def value(self, element, window=False):
"""
element: element是由int构成的list，如[1,1,1,1,1]
"""
k = "value/@%s/%s%d" % (self.ts, element, window)
res_v = get_Cache(k)
if res_v is not None:
return res_v
data_array = self._data.values
n_samples, _ = data_array.shape
#这里要小心，value可能是整数
# data_array = np.array(data_array, dtype=int)
ts_array = self._data["timestamp"].values
dim_array = self._data[DIM_LIST].values
value_array = self._data["value"].values
slice_data_array = sclice_dim_value(element, ts_array, dim_array, value_array)
if slice_data_array is None:
slice_data_array = np.array([self.ts, 0]).reshape(-1, 2)
ser = resample_array(slice_data_array, self.timestamp_list)
if window:
res = ser
else:
res = ser[-1, 1]
set_Cache(k, res)
return res
def predict(self, s_dimension_value, vol=False, window=3, frac=0.2):
k = "predict/@%s/%s%d" % (self.ts, s_dimension_value, window)
res_p = get_Cache(k)
if res_p is not None:
return res_p
s_dimension_value = np.array(s_dimension_value, dtype=int).reshape(-1, 5)
ser = self.value(s_dimension_value, window=True)
# if ser.empty:
if ser.size == 0:
pred, stand, real = 0, 0, 0
else:
ser_len = ser.shape[0]
# win_size = min(window+1, ser_len)
x = np.arange(ser_len).tolist()
y = ser[:,1]
filtered = lowess(y, x, is_sorted=True, frac=frac, it=2)
pred = filtered[:, 1][-1]
real = ser[-1, 1]
if ser_len <= 1:
stand = 0
else:
# stand = ser.std()
stand = np.std(filtered[:, 1])
if not vol:
res_pred = pred
else:
# print("pred, stand", pred, stand)
res_pred = (pred, stand, real)
set_Cache(k, res_pred)
return res_pred
def get_LEAF_contribution_vector(self):
LEAF_DIMENSION_LIST = self.LEAF_DIMENSION_LIST
k = "%s/contribution" % (self.ts)
v_vector = get_Cache(k)
if v_vector is None:
v_vector = [self.contribution(i) for i in LEAF_DIMENSION_LIST]
set_Cache(k, v_vector)
assert len(LEAF_DIMENSION_LIST) == len(v_vector)
return v_vector