add individual network speed params

matsim/scenariogen/network/ref_model/individual.py

@@ -0,0 +1,18 @@
+""" Reference model to fine tune individal links. This model does not use features and is not trasferable to other scenarios. """
+
+# Each link has its own target speed
+
+
+params = None
+
+def score(params, inputs):
+    return params[inputs[0]]
+
+
+def batch_loss(params, inputs, targets):
+    error = 0
+    inputs = inputs.astype(int)
+    for x, y in zip(inputs, targets):
+        preds = score(params, x)
+        error += (preds - y) ** 2
+    return error

matsim/scenariogen/network/runOPT.sh

@@ -1,19 +1,20 @@
 #!/bin/bash --login
-#$ -l h_rt=700000
-#$ -j y
-#$ -m a
-#$ -o ./logfile/logfile_$JOB_NAME.log
-#$ -cwd
-#$ -pe mp 6
-#$ -l mem_free=4G
-#$ -l cpuflag_avx2=1
-#$ -N network-opt
+#SBATCH --time=200:00:00
+#SBATCH --partition=smp
+#SBATCH --output=./logfile/logfile_%x-%j.log
+#SBATCH --nodes=1                       # How many computing nodes do you need (for MATSim usually 1)
+#SBATCH --ntasks=1                      # How many tasks should be run (For MATSim usually 1)
+#SBATCH --cpus-per-task=6              # Number of CPUs per task (For MATSim usually 8 - 12)
+#SBATCH --mem=12G                       # RAM for the job
+#SBATCH --job-name=network-opt         # name of your run, will be displayed in the joblist
+#SBATCH --constraint=cpuflag_avx2:1
+#SBATCH --mail-type=END,FAIL
 
 date
 hostname
 
 source venv/bin/activate
-module add java/17
+module add java/21
 
 jar="matsim-[name]-SNAPSHOT.jar"
 input="input/*"

matsim/scenariogen/network/run_opt_freespeed.py

@@ -61,7 +61,8 @@ def setup(parser: argparse.ArgumentParser):
     parser.add_argument("--resume", help="File with parameters to to resume", default=None)
     parser.add_argument("--port", type=int, nargs="+", help="Port to connect on", default=[9090])
     parser.add_argument("--ref-model", required=False, default=None,
-                        help="Use an integrated model instead of importing", choices=["tree", "germany"])
+                        help="Use an integrated model instead of importing", choices=["tree", "individual", "germany"])
+    parser.add_argument("--ref-size", type=int, help="Number of links (needed for individual model)", default=0)
     parser.add_argument("--learning-rate", type=float, help="Start learning rate", default=1e-4)
     parser.add_argument("--batch-size", type=int, help="Batch size", default=128)
     parser.add_argument("--output", help="Output folder for params", default="output-params")
@@ -77,6 +78,15 @@ def main(args):
     if args.ref_model == "tree":
         from .ref_model import tree as p
         rbl = tl = p
+    elif args.ref_model == "individual":
+        # Enables non relative import as well
+        try:
+            from .ref_model import individual as p
+            rbl = tl = p
+        except ImportError:
+            from ref_model import individual as p
+            rbl = tl = p
+
     elif args.ref_model == "germany":
         from .ref_model.germany import speedRelative_priority as p
         from .ref_model.germany import speedRelative_right_before_left as rbl
@@ -100,8 +110,13 @@ def main(args):
             staircase=False
         )
 
+        # Need to load default params statically
+        if module.params is None:
+            params = jnp.array(resume[name]) if name in resume else jnp.ones(args.ref_size) * 0.85
+        else:
+            params = jnp.array(resume[name] if name in resume else module.params)
+
         optimizer = optax.adam(schedule)
-        params = jnp.array(resume[name] if name in resume else module.params)
         opt_state = optimizer.init(params)
 
         models[name] = Model(