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temp: try to modify some features for the pred model

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alikia2x (寒寒) 2025-03-20 00:53:59 +08:00
parent 2ed909268e
commit ba6b8bd5b3
Signed by: alikia2x
GPG Key ID: 56209E0CCD8420C6
3 changed files with 12 additions and 12 deletions
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6
pred/dataset.py
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@ -20,7 +20,7 @@ class VideoPlayDataset(Dataset):
self.valid_series = [s for s in self.series_dict.values() if len(s['abs_time']) > 1] self.valid_series = [s for s in self.series_dict.values() if len(s['abs_time']) > 1]
self.term = term self.term = term
# Set time window based on term # Set time window based on term
self.time_window = 1000 * 24 * 3600 if term == 'long' else 7 * 24 * 3600 self.time_window = 1000 * 24 * 3600 if term == 'long' else 3 * 24 * 3600
MINUTE = 60 MINUTE = 60
HOUR = 3600 HOUR = 3600
DAY = 24 * HOUR DAY = 24 * HOUR
@ -37,7 +37,7 @@ class VideoPlayDataset(Dataset):
] ]
else: else:
self.feature_windows = [ self.feature_windows = [
( 5 * MINUTE, 0 * MINUTE), #( 5 * MINUTE, 0 * MINUTE),
( 15 * MINUTE, 0 * MINUTE), ( 15 * MINUTE, 0 * MINUTE),
( 40 * MINUTE, 0 * MINUTE), ( 40 * MINUTE, 0 * MINUTE),
( 1 * HOUR, 0 * HOUR), ( 1 * HOUR, 0 * HOUR),
@ -46,7 +46,7 @@ class VideoPlayDataset(Dataset):
( 3 * HOUR, 0 * HOUR), ( 3 * HOUR, 0 * HOUR),
#( 6 * HOUR, 3 * HOUR), #( 6 * HOUR, 3 * HOUR),
( 6 * HOUR, 0 * HOUR), ( 6 * HOUR, 0 * HOUR),
(18 * HOUR, 12 * HOUR), #(18 * HOUR, 12 * HOUR),
#( 1 * DAY, 6 * HOUR), #( 1 * DAY, 6 * HOUR),
( 1 * DAY, 0 * DAY), ( 1 * DAY, 0 * DAY),
#( 2 * DAY, 1 * DAY), #( 2 * DAY, 1 * DAY),

14
pred/inference.py
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@ -4,20 +4,20 @@ from model import CompactPredictor
import torch import torch
def main(): def main():
model = CompactPredictor(16).to('cpu', dtype=torch.float32) model = CompactPredictor(15).to('cpu', dtype=torch.float32)
model.load_state_dict(torch.load('./pred/checkpoints/model_20250315_0530.pt')) model.load_state_dict(torch.load('./pred/checkpoints/model_20250320_0045.pt'))
model.eval() model.eval()
# inference # inference
initial = 99906 initial = 999704
last = initial last = initial
start_time = '2025-03-16 14:48:42' start_time = '2025-03-19 22:00:42'
for i in range(1, 48): for i in range(1, 48):
hour = i / 4 hour = i / 6
sec = hour * 3600 sec = hour * 3600
time_d = np.log2(sec) time_d = np.log2(sec)
data = [time_d, np.log2(initial+1), # time_delta, current_views data = [time_d, np.log2(initial+1), # time_delta, current_views
2.456146, 3.562719, 4.106399, 1.0, 1.0, 5.634413, 6.619818, 1.0, 8.608774, 10.19127, 11.412958, # grows_feat 4.857981, 6.29067, 6.869476, 6.58392, 6.523051, 8.242355, 8.841574, 10.203909, 11.449314, 12.659556, # grows_feat
0.617153, 0.945308, 22.091431 # time_feat 0.916956, 0.416708, 28.003162 # time_feat
] ]
np_arr = np.array([data]) np_arr = np.array([data])
tensor = torch.from_numpy(np_arr).to('cpu', dtype=torch.float32) tensor = torch.from_numpy(np_arr).to('cpu', dtype=torch.float32)

4
pred/train.py
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@ -38,7 +38,7 @@ def train(model, dataloader, device, epochs=100):
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3, scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3,
total_steps=len(dataloader)*30) total_steps=len(dataloader)*30)
# Huber loss # Huber loss
criterion = asymmetricHuberLoss(delta=1.0, beta=2.1) criterion = asymmetricHuberLoss(delta=1.0, beta=2.2)
model.train() model.train()
global_step = 0 global_step = 0
@ -100,7 +100,7 @@ if __name__ == "__main__":
device = 'mps' device = 'mps'
# Initialize dataset and model # Initialize dataset and model
dataset = VideoPlayDataset('./data/pred', './data/pred/publish_time.csv', 'short') dataset = VideoPlayDataset('./data/pred', './data/pred/publish_time.csv', 'short', 712)
dataloader = DataLoader(dataset, batch_size=128, shuffle=True, collate_fn=collate_fn) dataloader = DataLoader(dataset, batch_size=128, shuffle=True, collate_fn=collate_fn)
# Get feature dimension # Get feature dimension