This page was generated from examples/anchor_text_movie.ipynb.

Anchor explanations for movie sentiment

In this example, we will explain why a certain sentence is classified by a logistic regression as having negative or positive sentiment. The logistic regression is trained on negative and positive movie reviews.

import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
import spacy
from alibi.explainers import AnchorText
from alibi.datasets import movie_sentiment
from alibi.utils.download import spacy_model

Load movie review dataset

data, labels = movie_sentiment()

Define shuffled training, validation and test set

train, test, train_labels, test_labels = train_test_split(data, labels, test_size=.2, random_state=42)
train, val, train_labels, val_labels = train_test_split(train, train_labels, test_size=.1, random_state=42)
train_labels = np.array(train_labels)
test_labels = np.array(test_labels)
val_labels = np.array(val_labels)

Apply CountVectorizer to training set

vectorizer = CountVectorizer(min_df=1)
vectorizer.fit(train)

CountVectorizer(analyzer='word', binary=False, decode_error='strict',
        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern='(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None)

Fit model

np.random.seed(0)
clf = LogisticRegression(solver='liblinear')
clf.fit(vectorizer.transform(train), train_labels)

LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='warn',
          n_jobs=None, penalty='l2', random_state=None, solver='liblinear',
          tol=0.0001, verbose=0, warm_start=False)

Define prediction function

predict_fn = lambda x: clf.predict(vectorizer.transform(x))

Make predictions on train and test sets

preds_train = predict_fn(train)
preds_val = predict_fn(val)
preds_test = predict_fn(test)
print('Train accuracy', accuracy_score(train_labels, preds_train))
print('Validation accuracy', accuracy_score(val_labels, preds_val))
print('Test accuracy', accuracy_score(test_labels, preds_test))

Train accuracy 0.9801624284382905
Validation accuracy 0.7544910179640718
Test accuracy 0.7589841878294202

Load spaCy model

English multi-task CNN trained on OntoNotes, with GloVe vectors trained on Common Crawl. Assigns word vectors, context-specific token vectors, POS tags, dependency parse and named entities.

model = 'en_core_web_md'
spacy_model(model=model)
nlp = spacy.load(model)

Initialize anchor text explainer

explainer = AnchorText(nlp, predict_fn)

Explain a prediction

class_names = ['negative', 'positive']

Prediction:

text = 'This is a good book .'
pred = class_names[predict_fn([text])[0]]
alternative =  class_names[1 - predict_fn([text])[0]]
print('Prediction: %s' % pred)

Prediction: positive

Explanation:

np.random.seed(0)
explanation = explainer.explain(text, threshold=0.95, use_proba=False, use_unk=True)

use_unk=True means we will perturb examples by replacing words with UNKs. Let us now take a look at the anchor. The word ‘good’ basically guarantees a positive prediction. This is because the UNKs do not take instances like ‘not good’ into account.

print('Anchor: %s' % (' AND '.join(explanation['names'])))
print('Precision: %.2f' % explanation['precision'])
print('\nExamples where anchor applies and model predicts %s:' % pred)
print('\n'.join([x[0] for x in explanation['raw']['examples'][-1]['covered_true']]))
print('\nExamples where anchor applies and model predicts %s:' % alternative)
print('\n'.join([x[0] for x in explanation['raw']['examples'][-1]['covered_false']]))

Anchor: good
Precision: 1.00

Examples where anchor applies and model predicts positive:
UNK UNK UNK good book UNK
UNK is a good book .
UNK is a good book UNK
UNK is UNK good book .
UNK UNK UNK good book .
UNK is a good book .
UNK is UNK good UNK UNK
UNK UNK UNK good UNK .
This UNK a good UNK UNK
This is a good UNK .

Examples where anchor applies and model predicts negative:

Changing the perturbation distribution

Let’s try this with another perturbation distribution, namely one that replaces words by similar words instead of UNKs.

Explanation:

np.random.seed(0)
explanation = explainer.explain(text, threshold=0.95, use_proba=True, use_unk=False)

The anchor now shows that we need more to guarantee the positive prediction:

print('Anchor: %s' % (' AND '.join(explanation['names'])))
print('Precision: %.2f' % explanation['precision'])
print('\nExamples where anchor applies and model predicts %s:' % pred)
print('\n'.join([x[0] for x in explanation['raw']['examples'][-1]['covered_true']]))
print('\nExamples where anchor applies and model predicts %s:' % alternative)
print('\n'.join([x[0] for x in explanation['raw']['examples'][-1]['covered_false']]))

Anchor: good AND book
Precision: 0.95

Examples where anchor applies and model predicts positive:
Another includes both good book .
Any explains that good book .
SOME refers another good book .
This makes an good book .
SOME encapsulates every good book .
That consists this good book .
THE carries this good book .
Both is another good book .
Every sits another good book .
BOTH leads the good book .

Examples where anchor applies and model predicts negative:
SOME falls another good book .
THE feels another good book .
This starts some good book .
THis starts another good book .
All requires a good book .
Some goes a good book .
This happens some good book .
Some starts a good book .
Both feels some good book .
Both feels another good book .