PROJECT_HOME = os.path.dirname(os.path.abspath(__file__)) + "/../"
sys.path.append(PROJECT_HOME)
from webapp.recommend.svm import SVM
model = SVM()
## load test data
data_filepath = os.path.dirname(
__file__) + "/../../resources/data/test-data.tsv"
labels_filepath = os.path.dirname(
__file__) + "/../../resources/data/test-label.tsv"
data = model.load_tsv_file(data_filepath)
labels = model.load_tsv_file(labels_filepath)
## restore model from file
result = model.predict_with_default_dumped_model(data)
num_true = 0
num_false = 0
for i in range(0, len(labels)):
if labels[i] == result[i]:
num_true += 1
else:
num_false += 1
print("# trues : %d" % (num_true))
print("# falses: %d" % (num_false))
print("accurary: %f" % (num_true / (num_true + num_false)))
def test_predict_with_default_dumped_model():
model = SVM()
target = [0, 2, 1]
result = model.predict_with_default_dumped_model(target)
assert_equal(result, [3])