def calculate_recall_for_kw_candidates(data_dir, recreate_ontology=False, verbose=False):
"""
Generate keyword candidates for files in a given directory
and compute their recall in reference to ground truth answers
:param data_dir: directory with .txt and .key files
:param recreate_ontology: boolean flag for recreating the ontology
:param verbose: whether to print computation times
:return average_recall: float
"""
average_recall = 0
total_kw_number = 0
ontology = get_ontology(recreate=recreate_ontology)
docs = get_documents(data_dir)
considered_keywords = set(get_keywords())
total_docs = 0
start_time = time.clock()
for doc in docs:
kw_candidates = {kw.get_canonical_form() for kw
in generate_keyword_candidates(doc, ontology)}
answers = get_answers_for_doc(doc.filename, data_dir, filtered_by=considered_keywords)
# print(document.get_meaningful_words())
# print(u"Candidates:")
# for kw in sorted(kw_candidates):
# print(u"\t" + unicode(kw))
# print
#
# print(u"Answers:")
# for kw in sorted(answers):
# print(u"\t" + unicode(kw))
# print
#
# print(u"Conjunction:")
# for kw in sorted(kw_candidates & answers):
# print(u"\t" + unicode(kw))
# print
recall = 1 if not answers else len(kw_candidates & answers) / (len(answers))
if verbose:
print
print("Paper: " + doc.filename)
print("Candidates: " + str(len(kw_candidates)))
print("Recall: " + unicode(recall * 100) + "%")
average_recall += recall
total_kw_number += len(kw_candidates)
total_docs += 1
average_recall /= total_docs
if verbose:
print
print("Total # of keywords: " + str(total_kw_number))
print("Time elapsed: " + str(time.clock() - start_time))
return average_recall
def build_train_matrices(docs, model, file_dir, ontology):
"""
Build X matrix and y vector from the input data
:param docs: documents to process. Either list of generator of Document obj
:param model: LearningModel object
:param file_dir: directory where the answer files are located
:param ontology: Ontology object
:return: X and y numpy arrays
"""
considered_keywords = set(get_keywords())
feature_matrices = []
output_vectors = []
for doc in docs:
inv_index = InvertedIndex(doc)
# Generate keyword candidates
kw_candidates = list(generate_keyword_candidates(doc, ontology))
# Get ground truth answers
doc_answers = get_answers_for_doc(
doc.filename,
file_dir,
filtered_by=considered_keywords,
)
# If an answer was not generated, add it anyway
add_gt_answers_to_candidates_set(kw_candidates, doc_answers, ontology)
# Create the output vector
output_vector = np.zeros((len(kw_candidates), 2), dtype=np.int16)
for i, kw in enumerate(kw_candidates):
if kw.get_canonical_form() in doc_answers:
output_vector[i][0] = True
output_vector[i][1] = doc.doc_id
output_vectors.append(output_vector)
X = build_feature_matrix(kw_candidates, inv_index, model)
feature_matrices.append(X)
# Merge the pandas
X = pd.concat(feature_matrices)
# Cast the output vector to numpy
y = np.concatenate(output_vectors)
return X, y
def build_test_matrices(docs, model, file_dir, ontology):
"""
Build the X feature matrix and answers & kw_vector variables, needed for
evaluating the predictions.
:param docs: documents to process. Either list or generator of Document obj
:param model: LearningModel object
:param file_dir: directory where the answer files are located
:param ontology: Ontology object
:return: X numpy array, answers dictionary and kw_vector tuple list
"""
considered_keywords = set(get_keywords())
feature_matrices = []
kw_vector = []
answers = dict()
for doc in docs:
inv_index = InvertedIndex(doc)
# Generate keyword candidates
kw_candidates = list(generate_keyword_candidates(doc, ontology))
X = build_feature_matrix(kw_candidates, inv_index, model)
feature_matrices.append(X)
# Get ground truth answers
answers[doc.doc_id] = get_answers_for_doc(
doc.filename,
file_dir,
filtered_by=considered_keywords,
)
kw_vector.extend([(doc.doc_id, kw.get_canonical_form())
for kw in kw_candidates])
# Merge feature matrices from different documents
X = pd.concat(feature_matrices)
return X, answers, kw_vector
def calculate_recall_for_kw_candidates(data_dir,
recreate_ontology=False,
verbose=False):
"""
Generate keyword candidates for files in a given directory
and compute their recall in reference to ground truth answers
:param data_dir: directory with .txt and .key files
:param recreate_ontology: boolean flag for recreating the ontology
:param verbose: whether to print computation times
:return average_recall: float
"""
average_recall = 0
total_kw_number = 0
ontology = get_ontology(recreate=recreate_ontology)
docs = get_documents(data_dir)
considered_keywords = set(get_keywords())
total_docs = 0
start_time = time.clock()
for doc in docs:
kw_candidates = {
kw.get_canonical_form()
for kw in generate_keyword_candidates(doc, ontology)
}
answers = get_answers_for_doc(doc.filename,
data_dir,
filtered_by=considered_keywords)
# print(document.get_meaningful_words())
# print(u"Candidates:")
# for kw in sorted(kw_candidates):
# print(u"\t" + unicode(kw))
# print
#
# print(u"Answers:")
# for kw in sorted(answers):
# print(u"\t" + unicode(kw))
# print
#
# print(u"Conjunction:")
# for kw in sorted(kw_candidates & answers):
# print(u"\t" + unicode(kw))
# print
recall = 1 if not answers else len(kw_candidates
& answers) / (len(answers))
if verbose:
print
print("Paper: " + doc.filename)
print("Candidates: " + str(len(kw_candidates)))
print("Recall: " + unicode(recall * 100) + "%")
average_recall += recall
total_kw_number += len(kw_candidates)
total_docs += 1
average_recall /= total_docs
if verbose:
print
print("Total # of keywords: " + str(total_kw_number))
print("Time elapsed: " + str(time.clock() - start_time))
return average_recall
