Add multilabel support to train/fit methods (#5)

The fit/train method now supports multilabel classification. It will
automatically determine if we're dealing with a multilabel
classification problem by looking at the first item of the `y_train`
list. If the first item is a list (of labels), i.e., if it's not a
single label, it will assume we're dealing with a multilabel
classification problem.

pyss3/__init__.py

@@ -2173,7 +2173,8 @@ def fit(self, x_train, y_train, n_grams=1, prep=True, leave_pbar=True):
  :param x_train: the list of documents
  :type x_train: list (of str)
  :param y_train: the list of document labels
- :type y_train: list (of str)
+ :type y_train: list of str for singlelabel classification;
+ list of list of str for multilabel classification.
  :param n_grams: indicates the maximum ``n``-grams to be learned
  (e.g. a value of ``1`` means only 1-grams (words),
  ``2`` means 1-grams and 2-grams,
@@ -2184,36 +2185,64 @@ def fit(self, x_train, y_train, n_grams=1, prep=True, leave_pbar=True):
  :param leave_pbar: controls whether to leave the progress bar or
  remove it after finishing.
  :type leave_pbar: bool
+ :raises: ValueError
  """
- cats = sorted(list(set(y_train)))
  stime = time()
-
  x_train, y_train = list(x_train), list(y_train)
 
- x_train = [
- "".join([
- x_train[i]
- if x_train[i] and x_train[i][-1] == '\n'
- else
- x_train[i] + '\n'
- for i in xrange(len(x_train))
- if y_train[i] == cat
- ])
- for cat in cats
- ]
- y_train = list(cats)
+ if len(x_train) != len(y_train):
+ raise ValueError("`x_train` and `y_train` must have the same length")
+
+ if len(y_train) == 0:
+ raise ValueError("`x_train` and `y_train` are empty")
+
+ # if it's a multi-label classification problem
+ if is_a_collection(y_train[0]):
+ # flattening y_train
+ labels = [l for y in y_train for l in y]
+ else:
+ labels = y_train
+
+ cats = sorted(list(set(labels)))
+
+ # if it's a single-label classification problem
+ if not is_a_collection(y_train[0]):
+ x_train = [
+ "".join([
+ x_train[i]
+ if x_train[i] and x_train[i][-1] == '\n'
+ else
+ x_train[i] + '\n'
+ for i in xrange(len(x_train))
+ if y_train[i] == cat
+ ])
+ for cat in cats
+ ]
+ y_train = list(cats)
 
  Print.info("about to start training", offset=1)
  Print.verbosity_region_begin(VERBOSITY.NORMAL)
  progress_bar = tqdm(total=len(x_train), desc="Training",
  leave=leave_pbar, disable=Print.is_quiet())
- for i in range(len(x_train)):
- progress_bar.set_description_str("Training on '%s'" % str(y_train[i]))
- self.learn(
- x_train[i], y_train[i],
- n_grams=n_grams, prep=prep, update=False
- )
- progress_bar.update(1)
+
+ # if it's a multi-label classification problem
+ if is_a_collection(y_train[0]):
+ __unknown__ = [STR_UNKNOWN_CATEGORY]
+ for i in range(len(x_train)):
+ for label in (y_train[i] if y_train[i] else __unknown__):
+ self.learn(
+ x_train[i], label,
+ n_grams=n_grams, prep=prep, update=False
+ )
+ progress_bar.update(1)
+ else:
+ for i in range(len(x_train)):
+ progress_bar.set_description_str("Training on '%s'" % str(y_train[i]))
+ self.learn(
+ x_train[i], y_train[i],
+ n_grams=n_grams, prep=prep, update=False
+ )
+ progress_bar.update(1)
  progress_bar.close()
  self.__prune_tries__()
  Print.verbosity_region_end()
@@ -2558,6 +2587,14 @@ def list_hash(str_list):
  return m.hexdigest()
 
 
+def is_a_collection(o):
+ """Return True when the object ``o`` is a collection."""
+ from sys import version_info
+ py2 = version_info[0] == 2
+ return hasattr(o, "__getitem__") and ((py2 and not isinstance(o, basestring)) or
+ (not py2 and not isinstance(o, (str, bytes))))
+
+
 def vsum(v0, v1):
  """Vectorial version of sum."""
  return [v0[i] + v1[i] for i in xrange(len(v0))]

tests/test_pyss3.py

@@ -16,8 +16,10 @@
 pyss3.set_verbosity(VERBOSITY.QUIET)
 
 DATASET_FOLDER = "dataset"
+DATASET_MULTILABEL_FOLDER = "dataset_ml"
 
 dataset_path = path.join(path.abspath(path.dirname(__file__)), DATASET_FOLDER)
+dataset_multilabel_path = path.join(path.abspath(path.dirname(__file__)), DATASET_MULTILABEL_FOLDER)
 
 x_train, y_train = Dataset.load_from_files(dataset_path, folder_label=False)
 x_test = [
@@ -253,6 +255,22 @@ def test_pyss3_functions():
  pyss3.mad([], 0)
 
 
+def test_multilabel():
+ """Test multilabel support."""
+ x_train, y_train = Dataset.load_from_files_multilabel(
+ path.join(dataset_multilabel_path, "train/docs.txt"),
+ path.join(dataset_multilabel_path, "train/labels.txt"),
+ sep_label=",",
+ sep_doc="\n>>>>>\n"
+ )
+
+ clf = SS3()
+ clf.fit(x_train, y_train)
+
+ assert sorted(clf.get_categories()) == ['[unknown]', 'insult', 'obscene',
+ 'severe_toxic', 'toxic']
+
+
 def test_pyss3_ss3(mockers):
  """Test SS3."""
  clf = SS3(
@@ -274,6 +292,10 @@ def test_pyss3_ss3(mockers):
  clf.predict(x_test)
  with pytest.raises(pyss3.EmptyModelError):
  clf.predict_proba(x_test)
+ with pytest.raises(ValueError):
+ clf.train(x_train, [])
+ with pytest.raises(ValueError):
+ clf.train([], [])
 
  # train and predict/classify tests (model: terms are single words)
  # cv_m=STR_NORM_GV_XAI, sn_m=STR_XAI

tests/test_util.py

@@ -9,9 +9,9 @@
 import pytest
 import pyss3
 
+PY3 = sys.version_info[0] >= 3
 DATASET_FOLDER = "dataset_mr"
 DATASET_MULTILABEL_FOLDER = "dataset_ml"
-PY3 = sys.version_info[0] >= 3
 DATASET_PATH = path.join(path.abspath(path.dirname(__file__)), DATASET_FOLDER)
 DATASET_MULTILABEL_PATH = path.join(path.abspath(path.dirname(__file__)), DATASET_MULTILABEL_FOLDER)
 TMP_FOLDER = "tests/ss3_models/"