【发布时间】：2021-07-06 13:35:11
【问题描述】：
我正在使用带有 jupyter notebook 的 python 3.8.5
spacy = 3.0.5
neuralcoref = 4.0
下面是我运行测试的代码
import datetime
import re
import time
import pandas as pd
from formative_assessment.dataset_extractor import ConvertDataType
from formative_assessment.feature_extractor import FeatureExtractor
class AEGrading:
"""
Automatically evaluates, grades and provides feedback to students' answers of the datasets.
Provides feedback as dict including total data of the student answer.
"""
def __init__(self, qid, stu_answer, dataset, dataset_path, score=5):
self.qid = qid
self.stu_answer = stu_answer
self.dataset = dataset
self.length_ratio = len(stu_answer) / len(dataset[qid]["desired_answer"])
self.score = score
self.fe = FeatureExtractor(qid, stu_answer, dataset, dataset_path)
self.wrong_terms = {}
self.feedback = {"id": self.qid, "question": self.dataset[self.qid]["question"],
"desired_answer": self.dataset[self.qid]["desired_answer"], "student_answer": stu_answer,
"length_ratio": self.length_ratio, "is_answered": "-", "is_wrong_answer": "not wrong answer",
"interchanged": "-", "missed_topics": "-", "missed_terms": "-", "irrelevant_terms": "-",
"score_avg": 0, "our_score": 0}
def is_answered(self, default="not answered"):
"""
Checks if the student answered or not given the default evaluator's string. Assigns score to 'zero' if not
answered.
:param default: str
String to be checked if student not answered
:return: bool
True if student answered, else False
"""
re_string = " *" + default + " *"
if re.match(re_string, self.stu_answer.lower()):
self.feedback["is_answered"] = "not answered"
self.score = 0
return False
else:
self.feedback["is_answered"] = "answered"
return True
def iot_score(self):
"""
Checks if there are any interchange of topics or missed topics and deduce the score accordingly. Deduce
nothing from the score if there are no interchange of topics or missed topics
:return: None
"""
iot = self.fe.get_interchanged_topics()
interchanged = iot["interchanged"]
missed_topics = iot["missed_topics"]
total_relations = iot["total_relations"]
topics_num = iot["total_topics"]
self.feedback["interchanged"] = interchanged
self.feedback["missed_topics"] = missed_topics
if interchanged:
iot_deduce = len(interchanged) / total_relations
self.score = self.score - (iot_deduce * self.score)
if missed_topics:
missed_deduce = len(missed_topics) / topics_num
self.score = self.score - (missed_deduce * self.score)
def missed_terms_score(self):
"""
Checks if there are any missed terms in the student answer and deduce score accordingly
:return: None
"""
missed_terms = self.fe.get_missed_terms()
self.feedback["missed_terms"] = missed_terms.keys()
total = round(sum(missed_terms.values()), 3)
self.score = self.score - (total * self.score) # self.score/2
def irrelevant_terms_score(self):
"""
Checks if there are any irrelevant terms in the student answer. We do not deduce score for this feature, as
we consider any irrelevant term as noise.
:return: None
"""
self.feedback["irrelevant_terms"] = self.fe.get_irrelevant_terms()
if __name__ == '__main__':
PATH = "dataset/mohler/cleaned/"
max_score = 5
# Convert the data into dictionary with ids, their corresponding questions, desired answers and student answers
convert_data = ConvertDataType(PATH)
dataset_dict = convert_data.to_dict()
id_list = list(dataset_dict.keys())
data = []
# random.seed(20)
for s_no in id_list[:7]:
# s_no = random.choice(id_list)
question = dataset_dict[s_no]["question"]
desired_answer = dataset_dict[s_no]["desired_answer"]
student_answers = dataset_dict[s_no]["student_answers"]
scores = dataset_dict[s_no]["scores"]
# score_me = dataset_dict[s_no]["score_me"]
# score_other = dataset_dict[s_no]["score_other"]
for index, _ in enumerate(student_answers):
# index = random.randint(0, 12)
start = time.time()
student_answer = str(student_answers[index])
print(s_no, student_answer)
aeg = AEGrading(s_no, student_answer, dataset_dict, PATH, max_score)
if aeg.is_answered():
aeg.iot_score()
aeg.missed_terms_score()
aeg.irrelevant_terms_score()
if aeg.score == 0:
aeg.feedback["is_wrong_answer"] = "wrong_answer"
# aeg.feedback["score_me"] = score_me[index] # Only for mohler data
# aeg.feedback["score_other"] = score_other[index]
aeg.feedback["score_avg"] = scores[index]
aeg.feedback["our_score"] = round((aeg.score * 4)) / 4 # Score in multiples of 0.25
data.append(aeg.feedback)
print(aeg.feedback)
print("It took ", time.time() - start, " secs")
print("----------------------------------------------------------")
if len(data) % 50 == 0:
df = pd.DataFrame(data)
SAVE_PATH = "outputs/automatic_evaluation/II_NN/" + str(datetime.datetime.now()) + ".csv"
df.to_csv(SAVE_PATH, sep=",")
df = pd.DataFrame(data)
SAVE_PATH = "outputs/automatic_evaluation/II_NN/" + str(datetime.datetime.now()) + ".csv"
df.to_csv(SAVE_PATH, sep=",")
运行上面的代码后，我得到如下错误
ValueError Traceback（最近一次调用最后一次） 在 9 将熊猫导入为 pd 10 ---> 11 从 formative_assessment.dataset_extractor 导入 ConvertDataType 12 从 formative_assessment.feature_extractor 导入 FeatureExtractor 13
~\Desktop\FYP\Automatic-Formative-Assessment-main\formative_assessment\dataset_extractor.py 在 8 将熊猫导入为 pd 9 ---> 10 从 formative_assessment.utilities.utils 导入实用程序 11
~\Desktop\FYP\Automatic-Formative-Assessment-main\formative_assessment\utilities\utils.py 在 11 从输入导入列表 12 ---> 13 导入神经核 14 将 numpy 导入为 np 15 导入pytextrank
~\anaconda3\lib\site-packages\neuralcoref_init_.py in 12 warnings.filterwarnings("ignore", message="spacy.strings.StringStore 大小已更改，") 13 ---> 14 从 .neuralcoref 导入 NeuralCoref 15 从 .file_utils 导入 NEURALCOREF_MODEL_URL、NEURALCOREF_MODEL_PATH、NEURALCOREF_CACHE、cached_path 16
strings.pxd in init neuralcoref.neuralcoref()
ValueError：spacy.strings.StringStore 大小已更改，可能表示二进制不兼容。预期 C 标头为 80，PyObject 为 64
**我曾尝试卸载以下方法
pip 卸载神经核
pip install neuralcoref --no-binary neuralcoref
但问题还是一样，希望有人能帮助我，非常感谢..**
【问题讨论】：
-
这里也一样，降级 Spacy 版本可能会有所帮助。
标签： python