def test_text_explainer_show_methods():
pytest.importorskip('IPython')
from IPython.display import HTML
text = "Hello, world!"
@_apply_to_list
def predict_proba(doc):
return [0.0, 1.0] if 'lo' in doc else [1.0, 0.0]
te = TextExplainer()
te.fit(text, predict_proba)
pred_expl = te.show_prediction()
assert isinstance(pred_expl, HTML)
assert 'lo' in pred_expl.data
weight_expl = te.show_weights()
assert isinstance(weight_expl, HTML)
assert 'lo' in weight_expl.data
# -*- coding: utf-8 -*-
"""
__title__ = 'eli5'
__author__ = 'JieYuan'
__mtime__ = '2018/8/21'
"""
from eli5.lime import TextExplainer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
X = [
"The dimension of the input documents is reduced to 100, and then a kernel SVM is used to classify the documents.",
"This is what the pipeline returns for a document - it is pretty sure the first message in test data belongs to sci.med:"
]
y = [0, 1]
piplie = make_pipeline(TfidfVectorizer(), LogisticRegression())
te = TextExplainer(random_state=42)
te.fit(X[0], piplie.predict_proba)
te.show_prediction()
te.show_weights()
eli5.show_prediction
exp.show_prediction() # In[381]: exp.show_prediction(target_names=target_names) # In[382]: exp.metrics_ # - ‘score’ is an accuracy score weighted by cosine distance between generated sample and the original document (i.e. texts which are closer to the example are more important). Accuracy shows how good are ‘top 1’ predictions. # - ‘mean_KL_divergence’ is a mean Kullback–Leibler divergence for all target classes; it is also weighted by distance. KL divergence shows how well are probabilities approximated; 0.0 means a perfect match. # In[46]: exp.show_weights() # In[47]: # Check For Vectorizer Used exp.vec_ # In[48]: # Check For Classifer Used exp.clf_ # In[49]: # In[ ]: