first experiments

Ordinal/evaluation.py

@@ -0,0 +1,43 @@
+import numpy as np
+
+
+def _check_arrays(prevs):
+    prevs = np.asarray(prevs)
+    if prevs.ndim==1:
+        prevs = prevs.reshape(1,-1)
+    return prevs
+
+
+def mnmd(prevs, prevs_hat):
+    prevs = _check_arrays(prevs)
+    prevs_hat = _check_arrays(prevs_hat)
+    assert prevs.shape == prevs_hat.shape, f'wrong shape; found {prevs.shape} and {prevs_hat.shape}'
+
+    nmds = [nmd(p, p_hat) for p, p_hat in zip(prevs, prevs_hat)]
+    return np.mean(nmds)
+
+
+def nmd(prev, prev_hat):
+    n = len(prev)
+    return (1./(n-1))*mdpa(prev, prev_hat)
+
+
+"""
+Minimum Distance of Pair Assignments (MDPA) [cha2002measuring] for ordinal pdfs `a` and `b`.
+The MDPA is a special case of the Earth Mover's Distance [rubner1998metric] that can be
+computed efficiently.
+[Mirko Bunse's code from Julia adapted]
+"""
+def mdpa(a, b):
+    assert len(a) == len(b), "histograms have to have the same length"
+    assert np.isclose(sum(a), sum(b)), "histograms have to have the same mass (difference is $(sum(a)-sum(b))"
+
+    # algorithm 1 in [cha2002measuring]
+    prefixsum = 0.0
+    distance  = 0.0
+    for i in range(len(a)):
+        prefixsum += a[i] - b[i]
+        distance += abs(prefixsum)
+
+    return distance / sum(a)  # the normalization is a fix to the original MDPA
+

Ordinal/main.py

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+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+import quapy as qp
+from method.aggregative import PACC, CC, EMQ
+from quapy.data import LabelledCollection
+from os.path import join
+from utils import load_samples
+from evaluation import nmd
+
+domain = 'Books'
+datapath = './data'
+protocol = 'app'
+
+tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, ngram_range=(1, 2))
+
+train = LabelledCollection.load(join(datapath, domain, 'training_data.txt'), loader_func=qp.data.reader.from_text)
+train.instances = tfidf.fit_transform(train.instances)
+
+
+def load_test_samples():
+    for sample in load_samples(join(datapath, domain, protocol, 'test_samples'), classes=train.classes_):
+        sample.instances = tfidf.transform(sample.instances)
+        yield sample.instances, sample.prevalence()
+
+
+q = EMQ(LogisticRegression())
+q.fit(train)
+report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd], eval_budget=100)
+mean_nmd = report['nmd'].mean()
+std_nmd = report['nmd'].std()
+
+print(f'{mean_nmd:.4f} +-{std_nmd:.4f}')
+
+
+

Ordinal/utils.py

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+import quapy as qp
+from quapy.data import LabelledCollection
+from glob import glob
+import os
+from os.path import join
+
+
+def load_samples(path_dir, classes):
+    nsamples = len(glob(join(path_dir, f'*.txt')))
+    for id in range(nsamples):
+        yield LabelledCollection.load(join(path_dir, f'{id}.txt'), loader_func=qp.data.reader.from_text, classes=classes)
+
+