From ba6b8bd5b32776ee068dd770d1476e0d0ba61396 Mon Sep 17 00:00:00 2001
From: alikia2x <alikia2x@outlook.com>
Date: Thu, 20 Mar 2025 00:53:59 +0800
Subject: [PATCH] temp: try to modify some features for the pred model

---
 pred/dataset.py   |  6 +++---
 pred/inference.py | 14 +++++++-------
 pred/train.py     |  4 ++--
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/pred/dataset.py b/pred/dataset.py
index 409e423..5ee30cc 100644
--- a/pred/dataset.py
+++ b/pred/dataset.py
@@ -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.term = 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
         HOUR = 3600
         DAY = 24 * HOUR
@@ -37,7 +37,7 @@ class VideoPlayDataset(Dataset):
             ]
         else:
             self.feature_windows = [
-                ( 5 * MINUTE,  0 * MINUTE),
+                #( 5 * MINUTE,  0 * MINUTE),
                 ( 15 * MINUTE,  0 * MINUTE),
                 ( 40 * MINUTE,  0 * MINUTE),
                 ( 1 * HOUR,  0 * HOUR),
@@ -46,7 +46,7 @@ class VideoPlayDataset(Dataset):
                 ( 3 * HOUR,  0 * HOUR),
                 #( 6 * HOUR,  3 * HOUR),
                 ( 6 * HOUR,  0 * HOUR),
-                (18 * HOUR, 12 * HOUR),
+                #(18 * HOUR, 12 * HOUR),
                 #( 1 * DAY,   6 * HOUR),
                 ( 1 * DAY,   0 * DAY),
                 #( 2 * DAY,   1 * DAY),
diff --git a/pred/inference.py b/pred/inference.py
index bf829cf..a6500d5 100644
--- a/pred/inference.py
+++ b/pred/inference.py
@@ -4,20 +4,20 @@ from model import CompactPredictor
 import torch
 
 def main():
-    model = CompactPredictor(16).to('cpu', dtype=torch.float32)
-    model.load_state_dict(torch.load('./pred/checkpoints/model_20250315_0530.pt'))
+    model = CompactPredictor(15).to('cpu', dtype=torch.float32)
+    model.load_state_dict(torch.load('./pred/checkpoints/model_20250320_0045.pt'))
     model.eval()
     # inference
-    initial = 99906
+    initial = 999704
     last = initial
-    start_time = '2025-03-16 14:48:42'
+    start_time = '2025-03-19 22:00:42'
     for i in range(1, 48):
-        hour = i / 4
+        hour = i / 6
         sec = hour * 3600
         time_d = np.log2(sec)
         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
-                0.617153, 0.945308, 22.091431 # time_feat
+                4.857981, 6.29067, 6.869476, 6.58392, 6.523051, 8.242355, 8.841574, 10.203909, 11.449314, 12.659556, # grows_feat
+                0.916956, 0.416708, 28.003162 # time_feat
         ]
         np_arr = np.array([data])
         tensor = torch.from_numpy(np_arr).to('cpu', dtype=torch.float32)
diff --git a/pred/train.py b/pred/train.py
index b162163..f52e51d 100644
--- a/pred/train.py
+++ b/pred/train.py
@@ -38,7 +38,7 @@ def train(model, dataloader, device, epochs=100):
     scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=1e-3,
                                                   total_steps=len(dataloader)*30)
     # Huber loss
-    criterion = asymmetricHuberLoss(delta=1.0, beta=2.1)
+    criterion = asymmetricHuberLoss(delta=1.0, beta=2.2)
     
     model.train()
     global_step = 0
@@ -100,7 +100,7 @@ if __name__ == "__main__":
     device = 'mps'
     
     # 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)
     
     # Get feature dimension