machine-learning - 如何在 Bert 模型中实现 LIME？

Question

我是机器学习的新手。我注意到以前也有人问过这样的问题，但没有得到适当的解决方案。下面是语义相似性的代码，我想实现 LIME 作为基础。请帮帮我。

from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('paraphrase-distilroberta-base-v1')

# Two lists of sentences
sentences1 = ['The cat sits outside',
             'A man is playing guitar',
             'The new movie is awesome']

sentences2 = ['The cat sits outside',
              'A woman watches TV',
              'The new movie is so great']

#Compute embedding for both lists
embeddings1 = model.encode(sentences1, convert_to_tensor=True)
embeddings2 = model.encode(sentences2, convert_to_tensor=True)

#Compute cosine-similarits
cosine_scores = util.pytorch_cos_sim(embeddings1, embeddings2)

#Output the pairs with their score
for i in range(len(sentences1)):
    print("{} \t\t {} \t\t Score: {:.4f}".format(sentences1[i], sentences2[i], cosine_scores[i][i]))

Answer 1

我不知道 Bert 是什么，但试试这个示例代码，看看它是否对你有帮助。

import pandas as pd
import numpy as np
import sklearn
import sklearn.ensemble
import sklearn.metrics
from sklearn.utils import shuffle
from io import StringIO
import re
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
import lime
from lime import lime_text
from lime.lime_text import LimeTextExplainer
from sklearn.pipeline import make_pipeline

df = pd.read_csv('C:\\Users\\ryans\\OneDrive\\Desktop\\Briefcase\\PDFs\\1-ALL PYTHON & R CODE SAMPLES\\A - GITHUB\\Natural Language Processing - Amazon Reviews\\Datafiniti_Amazon_Consumer_Reviews_of_Amazon_Products.csv')


# let's experiment with some sentiment analysis concepts
# first we need to clean up the stuff in the independent field of the DF we are workign with
df.replace('\'','', regex=True, inplace=True) 
df['review_title'] = df[['reviews.title']].astype(str)
df['review_text'] = df[['reviews.text']].astype(str)
df['review_title'] = df['reviews.title'].str.replace('\d+', '')
df['review_text'] = df['reviews.text'].str.replace('\d+', '')


# get rid of special characters
df['review_title'] = df['reviews.title'].str.replace(r'[^\w\s]+', '')
df['review_text'] = df['reviews.text'].str.replace(r'[^\w\s]+', '')

# get rid of double spaces
df['review_title'] = df['reviews.title'].str.replace(r'\^[a-zA-Z]\s+', '')
df['review_text'] = df['reviews.text'].str.replace(r'\^[a-zA-Z]\s+', '')

# convert all case to lower
df['review_title'] = df['reviews.title'].str.lower()
df['review_text'] = df['reviews.text'].str.lower()


list_corpus = df["review_text"].tolist()
list_labels = df["reviews.rating"].tolist()
X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2, random_state=40)
vectorizer = CountVectorizer(analyzer='word',token_pattern=r'\w{1,}', ngram_range=(1, 3), stop_words = 'english', binary=True)
train_vectors = vectorizer.fit_transform(X_train)
test_vectors = vectorizer.transform(X_test)

logreg = LogisticRegression(n_jobs=1, C=1e5)
logreg.fit(train_vectors, y_train)
pred = logreg.predict(test_vectors)
accuracy = accuracy_score(y_test, pred)
precision = precision_score(y_test, pred, average='weighted')
recall = recall_score(y_test, pred, average='weighted')
f1 = f1_score(y_test, pred, average='weighted')
print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy, precision, recall, f1))


list_corpus[3]


c = make_pipeline(vectorizer, logreg)
class_names=list(df.review_title.unique())
explainer = LimeTextExplainer(class_names=class_names)

idx = 3
exp = explainer.explain_instance(X_test[idx], c.predict_proba, num_features=6, labels=[1, 1])
print('Document id: %d' % idx)
print('Predicted class =', class_names[logreg.predict(test_vectors[idx]).reshape(1,-1)[0,0]])
print('True class: %s' % class_names[y_test[idx]])


print ('Explanation for class %s' % class_names[1])
print ('\n'.join(map(str, exp.as_list(label=1))))


exp = explainer.explain_instance(X_test[idx], c.predict_proba, num_features=6, top_labels=2)
print(exp.available_labels())


exp.show_in_notebook(text=False)

 

https://towardsdatascience.com/explain-nlp-models-with-lime-shap-5c5a9f84d59b

https://marcotcr.github.io/lime/tutorials/Lime%20-%20multiclass.html

https://towardsdatascience.com/understanding-model-predictions-with-lime-a582fdff3a3b