trying ordinal classification

Ordinal/main.py

@@ -1,35 +1,74 @@
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.linear_model import LogisticRegression
 import quapy as qp
-from method.aggregative import PACC, CC, EMQ
+import numpy as np
+
+from Ordinal.model import OrderedLogisticRegression, StackedClassifier, RegressionQuantification, RegressorClassifier
+from quapy.method.aggregative import PACC, CC, EMQ, PCC, ACC
 from quapy.data import LabelledCollection
 from os.path import join
-from utils import load_samples
+from utils import load_samples, load_samples_pkl
-from evaluation import nmd
+from evaluation import nmd, mnmd
+from time import time
+import pickle
+from tqdm import tqdm
 
-domain = 'Books'
+domain = 'Books-tfidf'
 datapath = './data'
 protocol = 'app'
+drift = 'low'
 
-tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5, ngram_range=(1, 2))
+train = pickle.load(open(join(datapath, domain, 'training_data.pkl'), 'rb'))
-
-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_):
+    ids = np.load(join(datapath, domain, protocol, f'{drift}drift.test.id.npy'))
-        sample.instances = tfidf.transform(sample.instances)
+    ids = set(ids)
+    for sample in tqdm(load_samples_pkl(join(datapath, domain, protocol, 'test_samples'), filter=ids), total=len(ids)):
         yield sample.instances, sample.prevalence()
 
 
-q = EMQ(LogisticRegression())
+def load_dev_samples():
+    ids = np.load(join(datapath, domain, protocol, f'{drift}drift.dev.id.npy'))
+    ids = set(ids)
+    for sample in tqdm(load_samples_pkl(join(datapath, domain, protocol, 'dev_samples'), filter=ids), total=len(ids)):
+        yield sample.instances, sample.prevalence()
+
+
+print('fitting the quantifier')
+
+# q = PACC(LogisticRegression(class_weight='balanced'))
+# q = PACC(OrderedLogisticRegression())
+# q = PACC(StackedClassifier(LogisticRegression(class_weight='balanced')))
+# q = RegressionQuantification(PCC(LogisticRegression(class_weight='balanced')), val_samples_generator=load_dev_samples)
+q = PACC(RegressorClassifier())
+
+q = qp.model_selection.GridSearchQ(
+    q,
+#     {'C': np.logspace(-3,3,7), 'class_weight': [None, 'balanced']},
+    {'C': np.logspace(-3,3,14)},
+    1000,
+    'gen',
+    error=mnmd,
+    val_split=load_dev_samples,
+    n_jobs=-1,
+    refit=False,
+    verbose=True)
+
 q.fit(train)
-report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd], eval_budget=100)
+
+print('[done]')
+
+report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
 mean_nmd = report['nmd'].mean()
 std_nmd = report['nmd'].std()
-
 print(f'{mean_nmd:.4f} +-{std_nmd:.4f}')
 
+# drift='high'
+# report = qp.evaluation.gen_prevalence_report(q, gen_fn=load_test_samples, error_metrics=[nmd])
+# mean_nmd = report['nmd'].mean()
+# std_nmd = report['nmd'].std()
+# print(f'{mean_nmd:.4f} +-{std_nmd:.4f}')
+
 
 

Ordinal/utils.py

@@ -3,6 +3,7 @@ from quapy.data import LabelledCollection
 from glob import glob
 import os
 from os.path import join
+import pickle
 
 
 def load_samples(path_dir, classes):
@@ -11,3 +12,11 @@ def load_samples(path_dir, classes):
         yield LabelledCollection.load(join(path_dir, f'{id}.txt'), loader_func=qp.data.reader.from_text, classes=classes)
 
 
+def load_samples_pkl(path_dir, filter=None):
+    nsamples = len(glob(join(path_dir, f'*.pkl')))
+    for id in range(nsamples):
+        if filter is not None:
+            if id not in filter:
+                continue
+        yield pickle.load(open(join(path_dir, f'{id}.pkl'), 'rb'))
+

quapy/data/reader.py

@@ -1,9 +1,10 @@
 import numpy as np
 from scipy.sparse import dok_matrix
 from tqdm import tqdm
+from time import time
 
 
-def from_text(path, encoding='utf-8', verbose=1, class2int=True):
+def from_text(path, encoding='utf-8', verbose=0, class2int=True):
     """
     Reads a labelled colletion of documents.
     File fomart <0 or 1>\t<document>\n