diff --git a/TweetSentQuant/experiments.py b/TweetSentQuant/experiments.py
new file mode 100644
index 0000000..511e60e
--- /dev/null
+++ b/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(),
+# }
+#
diff --git a/TweetSentQuant/tables.py b/TweetSentQuant/tables.py
new file mode 100644
index 0000000..12568b6
--- /dev/null
+++ b/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]")
\ No newline at end of file
diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py
index 54bfbfb..2af3de3 100644
--- a/quapy/data/datasets.py
+++ b/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:
diff --git a/quapy/data/preprocessing.py b/quapy/data/preprocessing.py
index 6206be0..972a3db 100644
--- a/quapy/data/preprocessing.py
+++ b/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)
diff --git a/quapy/evaluation.py b/quapy/evaluation.py
index a270663..498f284 100644
--- a/quapy/evaluation.py
+++ b/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):
diff --git a/quapy/model_selection.py b/quapy/model_selection.py
index 039dbb9..be330b5 100644
--- a/quapy/model_selection.py
+++ b/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())
diff --git a/tweet_sent_quant.py b/tweet_sent_quant.py
deleted file mode 100644
index ec35010..0000000
--- a/tweet_sent_quant.py
+++ /dev/null
@@ -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(),
-}
-