def get_role_directional_score(question_frame, answer_frame, experiment):
frame_scores = dict()
for frame_element in config.ROLES[experiment]:
q_element = question_frame[frame_element]
a_element = answer_frame[frame_element]
if pd.isnull(q_element) or pd.isnull(a_element):
score = np.NaN
else:
ret = entailment.get_ai2_textual_entailment(
utils.remove_filter_words(a_element),
utils.remove_filter_words(q_element))
a_scores = map(lambda x: x['score'], ret['alignments'])
if len(a_scores):
mean_a_score = np.mean(a_scores)
else:
mean_a_score = 0
confidence = ret['confidence'] if ret['confidence'] else 0
score1 = mean_a_score * confidence
ret = entailment.get_ai2_textual_entailment(
utils.remove_filter_words(q_element),
utils.remove_filter_words(a_element))
a_scores = map(lambda x: x['score'], ret['alignments'])
if len(a_scores):
mean_a_score = np.mean(a_scores)
else:
mean_a_score = 0
confidence = ret['confidence'] if ret['confidence'] else 0
score2 = mean_a_score * confidence
score = max(score1, score2)
if pd.notnull(q_element) and pd.notnull(q_element):
if q_element in ["process", "processes"]:
frame_scores[frame_element] = ("", a_element, np.NaN)
else:
frame_scores[frame_element] = (q_element, a_element, score)
elif pd.notnull(q_element):
frame_scores[frame_element] = (q_element, "", score)
elif pd.notnull(a_element):
frame_scores[frame_element] = ("", a_element, score)
else:
frame_scores[frame_element] = ("", "", np.NaN)
# frame_scores[utils.DEFINITION] = answer_frame[utils.DEFINITION]
return frame_scores
def get_role_directional_score(question_frame, answer_frame, experiment):
frame_scores = dict()
for frame_element in config.ROLES[experiment]:
q_element = question_frame[frame_element]
a_element = answer_frame[frame_element]
if config.ENTAILMENT_TYPE == 'BEST_TEXT_SPAN':
if pd.notnull(q_element):
q_spans = q_element.split(" | ")
else:
q_spans = [pd.NaT]
if pd.notnull(a_element):
a_spans = a_element.split(" | ")
else:
a_spans = [pd.NaT]
all_combinations = itertools.product(q_spans, a_spans)
best_combination = (None, None, 0)
for q_span, a_span in all_combinations:
if pd.isnull(q_span) or pd.isnull(a_span):
score = np.NaN
best_combination = (q_span, a_span, score)
else:
ret = entailment.get_ai2_textual_entailment(
utils.remove_filter_words(a_span),
utils.remove_filter_words(q_span))
a_scores = map(lambda x: x['score'], ret['alignments'])
if len(a_scores):
mean_a_score = np.mean(a_scores)
else:
mean_a_score = 0
confidence = ret['confidence'] if ret['confidence'] else 0
score1 = mean_a_score * confidence
ret = entailment.get_ai2_textual_entailment(
utils.remove_filter_words(q_span),
utils.remove_filter_words(a_span))
a_scores = map(lambda x: x['score'], ret['alignments'])
if len(a_scores):
mean_a_score = np.mean(a_scores)
else:
mean_a_score = 0
confidence = ret['confidence'] if ret['confidence'] else 0
score2 = mean_a_score * confidence
score = max(score1, score2)
if score > best_combination[2]:
best_combination = (q_span, a_span, score)
if pd.notnull(best_combination[0]) and pd.notnull(best_combination[1]):
frame_scores[frame_element] = best_combination
elif pd.notnull(best_combination[0]):
frame_scores[frame_element] = (best_combination[0], "", best_combination[2])
elif pd.notnull(best_combination[1]):
frame_scores[frame_element] = ("", best_combination[1], best_combination[2])
else:
frame_scores[frame_element] = ("", "", np.NaN)
else:
if pd.isnull(q_element) or pd.isnull(a_element):
score = np.NaN
else:
ret = entailment.get_ai2_textual_entailment(
utils.remove_filter_words(a_element),
utils.remove_filter_words(q_element))
a_scores = map(lambda x: x['score'], ret['alignments'])
if len(a_scores):
mean_a_score = np.mean(a_scores)
else:
mean_a_score = 0
confidence = ret['confidence'] if ret['confidence'] else 0
score1 = mean_a_score * confidence
ret = entailment.get_ai2_textual_entailment(
utils.remove_filter_words(q_element),
utils.remove_filter_words(a_element))
a_scores = map(lambda x: x['score'], ret['alignments'])
if len(a_scores):
mean_a_score = np.mean(a_scores)
else:
mean_a_score = 0
confidence = ret['confidence'] if ret['confidence'] else 0
score2 = mean_a_score * confidence
score = max(score1, score2)
if pd.notnull(q_element) and pd.notnull(a_element):
if q_element in ["process", "processes"]:
frame_scores[frame_element] = ("", a_element, np.NaN)
else:
frame_scores[frame_element] = (q_element, a_element, score)
elif pd.notnull(q_element):
frame_scores[frame_element] = (q_element, "", score)
elif pd.notnull(a_element):
frame_scores[frame_element] = ("", a_element, score)
else:
frame_scores[frame_element] = ("", "", np.NaN)
# frame_scores[utils.DEFINITION] = answer_frame[utils.DEFINITION]
return frame_scores
