def shift_reduce(queue, w_s, w_l, w_r):
heads = []
stack = [util.Token(0, util.ROOT, util.ROOT, None)]
while len(queue) > 0 or len(stack) > 1:
feats = make_feature(stack, queue)
action = get_action(feats, w_s, w_l, w_r, queue)
do_action(action, stack, queue, heads)
return heads
def shift_reduce(queue, w_s, w_l, w_r):
heads = []
stack = [util.Token(0, util.ROOT, util.ROOT, None)]
while len(queue) > 0 or len(stack) > 1:
feats = make_feature(stack, queue)
action = get_action(feats, w_s, w_l, w_r, queue)
do_action(action, stack, queue, heads)
return heads
def get_action(feats, w_s, w_l, w_r, queue):
feats = make_feature(stack, queue)
score_s = get_score(feats, w_s)
score_l = get_score(feats, w_l)
score_r = get_score(feats, w_r)
if score_s >= score_l and score_s >= score_r and len(queue) > 0:
return util.SHIFT
elif score_l >= score_s:
return util.LEFT_REDUCE
return util.RIGHT_REDUCE
def get_action(feats, w_s, w_l, w_r, queue):
feats = make_feature(stack, queue)
score_s = get_score(feats, w_s)
score_l = get_score(feats, w_l)
score_r = get_score(feats, w_r)
if score_s >= score_l and score_s >= score_r and len(queue) > 0:
return util.SHIFT
elif score_l >= score_s:
return util.LEFT_REDUCE
return util.RIGHT_REDUCE
def shift_reduce_train(queue, w_s, w_l, w_r):
heads = []
stack = [util.Token(0, util.ROOT, util.ROOT, util.ROOT)]
while len(queue) > 0 or len(stack) > 1:
feats = make_feature(stack, queue)
action = get_action(feats, w_s, w_l, w_s, queue)
correct_action = get_correct_action()
if action != correct_action:
w_a = get_weight_of(action, w_s, w_l, w_r)
w_ca = get_weight_of(correct_action, w_s, w_l, w_r)
update_weight(feats, w_a, w_ca)
do_action(action, stack, queue, heads)
run_time = datetime.now().strftime("%Y%m%d_%H%M%S")
# ... data params
n_data_size = 4000
n_seq = 50
n_test = 100
n_future = 5
n_fwd = 5
# ... ticker symbols in data set
# ticker = ['aapl', 'crm', 'dji', 'flir', 'nvda', 'sp500', 'vix']
ticker = DOW_30
df_feature = mf.make_feature(ticker, days=n_data_size, refresh=False)
feature_columns = df_feature.columns.to_list()
feature_columns.pop(0)
n_feature = len(feature_columns)
# ... model params
batch_size = 64
n_epochs = 4
# ... plot switch
save_plots = True
df_summary = pd.DataFrame()
for n_seq in [90]: