def pre_train(predict_day=date(2017, 11, 13), name="F"):
idou_week_n = 5
nn_train_num = 100
n_epochs = 50
batch_size = 10
mid = 30
hosei_rate = None
trend = func.make_trend_idou(name, idou_week_n, predict_day, hosei_rate)
dates = trend[0]
X_pred = []
for j in range(len(dates)):
holi = []
target_day = dates[j]
for k in range(1, 12):
if k == 5 or k == 6 or func.holiday_hantei(target_day +
dt.timedelta(days=k)):
holi.append(1)
else:
holi.append(0)
X_pred.append(holi)
#NNの定義
tf.reset_default_graph()
layers = [Dense(11, mid, tf.nn.sigmoid), Dense(mid, 1)]
x = tf.placeholder(tf.float32, [None, 11])
t = tf.placeholder(tf.float32, [None, 1])
def f_props(layers, x):
for layer in layers:
x = layer.f_prop(x)
return x
y_valid = f_props(layers, x)
cost = tf.reduce_mean((t - y_valid)**2) #,axis=0)
#train = tf.train.GradientDescentOptimizer(0.01).minimize(cost)
#train = tf.train.GradientDescentOptimizer(0.1).minimize(cost)
train = tf.train.AdamOptimizer().minimize(cost)
valid = tf.nn.relu(y_valid)
saver = tf.train.Saver()
sess = tf.Session()
# init = tf.global_variables_initializer()
init = tf.initialize_all_variables()
sess.run(init)
for i in range(nn_train_num):
#データセットの生成
trend = func.make_trend_idou(name, idou_week_n, predict_day,
hosei_rate)
dates = trend[0]
order_num = trend[1]
trend_num = trend[2]
X = []
y = []
for j in range(idou_week_n, len(dates) - idou_week_n):
target_day = dates[j]
rate = 1.0 + func.special_rate(target_day)
holi = []
for k in range(1, 12):
if k == 5 or k == 6 or func.holiday_hantei(
target_day + dt.timedelta(days=k)):
holi.append(1)
else:
holi.append(0)
train_rate = order_num[j] / trend_num[j] - rate
if abs(train_rate) < 0.4 and sum(holi) != 2:
X.append(holi)
if train_rate < 0:
train_rate = 0
y.append(train_rate)
#学習
X = np.array(X)
y = np.array(y).reshape((len(y), 1))
n_batches = X.shape[0] // batch_size
for epoch in range(n_epochs):
train_X, train_y = shuffle(X, y, random_state=random_state)
for j in range(n_batches):
start = j * batch_size
end = start + batch_size
_, train_cost = sess.run([train, cost],
feed_dict={
x: train_X[start:end],
t: train_y[start:end]
})
#補正レートの再定義
hosei_rate = []
pred_y = sess.run(valid, feed_dict={x: np.array(X_pred)})
for j in range(len(dates)):
target_day = dates[j]
rate = 1.0 + func.special_rate(target_day)
if sum(X_pred[j]) == 2:
pred_y[j] = 0
hosei_rate.append(rate + pred_y[j])
# test_y = sess.run(valid, feed_dict={x: np.array(func.test_X)})
# for p,q in zip(func.test_X,test_y):
# print(p,q)
# saver.save(sess, "./model/model_ckpt")
saver.save(sess, "./model_" + name + "/model_ckpt")
sess.close()
def weeksum(predict_day, predict_week_num=1, name="F"):
idou_week_n = 5
kaiki_week_n = 15
mid = 30
hosei_rate = None
trend = func.make_trend_idou(name, idou_week_n, predict_day, hosei_rate)
dates = trend[0]
X_pred = []
for j in range(len(dates)):
holi = []
target_day = dates[j]
for k in range(1, 12):
if k == 5 or k == 6 or func.holiday_hantei(target_day +
dt.timedelta(days=k)):
holi.append(1)
else:
holi.append(0)
X_pred.append(holi)
#NNの定義
tf.reset_default_graph()
layers = [Dense(11, mid, tf.nn.sigmoid), Dense(mid, 1)]
x = tf.placeholder(tf.float32, [None, 11])
def f_props(layers, x):
for layer in layers:
x = layer.f_prop(x)
return x
y_valid = f_props(layers, x)
valid = tf.nn.relu(y_valid)
saver = tf.train.Saver()
#補正レートの再定義
sess = tf.Session()
saver.restore(sess, "./model_" + name + "/model_ckpt")
hosei_rate = []
pred_y = sess.run(valid, feed_dict={x: np.array(X_pred)})
for j in range(len(dates)):
target_day = dates[j]
rate = 1 + func.special_rate(target_day)
if sum(X_pred[j]) == 2:
pred_y[j] = 0
hosei_rate.append(rate + pred_y[j])
#予測対象のトレンド値の取得
pred_trend = func.make_trend(name,
kaiki_week_n,
predict_day,
predict_week_num,
rate=hosei_rate)
trend_num = pred_trend[2][-1]
#予測対象の補正値の取得
hosei_rate = []
pred = []
target_day = predict_day
for j in range(predict_week_num):
rate = 1
holi = []
for i in range(1, 12):
if i == 5 or i == 6 or func.holiday_hantei(target_day +
dt.timedelta(days=i)):
holi.append(1)
else:
holi.append(0)
if sum(holi) != 2:
rate += sess.run(valid, feed_dict={x: np.array([holi])})[0][0]
rate += func.special_rate(target_day)
hosei_rate.append(rate)
pred.append(trend_num[j] * rate)
target_day += dt.timedelta(days=7)
sess.close()
return trend_num, hosei_rate, pred