def repl(env):
print('Lion Programming')
line = ''
results = None
while True:
line = input('*** ')
if len(line) != 0:
if line[-1] != ';':
line += ';'
results = eval_list(parse(lex(line)), env)
if results is not None:
print(token_str(results))
os.environ['PYTHONHASHSEED'] = '0'
np.random.seed(17)
rn.seed(12345)
if __name__ == "__main__":
set_reproductible()
datadir = "../data/"
trainfile = datadir + "traindata.csv"
devfile = datadir + "devdata.csv"
testfile = None
# Basic checking
start_time = time.perf_counter()
classifier = Classifier()
print("\n")
# Training
print("1. Training the classifier...\n")
classifier.train(trainfile)
# Evaluation on the dev dataset
print("\n2. Evaluation on the dev dataset...\n")
slabels = classifier.predict(devfile)
glabels = load_label_output(devfile)
eval_list(glabels, slabels)
if testfile is not None:
# Evaluation on the test data
print("\n3. Evaluation on the test dataset...\n")
slabels = classifier.predict(testfile)
glabels = load_label_output(testfile)
eval_list(glabels, slabels)
print("\nExec time: %.2f s." % (time.perf_counter() - start_time))
def add_native(expr, env): return eval_list(parse(lex(expr)), env)
def run(filename, env):
eval_list(parse(lex(get_chars(filename))), env)
def std_eval(string, env): if string[0] != 'string': return none; return eval_list(parse(lex(string[1])), env)
