def load_stems(self, data):
"""Returns all word stems used in the parsed XML data."""
# Get all word stems
stems = np.array([])
for txt in data.textfiles:
if self.annotations == "union":
txt.compute_union_relations()
elif self.annotations == "intersected":
txt.compute_intersection_relations()
for rel in txt.relations:
f = Feature(rel)
stems = np.append(stems, [f.get_stem_target()])
stems = np.append(stems, [f.get_stem_source()])
stems = np.unique(stems)
return stems
def parse_Features(data, new=False, annotations="union", features=["pos", "stem", "aspect", "tense", "distance", "similarity", "polarity", "modality"], distance=False):
"""Extracts the features out of the dataset and returns a list of features with the corresponding classes.
Args:
data (list): The parsed data from fables-100-temporal-dependency.xml.
new (bool): With new=True a new calculation of Pos() and Stem() can be enforced. Otherwise it will be loaded from a file.
annotations (str): Looking on all relations ("union") or at all relations in common between the annotators ("intersected").
features (list): Determines which features should be activated. Possible values: "pos", "stem", "aspect", "tense", "distance", "similarity", "polarity", "modality".
distance (bool): If set to True parse_Features() will return distance information for the data (needed for evaluation)
"""
# Only compute pos and stem if new flag is set
if "pos" in features or "stem" in features:
if new or not os.path.isfile("set.p"):
pos = Pos(data, 6, annotations)
stem = Stem(data, annotations)
pickle.dump((pos, stem), open("save.p", "wb"))
else:
pos, stem = pickle.load(open("save.p", "rb"))
if distance:
distance_diff = []
X = []
y = np.array([], dtype=int)
for txt in data.textfiles:
# Union or intersected relations?
if annotations == "union":
txt.compute_union_relations()
elif annotations == "intersected":
txt.compute_intersection_relations()
for rel in txt.relations:
f = Feature(rel)
feature = []
# Make polarity feature
if "polarity" in features:
feature = np.concatenate((feature, [f.get_polarity()]))
# Make distance feature
if "distance" in features:
feature = np.concatenate((feature, f.get_distance()))
# Make POS feature
if "pos" in features:
pos_feature = pos.transform(f.get_pos_target(), f.get_pos_source())
pos_feature = pos_feature.toarray()[0]
feature = np.concatenate((feature, pos_feature))
# Make Stem feature
if "stem" in features:
stem_feature = stem.transform(f.get_stem_source(), f.get_stem_target())
stem_feature = stem_feature[0]
feature = np.concatenate((feature, stem_feature))
# Make similarity feature
if "similarity" in features:
feature = np.concatenate((feature, [f.get_similarity_of_words()]))
# Make modality feature
if "modality" in features:
feature = np.concatenate((feature, [f.get_modality()]))
# Make aspect feature
if "aspect" in features:
feature = np.concatenate((feature, f.get_aspect()))
# Make tense feature
if "tense" in features:
feature = np.concatenate((feature, f.get_tense()))
# Append feature to X
X.append(feature)
y = np.append(y, [f.get_class()])
# Append distance information if needed
if distance:
distance_diff.append(f.get_distance_diff())
if distance:
return (X, y, distance_diff)
else:
return (X, y)
