refactor complex loss functions for improved readability; update settings and dataset classes for consistency

src/single-core-regen/hypertraining/settings.py

@@ -1,4 +1,4 @@
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from datetime import datetime
 
 
@@ -14,7 +14,7 @@ class DataSettings:
     config_path: str # = "data/*-128-16384-100000-0-0-17-0-PAM4-0.ini"
     dtype: tuple = ("complex64", "float64")
     symbols: tuple | float | int = 8
-    model_input_dim: tuple | float | int = 64
+    output_size: tuple | float | int = 64
     shuffle: bool = True
     in_out_delay: float = 0
     xy_delay: tuple | float | int = 0
@@ -33,6 +33,7 @@ class PytorchSettings:
     dataloader_workers: int = 2
     dataloader_prefetch: int = 2
 
+    save_models: bool = True
     model_dir: str = ".models"
 
     summary_dir: str = ".runs"
@@ -45,11 +46,10 @@ class PytorchSettings:
 @dataclass
 class ModelSettings:
     output_dim: int = 2
-    model_n_layers: tuple | int = 3
-    unit_count: tuple | int = 8
-    # n_units_range: tuple | int = (2, 32)
-    # activation_func_range: tuple = ("ModReLU", "ZReLU", "CReLU", "Mag", "Identity")
-    model_activation_func: tuple = ("ModReLU",)
+    n_hidden_layers: tuple | int = 3
+    n_hidden_nodes: tuple | int = 8
+    model_activation_func: tuple = "ModReLU"
+    overrides: dict = field(default_factory=dict)
 
 
 @dataclass
@@ -60,18 +60,36 @@ class OptimizerSettings:
     scheduler_kwargs: dict | None = None
 
 
+def _pruner_default_kwargs():
+    # MedianPruner
+    return {
+        "n_startup_trials": 0,
+        "n_warmup_steps":   5,
+    }
 # optuna settings
 @dataclass
 class OptunaSettings:
     n_trials: int = 128
-    n_threads: int = 4
-    timeout: int = 600
+    n_workers: int = 1
+    timeout: int = None
+    pruner: str = "MedianPruner"
+    pruner_kwargs: dict = field(default_factory=_pruner_default_kwargs)
     directions: tuple = ("minimize",)
     metrics_names: tuple = ("mse",)
     limit_examples: bool = True
-    n_train_batches: int = 100
-    n_valid_batches: int = 100
+    n_train_batches: int = float("inf")
+    n_valid_batches: int = float("inf")
     storage: str = "sqlite:///example.db"
     study_name: str = (
         f"optuna_study_{datetime.now().strftime('%Y-%m-%d_%H:%M:%S')}"
-    )
+    )
+    n_trials_filter: tuple|list = None, #(optuna.trial.TrialState.COMPLETE,)
+    remove_outliers: float|int = None
+    ## reserved, set by HyperTraining
+    _multi_objective = None
+    _parallel = None
+    _n_threads = None
+    _directions = None
+    _direction = None
+    _n_train_batches = None
+    _n_valid_batches = None