[Small PR] Logging dir and buffer outputs: fix issue and improvements

config/env/alaninedipeptide.yaml

@@ -26,5 +26,3 @@ buffer:
   test:
     type: grid
     n: 1000
-    output_csv: alaninedipeptide_test.csv
-    output_pkl: alaninedipeptide_test.pkl

config/env/ccube.yaml

@@ -35,5 +35,3 @@ buffer:
   test:
     type: grid
     n: 900
-    output_csv: ccube_test.csv
-    output_pkl: ccube_test.pkl

config/env/crystals/composition.yaml

@@ -20,7 +20,5 @@ buffer:
   data_path: null
   train:
     type: all
-    output_csv: composition_train.csv
   test:
     type: all
-    output_csv: composition_test.csv

config/env/crystals/lattice_parameters.yaml

@@ -39,5 +39,3 @@ buffer:
   test:
     type: grid
     n: 900
-    output_csv: clp_test.csv
-    output_pkl: clp_test.pkl

config/env/crystals/spacegroup.yaml

@@ -15,9 +15,5 @@ buffer:
   data_path: null
   train:
     type: all
-    output_csv: spacegroup_train.csv
-    output_pkl: spacegroup_train.pkl
   test:
     type: all
-    output_csv: spacegroup_test.csv
-    output_pkl: spacegroup_test.pkl

config/env/ctorus.yaml

@@ -27,5 +27,3 @@ buffer:
   test:
     type: grid
     n: 1000
-    output_csv: ctorus_test.csv
-    output_pkl: ctorus_test.pkl

config/env/grid.yaml

@@ -22,5 +22,3 @@ buffer:
   train: null
   test:
     type: all
-    output_csv: grid_test.csv
-    output_pkl: grid_test.pkl

config/env/htorus.yaml

@@ -27,5 +27,3 @@ buffer:
   test:
     type: grid
     n: 1000
-    output_csv: htorus_test.csv
-    output_pkl: htorus_test.pkl

config/env/scrabble.yaml

@@ -11,5 +11,3 @@ buffer:
   test:
     type: uniform
     n: 10
-    output_csv: scrabble_test.csv
-    output_pkl: scrabble_test.pkl

config/env/tetris.yaml

@@ -20,6 +20,3 @@ buffer:
   test:
     type: random
     n: 10
-    output_csv: tetris_test.csv
-    output_pkl: tetris_test.pkl
-

config/env/torus.yaml

@@ -20,8 +20,5 @@ buffer:
   data_path: null
   train:
     type: all
-    output_csv: torus_train.csv
   test:
     type: all
-    output_csv: torus_test.csv
-    output_pkl: torus_test.pkl

config/env/tree.yaml

@@ -35,5 +35,3 @@ buffer:
   test:
     type: random
     n: 1000
-    output_csv: tree_test.csv
-    output_pkl: tree_test.pkl

config/experiments/ccube/hyperparams_search_20230920_batch1.yaml

@@ -24,8 +24,6 @@ shared:
         test:
           type: grid
           n: 1000
-          output_csv: ccube_test.csv
-          output_pkl: ccube_test.pkl
     # Proxy
     proxy: corners
     # GFlowNet config

config/experiments/ccube/hyperparams_search_20230920_batch2.yaml

@@ -24,8 +24,6 @@ shared:
         test:
           type: grid
           n: 1000
-          output_csv: ccube_test.csv
-          output_pkl: ccube_test.pkl
     # Proxy
     proxy: corners
     # GFlowNet config

config/experiments/ccube/hyperparams_search_20230920_batch3.yaml

@@ -24,8 +24,6 @@ shared:
         test:
           type: grid
           n: 1000
-          output_csv: ccube_test.csv
-          output_pkl: ccube_test.pkl
     # Proxy
     proxy: corners
     # GFlowNet config

config/experiments/ccube/hyperparams_search_20230920_batch4.yaml

@@ -24,8 +24,6 @@ shared:
         test:
           type: grid
           n: 1000
-          output_csv: ccube_test.csv
-          output_pkl: ccube_test.pkl
     # Proxy
     proxy: corners
     # GFlowNet config

config/experiments/crystals/starling_bg.yaml

@@ -55,13 +55,9 @@ env:
     train:
       type: csv
       path: /network/projects/crystalgfn/data/bandgap/train.csv
-      output_csv: crystal_train.csv
-      output_pkl: crystal_train.pkl
     test:
       type: csv
       path: /network/projects/crystalgfn/data/bandgap/val.csv
-      output_csv: crystal_val.csv
-      output_pkl: crystal_val.pkl
 
 # GFlowNet hyperparameters
 gflownet:

config/experiments/crystals/starling_bg_no_constraints.yaml

@@ -55,13 +55,9 @@ env:
     train:
       type: csv
       path: /network/projects/crystalgfn/data/bandgap/train.csv
-      output_csv: crystal_train.csv
-      output_pkl: crystal_train.pkl
     test:
       type: csv
       path: /network/projects/crystalgfn/data/bandgap/val.csv
-      output_csv: crystal_val.csv
-      output_pkl: crystal_val.pkl
 
 # GFlowNet hyperparameters
 gflownet:

config/experiments/crystals/starling_density.yaml

@@ -54,13 +54,9 @@ env:
     train:
       type: csv
       path: /network/projects/crystalgfn/data/eform/train.csv
-      output_csv: crystal_train.csv
-      output_pkl: crystal_train.pkl
     test:
       type: csv
       path: /network/projects/crystalgfn/data/eform/val.csv
-      output_csv: crystal_val.csv
-      output_pkl: crystal_val.pkl
 
 # GFlowNet hyperparameters
 gflownet:

config/experiments/crystals/starling_density_no_constraints.yaml

@@ -54,13 +54,9 @@ env:
     train:
       type: csv
       path: /network/projects/crystalgfn/data/eform/train.csv
-      output_csv: crystal_train.csv
-      output_pkl: crystal_train.pkl
     test:
       type: csv
       path: /network/projects/crystalgfn/data/eform/val.csv
-      output_csv: crystal_val.csv
-      output_pkl: crystal_val.pkl
 
 # GFlowNet hyperparameters
 gflownet:

config/experiments/crystals/starling_fe.yaml

@@ -54,13 +54,9 @@ env:
     train:
       type: csv
       path: /network/projects/crystalgfn/data/eform/train.csv
-      output_csv: crystal_train.csv
-      output_pkl: crystal_train.pkl
     test:
       type: csv
       path: /network/projects/crystalgfn/data/eform/val.csv
-      output_csv: crystal_val.csv
-      output_pkl: crystal_val.pkl
 
 # GFlowNet hyperparameters
 gflownet:

config/experiments/crystals/starling_fe_no_constraints.yaml

@@ -54,13 +54,9 @@ env:
     train:
       type: csv
       path: /network/projects/crystalgfn/data/eform/train.csv
-      output_csv: crystal_train.csv
-      output_pkl: crystal_train.pkl
     test:
       type: csv
       path: /network/projects/crystalgfn/data/eform/val.csv
-      output_csv: crystal_val.csv
-      output_pkl: crystal_val.pkl
 
 # GFlowNet hyperparameters
 gflownet:

config/experiments/scrabble/jay.yaml

@@ -18,8 +18,6 @@ env:
     test:
       type: random
       n: 1000
-      output_csv: scrabble_test.csv
-      output_pkl: scrabble_test.pkl
 
 # Proxy
 proxy:

config/experiments/scrabble/penguin.yaml

@@ -17,8 +17,6 @@ env:
     test:
       type: random
       n: 1000
-      output_csv: scrabble_test.csv
-      output_pkl: scrabble_test.pkl
 
 # Proxy
 proxy:

gflownet/utils/buffer.py

@@ -3,6 +3,7 @@
 """
 
 import pickle
+from pathlib import Path
 from typing import List
 
 import numpy as np
@@ -21,14 +22,13 @@ def __init__(
         env,
         proxy,
         replay_capacity=0,
-        output_csv=None,
         data_path=None,
         train=None,
         test=None,
         logger=None,
         **kwargs,
     ):
-        self.logger = logger
+        self.datadir = logger.datadir
         self.env = env
         self.proxy = proxy
         self.replay_capacity = replay_capacity
@@ -43,7 +43,7 @@ def __init__(
         self.replay_states = {}
         self.replay_trajs = {}
         self.replay_rewards = {}
-        self.replay_pkl = "replay.pkl"
+        self.replay_pkl = self.datadir / "replay.pkl"
 
         self.train_csv = None
         self.train_pkl = None
@@ -58,28 +58,19 @@ def __init__(
         else:
             self.train_type = None
         self.train, dict_tr = self.make_data_set(train)
-        if (
-            self.train is not None
-            and "output_csv" in train
-            and train.output_csv is not None
-        ):
-            self.train.to_csv(train.output_csv)
-            self.train_csv = train.output_csv
-        if (
-            dict_tr is not None
-            and "output_pkl" in train
-            and train.output_pkl is not None
-        ):
-            with open(train.output_pkl, "wb") as f:
+        if self.train is not None:
+            self.train_csv = self.datadir / "train.csv"
+            self.train.to_csv(self.train_csv)
+        if dict_tr is not None:
+            self.train_pkl = self.datadir / "train.pkl"
+            with open(self.train_pkl, "wb") as f:
                 pickle.dump(dict_tr, f)
-                self.train_pkl = train.output_pkl
         else:
             print(
                 """
             Important: offline trajectories will NOT be sampled. In order to sample
             offline trajectories, the train configuration of the buffer should be
-            complete and feasible and an output pkl file should be defined in
-            env.buffer.train.output_pkl.
+            complete and feasible. It should at least specify env.buffer.train.type.
             """
             )
             self.train_pkl = None
@@ -90,24 +81,19 @@ def __init__(
         else:
             self.train_type = None
         self.test, dict_tt = self.make_data_set(test)
-        if (
-            self.test is not None
-            and "output_csv" in test
-            and test.output_csv is not None
-        ):
-            self.test_csv = test.output_csv
-            self.test.to_csv(test.output_csv)
-        if dict_tt is not None and "output_pkl" in test and test.output_pkl is not None:
-            with open(test.output_pkl, "wb") as f:
+        if self.test is not None:
+            self.test_csv = self.datadir / "test.csv"
+            self.test.to_csv(self.test_csv)
+        if dict_tt is not None:
+            self.test_pkl = self.datadir / "test.pkl"
+            with open(self.test_pkl, "wb") as f:
                 pickle.dump(dict_tt, f)
-                self.test_pkl = test.output_pkl
         else:
             print(
                 """
             Important: test metrics will NOT be computed. In order to compute
             test metrics the test configuration of the buffer should be complete and
-            feasible and an output pkl file should be defined in
-            env.buffer.test.output_pkl.
+            feasible. It should at least specify env.buffer.test.type.
             """
             )
             self.test_pkl = None

gflownet/utils/logger.py

@@ -71,14 +71,21 @@ def __init__(
         self.lightweight = lightweight
         self.debug = debug
         # Log directory
-        self.logdir = Path(logdir.root)
+        if "path" in logdir:
+            self.logdir = Path(logdir.path)
+        else:
+            self.logdir = Path(logdir.root)
         if not self.logdir.exists() or logdir.overwrite:
             self.logdir.mkdir(parents=True, exist_ok=True)
         else:
             print(f"logdir {logdir} already exists! - Ending run...")
             sys.exit(1)
+        # Checkpoints directory
         self.ckpts_dir = self.logdir / logdir.ckpts
         self.ckpts_dir.mkdir(parents=True, exist_ok=True)
+        # Data directory
+        self.datadir = self.logdir / "data"
+        self.datadir.mkdir(parents=True, exist_ok=True)
         # Write wandb URL
         self.write_url_file()
 

main.py

@@ -9,19 +9,21 @@
 
 import hydra
 import pandas as pd
+from omegaconf import open_dict
 
 from gflownet.utils.policy import parse_policy_config
 
 
 @hydra.main(config_path="./config", config_name="main", version_base="1.1")
 def main(config):
 
-    # Print working and logging directory
+    # Set and print working and logging directory
+    with open_dict(config):
+        config.logger.logdir.path = (
+            hydra.core.hydra_config.HydraConfig.get().runtime.output_dir
+        )
     print(f"\nWorking directory of this run: {os.getcwd()}")
-    print(
-        "Logging directory of this run: "
-        f"{hydra.core.hydra_config.HydraConfig.get().runtime.output_dir}"
-    )
+    print(f"Logging directory of this run: {config.logger.logdir.path}\n")
 
     # Reset seed for job-name generation in multirun jobs
     random.seed(None)
@@ -48,7 +50,7 @@ def main(config):
         _partial_=True,
     )
     env = env_maker()
-    
+
     # The evaluator is used to compute metrics and plots
     evaluator = hydra.utils.instantiate(config.evaluator)
 

tests/gflownet/evaluator/test_base.py

@@ -221,7 +221,7 @@ def test__should_eval_top_k(constant_evaluator, period, step, target, first_it,
     ],
 )
 def test__eval(gflownet_for_tests, parameterization):
-    assert Path("./replay.pkl").exists()
+    assert gflownet_for_tests.buffer.replay_pkl.exists()
     # results: {"metrics": dict[str, float], "figs": list[plt.Figure]}
     results = gflownet_for_tests.evaluator.eval()
     figs = gflownet_for_tests.evaluator.plot(**results["data"])