Add default parameters

algorithm_params.json

@@ -6,6 +6,10 @@
 			"window_size": [5, 100]
 		},
 		"const": {
+		},
+		"default": {
+			"n_clusters": 50,
+			"window_size": 10
 		}
 	},
 	"iforest": {
@@ -15,7 +19,11 @@
 		},
 		"const": {
 			"max_features": 1.0
+		},
+		"default": {
+			"n_trees": 500
 		}
+
 	},
 	"deepant": {
 		"training": true,
@@ -26,7 +34,12 @@
 		"const": {
 			"epochs": 500,
 			"learning_rate": 0.001
+		},
+		"default": {
+			"window_size": 50,
+			"prediction_window_size": 50
 		}
+
 	},
 	"lstm_ad": {
 		"training": true,
@@ -38,7 +51,13 @@
 		"const": {
 			"epochs": 500,
 			"learning_rate": 0.001
+		},
+		"default": {
+			"lstm_layers": 1,
+			"window_size": 200,
+			"prediction_window_size": 50 
 		}
+
 	},
 	"health_esn": {
 		"training": true,
@@ -46,7 +65,11 @@
 			"prediction_window_size": [10, 100]
 		},
 		"const": {
+		},
+		"default": {
+			"prediction_window_size": 50
 		}
+
 	},
 	"random_black_forest": {
 		"training": true,
@@ -59,7 +82,12 @@
 			"n_neighbors": [10, 100],
 			"n_trees": [50, 1000]
 		},
-		"const": {}
+		"const": {},
+		"default": {
+			"n_trees": 500,
+			"n_neighbors": 50
+		}
+
 	},
 	"lof": {
 		"training": false,
@@ -67,7 +95,12 @@
 			"n_neighbors": [10, 100],
 			"leaf_size": [5, 50]
 		},
-		"const": {}
+		"const": {},
+		"default": {
+			"n_neighbors": 50,
+			"leaf_size": 20
+		}
+
 	}
 
 }

scripts/synth_optim.sh

 #!/bin/sh
 # Options SBATCH:
-#SBATCH --job-name=Synthetique
+#SBATCH --job-name=Synthetique_avec_optim
 #SBATCH --cpus-per-task=8
-#SBATCH --array=1-20%9
-#SBATCH --nodelist=r9nc-24-9,r9nc-24-10,r9nc-24-11
+#SBATCH --array=1-20%6
 
 #SBATCH --mail-type=END
 #SBATCH --mail-user=pierre.lotte@irit.fr

scripts/synthetiques_full_run.sh

 #!/bin/sh
 # Options SBATCH:
-#SBATCH --job-name=Synthetique
+#SBATCH --job-name=Synthetique_param_default
 #SBATCH --cpus-per-task=8
 #SBATCH --array=1-20%6
 
@@ -12,4 +12,4 @@
 #SBATCH --partition=24CPUNodes
 
 # Traitement
-srun singularity exec /apps/containerCollections/CUDA12/pytorch2-NGC-24-02.sif /projects/sig/plotte/tsg_env/bin/python ./main.py -c config/generated_config_$SLURM_ARRAY_TASK_ID.json -a kmeans health_esn deepant lstm_ad iforest lof if_lof -o synth_generated -s all
+srun singularity exec /apps/containerCollections/CUDA12/pytorch2-NGC-24-02.sif /projects/sig/plotte/tsg_env/bin/python ./main.py -c config/generated_config_$SLURM_ARRAY_TASK_ID.json -a kmeans health_esn deepant lstm_ad iforest lof if_lof -o synth_generated_default -s all

trainers/base.py

@@ -26,6 +26,7 @@ class BaseTrainer():
         self.data_path = data_path
         self.params = kwargs["const"]
         self.optim_params = kwargs["optimize"]
+        self.default_params = kwargs["default"]
         self.log_path = f"{'/'.join(data_path.split('/')[:-1])}/{self.__class__.__name__}_logs"
         self.algorithm = algorithm
         self.train = train
@@ -50,6 +51,8 @@ class BaseTrainer():
             optimize_time = self.optimize("dataset", path)
             with open(f"{path}/time.csv", "a", encoding="utf-8") as f:
                 f.write(f"{self.algorithm},dataset,optimize,{optimize_time}\n")
+        else:
+            self.params.update(self.default_params)
 
         files = __exec(f"find -L {self.data_path} -regex '^.*dataset[-_0-9]*\(_auto_split\)*.csv'")