def main():
with open(PASSAGES_PATH, "rb") as f:
passages = pickle.load(f)
passages = passages[:NUM_PASSAGES]
terminals, token_labels = tokeneval.get_terminals_labels(passages)
tokens = [x.text for x in terminals]
clas = classify.train_classifier(FMAT[:len(LABELS)],
LABELS,
METHOD,
c_param=PARAM,
nu_param=PARAM,
learn_rate=PARAM,
n_estimators=500)
if TOKENS_FMAT is not None: # use token evaluation, not type
stats = tokeneval.evaluate_with_classifier(tokens, token_labels,
TARGETS, TOKENS_FMAT, clas)
else:
target_labels = LABELS.tolist()
target_labels += classify.predict_labels(clas,
FMAT[len(LABELS):]).tolist()
stats = tokeneval.evaluate_with_type(tokens, token_labels, TARGETS,
target_labels)
print("\t".join(str(len(x)) for x in stats))
def main():
# Getting required data
with open(TARGETS_PATH, "rb") as f:
target_array, scores = pickle.load(f)
target_list = target_array.tolist()
with open(FMAT_PATH, "rb") as f:
fmat = pickle.load(f)
with open(PASSAGES_PATH, "rb") as f:
passages = pickle.load(f)
with open(TOKENS_FMAT, "rb") as f:
tokens_fmat = pickle.load(f)
passages = passages[:NUM_PASSAGES]
terminals, token_labels = tokeneval.get_terminals_labels(passages)
tokens = [x.text for x in terminals]
# Running through random parameters settings
#for i, params in enumerate(params_generator(NUM_SAMPLING)):
for i, params in enumerate(PARAMS):
sys.stderr.write('{} {}\n'.format(METHOD, i))
clas, _, _ = classify.self_train_classifier(
fmat, scores, target_array, params, method=METHOD,
c_param=CLS_PRM, nu_param=CLS_PRM, learn_rate=CLS_PRM,
n_estimators=500)
target_labels = [int(x >= classify.PRE_LABELS_THRESH) for x in scores]
target_labels += list(classify.predict_labels(clas,
fmat[len(scores):]))
stats = tokeneval.evaluate_with_classifier(
tokens, token_labels, target_list, tokens_fmat, clas)
print("\t".join([str(x)
for x in params] + [str(len(x)) for x in stats]))
def evaluate_with_classifier(tokens, token_labels, targets, token_features, classifier):
tp, tn, fp, fn = [], [], [], [] # True/Flase positive/negative labels
found, not_found = [], []
pred = classify.predict_labels(classifier, token_features).tolist()
for token, token_label, guessed_label in zip(tokens, token_labels, pred):
lemma = lemmatize(token, targets)
if lemma in targets:
found.append((token, token_label))
if guessed_label == token_label == 0:
tn.append(token)
elif guessed_label == token_label == 1:
tp.append(token)
elif token_label == 0:
fp.append(token)
else:
fn.append(token)
else:
not_found.append((token, token_label))
return found, not_found, tp, tn, fp, fn
def main():
with open(PASSAGES_PATH, "rb") as f:
passages = pickle.load(f)
passages = passages[:NUM_PASSAGES]
terminals, token_labels = tokeneval.get_terminals_labels(passages)
tokens = [x.text for x in terminals]
clas = classify.train_classifier(FMAT[:len(LABELS)], LABELS, METHOD,
c_param=PARAM, nu_param=PARAM,
learn_rate=PARAM, n_estimators=500)
if TOKENS_FMAT is not None: # use token evaluation, not type
stats = tokeneval.evaluate_with_classifier(tokens, token_labels,
TARGETS, TOKENS_FMAT, clas)
else:
target_labels = LABELS.tolist()
target_labels += classify.predict_labels(clas, FMAT[len(LABELS):]).tolist()
stats = tokeneval.evaluate_with_type(tokens, token_labels, TARGETS,
target_labels)
print("\t".join(str(len(x)) for x in stats))
def evaluate_with_classifier(tokens, token_labels, targets,
token_features, classifier):
tp, tn, fp, fn = [], [], [], [] # True/Flase positive/negative labels
found, not_found = [], []
pred = classify.predict_labels(classifier, token_features).tolist()
for token, token_label, guessed_label in zip(tokens, token_labels, pred):
lemma = lemmatize(token, targets)
if lemma in targets:
found.append((token, token_label))
if guessed_label == token_label == 0:
tn.append(token)
elif guessed_label == token_label == 1:
tp.append(token)
elif token_label == 0:
fp.append(token)
else:
fn.append(token)
else:
not_found.append((token, token_label))
return found, not_found, tp, tn, fp, fn