QuaPy/test.py

from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
import quapy as qp
import quapy.functional as F


SAMPLE_SIZE=500
binary = False

if binary:
    # load a textual binary dataset and create a tfidf bag of words
    train_path = './datasets/reviews/kindle/train.txt'
    test_path = './datasets/reviews/kindle/test.txt'
    dataset = qp.Dataset.load(train_path, test_path, qp.reader.from_text)
    qp.preprocessing.text2tfidf(dataset, inplace=True)
    qp.preprocessing.reduce_columns(dataset, min_df=10, inplace=True)

else:
    # load a sparse matrix ternary dataset
    train_path = './datasets/twitter/train/sst.train+dev.feature.txt'
    test_path = './datasets/twitter/test/sst.test.feature.txt'
    dataset = qp.Dataset.load(train_path, test_path, qp.reader.from_sparse)

# training a quantifier
learner = LogisticRegression()
model = qp.method.aggregative.ClassifyAndCount(learner)
# model = qp.method.aggregative.AdjustedClassifyAndCount(learner)
# model = qp.method.aggregative.AdjustedClassifyAndCount(learner)
# model = qp.method.aggregative.ProbabilisticClassifyAndCount(learner)
# model = qp.method.aggregative.ProbabilisticAdjustedClassifyAndCount(learner)
# model = qp.method.aggregative.ExpectationMaximizationQuantifier(learner)

model.fit(dataset.training)

# estimating class prevalences
prevalences_estim = model.quantify(dataset.test.instances)
prevalences_true  = dataset.test.prevalence()

# evaluation (one single prediction)
error = qp.error.mae(prevalences_true, prevalences_estim)

print(f'method {model.__class__.__name__}')

print(f'Evaluation in test (1 eval)')
print(f'true prevalence {F.strprev(prevalences_true)}')
print(f'estim prevalence {F.strprev(prevalences_estim)}')
print(f'mae={error:.3f}')

true_prev, estim_prev = qp.evaluation.artificial_sampling_prediction(model, dataset.test, SAMPLE_SIZE)

qp.error.SAMPLE_SIZE=SAMPLE_SIZE
print(f'Evaluation according to the artificial sampling protocol ({len(true_prev)} evals)')
for error in qp.error.QUANTIFICATION_ERROR:
    score = error(true_prev, estim_prev)
    print(f'{error.__name__}={score:.5f}')