full example of training, model selection, and evaluation using the lequa2022 dataset with the new protocols

examples/lequa2022_experiments.py

@@ -0,0 +1,26 @@
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+import quapy as qp
+from data.datasets import LEQUA2022_SAMPLE_SIZE, fetch_lequa2022
+from evaluation import evaluation_report
+from method.aggregative import EMQ
+from model_selection import GridSearchQ
+
+
+task = 'T1A'
+
+qp.environ['SAMPLE_SIZE']=LEQUA2022_SAMPLE_SIZE[task]
+training, val_generator, test_generator = fetch_lequa2022(task=task)
+
+# define the quantifier
+quantifier = EMQ(learner=LogisticRegression())
+
+# model selection
+param_grid = {'C': np.logspace(-3, 3, 7), 'class_weight': ['balanced', None]}
+model_selection = GridSearchQ(quantifier, param_grid, protocol=val_generator, n_jobs=-1, refit=False, verbose=True)
+quantifier = model_selection.fit(training)
+
+# evaluation
+report = evaluation_report(quantifier, protocol=test_generator, error_metrics=['mae', 'mrae'], verbose=True)
+
+print(report)

quapy/data/datasets.py

@@ -12,6 +12,7 @@ from quapy.data.preprocessing import text2tfidf, reduce_columns
 from quapy.data.reader import *
 from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
 
+
 REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
 TWITTER_SENTIMENT_DATASETS_TEST = ['gasp', 'hcr', 'omd', 'sanders',
                               'semeval13', 'semeval14', 'semeval15', 'semeval16',
@@ -45,6 +46,20 @@ UCI_DATASETS = ['acute.a', 'acute.b',
 
 LEQUA2022_TASKS = ['T1A', 'T1B', 'T2A', 'T2B']
 
+_TXA_SAMPLE_SIZE = 250
+_TXB_SAMPLE_SIZE = 1000
+
+LEQUA2022_SAMPLE_SIZE = {
+    'TXA': _TXA_SAMPLE_SIZE,
+    'TXB': _TXB_SAMPLE_SIZE,
+    'T1A': _TXA_SAMPLE_SIZE,
+    'T1B': _TXB_SAMPLE_SIZE,
+    'T2A': _TXA_SAMPLE_SIZE,
+    'T2B': _TXB_SAMPLE_SIZE,
+    'binary': _TXA_SAMPLE_SIZE,
+    'multiclass': _TXB_SAMPLE_SIZE
+}
+
 
 def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False) -> Dataset:
     """
@@ -578,7 +593,7 @@ def fetch_lequa2022(task, data_home=None):
     val_true_prev_path = join(lequa_dir, task, 'public', 'dev_prevalences.txt')
     val_gen = SamplesFromDir(val_samples_path, val_true_prev_path, load_fn=load_fn)
 
-    test_samples_path = join(lequa_dir, task, 'public', 'dev_samples')
+    test_samples_path = join(lequa_dir, task, 'public', 'test_samples')
     test_true_prev_path = join(lequa_dir, task, 'public', 'test_prevalences.txt')
     test_gen = SamplesFromDir(test_samples_path, test_true_prev_path, load_fn=load_fn)
 

quapy/error.py

@@ -11,11 +11,11 @@ def from_name(err_name):
     """
     assert err_name in ERROR_NAMES, f'unknown error {err_name}'
     callable_error = globals()[err_name]
-    if err_name in QUANTIFICATION_ERROR_SMOOTH_NAMES:
-        eps = __check_eps()
-        def bound_callable_error(y_true, y_pred):
-            return callable_error(y_true, y_pred, eps)
-        return bound_callable_error
+    # if err_name in QUANTIFICATION_ERROR_SMOOTH_NAMES:
+    #     eps = __check_eps()
+    #     def bound_callable_error(y_true, y_pred):
+    #         return callable_error(y_true, y_pred, eps)
+    #     return bound_callable_error
     return callable_error
 
 

quapy/evaluation.py

@@ -41,7 +41,7 @@ def prediction(model: BaseQuantifier, protocol: AbstractProtocol, aggr_speedup='
 
 def __prediction_helper(quantification_fn, protocol: AbstractProtocol, verbose=False):
     true_prevs, estim_prevs = [], []
-    for sample_instances, sample_prev in tqdm(protocol(), total=protocol.total()) if verbose else protocol():
+    for sample_instances, sample_prev in tqdm(protocol(), total=protocol.total(), desc='predicting') if verbose else protocol():
         estim_prevs.append(quantification_fn(sample_instances))
         true_prevs.append(sample_prev)