fixes

2025-05-19 13:59:16 +02:00
parent 426f4d6963
commit c6defa2065
196 changed files with 18625 additions and 1 deletions
@@ -0,0 +1,109 @@
+from functools import partial
+
+from configs.feature_config import feature_config
+
+from utils.data_utils import *
+from utils.training import *
+
+from models.third_party.patch_tst.models.PatchTST import Model as PatchTST
+from models.utils import *
+from models.collation import *
+
+take_every_nth = int(288 / 12)
+shift_in_hours = 12
+input_window_length = (288 // take_every_nth) * 80
+# output_window_length = (288 // take_every_nth) * 1
+output_window_length = 1
+output_window_offset = input_window_length + (288 // take_every_nth) * 0
+window_shift = int((288 // take_every_nth) / 24 * shift_in_hours)
+min_input_length_fraction_for_padding = ((288 // take_every_nth) * 4) / input_window_length
+
+max_lr = 1e-5
+batch_size = 64
+
+run_configuration = {
+    "item_limit": 100,
+    "runs": [
+        {
+            "name": "lstm_ovulation_regression",
+            "description": "LSTM model for ovulation regression",
+            "model_configuration": {
+                "model_name": "patch_tst_regressor",
+                "version": "1.0.0",
+                "model_class": PatchTST,
+                "feature_config": feature_config | {"ignored_features":
+                    [
+                        "fertility_probability",
+                        "ov_over_probability",
+                        # "days_relative_to_ov",
+                        # "is_biphasic",
+                        "ov_day",
+                    ]},
+                "preprocessing": {
+                    "window_shift": int((288 // take_every_nth) / 24 * shift_in_hours),
+                    "take_every_nth": take_every_nth,
+                    "min_input_length_fraction_for_padding": ((288 // take_every_nth) * 4) / input_window_length,
+                },
+                "batch_fn": produce_window_batches,
+                "collate_fn": simple_x_y_collate,
+                "model_creation_fn": simple_model_creation,
+                "model_save_fn": simple_model_save,
+                "model_load_fn": partial(simple_model_load, model_creation_fn=simple_model_creation),
+                "batch_loss_fn": get_model_loss,
+                "actual_fn": simple_get_y,
+                "predict_fn": simple_x_y_predict,
+                "model_parameters": {
+                    "configs": {
+                        # core
+                        "seq_len": input_window_length,
+                        "pred_len": output_window_length,
+                        "seq_pred": False,
+                        # model
+                        "e_layers": 4,
+                        "n_heads": 4,
+                        "d_model": 128,
+                        "d_ff": 128,
+                        "dropout": 0.2,
+                        "fc_dropout": 0.2,
+                        "head_dropout": 0.0,
+                        "individual": True,
+                        # patch
+                        # "patch_len": input_window_length,
+                        "patch_len": int(288 / take_every_nth),
+                        "stride": int(288 / take_every_nth / 2),
+                        "padding_patch": 0,
+                        # preprocessing
+                        "revin": False,
+                        "affine": False,
+                        "subtract_last": False,
+                        # decomp
+                        "decomposition": True,
+                        "kernel_size": 3,
+                    }
+                },
+                "input_window_length": input_window_length,
+                "output_window_length": output_window_length,
+                "output_window_offset": output_window_offset,
+            },
+            "training_configuration": {
+                "batch_size": batch_size,
+                "model_class": PatchTST,
+                "learning_parameters": {
+                    # "learning_rate": base_lr,
+                    "learning_rate": max_lr * (batch_size / 4),
+                    "epochs": 10,
+                    "patience": 3,
+                },
+                "loss_functions": [
+                    nn.MSELoss(),
+                    nn.BCEWithLogitsLoss(),
+                ],
+                "max_grad_norm": 1.0,
+                "train_size": 0.7,
+                "val_size": 0.15,
+                "test_size": 0.15,
+            }
+
+        }
+    ]
+}