From c63325e364d2a56eaeb239f7156798fc3756d986 Mon Sep 17 00:00:00 2001
From: Alex Moreo <alejandro.moreo@isti.cnr.it>
Date: Thu, 3 Mar 2022 14:40:11 +0100
Subject: [PATCH] script for genearting the datasets

---
 Ordinal/build_Amazon_datasets.py | 116 +++++++++++++++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 Ordinal/build_Amazon_datasets.py

diff --git a/Ordinal/build_Amazon_datasets.py b/Ordinal/build_Amazon_datasets.py
new file mode 100644
index 0000000..465c797
--- /dev/null
+++ b/Ordinal/build_Amazon_datasets.py
@@ -0,0 +1,116 @@
+import gzip
+import quapy as qp
+from quapy.data import LabelledCollection
+import quapy.functional as F
+import os
+from os.path import join
+from pathlib import Path
+
+
+datadir = '/mnt/1T/Datasets/Amazon/reviews'
+outdir  = './data/'
+domain = 'Books'
+seed = 7
+
+tr_size = 20000
+val_size = 1000
+te_size = 1000
+nval = 1000
+nte = 5000
+
+# domain = 'Gift_Cards'
+# tr_size = 200
+# val_size = 100
+# te_size = 100
+# nval = 20
+# nte = 40
+
+
+def from_gz_text(path, encoding='utf-8', class2int=True):
+    """
+    Reads a labelled colletion of documents.
+    File fomart <0-4>\t<document>\n
+
+    :param path: path to the labelled collection
+    :param encoding: the text encoding used to open the file
+    :return: a list of sentences, and a list of labels
+    """
+    all_sentences, all_labels = [], []
+    file = gzip.open(path, 'rt', encoding=encoding).readlines()
+    for line in file:
+        line = line.strip()
+        if line:
+            try:
+                label, sentence = line.split('\t')
+                sentence = sentence.strip()
+                if class2int:
+                    label = int(label) - 1
+                if label >= 0:
+                    if sentence:
+                        all_sentences.append(sentence)
+                        all_labels.append(label)
+            except ValueError:
+                print(f'format error in {line}')
+    return all_sentences, all_labels
+
+
+def write_txt_sample(sample: LabelledCollection, path):
+    os.makedirs(Path(path).parent, exist_ok=True)
+    with open(path, 'wt') as foo:
+        for document, label in zip(*sample.Xy):
+            foo.write(f'{label}\t{document}\n')
+
+
+def gen_samples_APP(pool: LabelledCollection, nsamples, sample_size, outdir, prevpath):
+    os.makedirs(outdir, exist_ok=True)
+    with open(prevpath, 'wt') as prevfile:
+        prevfile.write('id,' + ','.join(f'{c}' for c in pool.classes_) + '\n')
+        for i, prev in enumerate(F.uniform_simplex_sampling(n_classes=pool.n_classes, size=nsamples)):
+            sample = pool.sampling(sample_size, *prev)
+            write_txt_sample(sample, join(outdir, f'{i}.txt'))
+            prevfile.write(f'{i},' + ','.join(f'{p:.3f}' for p in sample.prevalence()) + '\n')
+
+
+def gen_samples_NPP(pool: LabelledCollection, nsamples, sample_size, outdir, prevpath):
+    os.makedirs(outdir, exist_ok=True)
+    with open(prevpath, 'wt') as prevfile:
+        prevfile.write('id,' + ','.join(f'{c}' for c in pool.classes_) + '\n')
+        for i, sample in enumerate(pool.natural_sampling_generator(sample_size, repeats=nsamples)):
+            write_txt_sample(sample, join(outdir, f'{i}.txt'))
+            prevfile.write(f'{i},' + ','.join(f'{p:.3f}' for p in sample.prevalence()) + '\n')
+
+
+
+fullpath = join(datadir,domain)+'.txt.gz'
+
+data = LabelledCollection.load(fullpath, from_gz_text)
+print(len(data))
+print(data.classes_)
+print(data.prevalence())
+
+with qp.util.temp_seed(seed):
+    train, rest = data.split_stratified(train_prop=tr_size)
+
+    devel, test = rest.split_stratified(train_prop=0.5)
+    print(len(train))
+    print(len(devel))
+    print(len(test))
+
+    domaindir = join(outdir, domain)
+
+    write_txt_sample(train, join(domaindir, 'training_data.txt'))
+    write_txt_sample(devel, join(domaindir, 'development_data.txt'))
+    write_txt_sample(test, join(domaindir, 'test_data.txt'))
+
+    gen_samples_APP(devel, nsamples=nval, sample_size=val_size, outdir=join(domaindir, 'app', 'dev_samples'),
+                    prevpath=join(domaindir, 'app', 'dev_prevalences.txt'))
+    gen_samples_APP(test, nsamples=nte, sample_size=te_size, outdir=join(domaindir, 'app', 'test_samples'),
+                    prevpath=join(domaindir, 'app', 'test_prevalences.txt'))
+
+    gen_samples_NPP(devel, nsamples=nval, sample_size=val_size, outdir=join(domaindir, 'npp', 'dev_samples'),
+                    prevpath=join(domaindir, 'npp', 'dev_prevalences.txt'))
+    gen_samples_NPP(test, nsamples=nte, sample_size=te_size, outdir=join(domaindir, 'npp', 'test_samples'),
+                    prevpath=join(domaindir, 'npp', 'test_prevalences.txt'))
+
+
+