def test_SVC_predict():
from b4msa.classifier import SVC
from b4msa.textmodel import TextModel
from b4msa.utils import read_data_labels
import os
fname = os.path.dirname(__file__) + '/text.json'
X, y = read_data_labels(fname)
t = TextModel(X)
c = SVC(t)
c.fit_file(fname)
y = c.predict_text('Excelente dia b4msa')
assert y == 'POS'
def test_SVC_predict_from_file():
from b4msa.classifier import SVC
from b4msa.textmodel import TextModel
from b4msa.utils import read_data_labels
import os
fname = os.path.dirname(__file__) + '/text.json'
X, y = read_data_labels(fname)
t = TextModel(X)
c = SVC(t)
c.fit_file(fname)
y = c.predict_file(fname)
for i in y:
assert i in ['POS', 'NEU', 'NEG']
def test_SVC_predict_from_file():
from b4msa.classifier import SVC
from b4msa.textmodel import TextModel
from b4msa.utils import read_data_labels
import os
#fname = os.path.dirname(__file__) + '/text.json'
fname = 'text.json'
#fname = 'test_text.json'
X, y = read_data_labels(fname)
t = TextModel(X)
c = SVC(t)
c.fit_file(fname)
y = c.predict_file("test_text.json")
print "Final Labels"
print y