def __init__(self, data):
# Word-level features module
self.feat_word = WordFeatures()
# Only run GENIA tagger if module is available
if data and enabled['GENIA']:
tagger = enabled['GENIA']
self.feat_genia = GeniaFeatures(tagger, data)
# Only create UMLS cache if module is available
if enabled['UMLS']:
self.feat_umls = UMLSFeatures()
self.enabled_IOB_nonprose_sentence_features = []
#self.enabled_IOB_nonprose_sentence_features.append('pos')
#self.enabled_IOB_nonprose_sentence_features.append('pos_context')
self.enabled_IOB_nonprose_sentence_features.append('prev')
self.enabled_IOB_nonprose_sentence_features.append('next')
self.enabled_IOB_nonprose_sentence_features.append('unigram_context')
self.enabled_IOB_nonprose_sentence_features.append('UMLS')
self.enabled_IOB_prose_sentence_features = []
self.enabled_IOB_prose_sentence_features.append('unigram_context')
self.enabled_IOB_prose_sentence_features.append('pos')
self.enabled_IOB_prose_sentence_features.append('pos_context')
self.enabled_IOB_prose_sentence_features.append('prev')
self.enabled_IOB_prose_sentence_features.append('prev2')
self.enabled_IOB_prose_sentence_features.append('next')
self.enabled_IOB_prose_sentence_features.append('next2')
self.enabled_IOB_prose_sentence_features.append('GENIA')
self.enabled_IOB_prose_sentence_features.append('UMLS')
def test(weights_file=None):
if weights_file is None:
weights_file = pick_best_model_from_dir()
if DEBUG:
print("Best model detected: {}".format(weights_file))
test_data = read_data_from_json(TEST_DATA_PATH)
if DEBUG:
print_data_stats(test_data, "Test")
# Tokenize data (rudimentary tokenizer).
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_sentences(test_data))
if DEBUG:
print("Tokenizer found {} words.".format(len(tokenizer.word_counts)))
print("")
# Convert to Keras input arrays (or dict).
wf = WordFeatures()
wf.train_PMI(test_data)
test_data, test_labels, _ = preprocess_data(test_data, tokenizer, wf,
"test")
embeddings_matrix = build_embeddings_matrix(tokenizer)
num_words = len(tokenizer.word_counts)
model = define_model(num_words, embeddings_matrix, "test",
WORD_EMBEDDINGS_DIM)
model.load_weights(weights_file, by_name=True)
model.summary()
num_tests = test_data["question_input"].shape[0]
y = model.predict(test_data)
assert (y.shape[0] == num_tests)
correct = 0
total = 0
exp_acc = 0.0
lin_acc = 0.0
for i in range(0, num_tests):
predicted = np.argmax(y[i])
expected = np.argmax(test_labels[i])
# Expected value (treat y[i] as a random variable).
value = np.dot(y[i], [0, 1, 2, 3, 4, 5])
expected_value = np.dot(test_labels[i], [0, 1, 2, 3, 4, 5])
exp_acc += (np.exp(abs(value - expected_value)) - 1.0)
lin_acc += abs(value - expected_value)
if predicted == expected:
correct += 1
total += 1
assert (total == num_tests)
print("\nEvaluated on {} terms.".format(total))
print("Accuracy: {0:.3f}%".format(100 * correct / total))
print("Exp accuracy: {0:.3f}".format(exp_acc / total))
print("Linear accuracy: {0:.3f}".format(lin_acc / total))
def predict(entry, sort=False, weights_file=None, show_plot=True):
if weights_file is None:
weights_file = pick_best_model_from_dir()
if DEBUG:
print("Best model detected: {}".format(weights_file))
assert ("question" in entry)
question = entry["question"]
if "terms" not in entry:
nlp = spacy.load("en_core_web_sm")
doc = nlp(question)
entry["terms"] = {}
for token in doc:
entry["terms"][token.text] = 0
data = [entry]
# Tokenize data (rudimentary tokenizer).
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_sentences(data))
# Convert to Keras input arrays (or dict).
wf = WordFeatures()
wf.train_PMI(data)
data, _, words = preprocess_data(data, tokenizer, wf, "predict")
embeddings_matrix = build_embeddings_matrix(tokenizer)
num_words = len(tokenizer.word_counts)
model = define_model(num_words, embeddings_matrix, "predict",
WORD_EMBEDDINGS_DIM)
model.load_weights(weights_file, by_name=True)
y = model.predict(data)
idx = 0
essentiality = []
for word in entry["terms"]:
value = np.dot(y[idx], [0, 1, 2, 3, 4, 5])
essentiality.append(value / 5.0)
idx += 1
if sort:
zipped = list(zip(words, essentiality))
zipped.sort(key=lambda x: x[1], reverse=True)
words = [x[0] for x in zipped]
essentiality = [x[1] for x in zipped]
import matplotlib.pyplot as plt
plt.bar(range(len(words)), essentiality, align='center')
plt.title("Predicted values")
plt.xticks(range(len(words)),
words,
rotation=45,
horizontalalignment='right')
plt.tight_layout()
if show_plot:
plt.show()
def plot_pmi_values(dataset="val", index=None, sort=False):
assert (dataset in ["train", "val", "test"])
data = None
if dataset == "train":
data = read_data_from_json(TRAIN_DATA_PATH)
elif dataset == "val":
data = read_data_from_json(VALIDATION_DATA_PATH)
elif dataset == "test":
data = read_data_from_json(TEST_DATA_PATH)
assert (data is not None)
if index is None:
entry = random.choice(data)
else:
entry = data[index]
entry_copy = deepcopy(entry)
predict(entry, sort=sort, show_plot=False)
entry = entry_copy
wf = WordFeatures()
wf.train_PMI([entry])
from pmi_utils import reduce_positive_avg
idx = 0
values = []
words = []
for word in entry["terms"]:
pmi_values = wf.get_PMI(word,
entry,
use_question=True,
reduce_f=reduce_positive_avg)
values.append(pmi_values[1])
words.append(word)
idx += 1
if sort:
zipped = list(zip(words, values))
zipped.sort(key=lambda x: x[1], reverse=True)
words = [x[0] for x in zipped]
values = [x[1] for x in zipped]
print("\nQuestion: {}\n".format(entry["question"]))
import matplotlib.pyplot as plt
plt.figure()
plt.bar(range(len(words)), values, align='center')
plt.title("PMI values")
plt.xticks(range(len(words)),
words,
rotation=45,
horizontalalignment='right')
plt.tight_layout()
plt.show()
def predict_batch(data, weights_file=None):
if weights_file is None:
weights_file = pick_best_model_from_dir()
if DEBUG:
print("Best model detected: {}".format(weights_file))
# Tokenize data (rudimentary tokenizer).
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_sentences(data))
# Convert to Keras input arrays (or dict).
wf = WordFeatures()
wf.train_PMI(data)
data2, _, words = preprocess_data(data, tokenizer, wf, "predict_batch")
embeddings_matrix = build_embeddings_matrix(tokenizer)
num_words = len(tokenizer.word_counts)
model = define_model(num_words, embeddings_matrix, "predict_batch",
WORD_EMBEDDINGS_DIM)
model.load_weights(weights_file, by_name=True)
y = model.predict(data2, batch_size=128)
idx = 0
out = []
for entry in data:
out_set = {}
for _ in entry["terms"]:
value = np.dot(y[idx], [0, 1, 2, 3, 4, 5])
word = words[idx]
assert (word not in out_set)
out_set[word] = value / 5.0
idx += 1
out.append(out_set)
num_entries = 0
for entry in data:
num_entries += len(entry['terms'])
assert (num_entries == idx)
assert (len(data) == len(out))
for i in range(0, len(data)):
assert (len(out[i]) == len(data[i]["terms"]))
for out_set in out:
num_entries -= len(out_set)
assert (num_entries == 0)
return out
class TestWordEmbeddings(unittest.TestCase):
wf = WordFeatures()
def setUp(self):
pass
def tearDown(self):
pass
def test_is_science_term(self):
self.assertFalse(DEBUG)
science_terms = [
"aardvarks",
"ab initio",
"center of curvature",
"force",
"gravity",
"geo-science",
"origins of the solar system",
"atom",
"protons",
"seahorses",
"newton's law of universal gravitation",
"nucleus",
"zwitterion",
"zygomorphic",
"zygomycetes",
"zygospore",
]
for word in science_terms:
self.assertTrue(self.wf.is_science_term(word))
not_science_terms = ["love", "beauty", "nice", "language", "glasses"]
for word in not_science_terms:
self.assertFalse(self.wf.is_science_term(word))
def test_concreteness_ratings(self):
self.assertFalse(DEBUG)
to_check = {
"roadsweeper": 4.85,
"treeless": 4.24,
"divisional": 2.04,
"hopeful": 1.7,
"essentialness": 1.04,
"interpretively": 1.21,
"traindriver": 4.54,
"chocolaty": 3.45,
"mathematical": 2.9,
"baking soda": 5.0,
"beach ball": 5.0,
"birth certificate": 5.0,
"adaptive": 1.97,
"bucharest": 2.5,
"soccer": 4.76,
"sebi": 2.5,
"fasole": 2.5
}
for word in to_check:
r = to_check[word]
self.assertAlmostEqual(self.wf.get_concretness_rating(word), r)
def plot_F1_scores(dataset, weights_file=None):
assert (dataset in ["val", "test"])
if weights_file is None:
weights_file = pick_best_model_from_dir()
if DEBUG:
print("Best model detected: {}".format(weights_file))
data = None
if dataset == "val":
data = read_data_from_json(VALIDATION_DATA_PATH)
elif dataset == "test":
data = read_data_from_json(TEST_DATA_PATH)
assert (data is not None)
if DEBUG:
print_data_stats(data, "F1 scores data")
# Tokenize data (rudimentary tokenizer).
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_sentences(data))
if DEBUG:
print("Tokenizer found {} words.".format(len(tokenizer.word_counts)))
print("")
# Convert to Keras input arrays (or dict).
wf = WordFeatures()
wf.train_PMI(data)
data, labels, words = preprocess_data(data, tokenizer, wf, "F1 scores")
embeddings_matrix = build_embeddings_matrix(tokenizer)
num_words = len(tokenizer.word_counts)
model = define_model(num_words, embeddings_matrix, "F1_scores_data",
WORD_EMBEDDINGS_DIM)
model.load_weights(weights_file, by_name=True)
model.summary()
num_tests = data["question_input"].shape[0]
y = model.predict(data)
assert (y.shape[0] == num_tests)
threshold = 0.0
f1 = []
thresholds = []
best_f1 = None
best_threshold = None
acc_at_max_f1 = None
while threshold <= 1.0:
correct = 0
total = 0
true_positive = 0
false_positive = 0
true_negative = 0
false_negative = 0
for i in range(0, num_tests):
# Expected value (treat y[i] as a random variable).
value = np.dot(y[i], [0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
if value >= threshold:
predicted = 1
else:
predicted = 0
expected_value = np.argmax(labels[i])
if expected_value >= 2.5:
expected = 1
else:
expected = 0
if predicted == expected:
correct += 1
if predicted == 1:
if expected == 1:
true_positive += 1
else:
false_positive += 1
else:
if expected == 0:
true_negative += 1
else:
false_negative += 1
total += 1
assert (total == num_tests)
assert (correct == true_positive + true_negative)
if true_positive + false_positive == 0:
threshold += 0.001
continue
if true_positive + false_negative == 0:
threshold += 0.001
continue
precision = 1.0 * true_positive / (true_positive + false_positive)
recall = 1.0 * true_positive / (true_positive + false_negative)
f1_score = 2.0 * precision * recall / (precision + recall)
if best_f1 is None or f1_score > best_f1:
best_f1 = f1_score
best_threshold = threshold
acc_at_max_f1 = 1.0 * correct / max(total, 1.0)
f1.append(f1_score)
thresholds.append(threshold)
threshold += 0.001
print("Best F1 score: {}, at t = {}".format(round(best_f1, 3),
round(best_threshold, 4)))
print("Accuracy at max F1: {}".format(round(acc_at_max_f1, 3)))
import matplotlib.pyplot as plt
plt.title("F1 score")
plt.xlabel("Threshold")
plt.ylabel("F1")
plt.plot(thresholds, f1)
plt.show()
def binary_test(weights_file=None):
if weights_file is None:
weights_file = pick_best_model_from_dir()
if DEBUG:
print("Best model detected: {}".format(weights_file))
test_data = read_data_from_json(TEST_DATA_PATH)
# test_data = undersample_dataset(test_data, prob=0.84)
if DEBUG:
print_data_stats(test_data, "Test")
# Tokenize data (rudimentary tokenizer).
tokenizer = Tokenizer()
tokenizer.fit_on_texts(all_sentences(test_data))
if DEBUG:
print("Tokenizer found {} words.".format(len(tokenizer.word_counts)))
print("")
# Convert to Keras input arrays (or dict).
wf = WordFeatures()
wf.train_PMI(test_data)
test_data, test_labels, words = preprocess_data(test_data, tokenizer, wf,
"test")
embeddings_matrix = build_embeddings_matrix(tokenizer)
num_words = len(tokenizer.word_counts)
model = define_model(num_words, embeddings_matrix, "test",
WORD_EMBEDDINGS_DIM)
model.load_weights(weights_file, by_name=True)
model.summary()
num_tests = test_data["question_input"].shape[0]
y = model.predict(test_data)
assert (y.shape[0] == num_tests)
correct = 0
total = 0
true_positive = 0
false_positive = 0
true_negative = 0
false_negative = 0
correct_confidence = 0.0
wrong_confidence = 0.0
false_positive_words = []
for i in range(0, num_tests):
# Expected value (treat y[i] as a random variable).
value = np.dot(y[i], [0.0, 0.2, 0.4, 0.6, 0.8, 1.0])
if value >= 0.5:
predicted = 1
else:
predicted = 0
confidence = None
if predicted == 1:
confidence = np.dot(y[i], [0, 0, 0, 1, 1, 1])
else:
confidence = np.dot(y[i], [1, 1, 1, 0, 0, 0])
expected_value = np.argmax(test_labels[i])
if expected_value >= 2.5:
expected = 1
else:
expected = 0
if predicted == expected:
correct += 1
correct_confidence += confidence
else:
wrong_confidence += confidence
if predicted == 1:
if expected == 1:
true_positive += 1
else:
false_positive += 1
false_positive_words.append(words[i])
else:
if expected == 0:
true_negative += 1
else:
false_negative += 1
total += 1
assert (total == num_tests)
assert (correct == true_positive + true_negative)
precision = 100.0 * true_positive / (true_positive + false_positive)
recall = 100.0 * true_positive / (true_positive + false_negative)
f1 = 2.0 * precision * recall / (precision + recall)
print("")
print(" | Correct class |")
print(" | 1 | 0 |")
print("Predicted 1 |{} |{} |".format(
str(true_positive).rjust(6),
str(false_positive).rjust(6)))
print("Predicted 0 |{} |{} |".format(
str(false_negative).rjust(6),
str(true_negative).rjust(6)))
print("\nEvaluated on {} terms.".format(total))
print("Binary accuracy: {0:.3f}%".format(100 * correct / total))
print("Precision: {0:.3f}%".format(precision))
print("Recall: {0:.3f}%".format(recall))
print("F1: {0:.3f}".format(f1 / 100.0))
if correct >= 1:
print("Correct confidence {0:.3f}%".format(100.0 * correct_confidence /
correct))
if correct < total:
print("Wrong confidence {0:.3f}%".format(100.0 * wrong_confidence /
(total - correct)))
print("")
random.shuffle(false_positive_words)
print("Some false positive words: ", str(false_positive_words[:10]))
def train():
train_data = read_data_from_json(TRAIN_DATA_PATH)
val_data = read_data_from_json(VALIDATION_DATA_PATH)
test_data = read_data_from_json(TEST_DATA_PATH)
# train_data = undersample_dataset(train_data, prob=0.68)
# val_data = undersample_dataset(val_data, prob=0.68)
# test_data = undersample_dataset(test_data, prob=0.68)
# train_data = train_data[:2]
# val_data = val_data[:2]
# test_data = test_data[:1]
if DEBUG:
print_data_stats(train_data, "Train")
print_data_stats(val_data, "Validation")
print_data_stats(test_data, "Test")
if False:
print(dataset_similarity(val_data, train_data)) # 0.5714%
print(dataset_similarity(test_data, train_data)) # 2.112%
# Tokenize data (rudimentary tokenizer).
tokenizer = Tokenizer()
tokenizer.fit_on_texts(
all_sentences(train_data) + all_sentences(val_data) +
all_sentences(test_data))
if DEBUG:
print("Tokenizer found {} words.".format(len(tokenizer.word_counts)))
print("")
# Convert to Keras input arrays (or dict).
wf = WordFeatures()
wf.train_PMI(train_data + val_data + test_data)
train_data, train_labels, _ = preprocess_data(train_data, tokenizer, wf,
"train")
val_data, val_labels, _ = preprocess_data(val_data, tokenizer, wf,
"validation")
test_data, test_labels, _ = preprocess_data(test_data, tokenizer, wf,
"test")
# Equalize training data labels to the same frequency.
if False:
from utils import equalize
train_data, train_labels = equalize(train_data, train_labels)
if DEBUG:
print("Train data has been equalized. New freq: {}.".format(
np.asarray(np.sum(train_labels, axis=0), dtype=np.int32)))
if False:
from utils import oversample_dataset
train_data, train_labels = oversample_dataset(train_data, train_labels,
[6000, 8000])
if DEBUG:
print("Train data has been oversampled. New freq: {}.".format(
np.asarray(np.sum(train_labels, axis=0), dtype=np.int32)))
embeddings_matrix = build_embeddings_matrix(tokenizer)
num_words = len(tokenizer.word_counts)
model = define_model(num_words, embeddings_matrix, "train",
WORD_EMBEDDINGS_DIM)
model.summary()
plot_model(model, to_file='model.png', show_shapes=True)
filepath = "models/" + "model.{val_acc:.3f}-{epoch:03d}.hdf5"
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=0,
mode='max',
save_best_only=True,
save_weights_only=True)
model.fit(train_data,
train_labels,
batch_size=4000,
epochs=450,
verbose=2,
validation_data=(val_data, val_labels),
callbacks=[checkpoint])
score = model.evaluate(test_data, test_labels, verbose=0)
if score:
print('Test loss:', score[0])
print('Test accuracy:', score[1])
class SentenceFeatures:
# Feature Enabling
enabled_concept_features = frozenset(["UMLS"])
# Instantiate an Sentence object
def __init__(self, data):
# Word-level features module
self.feat_word = WordFeatures()
# Only run GENIA tagger if module is available
if data and enabled['GENIA']:
tagger = enabled['GENIA']
self.feat_genia = GeniaFeatures(tagger, data)
# Only create UMLS cache if module is available
if enabled['UMLS']:
self.feat_umls = UMLSFeatures()
self.enabled_IOB_nonprose_sentence_features = []
#self.enabled_IOB_nonprose_sentence_features.append('pos')
#self.enabled_IOB_nonprose_sentence_features.append('pos_context')
self.enabled_IOB_nonprose_sentence_features.append('prev')
self.enabled_IOB_nonprose_sentence_features.append('next')
self.enabled_IOB_nonprose_sentence_features.append('unigram_context')
self.enabled_IOB_nonprose_sentence_features.append('UMLS')
self.enabled_IOB_prose_sentence_features = []
self.enabled_IOB_prose_sentence_features.append('unigram_context')
self.enabled_IOB_prose_sentence_features.append('pos')
self.enabled_IOB_prose_sentence_features.append('pos_context')
self.enabled_IOB_prose_sentence_features.append('prev')
self.enabled_IOB_prose_sentence_features.append('prev2')
self.enabled_IOB_prose_sentence_features.append('next')
self.enabled_IOB_prose_sentence_features.append('next2')
self.enabled_IOB_prose_sentence_features.append('GENIA')
self.enabled_IOB_prose_sentence_features.append('UMLS')
def IOB_prose_features(self, sentence):
"""
IOB_prose_features
@param sentence. A list of strings
@return A list of dictionaries of features
"""
features_list = []
# Get a feature set for each word in the sentence
for i, word in enumerate(sentence):
features_list.append(self.feat_word.IOB_prose_features(
sentence[i]))
# Feature: Bag of Words unigram conext (window=3)
if 'unigram_context' in self.enabled_IOB_prose_sentence_features:
window = 3
n = len(sentence)
# Previous unigrams
for i in range(n):
end = min(i, window)
unigrams = sentence[i - end:i]
for j, u in enumerate(unigrams):
features_list[i][('prev_unigrams-%d' % j, u)] = 1
# Next unigrams
for i in range(n):
end = min(i + window, n - 1)
unigrams = sentence[i + 1:end + 1]
for j, u in enumerate(unigrams):
features_list[i][('next_unigrams-%d' % j, u)] = 1
# Only POS tag once
if 'pos' in self.enabled_IOB_prose_sentence_features:
pos_tagged = nltk_tagger.tag(sentence)
# Allow for particular features to be enabled
for feature in self.enabled_IOB_prose_sentence_features:
# Feature: Part of Speech
if feature == 'pos':
for (i, (_, pos)) in enumerate(pos_tagged):
features_list[i].update({('pos', pos): 1})
# Feature: POS context
if 'pos_context' in self.enabled_IOB_prose_sentence_features:
window = 3
n = len(sentence)
# Previous POS
for i in range(n):
end = min(i, window)
for j, p in enumerate(pos_tagged[i - end:i]):
pos = p[1]
features_list[i][('prev_pos_context-%d' % j, pos)] = 1
# Next POS
for i in range(n):
end = min(i + window, n - 1)
for j, p in enumerate(pos_tagged[i + 1:i + end + 1]):
pos = p[1]
features_list[i][('prev_pos_context-%d' % j, pos)] = 1
# GENIA features
if (feature == 'GENIA') and enabled['GENIA']:
# Get GENIA features
genia_feat_list = self.feat_genia.features(sentence)
'''
print '\t', sentence
print '\n\n'
for gf in genia_feat_list:
print '\t', gf
print
print '\n\n'
'''
for i, feat_dict in enumerate(genia_feat_list):
features_list[i].update(feat_dict)
# Feature: UMLS Word Features (only use prose ones)
if (feature == "UMLS") and enabled['UMLS']:
umls_features = self.feat_umls.IOB_prose_features(sentence)
for i in range(len(sentence)):
features_list[i].update(umls_features[i])
# Used for 'prev' and 'next' features
ngram_features = [{} for i in range(len(features_list))]
if "prev" in self.enabled_IOB_prose_sentence_features:
prev = lambda f: {("prev_" + k[0], k[1]): v for k, v in f.items()}
prev_list = map(prev, features_list)
for i in range(len(features_list)):
if i == 0:
ngram_features[i][("prev", "*")] = 1
else:
ngram_features[i].update(prev_list[i - 1])
if "prev2" in self.enabled_IOB_prose_sentence_features:
prev2 = lambda f: {("prev2_" + k[0], k[1]): v / 2.0
for k, v in f.items()}
prev_list = map(prev2, features_list)
for i in range(len(features_list)):
if i == 0:
ngram_features[i][("prev2", "*")] = 1
elif i == 1:
ngram_features[i][("prev2", "*")] = 1
else:
ngram_features[i].update(prev_list[i - 2])
if "next" in self.enabled_IOB_prose_sentence_features:
next = lambda f: {("next_" + k[0], k[1]): v for k, v in f.items()}
next_list = map(next, features_list)
for i in range(len(features_list)):
if i < len(features_list) - 1:
ngram_features[i].update(next_list[i + 1])
else:
ngram_features[i][("next", "*")] = 1
if "next2" in self.enabled_IOB_prose_sentence_features:
next2 = lambda f: {("next2_" + k[0], k[1]): v / 2.0
for k, v in f.items()}
next_list = map(next2, features_list)
for i in range(len(features_list)):
if i < len(features_list) - 2:
ngram_features[i].update(next_list[i + 2])
elif i == len(features_list) - 2:
ngram_features[i][("next2", "**")] = 1
else:
ngram_features[i][("next2", "*")] = 1
merged = lambda d1, d2: dict(d1.items() + d2.items())
features_list = [
merged(features_list[i], ngram_features[i])
for i in range(len(features_list))
]
'''
for f in features_list:
print sorted(f.items())
print
print '\n\n\n'
'''
return features_list
def IOB_nonprose_features(self, sentence):
"""
IOB_nonprose_features
@param sentence. A list of strings
@return A list of dictionaries of features
"""
# Get a feature set for each word in the sentence
features_list = []
for i, word in enumerate(sentence):
word_feats = self.feat_word.IOB_nonprose_features(sentence[i])
features_list.append(word_feats)
# Feature: Bag of Words unigram conext (window=3)
if 'unigram_context' in self.enabled_IOB_nonprose_sentence_features:
window = 3
n = len(sentence)
# Previous unigrams
for i in range(n):
end = min(i, window)
unigrams = sentence[i - end:i]
for j, u in enumerate(unigrams):
features_list[i][('prev_unigrams-%d' % j, u)] = 1
# Next unigrams
for i in range(n):
end = min(i + window, n - 1)
unigrams = sentence[i + 1:end + 1]
for u in unigrams:
features_list[i][('next_unigrams-%d' % j, u)] = 1
# Feature: UMLS Word Features (only use nonprose ones)
if enabled[
'UMLS'] and 'UMLS' in self.enabled_IOB_nonprose_sentence_features:
umls_features = self.feat_umls.IOB_nonprose_features(sentence)
for i in range(len(sentence)):
features_list[i].update(umls_features[i])
#return features_list
if 'pos' in self.enabled_IOB_nonprose_sentence_features:
pos_tagged = nltk_tagger.tag(sentence)
# Allow for particular features to be enabled
for feature in self.enabled_IOB_nonprose_sentence_features:
# Feature: Part of Speech
if feature == 'pos':
for (i, (_, pos)) in enumerate(pos_tagged):
features_list[i][('pos', pos)] = 1
# Feature: POS context
if 'pos_context' in self.enabled_IOB_nonprose_sentence_features:
window = 3
n = len(sentence)
# Previous POS
for i in range(n):
end = min(i, window)
for j, p in enumerate(pos_tagged[i - end:i]):
pos = p[1]
features_list[i][('prev_pos_context-%d' % j, pos)] = 1
# Next POS
for i in range(n):
end = min(i + window, n - 1)
for j, p in enumerate(pos_tagged[i + 1:i + end + 1]):
pos = p[1]
features_list[i][('prev_pos_context-%d' % j, pos)] = 1
ngram_features = [{} for _ in range(len(features_list))]
if "prev" in self.enabled_IOB_nonprose_sentence_features:
prev = lambda f: {("prev_" + k[0], k[1]): v for k, v in f.items()}
prev_list = map(prev, features_list)
for i in range(len(features_list)):
if i == 0:
ngram_features[i][("prev", "*")] = 1
else:
ngram_features[i].update(prev_list[i - 1])
if "next" in self.enabled_IOB_nonprose_sentence_features:
next = lambda f: {("next_" + k[0], k[1]): v for k, v in f.items()}
next_list = map(next, features_list)
for i in range(len(features_list)):
if i == len(features_list) - 1:
ngram_features[i][("next", "*")] = 1
else:
ngram_features[i].update(next_list[i + 1])
merged = lambda d1, d2: dict(d1.items() + d2.items())
features_list = [
merged(features_list[i], ngram_features[i])
for i in range(len(features_list))
]
return features_list
def concept_features_for_sentence(self, sentence, chunk_inds):
"""
concept_features()
@param sentence. A sentence in list of chunk format
@param chunk_inds. A list of indices for non-None-labeled chunks
@return A list of feature dictionaries
"""
# Get a feature set for each word in the sentence
features_list = []
for ind in chunk_inds:
features_list.append(
self.feat_word.concept_features_for_chunk(sentence, ind))
# Allow for particular features to be enabled
for feature in self.enabled_concept_features:
# Features: UMLS features
if (feature == "UMLS") and enabled['UMLS']:
umls_features = self.feat_umls.concept_features_for_chunks(
sentence, chunk_inds)
for i in range(len(chunk_inds)):
features_list[i].update(umls_features[i])
return features_list