commit

datasets/d_2026_03_05_09_02_38_retail_jetskiing/{script.bash → create_dataset.bash}

@@ -1,8 +1,9 @@
-python create_dataset.py --nb_jobs 7\
+python create_dataset.py\
+    --nb_jobs 10\
     --nb_machines 2\
     --time_min 0\
     --time_max 1\
-    --nb_base_samples 1000\
+    --nb_base_samples 10000\
     --duplication_factor 0.0\
     --init_type exhaustive\
     --data_folder_location "./"\

datasets/d_2026_03_05_09_02_38_retail_jetskiing/create_dataset.log

@@ -47,3 +47,63 @@
 2026-03-05 09:46:42.664 | INFO     | __main__:<module>:535 - seed: 97
 2026-03-05 09:46:42.664 | INFO     | __main__:<module>:535 - normalize_makespans: True
 2026-03-05 09:46:42.678 | INFO     | __main__:<module>:543 - Normalizing makespans by the sum of processing times 7.829251766204834
+2026-03-05 22:46:34.329 | INFO     | __main__:<module>:535 - nb_jobs: 10
+2026-03-05 22:46:34.339 | INFO     | __main__:<module>:535 - nb_machines: 10
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - time_min: 0
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - time_max: 1
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - nb_base_samples: 1000
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - duplication_factor: 0.0
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - init_type: exhaustive
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - data_folder_location: ./
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - data_folder_name: 
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - seed: 97
+2026-03-05 22:46:34.340 | INFO     | __main__:<module>:535 - normalize_makespans: True
+2026-03-05 22:46:34.348 | INFO     | __main__:<module>:543 - Normalizing makespans by the sum of processing times 47.58796691894531
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - nb_jobs: 7
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - nb_machines: 2
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - time_min: 0
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - time_max: 1
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - nb_base_samples: 1000
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - duplication_factor: 0.0
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - init_type: exhaustive
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - data_folder_location: ./
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - data_folder_name: 
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - seed: 97
+2026-03-05 22:47:08.414 | INFO     | __main__:<module>:535 - normalize_makespans: True
+2026-03-05 22:47:08.419 | INFO     | __main__:<module>:543 - Normalizing makespans by the sum of processing times 7.829251766204834
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - nb_jobs: 10
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - nb_machines: 2
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - time_min: 0
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - time_max: 1
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - nb_base_samples: 10000
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - duplication_factor: 0.0
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - init_type: random
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - data_folder_location: ./
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - data_folder_name: 
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - seed: 97
+2026-03-05 22:47:45.145 | INFO     | __main__:<module>:535 - normalize_makespans: True
+2026-03-05 22:47:45.154 | INFO     | __main__:<module>:543 - Normalizing makespans by the sum of processing times 9.149216651916504
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - nb_jobs: 10
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - nb_machines: 2
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - time_min: 0
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - time_max: 1
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - nb_base_samples: 10000
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - duplication_factor: 0.0
+2026-03-05 22:47:56.485 | INFO     | __main__:<module>:535 - init_type: palmer
+2026-03-05 22:47:56.486 | INFO     | __main__:<module>:535 - data_folder_location: ./
+2026-03-05 22:47:56.486 | INFO     | __main__:<module>:535 - data_folder_name: 
+2026-03-05 22:47:56.486 | INFO     | __main__:<module>:535 - seed: 97
+2026-03-05 22:47:56.486 | INFO     | __main__:<module>:535 - normalize_makespans: True
+2026-03-05 22:47:56.495 | INFO     | __main__:<module>:543 - Normalizing makespans by the sum of processing times 9.149216651916504
+2026-03-05 22:48:09.563 | INFO     | __main__:<module>:535 - nb_jobs: 10
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - nb_machines: 2
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - time_min: 0
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - time_max: 1
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - nb_base_samples: 10000
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - duplication_factor: 0.0
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - init_type: neh
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - data_folder_location: ./
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - data_folder_name: 
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - seed: 97
+2026-03-05 22:48:09.564 | INFO     | __main__:<module>:535 - normalize_makespans: True
+2026-03-05 22:48:09.570 | INFO     | __main__:<module>:543 - Normalizing makespans by the sum of processing times 9.149216651916504

datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_/makespans.npy

@@ -1,3 +1,3 @@
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+size 400128

datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_/metadata.json

@@ -1,11 +1,11 @@
 {
-    "nb_base_samples": 5040,
+    "nb_base_samples": 10000,
     "duplication_factor": 0.0,
-    "nb_samples": 5040,
-    "nb_jobs": 7,
+    "nb_samples": 10000,
+    "nb_jobs": 10,
     "nb_machines": 2,
-    "init_type": "exhaustive",
+    "init_type": "neh",
     "data_path": "./ftd_",
     "seed": 97,
-    "date_time": "2026_03_05_09_46_42"
+    "date_time": "2026_03_05_22_48_09"
 }

datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_/pfsp_instance.npy

@@ -1,3 +1,3 @@
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+size 208

datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_/schedules.npy

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studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/checkpoints/checkpoint_epoch_1.96.pth → datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split/makespans_train.npy

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studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/checkpoints/checkpoint_epoch_0.72.pth → datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split/makespans_val.npy

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datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split/metadata.json

@@ -0,0 +1,14 @@
+{
+    "nb_base_samples": 5040,
+    "duplication_factor": 0.0,
+    "nb_samples": 5040,
+    "nb_jobs": 7,
+    "nb_machines": 2,
+    "init_type": "exhaustive",
+    "data_path": "./ftd_",
+    "seed": 97,
+    "date_time": "2026_03_05_09_46_42",
+    "nb_train_samples": 4032,
+    "nb_val_samples": 1008,
+    "data_source": "/home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_"
+}

studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/checkpoints/checkpoint_epoch_0.96.pth → datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split/pfsp_instance.npy

@@ -1,3 +1,3 @@
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studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/checkpoints/checkpoint_epoch_1.72.pth → datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split/schedules_train.npy

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datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split/schedules_val.npy

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datasets/d_2026_03_05_09_02_38_retail_jetskiing/split_dataset.bash

@@ -0,0 +1,7 @@
+python split_dataset.py\
+    --input_dir "./ftd_"
+    --output_dir "./ftd_split"\
+    --ratio 0.85\
+    --seed 97\
+    --keep_best_makespan True
+    

datasets/d_2026_03_05_09_02_38_retail_jetskiing/split_dataset.log

@@ -0,0 +1,20 @@
+2026-03-05 12:30:44.495 | INFO     | __main__:split_dataset:15 - Starting dataset split. Input: /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_ -> Output: /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split
+2026-03-05 12:30:44.495 | INFO     | __main__:split_dataset:31 - Loading data from .npy files...
+2026-03-05 12:30:44.495 | INFO     | __main__:split_dataset:48 - Shuffling with seed 97...
+2026-03-05 12:30:44.500 | INFO     | __main__:split_dataset:61 - Saving 4032 training samples...
+2026-03-05 12:30:44.500 | INFO     | __main__:split_dataset:66 - Saving 1008 validation samples...
+2026-03-05 12:30:44.501 | SUCCESS  | __main__:split_dataset:83 - Successfully split dataset into 4032 train and 1008 val samples in /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split.
+2026-03-05 12:31:57.006 | INFO     | __main__:split_dataset:15 - Starting dataset split. Input: /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_ -> Output: /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split
+2026-03-05 12:31:57.006 | INFO     | __main__:split_dataset:31 - Loading data from .npy files...
+2026-03-05 12:31:57.007 | INFO     | __main__:split_dataset:34 - Loaded 5040 = 5040 samples.
+2026-03-05 12:31:57.007 | INFO     | __main__:split_dataset:49 - Shuffling with seed 97...
+2026-03-05 12:31:57.011 | INFO     | __main__:split_dataset:62 - Saving 4032 training samples...
+2026-03-05 12:31:57.011 | INFO     | __main__:split_dataset:67 - Saving 1008 validation samples...
+2026-03-05 12:31:57.012 | SUCCESS  | __main__:split_dataset:84 - Successfully split dataset into 4032 train and 1008 val samples in /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split.
+2026-03-05 12:35:10.809 | INFO     | __main__:split_dataset:15 - Starting dataset split. Input: /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_ -> Output: /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split
+2026-03-05 12:35:10.809 | INFO     | __main__:split_dataset:31 - Loading data from .npy files...
+2026-03-05 12:35:10.810 | INFO     | __main__:split_dataset:34 - Loaded 5040 = 5040 samples.
+2026-03-05 12:35:10.810 | INFO     | __main__:split_dataset:49 - Shuffling with seed 97...
+2026-03-05 12:35:10.814 | INFO     | __main__:split_dataset:62 - Saving 4032 training samples...
+2026-03-05 12:35:10.814 | INFO     | __main__:split_dataset:67 - Saving 1008 validation samples...
+2026-03-05 12:35:10.815 | SUCCESS  | __main__:split_dataset:84 - Successfully split dataset into 4032 train and 1008 val samples in /home/younes/MVA/semester_2/advanced_deep_learning/flowshop_transformer/datasets/d_2026_03_05_09_02_38_retail_jetskiing/ftd_split.

datasets/d_2026_03_05_09_02_38_retail_jetskiing/split_dataset.py

@@ -0,0 +1,102 @@
+import os
+import numpy as np
+import json
+import argparse
+import shutil
+from loguru import logger
+
+
+def split_dataset(input_dir, output_dir, split_ratio=0.8, seed=42, keep_best_makespan=True):
+    """
+    Splits the dataset in input_dir into train and validation sets.
+    Saves the splits in output_dir with names like schedules_train.npy, etc.
+    """
+    logger.add("split_dataset.log")
+    logger.info(f"Starting dataset split. Input: {input_dir} -> Output: {output_dir}")
+    
+    # paths
+    metadata_path = os.path.join(input_dir, "metadata.json")
+    schedules_path = os.path.join(input_dir, "schedules.npy")
+    makespans_path = os.path.join(input_dir, "makespans.npy")
+    
+    if not os.path.exists(metadata_path):
+        logger.error(f"Metadata file not found: {metadata_path}")
+        return
+
+    # load metadata
+    with open(metadata_path, "r") as f:
+        metadata = json.load(f)
+    
+    # load data
+    logger.info("Loading data from .npy files...")
+    schedules = np.load(schedules_path)
+    makespans = np.load(makespans_path)
+    logger.info(f"Loaded {schedules.shape[0]} = {makespans.shape[0]} samples.")
+    
+    # filtering logic: remove best makespans if requested
+    if not keep_best_makespan:
+        min_makespan = np.min(makespans)
+        keep_indices = makespans != min_makespan
+        removed_count = np.sum(~keep_indices)
+        logger.info(f"Removing {removed_count} samples with best makespan ({min_makespan}).")
+        schedules = schedules[keep_indices]
+        makespans = makespans[keep_indices]
+
+    num_samples = schedules.shape[0]
+    indices = np.arange(num_samples)
+    
+    # shuffle
+    logger.info(f"Shuffling with seed {seed}...")
+    np.random.seed(seed)
+    np.random.shuffle(indices)
+    
+    split_idx = int(num_samples * split_ratio)
+    
+    train_indices = indices[:split_idx]
+    val_indices = indices[split_idx:]
+    
+    # create output directory
+    os.makedirs(output_dir, exist_ok=True)
+    
+    # save train data
+    logger.info(f"Saving {len(train_indices)} training samples...")
+    np.save(os.path.join(output_dir, "schedules_train.npy"), schedules[train_indices])
+    np.save(os.path.join(output_dir, "makespans_train.npy"), makespans[train_indices])
+    
+    # save val data
+    logger.info(f"Saving {len(val_indices)} validation samples...")
+    np.save(os.path.join(output_dir, "schedules_val.npy"), schedules[val_indices])
+    np.save(os.path.join(output_dir, "makespans_val.npy"), makespans[val_indices])
+    
+    # copy pfsp_instance.npy if it exists
+    pfsp_path = os.path.join(input_dir, "pfsp_instance.npy")
+    if os.path.exists(pfsp_path):
+        shutil.copy(pfsp_path, os.path.join(output_dir, "pfsp_instance.npy"))
+    
+    # create metadata file for the split dataset
+    split_metadata = metadata.copy()
+    split_metadata["nb_train_samples"] = len(train_indices)
+    split_metadata["nb_val_samples"] = len(val_indices)
+    split_metadata["data_source"] = input_dir
+    with open(os.path.join(output_dir, "metadata.json"), "w") as f:
+        json.dump(split_metadata, f, indent=4)
+            
+    logger.success(f"Successfully split dataset into {len(train_indices)} train and {len(val_indices)} val samples in {output_dir}.")
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Split flowshop dataset into train and validation sets.")
+    parser.add_argument("--input_dir", type=str, default="ftd_", help="Path to the source dataset directory")
+    parser.add_argument("--output_dir", type=str, default="ftd_split", help="Path to the output directory")
+    parser.add_argument("--ratio", type=float, default=0.8, help="Train/Val split ratio")
+    parser.add_argument("--seed", type=int, default=97, help="Random seed for shuffling")
+    parser.add_argument("--keep_best_makespan", type=lambda x: (str(x).lower() == 'true'), default=True, help="Whether to keep samples with the best makespan")
+    
+    args = parser.parse_args()
+    
+    split_dataset(
+        os.path.abspath(args.input_dir), 
+        os.path.abspath(args.output_dir), 
+        args.ratio, 
+        args.seed,
+        args.keep_best_makespan
+    )

studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/batch_losses.npy

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studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/checkpoints/checkpoint_epoch_0.24.pth

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studies/s_2026_02_18_01_31_19_frightened_stork/e_2026_03_05_10_19_37_printed_pepato/checkpoints/checkpoint_epoch_0.48.pth

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