\\\\\hline\n'
+#
+#     for dataset in datasets:
+#         tabular += nice.get(dataset, dataset.upper()) + ' '
+#         ranks_no_gap = []
+#         for method in methods:
+#             learner = 'lr' if not method.startswith('svm') else 'svmperf'
+#             key = f'{dataset}-{method}-{learner}-{}-{eval_name}'
+#             ranks_no_gap.append(stats[dataset][eval_name].get(key, (None, None, len(methods)))[2])
+#         ranks_no_gap = sorted(ranks_no_gap)
+#         ranks_no_gap = {rank:i+1 for i,rank in enumerate(ranks_no_gap)}
+#         for method in methods:
+#             learner = 'lr' if not method.startswith('svm') else 'svmperf'
+#             key = f'{dataset}-{method}-{learner}-{sample_size}-{eval_name}'
+#             if key in stats[dataset][eval_name]:
+#                 _, _, abs_rank = stats[dataset][eval_name][key]
+#                 real_rank = ranks_no_gap[abs_rank]
+#                 tabular += f' & {real_rank}'
+#                 tabular += color_from_abs_rank(real_rank, len(methods), maxtone=MAXTONE)
+#             else:
+#                 tabular += ' & --- '
+#             old_rank = gao_seb_ranks.get(f'{dataset}-{method}-{eval_name}', 'error')
+#             tabular += f' ({old_rank})'
+#         tabular += '\\\\\hline\n'
+#     tabular += "\end{tabularx}"
+#     table += tabular + """
+#         }
+#       \end{center}
+#       \label{tab:""" + eval_name + """ranks}
+#     \end{table}
+#     """
+#     save_table(f'../tables/tab_rank_{eval_name}.tex', table)
+#
+#
+# print("[Done]")

quapy/data/datasets.py

@@ -9,9 +9,12 @@ import pandas as pd
 
 
 REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
-TWITTER_SENTIMENT_DATASETS = ['gasp', 'hcr', 'omd', 'sanders',
+TWITTER_SENTIMENT_DATASETS_TEST = ['gasp', 'hcr', 'omd', 'sanders',
                               'semeval13', 'semeval14', 'semeval15', 'semeval16',
                               'sst', 'wa', 'wb']
+TWITTER_SENTIMENT_DATASETS_TRAIN = ['gasp', 'hcr', 'omd', 'sanders',
+                                 'semeval', 'semeval16',
+                                 'sst', 'wa', 'wb']
 
 
 def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False):
@@ -63,6 +66,7 @@ def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_hom
     Load a Twitter dataset as a Dataset instance, as used in:
     Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
     Social Network Analysis and Mining6(19), 1–22 (2016)
+    The datasets 'semeval13', 'semeval14', 'semeval15' share the same training set.
 
     :param dataset_name: the name of the dataset: valid ones are 'gasp', 'hcr', 'omd', 'sanders', 'semeval13',
     'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb'
@@ -76,9 +80,11 @@ def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_hom
     faster subsequent invokations
     :return: a Dataset instance
     """
-    assert dataset_name in TWITTER_SENTIMENT_DATASETS, \
+    assert dataset_name in TWITTER_SENTIMENT_DATASETS_TRAIN + TWITTER_SENTIMENT_DATASETS_TEST, \
         f'Name {dataset_name} does not match any known dataset for sentiment twitter. ' \
-        f'Valid ones are {TWITTER_SENTIMENT_DATASETS}'
+        f'Valid ones are {TWITTER_SENTIMENT_DATASETS_TRAIN} for model selection and ' \
+        f'{TWITTER_SENTIMENT_DATASETS_TEST} for test (datasets "semeval14", "semeval15", "semeval16" share ' \
+        f'a common training set "semeval")'
     if data_home is None:
         data_home = get_quapy_home()
 
@@ -97,6 +103,9 @@ def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_hom
         print(f"the training and development sets for datasets 'semeval13', 'semeval14', 'semeval15' are common "
               f"(called 'semeval'); returning trainin-set='{trainset_name}' and test-set={testset_name}")
     else:
+        if dataset_name == 'semeval' and for_model_selection==False:
+            raise ValueError('dataset "semeval" can only be used for model selection. '
+                             'Use "semeval13", "semeval14", or "semeval15" for model evaluation.')
         trainset_name = testset_name = dataset_name
 
     if for_model_selection:

quapy/data/preprocessing.py

@@ -137,7 +137,7 @@ class IndexTransformer:
 
     def index(self, documents):
         vocab = self.vocabulary_.copy()
-        return [[vocab.get(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
+        return [[vocab.getscore(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
 
     def fit_transform(self, X, n_jobs=-1):
         return self.fit(X).transform(X, n_jobs=n_jobs)

quapy/evaluation.py

@@ -39,17 +39,17 @@ def artificial_sampling_prediction(
         indexes = list(test.artificial_sampling_index_generator(sample_size, n_prevpoints, n_repetitions))
 
     if isinstance(model, qp.method.aggregative.AggregativeQuantifier):
-        print('\tinstance of aggregative-quantifier')
+        # print('\tinstance of aggregative-quantifier')
         quantification_func = model.aggregate
         if isinstance(model, qp.method.aggregative.AggregativeProbabilisticQuantifier):
-            print('\t\tinstance of probabilitstic-aggregative-quantifier')
+            # print('\t\tinstance of probabilitstic-aggregative-quantifier')
             preclassified_instances = model.posterior_probabilities(test.instances)
         else:
-            print('\t\tinstance of hard-aggregative-quantifier')
+            # print('\t\tinstance of hard-aggregative-quantifier')
             preclassified_instances = model.classify(test.instances)
         test = LabelledCollection(preclassified_instances, test.labels)
     else:
-        print('\t\tinstance of base-quantifier')
+        # print('\t\tinstance of base-quantifier')
         quantification_func = model.quantify
 
     def _predict_prevalences(index):

quapy/model_selection.py

@@ -112,7 +112,7 @@ class GridSearchQ(BaseQuantifier):
             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.QUANTIFICATION_ERROR_NAMES}')
 
-    def fit(self, training: LabelledCollection, validation: Union[LabelledCollection, float]=0.3):
+    def fit(self, training: LabelledCollection, validation: Union[LabelledCollection, float]=0.4):
         """
         :param training: the training set on which to optimize the hyperparameters
         :param validation: either a LabelledCollection on which to test the performance of the different settings, or
@@ -121,6 +121,8 @@ class GridSearchQ(BaseQuantifier):
         training, validation = self.__check_training_validation(training, validation)
         self.__check_num_evals(self.n_prevpoints, self.eval_budget, self.n_repetitions, training.n_classes)
 
+        print(f'training size={len(training)}')
+        print(f'validation size={len(validation)}')
         params_keys = list(self.param_grid.keys())
         params_values = list(self.param_grid.values())
 

tweet_sent_quant.py

@@ -1,137 +0,0 @@
-from sklearn.linear_model import LogisticRegression
-import quapy as qp
-import quapy.functional as F
-import numpy as np
-import os
-import sys
-import pickle
-
-qp.environ['SAMPLE_SIZE'] = 100
-sample_size = qp.environ['SAMPLE_SIZE']
-
-
-
-def evaluate_experiment(true_prevalences, estim_prevalences, n_repetitions=25):
-    #n_classes = true_prevalences.shape[1]
-    #true_ave = true_prevalences.reshape(-1, n_repetitions, n_classes).mean(axis=1)
-    #estim_ave = estim_prevalences.reshape(-1, n_repetitions, n_classes).mean(axis=1)
-    #estim_std = estim_prevalences.reshape(-1, n_repetitions, n_classes).std(axis=1)
-    #print('\nTrueP->mean(Phat)(std(Phat))\n'+'='*22)
-    #for true, estim, std in zip(true_ave, estim_ave, estim_std):
-    #    str_estim = ', '.join([f'{mean:.3f}+-{std:.4f}' for mean, std in zip(estim, std)])
-    #    print(f'{F.strprev(true)}->[{str_estim}]')
-
-    print('\nEvaluation Metrics:\n'+'='*22)
-    for eval_measure in [qp.error.mae, qp.error.mrae]:
-        err = eval_measure(true_prevalences, estim_prevalences)
-        print(f'\t{eval_measure.__name__}={err:.4f}')
-    print()
-
-
-def evaluate_method_point_test(method, test):
-    estim_prev = method.quantify(test.instances)
-    true_prev = F.prevalence_from_labels(test.labels, test.n_classes)
-    print('\nPoint-Test evaluation:\n' + '=' * 22)
-    print(f'true-prev={F.strprev(true_prev)}, estim-prev={F.strprev(estim_prev)}')
-    for eval_measure in [qp.error.mae, qp.error.mrae]:
-        err = eval_measure(true_prev, estim_prev)
-        print(f'\t{eval_measure.__name__}={err:.4f}')
-
-
-def quantification_models():
-    def newLR():
-        return LogisticRegression(max_iter=1000, solver='lbfgs', n_jobs=-1)
-    __C_range = np.logspace(-4, 5, 10)
-    lr_params = {'C': __C_range, 'class_weight': [None, 'balanced']}
-    #yield 'cc', qp.method.aggregative.CC(newLR()), lr_params
-    #yield 'acc', qp.method.aggregative.ACC(newLR()), lr_params
-    #yield 'pcc', qp.method.aggregative.PCC(newLR()), lr_params
-    yield 'pacc', qp.method.aggregative.PACC(newLR()), lr_params
-
-
-def result_path(dataset_name, model_name, optim_metric):
-    return f'{dataset_name}-{model_name}-{optim_metric}.pkl'
-
-
-def check_already_computed(dataset_name, model_name, optim_metric):
-    path = result_path(dataset_name, model_name, optim_metric)
-    return os.path.exists(path)
-
-
-def save_results(dataset_name, model_name, optim_metric, *results):
-    path = result_path(dataset_name, model_name, optim_metric)
-    qp.util.create_parent_dir(path)
-    with open(path, 'wb') as foo:
-        pickle.dump(tuple(results), foo, pickle.HIGHEST_PROTOCOL)
-
-
-if __name__ == '__main__':
-
-    np.random.seed(0)
-
-    for dataset_name in ['sanders']:  # qp.datasets.TWITTER_SENTIMENT_DATASETS:
-
-        benchmark_devel = qp.datasets.fetch_twitter(dataset_name, for_model_selection=True, min_df=5, pickle=True)
-        benchmark_devel.stats()
-
-        for model_name, model, hyperparams in quantification_models():
-
-            model_selection = qp.model_selection.GridSearchQ(
-                model,
-                param_grid=hyperparams,
-                sample_size=sample_size,
-                n_prevpoints=21,
-                n_repetitions=5,
-                error='mae',
-                refit=False,
-                verbose=True
-            )
-
-            model_selection.fit(benchmark_devel.training, benchmark_devel.test)
-            model = model_selection.best_model()
-
-            benchmark_eval = qp.datasets.fetch_twitter(dataset_name, for_model_selection=False, min_df=5, pickle=True)
-            model.fit(benchmark_eval.training)
-            true_prevalences, estim_prevalences = qp.evaluation.artificial_sampling_prediction(
-                model,
-                test=benchmark_eval.test,
-                sample_size=sample_size,
-                n_prevpoints=21,
-                n_repetitions=25
-            )
-
-            evaluate_experiment(true_prevalences, estim_prevalences, n_repetitions=25)
-            evaluate_method_point_test(model, benchmark_eval.test)
-
-            #save_arrays(FLAGS.results, true_prevalences, estim_prevalences, test_name)
-
-            sys.exit(0)
-
-            # decide the test to be performed (in the case of 'semeval', tests are 'semeval13', 'semeval14', 'semeval15')
-            if FLAGS.dataset == 'semeval':
-                test_sets = ['semeval13', 'semeval14', 'semeval15']
-            else:
-                test_sets = [FLAGS.dataset]
-
-                evaluate_method_point_test(method, benchmark_eval.test, test_name=test_set)
-
-
-
-
-# quantifiers:
-# ----------------------------------------
-# alias for quantifiers and default configurations
-QUANTIFIER_ALIASES = {
-    'cc': lambda learner: ClassifyAndCount(learner),
-    'acc': lambda learner: AdjustedClassifyAndCount(learner),
-    'pcc': lambda learner: ProbabilisticClassifyAndCount(learner),
-    'pacc': lambda learner: ProbabilisticAdjustedClassifyAndCount(learner),
-    'emq': lambda learner: ExpectationMaximizationQuantifier(learner),
-    'svmq': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='q'),
-    'svmkld': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='kld'),
-    'svmnkld': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='nkld'),
-    'svmmae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mae'),
-    'svmmrae': lambda learner: OneVsAllELM(settings.SVM_PERF_HOME, loss='mrae'),
-    'mlpe': lambda learner: MaximumLikelihoodPrevalenceEstimation(),
-}
-