def __init__(self):
abs_dir = os.path.dirname(__file__) # absolute path to here
self.sent_clf = MiniClassifier(os.path.join(abs_dir, 'robots/bias_sent_level.npz'))
self.doc_clf = MiniClassifier(os.path.join(abs_dir, 'robots/bias_doc_level.npz'))
self.vec = ModularVectorizer(norm=None, non_negative=True, binary=True, ngram_range=(1, 2), n_features=2**26)
self.bias_domains = ['Random sequence generation', 'Allocation concealment', 'Blinding of participants and personnel', 'Blinding of outcome assessment', 'Incomplete outcome data', 'Selective reporting']
class BiasRobot:
def __init__(self):
abs_dir = os.path.dirname(__file__) # absolute path to here
self.sent_clf = MiniClassifier(os.path.join(abs_dir, 'robots/bias_sent_level.npz'))
self.doc_clf = MiniClassifier(os.path.join(abs_dir, 'robots/bias_doc_level.npz'))
self.vec = ModularVectorizer(norm=None, non_negative=True, binary=True, ngram_range=(1, 2), n_features=2**26)
self.bias_domains = ['Random sequence generation', 'Allocation concealment', 'Blinding of participants and personnel', 'Blinding of outcome assessment', 'Incomplete outcome data', 'Selective reporting']
def annotate(self, doc_text, top_k=3):
"""
Annotate full text of clinical trial report
`top_k` refers to 'top-k recall'.
top-1 recall will return the single most relevant sentence
in the document, and top-3 recall the 3 most relevant.
The validation study assessed the accuracy of top-3 and top-1
and we suggest top-3 as default
"""
marginalia = []
doc_sents = sent_tokenize(doc_text)
for domain in self.bias_domains:
doc_domains = [domain] * len(doc_sents)
doc_X_i = izip(doc_sents, doc_domains)
#
# build up sentence feature set
#
self.vec.builder_clear()
# uni-bigrams
self.vec.builder_add_docs(doc_sents)
# uni-bigrams/domain interactions
self.vec.builder_add_docs(doc_X_i)
doc_sents_X = self.vec.builder_transform()
doc_sents_preds = self.sent_clf.decision_function(doc_sents_X)
high_prob_sent_indices = np.argsort(doc_sents_preds)[:-top_k-1:-1] # top k, with no 1 first
high_prob_sents = [doc_sents[i] for i in high_prob_sent_indices]
high_prob_sents_j = " ".join(high_prob_sents)
sent_domain_interaction = "-s-" + domain
#
# build up document feature set
#
self.vec.builder_clear()
# uni-bigrams
self.vec.builder_add_docs([doc_text])
# uni-bigrams/domain interaction
self.vec.builder_add_docs([(doc_text, domain)])
# uni-bigrams/relevance interaction
self.vec.builder_add_docs([(high_prob_sents_j, sent_domain_interaction)])
X = self.vec.builder_transform()
bias_pred = self.doc_clf.predict(X)
bias_class = ["high/unclear", "low"][bias_pred[0]]
marginalia.append({
"type": "Risk of Bias",
"title": domain,
"annotations": [{"content": sent, "uuid": str(uuid.uuid1())} for sent in high_prob_sents],
"description": "**Overall risk of bias prediction**: " + bias_class
})
return {"marginalia": marginalia}
