AEPSA v0.1
This commit is contained in:
parent
88daa53dd5
commit
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@ -3,9 +3,11 @@
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// 悬停以查看现有属性的描述。
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// 欲了解更多信息，请访问: https://go.microsoft.com/fwlink/?linkid=830387
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"version": "0.2.0",
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"configurations": [
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// STID 模型组
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{
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"name": "STID_PEMS-BAY",
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"name": "STID: PEMS-BAY",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -13,7 +15,7 @@
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"args": "--config ./config/STID/PEMS-BAY.yaml"
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},
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{
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"name": "STID_PEMSD4",
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"name": "STID: PEMSD4",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -21,15 +23,7 @@
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"args": "--config ./config/STID/PEMSD4.yaml"
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},
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{
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"name": "REPST",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/REPST/PEMSD8.yaml"
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},
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{
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"name": "STID-BJTaxi-InFlow",
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"name": "STID: BJTaxi-InFlow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -37,7 +31,7 @@
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"args": "--config ./config/STID/BJTaxi_Inflow.yaml"
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},
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{
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"name": "STID-BJTaxi-OutFlow",
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"name": "STID: BJTaxi-OutFlow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -45,7 +39,7 @@
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"args": "--config ./config/STID/BJTaxi_Outflow.yaml"
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},
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{
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"name": "STID-NYCBike-InFlow",
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"name": "STID: NYCBike-InFlow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -53,7 +47,7 @@
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"args": "--config ./config/STID/NYCBike_Inflow.yaml"
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},
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{
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"name": "STID-NYCBike-OutFlow",
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"name": "STID: NYCBike-OutFlow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -61,15 +55,25 @@
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"args": "--config ./config/STID/NYCBike_Outflow.yaml"
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},
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{
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"name": "STID-SolarEnergy",
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"name": "STID: SolarEnergy",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/STID/SolarEnergy.yaml"
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},
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// REPST 模型组
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{
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"name": "REPST-BJTaxi-InFlow",
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"name": "REPST: PEMSD8",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/REPST/PEMSD8.yaml"
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},
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{
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"name": "REPST: BJTaxi-InFlow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -77,7 +81,7 @@
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"args": "--config ./config/REPST/BJTaxi-Inflow.yaml"
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},
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{
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"name": "REPST-NYCBike-outflow",
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"name": "REPST: NYCBike-outflow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -85,7 +89,7 @@
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"args": "--config ./config/REPST/NYCBike-outflow.yaml"
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},
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{
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"name": "REPST-NYCBike-inflow",
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"name": "REPST: NYCBike-inflow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -93,7 +97,7 @@
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"args": "--config ./config/REPST/NYCBike-inflow.yaml"
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},
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{
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"name": "REPST-PEMSBAY",
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"name": "REPST: PEMS-BAY",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -101,7 +105,7 @@
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"args": "--config ./config/REPST/PEMS-BAY.yaml"
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},
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{
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"name": "REPST-METR",
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"name": "REPST: METR-LA",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -109,7 +113,7 @@
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"args": "--config ./config/REPST/METR-LA.yaml"
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},
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{
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"name": "REPST-Solar",
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"name": "REPST: SolarEnergy",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -117,7 +121,7 @@
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"args": "--config ./config/REPST/SolarEnergy.yaml"
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},
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{
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"name": "BeijingAirQuality",
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"name": "REPST: BeijingAirQuality",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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@ -125,20 +129,78 @@
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"args": "--config ./config/REPST/BeijingAirQuality.yaml"
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},
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{
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"name": "AirQuality",
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"name": "REPST: AirQuality",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/REPST/AirQuality.yaml"
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},
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// AEPSA 模型组
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{
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"name": "AEPSA-PEMSBAY",
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"name": "AEPSA: PEMS-BAY",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/PEMS-BAY.yaml"
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},
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{
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"name": "AEPSA: METR-LA",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/METR-LA.yaml"
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},
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{
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"name": "AEPSA: AirQuality",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/AirQuality.yaml"
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},
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{
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"name": "AEPSA: BJTaxi-Inflow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/BJTaxi-Inflow.yaml"
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},
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{
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"name": "AEPSA: BJTaxi-outflow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/BJTaxi-outflow.yaml"
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},
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{
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"name": "AEPSA: NYCBike-inflow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/NYCBike-inflow.yaml"
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},
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{
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"name": "AEPSA: NYCBike-outflow",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/NYCBike-outflow.yaml"
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},
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{
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"name": "AEPSA: SolarEnergy",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/AEPSA/SolarEnergy.yaml"
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}
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]
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}
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basic:
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dataset: "AirQuality"
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mode : "train"
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device : "cuda:0"
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model: "AEPSA"
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seed: 2023
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data:
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add_day_in_week: true
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add_time_in_day: true
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column_wise: false
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days_per_week: 7
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default_graph: true
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horizon: 24
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lag: 24
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normalizer: std
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num_nodes: 35
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steps_per_day: 24
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test_ratio: 0.2
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tod: false
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val_ratio: 0.2
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sample: 1
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input_dim: 6
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batch_size: 16
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model:
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pred_len: 24
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seq_len: 24
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patch_len: 6
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stride: 7
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dropout: 0.2
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gpt_layers: 9
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d_ff: 128
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gpt_path: ./GPT-2
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d_model: 64
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n_heads: 1
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input_dim: 6
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word_num: 1000
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train:
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batch_size: 16
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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grad_norm: false
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loss_func: mae
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lr_decay: true
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lr_decay_rate: 0.3
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lr_decay_step: "5,20,40,70"
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lr_init: 0.003
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max_grad_norm: 5
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weight_decay: 0
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debug: false
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output_dim: 6
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log_step: 100
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plot: false
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mae_thresh: None
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mape_thresh: 0.001
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basic:
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dataset: "PEMSD8"
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dataset: "BJTaxi-Inflow"
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mode : "train"
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device : "cuda:0"
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model: "AEPSA"
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seed: 2023
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data:
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add_day_in_week: true
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@ -13,14 +14,14 @@ data:
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horizon: 12
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lag: 12
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normalizer: std
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num_nodes: 170
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steps_per_day: 288
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num_nodes: 142
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steps_per_day: 48
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test_ratio: 0.2
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tod: false
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val_ratio: 0.2
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sample: 1
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input_dim: 1
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batch_size: 64
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batch_size: 32
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model:
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pred_len: 12
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gpt_path: ./GPT-2
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d_model: 64
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n_heads: 1
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input_dim: 1
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word_num: 1000
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train:
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batch_size: 64
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batch_size: 32
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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lr_decay_step: "5,20,40,70"
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lr_init: 0.003
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max_grad_norm: 5
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real_value: true
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seed: 12
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weight_decay: 0
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debug: false
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output_dim: 1
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log_step: 2000
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log_step: 100
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plot: false
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mae_thresh: None
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mape_thresh: 0.001
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@ -1,8 +1,9 @@
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basic:
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dataset: "PEMSD8"
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dataset: "BJTaxi-outflow"
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mode : "train"
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device : "cuda:0"
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model: "REPST"
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model: "AEPSA"
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seed: 2023
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data:
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add_day_in_week: true
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@ -13,14 +14,14 @@ data:
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horizon: 12
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lag: 12
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normalizer: std
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num_nodes: 170
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steps_per_day: 288
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num_nodes: 142
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steps_per_day: 48
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test_ratio: 0.2
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tod: false
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val_ratio: 0.2
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sample: 1
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input_dim: 1
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batch_size: 64
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batch_size: 32
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model:
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pred_len: 12
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gpt_path: ./GPT-2
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d_model: 64
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n_heads: 1
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input_dim: 1
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word_num: 1000
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train:
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batch_size: 64
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batch_size: 32
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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@ -46,13 +49,10 @@ train:
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lr_decay_step: "5,20,40,70"
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lr_init: 0.003
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max_grad_norm: 5
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real_value: true
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seed: 12
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weight_decay: 0
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debug: false
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output_dim: 1
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log_step: 2000
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log_step: 100
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plot: false
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mae_thresh: None
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mape_thresh: 0.001
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@ -0,0 +1,59 @@
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basic:
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dataset: "METR-LA"
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mode : "train"
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device : "cuda:0"
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model: "AEPSA"
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seed: 2023
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data:
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add_day_in_week: true
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add_time_in_day: true
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column_wise: false
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days_per_week: 7
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default_graph: true
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horizon: 24
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lag: 24
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normalizer: std
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num_nodes: 207
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steps_per_day: 288
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test_ratio: 0.2
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tod: false
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val_ratio: 0.2
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sample: 1
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input_dim: 1
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batch_size: 16
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model:
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pred_len: 24
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seq_len: 24
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patch_len: 6
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stride: 7
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dropout: 0.2
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gpt_layers: 9
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d_ff: 128
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gpt_path: ./GPT-2
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d_model: 64
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n_heads: 1
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input_dim: 1
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word_num: 1000
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train:
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batch_size: 16
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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grad_norm: false
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loss_func: mae
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lr_decay: true
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lr_decay_rate: 0.3
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lr_decay_step: "5,20,40,70"
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lr_init: 0.003
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max_grad_norm: 5
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real_value: true
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weight_decay: 0
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debug: false
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output_dim: 1
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log_step: 1000
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plot: false
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mae_thresh: None
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mape_thresh: 0.001
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@ -0,0 +1,58 @@
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basic:
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dataset: "NYCBike-inflow"
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mode : "train"
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device : "cuda:0"
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model: "AEPSA"
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seed: 2023
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data:
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add_day_in_week: true
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add_time_in_day: true
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column_wise: false
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days_per_week: 7
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default_graph: true
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horizon: 12
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lag: 12
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normalizer: std
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num_nodes: 200
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steps_per_day: 24
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test_ratio: 0.2
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tod: false
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val_ratio: 0.2
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sample: 1
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input_dim: 1
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batch_size: 32
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model:
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pred_len: 12
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seq_len: 12
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patch_len: 6
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stride: 7
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dropout: 0.2
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gpt_layers: 9
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d_ff: 128
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gpt_path: ./GPT-2
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d_model: 64
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n_heads: 1
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input_dim: 1
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word_num: 1000
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train:
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batch_size: 32
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early_stop: true
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early_stop_patience: 15
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epochs: 100
|
||||
grad_norm: false
|
||||
loss_func: mae
|
||||
lr_decay: true
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: "5,20,40,70"
|
||||
lr_init: 0.003
|
||||
max_grad_norm: 5
|
||||
weight_decay: 0
|
||||
debug: false
|
||||
output_dim: 1
|
||||
log_step: 100
|
||||
plot: false
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
|
|
@ -0,0 +1,58 @@
|
|||
basic:
|
||||
dataset: "NYCBike-outflow"
|
||||
mode : "train"
|
||||
device : "cuda:0"
|
||||
model: "AEPSA"
|
||||
seed: 2023
|
||||
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
column_wise: false
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
horizon: 12
|
||||
lag: 12
|
||||
normalizer: std
|
||||
num_nodes: 200
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_ratio: 0.2
|
||||
sample: 1
|
||||
input_dim: 1
|
||||
batch_size: 32
|
||||
|
||||
model:
|
||||
pred_len: 12
|
||||
seq_len: 12
|
||||
patch_len: 6
|
||||
stride: 7
|
||||
dropout: 0.2
|
||||
gpt_layers: 9
|
||||
d_ff: 128
|
||||
gpt_path: ./GPT-2
|
||||
d_model: 64
|
||||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
|
||||
train:
|
||||
batch_size: 32
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
loss_func: mae
|
||||
lr_decay: true
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: "5,20,40,70"
|
||||
lr_init: 0.003
|
||||
max_grad_norm: 5
|
||||
weight_decay: 0
|
||||
debug: false
|
||||
output_dim: 1
|
||||
log_step: 100
|
||||
plot: false
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
|
|
@ -3,6 +3,7 @@ basic:
|
|||
mode : "train"
|
||||
device : "cuda:0"
|
||||
model: "AEPSA"
|
||||
seed: 2023
|
||||
|
||||
data:
|
||||
add_day_in_week: true
|
||||
|
|
@ -34,6 +35,7 @@ model:
|
|||
d_model: 64
|
||||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
@ -47,8 +49,6 @@ train:
|
|||
lr_decay_step: "5,20,40,70"
|
||||
lr_init: 0.003
|
||||
max_grad_norm: 5
|
||||
real_value: true
|
||||
seed: 12
|
||||
weight_decay: 0
|
||||
debug: false
|
||||
output_dim: 1
|
||||
|
|
|
|||
|
|
@ -0,0 +1,59 @@
|
|||
basic:
|
||||
dataset: "SolarEnergy"
|
||||
mode : "train"
|
||||
device : "cuda:0"
|
||||
model: "AEPSA"
|
||||
seed: 2023
|
||||
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
column_wise: false
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
horizon: 24
|
||||
lag: 24
|
||||
normalizer: std
|
||||
num_nodes: 137
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_ratio: 0.2
|
||||
sample: 1
|
||||
input_dim: 1
|
||||
batch_size: 64
|
||||
|
||||
model:
|
||||
pred_len: 24
|
||||
seq_len: 24
|
||||
patch_len: 6
|
||||
stride: 7
|
||||
dropout: 0.2
|
||||
gpt_layers: 9
|
||||
d_ff: 128
|
||||
gpt_path: ./GPT-2
|
||||
d_model: 64
|
||||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 137
|
||||
|
||||
train:
|
||||
batch_size: 64
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
loss_func: mae
|
||||
lr_decay: true
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: "5,20,40,70"
|
||||
lr_init: 0.003
|
||||
max_grad_norm: 5
|
||||
weight_decay: 0
|
||||
debug: false
|
||||
output_dim: 1
|
||||
log_step: 100
|
||||
plot: false
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
|
|
@ -37,6 +37,7 @@ model:
|
|||
input_dim: 6
|
||||
output_dim: 3
|
||||
word_num: 1000
|
||||
num_nodes: 35
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 1024
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 1024
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -37,6 +37,7 @@ model:
|
|||
input_dim: 3
|
||||
output_dim: 3
|
||||
word_num: 1000
|
||||
num_nodes: 7
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 207
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 128
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 128
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 325
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -35,6 +35,7 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
t_max: 5
|
||||
num_nodes: 325
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ data:
|
|||
val_ratio: 0.2
|
||||
sample: 1
|
||||
input_dim: 1
|
||||
batch_size: 16
|
||||
batch_size: 64
|
||||
|
||||
model:
|
||||
pred_len: 24
|
||||
|
|
@ -36,9 +36,10 @@ model:
|
|||
n_heads: 1
|
||||
input_dim: 1
|
||||
word_num: 1000
|
||||
num_nodes: 137
|
||||
|
||||
train:
|
||||
batch_size: 16
|
||||
batch_size: 64
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
|
|
|
|||
|
|
@ -0,0 +1,251 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
|
||||
from einops import rearrange
|
||||
from model.AEPSA.normalizer import GumbelSoftmax
|
||||
from model.AEPSA.reprogramming import PatchEmbedding, ReprogrammingLayer
|
||||
import torch.nn.functional as F
|
||||
|
||||
class DynamicGraphEnhancer(nn.Module):
|
||||
"""
|
||||
动态图增强器，基于节点嵌入自动生成图结构
|
||||
参考DDGCRN的设计，使用节点嵌入和特征信息动态计算邻接矩阵
|
||||
"""
|
||||
def __init__(self, num_nodes, in_dim, embed_dim=10):
|
||||
super().__init__()
|
||||
self.num_nodes = num_nodes
|
||||
self.embed_dim = embed_dim
|
||||
|
||||
# 节点嵌入参数
|
||||
self.node_embeddings = nn.Parameter(
|
||||
torch.randn(num_nodes, embed_dim), requires_grad=True
|
||||
)
|
||||
|
||||
# 特征转换层，用于生成动态调整的嵌入
|
||||
self.feature_transform = nn.Sequential(
|
||||
nn.Linear(in_dim, 16),
|
||||
nn.Sigmoid(),
|
||||
nn.Linear(16, 2),
|
||||
nn.Sigmoid(),
|
||||
nn.Linear(2, embed_dim)
|
||||
)
|
||||
|
||||
# 注册单位矩阵作为固定的支持矩阵
|
||||
self.register_buffer("eye", torch.eye(num_nodes))
|
||||
|
||||
def get_laplacian(self, graph, I, normalize=True):
|
||||
"""
|
||||
计算归一化拉普拉斯矩阵
|
||||
"""
|
||||
# 计算度矩阵的逆平方根
|
||||
D_inv = torch.diag_embed(torch.sum(graph, -1) ** (-0.5))
|
||||
D_inv[torch.isinf(D_inv)] = 0.0 # 处理零除问题
|
||||
|
||||
if normalize:
|
||||
return torch.matmul(torch.matmul(D_inv, graph), D_inv)
|
||||
else:
|
||||
return torch.matmul(torch.matmul(D_inv, graph + I), D_inv)
|
||||
|
||||
def forward(self, X):
|
||||
"""
|
||||
X: 输入特征 [B, N, D]
|
||||
返回: 动态生成的归一化拉普拉斯矩阵 [B, N, N]
|
||||
"""
|
||||
batch_size = X.size(0)
|
||||
laplacians = []
|
||||
|
||||
# 获取单位矩阵
|
||||
I = self.eye.to(X.device)
|
||||
|
||||
for b in range(batch_size):
|
||||
# 使用特征转换层生成动态嵌入调整因子
|
||||
filt = self.feature_transform(X[b]) # [N, embed_dim]
|
||||
|
||||
# 计算节点嵌入向量
|
||||
nodevec = torch.tanh(self.node_embeddings * filt)
|
||||
|
||||
# 通过节点嵌入的点积计算邻接矩阵
|
||||
adj = F.relu(torch.matmul(nodevec, nodevec.transpose(0, 1)))
|
||||
|
||||
# 计算归一化拉普拉斯矩阵
|
||||
laplacian = self.get_laplacian(adj, I)
|
||||
laplacians.append(laplacian)
|
||||
|
||||
return torch.stack(laplacians, dim=0)
|
||||
|
||||
class GraphEnhancedEncoder(nn.Module):
|
||||
"""
|
||||
基于Chebyshev多项式和动态拉普拉斯矩阵的图增强编码器
|
||||
用于将动态图结构信息整合到特征编码中
|
||||
"""
|
||||
def __init__(self, K=3, in_dim=64, hidden_dim=32, num_nodes=325, embed_dim=10, device='cpu'):
|
||||
super().__init__()
|
||||
self.K = K # Chebyshev多项式阶数
|
||||
self.in_dim = in_dim
|
||||
self.hidden_dim = hidden_dim
|
||||
self.device = device
|
||||
|
||||
# 动态图增强器
|
||||
self.graph_enhancer = DynamicGraphEnhancer(num_nodes, in_dim, embed_dim)
|
||||
|
||||
# 谱系数 α_k (可学习参数)
|
||||
self.alpha = nn.Parameter(torch.randn(K + 1, 1))
|
||||
|
||||
# 传播权重 W_k (可学习参数)
|
||||
self.W = nn.ParameterList([
|
||||
nn.Parameter(torch.randn(in_dim, hidden_dim)) for _ in range(K + 1)
|
||||
])
|
||||
|
||||
self.to(device)
|
||||
|
||||
def chebyshev_polynomials(self, L_tilde, X):
|
||||
"""递归计算 [T_0(L_tilde)X, ..., T_K(L_tilde)X]"""
|
||||
T_k_list = [X]
|
||||
if self.K >= 1:
|
||||
T_k_list.append(torch.matmul(L_tilde, X))
|
||||
for k in range(2, self.K + 1):
|
||||
T_k_list.append(2 * torch.matmul(L_tilde, T_k_list[-1]) - T_k_list[-2])
|
||||
return T_k_list
|
||||
|
||||
def forward(self, X):
|
||||
"""
|
||||
X: 输入特征 [B, N, D]
|
||||
返回: 增强后的特征 [B, N, hidden_dim*(K+1)]
|
||||
"""
|
||||
batch_size, num_nodes, _ = X.shape
|
||||
enhanced_features = []
|
||||
|
||||
# 动态生成拉普拉斯矩阵
|
||||
laplacians = self.graph_enhancer(X)
|
||||
|
||||
for b in range(batch_size):
|
||||
L = laplacians[b]
|
||||
|
||||
# 特征值缩放
|
||||
try:
|
||||
lambda_max = torch.linalg.eigvalsh(L).max().real
|
||||
# 避免除零问题
|
||||
if lambda_max < 1e-6:
|
||||
lambda_max = 1.0
|
||||
L_tilde = (2.0 / lambda_max) * L - torch.eye(L.size(0), device=L.device)
|
||||
except:
|
||||
# 如果计算特征值失败，使用单位矩阵
|
||||
L_tilde = torch.eye(num_nodes, device=X.device)
|
||||
|
||||
# 计算Chebyshev多项式展开
|
||||
T_k_list = self.chebyshev_polynomials(L_tilde, X[b])
|
||||
H_list = []
|
||||
|
||||
# 应用传播权重
|
||||
for k in range(self.K + 1):
|
||||
H_k = torch.matmul(T_k_list[k], self.W[k])
|
||||
H_list.append(H_k)
|
||||
|
||||
# 拼接所有尺度的特征
|
||||
X_enhanced = torch.cat(H_list, dim=-1) # [N, hidden_dim*(K+1)]
|
||||
enhanced_features.append(X_enhanced)
|
||||
|
||||
return torch.stack(enhanced_features, dim=0)
|
||||
|
||||
class AEPSA(nn.Module):
|
||||
|
||||
def __init__(self, configs):
|
||||
super(AEPSA, self).__init__()
|
||||
self.device = configs['device']
|
||||
self.pred_len = configs['pred_len']
|
||||
self.seq_len = configs['seq_len']
|
||||
self.patch_len = configs['patch_len']
|
||||
self.input_dim = configs['input_dim']
|
||||
self.stride = configs['stride']
|
||||
self.dropout = configs['dropout']
|
||||
self.gpt_layers = configs['gpt_layers']
|
||||
self.d_ff = configs['d_ff']
|
||||
self.gpt_path = configs['gpt_path']
|
||||
self.num_nodes = configs.get('num_nodes', 325) # 添加节点数量配置
|
||||
|
||||
self.word_choice = GumbelSoftmax(configs['word_num'])
|
||||
|
||||
self.d_model = configs['d_model']
|
||||
self.n_heads = configs['n_heads']
|
||||
self.d_keys = None
|
||||
self.d_llm = 768
|
||||
|
||||
self.patch_nums = int((self.seq_len - self.patch_len) / self.stride + 2)
|
||||
self.head_nf = self.d_ff * self.patch_nums
|
||||
|
||||
# 词嵌入
|
||||
self.patch_embedding = PatchEmbedding(self.d_model, self.patch_len, self.stride, self.dropout, self.patch_nums, self.input_dim)
|
||||
|
||||
# GPT2初始化
|
||||
self.gpts = GPT2Model.from_pretrained(self.gpt_path, output_attentions=True, output_hidden_states=True)
|
||||
self.gpts.h = self.gpts.h[:self.gpt_layers]
|
||||
self.gpts.apply(self.reset_parameters)
|
||||
|
||||
self.word_embeddings = self.gpts.get_input_embeddings().weight.to(self.device)
|
||||
self.vocab_size = self.word_embeddings.shape[0]
|
||||
self.mapping_layer = nn.Linear(self.vocab_size, 1)
|
||||
self.reprogramming_layer = ReprogrammingLayer(self.d_model, self.n_heads, self.d_keys, self.d_llm)
|
||||
|
||||
# 添加动态图增强编码器
|
||||
self.graph_encoder = GraphEnhancedEncoder(
|
||||
K=configs.get('chebyshev_order', 3),
|
||||
in_dim=self.d_model,
|
||||
hidden_dim=configs.get('graph_hidden_dim', 32),
|
||||
num_nodes=self.num_nodes,
|
||||
embed_dim=configs.get('graph_embed_dim', 10),
|
||||
device=self.device
|
||||
)
|
||||
|
||||
# 特征融合层
|
||||
self.feature_fusion = nn.Linear(
|
||||
self.d_model + configs.get('graph_hidden_dim', 32) * (configs.get('chebyshev_order', 3) + 1),
|
||||
self.d_model
|
||||
)
|
||||
|
||||
self.out_mlp = nn.Sequential(
|
||||
nn.Linear(self.d_llm, 128),
|
||||
nn.ReLU(),
|
||||
nn.Linear(128, self.pred_len)
|
||||
)
|
||||
|
||||
for i, (name, param) in enumerate(self.gpts.named_parameters()):
|
||||
if 'wpe' in name:
|
||||
param.requires_grad = True
|
||||
else:
|
||||
param.requires_grad = False
|
||||
|
||||
def reset_parameters(self, module):
|
||||
if hasattr(module, 'weight') and module.weight is not None:
|
||||
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
torch.nn.init.zeros_(module.bias)
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
x: 输入数据 [B, T, N, C]
|
||||
自动生成图结构，无需外部提供邻接矩阵
|
||||
"""
|
||||
x = x[..., :1]
|
||||
x_enc = rearrange(x, 'b t n c -> b n c t')
|
||||
enc_out, n_vars = self.patch_embedding(x_enc) # (B, N, C)
|
||||
# 应用图增强编码器（自动生成图结构）
|
||||
graph_enhanced = self.graph_encoder(enc_out)
|
||||
# 保持相同的维度
|
||||
|
||||
# 特征融合 - 现在两个张量都是三维的 [B, N, d_model]
|
||||
enc_out = torch.cat([enc_out, graph_enhanced], dim=-1)
|
||||
enc_out = self.feature_fusion(enc_out)
|
||||
|
||||
self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
|
||||
masks = self.word_choice(self.mapping_layer.weight.data.permute(1,0))
|
||||
source_embeddings = self.word_embeddings[masks==1]
|
||||
|
||||
enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)
|
||||
enc_out = self.gpts(inputs_embeds=enc_out).last_hidden_state
|
||||
|
||||
dec_out = self.out_mlp(enc_out)
|
||||
outputs = dec_out.unsqueeze(dim=-1)
|
||||
outputs = outputs.repeat(1, 1, 1, n_vars)
|
||||
outputs = outputs.permute(0,2,1,3)
|
||||
|
||||
return outputs
|
||||
|
|
@ -1,103 +0,0 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
from transformers.models.gpt2.modeling_gpt2 import GPT2Model
|
||||
from einops import rearrange
|
||||
from model.REPST.normalizer import GumbelSoftmax
|
||||
from model.REPST.reprogramming import PatchEmbedding, ReprogrammingLayer
|
||||
|
||||
class repst(nn.Module):
|
||||
|
||||
def __init__(self, configs):
|
||||
super(repst, self).__init__()
|
||||
self.device = configs['device']
|
||||
self.pred_len = configs['pred_len']
|
||||
self.seq_len = configs['seq_len']
|
||||
self.patch_len = configs['patch_len']
|
||||
self.input_dim = configs['input_dim']
|
||||
self.stride = configs['stride']
|
||||
self.dropout = configs['dropout']
|
||||
self.gpt_layers = configs['gpt_layers']
|
||||
self.d_ff = configs['d_ff']
|
||||
self.gpt_path = configs['gpt_path']
|
||||
|
||||
self.word_choice = GumbelSoftmax(configs['word_num'])
|
||||
|
||||
self.d_model = configs['d_model']
|
||||
self.n_heads = configs['n_heads']
|
||||
self.d_keys = None
|
||||
self.d_llm = 768
|
||||
|
||||
self.patch_nums = int((self.seq_len - self.patch_len) / self.stride + 2)
|
||||
self.head_nf = self.d_ff * self.patch_nums
|
||||
|
||||
# 词嵌入
|
||||
self.patch_embedding = PatchEmbedding(self.d_model, self.patch_len, self.stride, self.dropout, self.patch_nums, self.input_dim)
|
||||
|
||||
# GPT2初始化
|
||||
self.gpts = GPT2Model.from_pretrained(self.gpt_path, output_attentions=True, output_hidden_states=True)
|
||||
self.gpts.h = self.gpts.h[:self.gpt_layers]
|
||||
self.gpts.apply(self.reset_parameters)
|
||||
|
||||
self.word_embeddings = self.gpts.get_input_embeddings().weight.to(self.device)
|
||||
self.vocab_size = self.word_embeddings.shape[0]
|
||||
self.mapping_layer = nn.Linear(self.vocab_size, 1)
|
||||
self.reprogramming_layer = ReprogrammingLayer(self.d_model, self.n_heads, self.d_keys, self.d_llm)
|
||||
|
||||
self.out_mlp = nn.Sequential(
|
||||
nn.Linear(self.d_llm, 128),
|
||||
nn.ReLU(),
|
||||
nn.Linear(128, self.pred_len)
|
||||
)
|
||||
|
||||
for i, (name, param) in enumerate(self.gpts.named_parameters()):
|
||||
if 'wpe' in name:
|
||||
param.requires_grad = True
|
||||
else:
|
||||
param.requires_grad = False
|
||||
|
||||
def reset_parameters(self, module):
|
||||
if hasattr(module, 'weight') and module.weight is not None:
|
||||
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
|
||||
if hasattr(module, 'bias') and module.bias is not None:
|
||||
torch.nn.init.zeros_(module.bias)
|
||||
|
||||
def forward(self, x):
|
||||
x = x[..., :1]
|
||||
x_enc = rearrange(x, 'b t n c -> b n c t')
|
||||
enc_out, n_vars = self.patch_embedding(x_enc)
|
||||
self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
|
||||
masks = self.word_choice(self.mapping_layer.weight.data.permute(1,0))
|
||||
source_embeddings = self.word_embeddings[masks==1]
|
||||
|
||||
enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)
|
||||
enc_out = self.gpts(inputs_embeds=enc_out).last_hidden_state
|
||||
|
||||
dec_out = self.out_mlp(enc_out)
|
||||
outputs = dec_out.unsqueeze(dim=-1)
|
||||
outputs = outputs.repeat(1, 1, 1, n_vars)
|
||||
outputs = outputs.permute(0,2,1,3)
|
||||
|
||||
return outputs
|
||||
|
||||
if __name__ == '__main__':
|
||||
configs = {
|
||||
'device': 'cuda:0',
|
||||
'pred_len': 24,
|
||||
'seq_len': 24,
|
||||
'patch_len': 6,
|
||||
'stride': 7,
|
||||
'dropout': 0.2,
|
||||
'gpt_layers': 9,
|
||||
'd_ff': 128,
|
||||
'gpt_path': './GPT-2',
|
||||
'd_model': 64,
|
||||
'n_heads': 1,
|
||||
'input_dim': 1
|
||||
}
|
||||
model = repst(configs)
|
||||
x = torch.randn(16, 24, 325, 1)
|
||||
y = model(x)
|
||||
|
||||
print(y.shape)
|
||||
|
||||
|
||||
|
|
@ -23,7 +23,7 @@ from model.ST_SSL.ST_SSL import STSSLModel
|
|||
from model.STGNRDE.Make_model import make_model as make_nrde_model
|
||||
from model.STAWnet.STAWnet import STAWnet
|
||||
from model.REPST.repst import repst as REPST
|
||||
from model.AEPSA.repst import repst as AEPSA
|
||||
from model.AEPSA.aepsa import AEPSA as AEPSA
|
||||
|
||||
|
||||
def model_selector(config):
|
||||
|
|
|
|||
|
|
@ -160,6 +160,7 @@ def check_and_download_data():
|
|||
file_path = f"Datasets/TaxiBJ/{file}"
|
||||
download_github_data(file_path, taxi_bj_floder)
|
||||
# 下载后更新缺失列表
|
||||
# download_and_extract("http://code.zhang-heng.com/static/BeijingTaxi.7z", data_dir)
|
||||
missing_list = detect_data_integrity(data_dir, file_tree)
|
||||
|
||||
# 检查并下载TaxiBJ数据
|
||||
|
|
|
|||
Loading…
Reference in New Issue