def test_explain_regression(self, sentence, num_samples, positive_token,
negative_token):
"""Tests explaining text classifiers with various output dimensions."""
def _predict_fn(sentences):
"""Mock prediction function."""
rs = np.random.RandomState(seed=0)
predictions = []
for sentence in sentences:
output = rs.uniform(-2., 2.)
# To check if LIME finds the right positive/negative correlations.
if negative_token in sentence:
output -= rs.uniform(0., 2.)
if positive_token in sentence:
output += rs.uniform(0., 2.)
predictions.append(output)
predictions = np.stack(predictions, axis=0)
return predictions
explanation = lime.explain(sentence,
_predict_fn,
num_samples=num_samples,
tokenizer=str.split)
self.assertLen(explanation.feature_importance, len(sentence.split()))
# The positive word should have the highest attribution score.
positive_token_idx = sentence.split().index(positive_token)
self.assertEqual(positive_token_idx,
np.argmax(explanation.feature_importance))
# The negative word should have the lowest attribution score.
negative_token_idx = sentence.split().index(negative_token)
self.assertEqual(negative_token_idx,
np.argmin(explanation.feature_importance))
def test_explain_returns_explanation_with_intercept(self):
"""Tests if the explanation contains an intercept value."""
def _predict_fn(sentences):
return np.random.uniform(0., 1., [len(list(sentences)), 2])
explanation = lime.explain('Test sentence',
_predict_fn,
1,
num_samples=5)
self.assertNotEqual(explanation.intercept, 0.)
def test_explain_returns_explanation_with_prediction(self):
"""Tests if the explanation contains a prediction."""
def _predict_fn(sentences):
return np.random.uniform(0., 1., [len(list(sentences)), 2])
explanation = lime.explain('Test sentence',
_predict_fn,
class_to_explain=1,
num_samples=5,
return_prediction=True)
self.assertIsNotNone(explanation.prediction)
def test_explain(self, sentence, num_samples, positive_token,
negative_token, num_classes, class_to_explain):
"""Tests explaining text classifiers with various output dimensions."""
def _predict_fn(sentences):
"""Mock prediction function."""
rs = np.random.RandomState(seed=0)
predictions = []
for sentence in sentences:
probs = rs.uniform(0., 1., num_classes)
# To check if LIME finds the right positive/negative correlations.
if negative_token in sentence:
probs[class_to_explain] = probs[class_to_explain] - 1.
if positive_token in sentence:
probs[class_to_explain] = probs[class_to_explain] + 1.
predictions.append(probs)
predictions = np.stack(predictions, axis=0)
if num_classes == 1:
return np.squeeze(special.expit(predictions), -1)
else:
return special.softmax(predictions, axis=-1)
explanation = lime.explain(sentence,
_predict_fn,
class_to_explain=class_to_explain,
num_samples=num_samples,
tokenizer=str.split)
self.assertLen(explanation.feature_importance, len(sentence.split()))
# The positive word should have the highest attribution score.
positive_token_idx = sentence.split().index(positive_token)
self.assertEqual(positive_token_idx,
np.argmax(explanation.feature_importance))
# The negative word should have the lowest attribution score.
negative_token_idx = sentence.split().index(negative_token)
self.assertEqual(negative_token_idx,
np.argmin(explanation.feature_importance))
def test_explain_matches_original_lime(self, sentence, num_samples,
num_classes, class_to_explain):
"""Tests if Citrus LIME matches the original implementation."""
# Assign some weight to each token a-z.
# Each token contributes positively/negatively to the prediction.
rs = np.random.RandomState(seed=0)
token_weights = {token: rs.normal() for token in sentence.split()}
token_weights[lime.DEFAULT_MASK_TOKEN] = 0.
def _predict_fn(sentences):
"""Mock prediction function."""
rs = np.random.RandomState(seed=0)
predictions = []
for sentence in sentences:
probs = rs.normal(0., 0.1, size=num_classes)
# To check if LIME finds the right positive/negative correlations.
for token in sentence.split():
probs[class_to_explain] += token_weights[token]
predictions.append(probs)
return np.stack(predictions, axis=0)
# Explain the prediction using Citrus LIME.
explanation = lime.explain(
sentence,
_predict_fn,
class_to_explain=class_to_explain,
num_samples=num_samples,
tokenizer=str.split,
mask_token=lime.DEFAULT_MASK_TOKEN,
kernel=functools.partial(
lime.exponential_kernel, kernel_width=lime.DEFAULT_KERNEL_WIDTH))
scores = explanation.feature_importance # <float32>[seq_len]
scores = utils.normalize_scores(scores, make_positive=False)
# Explain the prediction using original LIME.
original_lime_explainer = lime_text.LimeTextExplainer(
class_names=map(str, np.arange(num_classes)),
mask_string=lime.DEFAULT_MASK_TOKEN,
kernel_width=lime.DEFAULT_KERNEL_WIDTH,
split_expression=str.split,
bow=False)
num_features = len(sentence.split())
original_explanation = original_lime_explainer.explain_instance(
sentence,
_predict_fn,
labels=(class_to_explain,),
num_features=num_features,
num_samples=num_samples)
# original_explanation.local_exp is a dict that has a key class_to_explain,
# which gives a sequence of (index, score) pairs.
# We convert it to an array <float32>[seq_len] with a score per position.
original_scores = np.zeros(num_features)
for index, score in original_explanation.local_exp[class_to_explain]:
original_scores[index] = score
original_scores = utils.normalize_scores(
original_scores, make_positive=False)
# Test that Citrus LIME and original LIME match.
np.testing.assert_allclose(scores, original_scores, atol=0.01)