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Stat_models.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
import pmdarima as pm
import threading
from sklearn.ensemble import GradientBoostingRegressor
class Naive_repeat(nn.Module):
def __init__(self, configs):
super(Naive_repeat, self).__init__()
self.pred_len = configs.pred_len
def forward(self, x):
B,L,D = x.shape
x = x[:,-1,:].reshape(B,1,D).repeat(self.pred_len,axis=1)
return x # [B, L, D]
class Naive_thread(threading.Thread):
def __init__(self,func,args=()):
super(Naive_thread,self).__init__()
self.func = func
self.args = args
def run(self):
self.results = self.func(*self.args)
def return_result(self):
threading.Thread.join(self)
return self.results
def _arima(seq,pred_len,bt,i):
model = pm.auto_arima(seq)
forecasts = model.predict(pred_len)
return forecasts,bt,i
class Arima(nn.Module):
"""
Extremely slow, please sample < 0.1
"""
def __init__(self, configs):
super(Arima, self).__init__()
self.pred_len = configs.pred_len
def forward(self, x):
result = np.zeros([x.shape[0],self.pred_len,x.shape[2]])
threads = []
for bt,seqs in tqdm(enumerate(x)):
for i in range(seqs.shape[-1]):
seq = seqs[:,i]
one_seq = Naive_thread(func=_arima,args=(seq,self.pred_len,bt,i))
threads.append(one_seq)
threads[-1].start()
for every_thread in tqdm(threads):
forcast,bt,i = every_thread.return_result()
result[bt,:,i] = forcast
return result # [B, L, D]
def _sarima(season,seq,pred_len,bt,i):
model = pm.auto_arima(seq, seasonal=True, m=season)
forecasts = model.predict(pred_len)
return forecasts,bt,i
class SArima(nn.Module):
"""
Extremely extremely slow, please sample < 0.01
"""
def __init__(self, configs):
super(SArima, self).__init__()
self.pred_len = configs.pred_len
self.seq_len = configs.seq_len
self.season = 24
if 'Ettm' in configs.data_path:
self.season = 12
elif 'ILI' in configs.data_path:
self.season = 1
if self.season >= self.seq_len:
self.season = 1
def forward(self, x):
result = np.zeros([x.shape[0],self.pred_len,x.shape[2]])
threads = []
for bt,seqs in tqdm(enumerate(x)):
for i in range(seqs.shape[-1]):
seq = seqs[:,i]
one_seq = Naive_thread(func=_sarima,args=(self.season,seq,self.pred_len,bt,i))
threads.append(one_seq)
threads[-1].start()
for every_thread in tqdm(threads):
forcast,bt,i = every_thread.return_result()
result[bt,:,i] = forcast
return result # [B, L, D]
def _gbrt(seq,seq_len,pred_len,bt,i):
model = GradientBoostingRegressor()
model.fit(np.arange(seq_len).reshape(-1,1),seq.reshape(-1,1))
forecasts = model.predict(np.arange(seq_len,seq_len+pred_len).reshape(-1,1))
return forecasts,bt,i
class GBRT(nn.Module):
def __init__(self, configs):
super(GBRT, self).__init__()
self.seq_len = configs.seq_len
self.pred_len = configs.pred_len
def forward(self, x):
result = np.zeros([x.shape[0],self.pred_len,x.shape[2]])
threads = []
for bt,seqs in tqdm(enumerate(x)):
for i in range(seqs.shape[-1]):
seq = seqs[:,i]
one_seq = Naive_thread(func=_gbrt,args=(seq,self.seq_len,self.pred_len,bt,i))
threads.append(one_seq)
threads[-1].start()
for every_thread in tqdm(threads):
forcast,bt,i = every_thread.return_result()
result[bt,:,i] = forcast
return result # [B, L, D]