machine-learning - Bert モデルに LIME を実装する方法は?

Question

私は機械学習が初めてです。以前にも同様の質問がありましたが、適切な解決策が得られていないことに気付きました。以下は意味的類似性のコードで、LIME をベースとして実装したいと考えています。私を助けてください。

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

Bert が何かはわかりませんが、このサンプル コードを試してみてください。

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