[Major] Support Re-Training

neuralprophet/configure.py

@@ -23,6 +23,24 @@
 @dataclass
 class Model:
     lagged_reg_layers: Optional[List[int]]
+    quantiles: Optional[List[float]] = None
+
+    def setup_quantiles(self):
+        # convert quantiles to empty list [] if None
+        if self.quantiles is None:
+            self.quantiles = []
+        # assert quantiles is a list type
+        assert isinstance(self.quantiles, list), "Quantiles must be provided as list."
+        # check if quantiles are float values in (0, 1)
+        assert all(
+            0 < quantile < 1 for quantile in self.quantiles
+        ), "The quantiles specified need to be floats in-between (0, 1)."
+        # sort the quantiles
+        self.quantiles.sort()
+        # check if quantiles contain 0.5 or close to 0.5, remove if so as 0.5 will be inserted again as first index
+        self.quantiles = [quantile for quantile in self.quantiles if not math.isclose(0.5, quantile)]
+        # 0 is the median quantile index
+        self.quantiles.insert(0, 0.5)
 
 
 @dataclass
@@ -92,9 +110,9 @@
     batch_size: Optional[int]
     loss_func: Union[str, torch.nn.modules.loss._Loss, Callable]
     optimizer: Union[str, Type[torch.optim.Optimizer]]
-    quantiles: List[float] = field(default_factory=list)
+    # quantiles: List[float] = field(default_factory=list)
     optimizer_args: dict = field(default_factory=dict)
-    scheduler: Optional[Type[torch.optim.lr_scheduler.OneCycleLR]] = None
+    scheduler: Optional[Union[str, Type[torch.optim.lr_scheduler.LRScheduler]]] = None
     scheduler_args: dict = field(default_factory=dict)
     newer_samples_weight: float = 1.0
     newer_samples_start: float = 0.0
@@ -104,18 +122,21 @@
     n_data: int = field(init=False)
     loss_func_name: str = field(init=False)
     lr_finder_args: dict = field(default_factory=dict)
+    optimizer_state: dict = field(default_factory=dict)
+    continue_training: bool = False
+    trainer_config: dict = field(default_factory=dict)
 
     def __post_init__(self):
-        # assert the uncertainty estimation params and then finalize the quantiles
-        self.set_quantiles()
         assert self.newer_samples_weight >= 1.0
         assert self.newer_samples_start >= 0.0
         assert self.newer_samples_start < 1.0
-        self.set_loss_func()
-        self.set_optimizer()
-        self.set_scheduler()
+        # self.set_loss_func(self.quantiles)
 
-    def set_loss_func(self):
+        # called in TimeNet configure_optimizers:
+        # self.set_optimizer()
+        # self.set_scheduler()
+
+    def set_loss_func(self, quantiles: List[float]):
         if isinstance(self.loss_func, str):
             if self.loss_func.lower() in ["smoothl1", "smoothl1loss", "huber"]:
                 # keeping 'huber' for backwards compatiblility, though not identical
@@ -135,25 +156,8 @@
                 self.loss_func_name = type(self.loss_func).__name__
             else:
                 raise NotImplementedError(f"Loss function {self.loss_func} not found")
-        if len(self.quantiles) > 1:
-            self.loss_func = PinballLoss(loss_func=self.loss_func, quantiles=self.quantiles)
-
-    def set_quantiles(self):
-        # convert quantiles to empty list [] if None
-        if self.quantiles is None:
-            self.quantiles = []
-        # assert quantiles is a list type
-        assert isinstance(self.quantiles, list), "Quantiles must be in a list format, not None or scalar."
-        # check if quantiles contain 0.5 or close to 0.5, remove if so as 0.5 will be inserted again as first index
-        self.quantiles = [quantile for quantile in self.quantiles if not math.isclose(0.5, quantile)]
-        # check if quantiles are float values in (0, 1)
-        assert all(
-            0 < quantile < 1 for quantile in self.quantiles
-        ), "The quantiles specified need to be floats in-between (0, 1)."
-        # sort the quantiles
-        self.quantiles.sort()
-        # 0 is the median quantile index
-        self.quantiles.insert(0, 0.5)
+        if len(quantiles) > 1:
+            self.loss_func = PinballLoss(loss_func=self.loss_func, quantiles=quantiles)
 
     def set_auto_batch_epoch(
         self,
@@ -182,26 +186,87 @@
         """
         Set the optimizer and optimizer args. If optimizer is a string, then it will be converted to the corresponding
         torch optimizer. The optimizer is not initialized yet as this is done in configure_optimizers in TimeNet.
+
+        Parameters
+            ----------
+                optimizer_name : int
+                    Object provided to NeuralProphet as optimizer.
+                optimizer_args : dict
+                    Arguments for the optimizer.
+
         """
-        self.optimizer, self.optimizer_args = utils_torch.create_optimizer_from_config(
-            self.optimizer, self.optimizer_args
-        )
+        if isinstance(self.optimizer, str):
+            if self.optimizer.lower() == "adamw":
+                # Tends to overfit, but reliable
+                self.optimizer = torch.optim.AdamW
+                self.optimizer_args["weight_decay"] = 1e-3
+            elif self.optimizer.lower() == "sgd":
+                # better validation performance, but diverges sometimes
+                self.optimizer = torch.optim.SGD
+                self.optimizer_args["momentum"] = 0.9
+                self.optimizer_args["weight_decay"] = 1e-4
+            else:
+                raise ValueError(
+                    f"The optimizer name {self.optimizer} is not supported. Please pass the optimizer class."
+                )
+        elif not issubclass(self.optimizer, torch.optim.Optimizer):
+            raise ValueError("The provided optimizer is not supported.")
 
     def set_scheduler(self):
         """
-        Set the scheduler and scheduler args.
+        Set the scheduler and scheduler arg depending on the user selection.
         The scheduler is not initialized yet as this is done in configure_optimizers in TimeNet.
         """
-        self.scheduler = torch.optim.lr_scheduler.OneCycleLR
-        self.scheduler_args.update(
-            {
-                "pct_start": 0.3,
-                "anneal_strategy": "cos",
-                "div_factor": 10.0,
-                "final_div_factor": 10.0,
-                "three_phase": True,
-            }
-        )
+        if self.continue_training:
+            if (isinstance(self.scheduler, str) and self.scheduler.lower() == "onecyclelr") or isinstance(
+                self.scheduler, torch.optim.lr_scheduler.OneCycleLR
+            ):
+                log.warning(
+                    "OneCycleLR scheduler is not supported for continued training. Please set another scheduler. Falling back to ExponentialLR scheduler"
+                )
+                self.scheduler = "exponentiallr"
+
+        if self.scheduler is None:
+            log.warning("No scheduler specified. Falling back to ExponentialLR scheduler.")
+            self.scheduler = "exponentiallr"
+
+        if isinstance(self.scheduler, str):
+            if self.scheduler.lower() == "onecyclelr":
+                self.scheduler = torch.optim.lr_scheduler.OneCycleLR
+                defaults = {
+                    "pct_start": 0.3,
+                    "anneal_strategy": "cos",
+                    "div_factor": 10.0,
+                    "final_div_factor": 10.0,
+                    "three_phase": True,
+                }
+            elif self.scheduler.lower() == "steplr":
+                self.scheduler = torch.optim.lr_scheduler.StepLR
+                defaults = {
+                    "step_size": 10,
+                    "gamma": 0.1,
+                }
+            elif self.scheduler.lower() == "exponentiallr":
+                self.scheduler = torch.optim.lr_scheduler.ExponentialLR
+                defaults = {
+                    "gamma": 0.95,
+                }
+            elif self.scheduler.lower() == "cosineannealinglr":
+                self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR
+                defaults = {
+                    "T_max": 50,
+                }
+            else:
+                raise NotImplementedError(
+                    f"Scheduler {self.scheduler} is not supported from string. Please pass the scheduler class."
+                )
+            if self.scheduler_args is not None:
+                defaults.update(self.scheduler_args)
+            self.scheduler_args = defaults
+        else:
+            assert issubclass(
+                self.scheduler, torch.optim.lr_scheduler.LRScheduler
+            ), "Scheduler must be a subclass of torch.optim.lr_scheduler.LRScheduler"
 
     def set_lr_finder_args(self, dataset_size, num_batches):
         """
@@ -239,6 +304,9 @@
             delay_weight = 1
         return delay_weight
 
+    def set_optimizer_state(self, optimizer_state: dict):
+        self.optimizer_state = optimizer_state
+
 
 @dataclass
 class Trend:
@@ -304,7 +372,7 @@
             log.error("Invalid growth for global_local mode '{}'. Set to 'global'".format(self.trend_global_local))
             self.trend_global_local = "global"
 
         if self.trend_local_reg < 0:
             log.error("Invalid  negative trend_local_reg '{}'. Set to False".format(self.trend_local_reg))
             self.trend_local_reg = False
 
@@ -353,13 +421,13 @@
             log.error("Invalid global_local mode '{}'. Set to 'global'".format(self.global_local))
             self.global_local = "global"
 
         self.periods = OrderedDict(
             {
                 "yearly": Season(
                     resolution=6,
                     period=365.25,
                     arg=self.yearly_arg,
                     global_local=(
                         self.yearly_global_local
                         if self.yearly_global_local in ["global", "local"]
                         else self.global_local
@@ -370,7 +438,7 @@
                     resolution=3,
                     period=7,
                     arg=self.weekly_arg,
                     global_local=(
                         self.weekly_global_local
                         if self.weekly_global_local in ["global", "local"]
                         else self.global_local
@@ -381,7 +449,7 @@
                     resolution=6,
                     period=1,
                     arg=self.daily_arg,
                     global_local=(
                         self.daily_global_local if self.daily_global_local in ["global", "local"] else self.global_local
                     ),
                     condition_name=None,
@@ -389,7 +457,7 @@
             }
         )
 
         assert self.seasonality_local_reg >= 0, "Invalid seasonality_local_reg '{}'.".format(self.seasonality_local_reg)
 
         if self.seasonality_local_reg is True:
             log.warning("seasonality_local_reg = True. Default seasonality_local_reg value set to 1")
@@ -407,7 +475,7 @@
             resolution=resolution,
             period=period,
             arg=arg,
             global_local=global_local if global_local in ["global", "local"] else self.global_local,
             condition_name=condition_name,
         )
 
@@ -483,7 +551,7 @@
     regressors: OrderedDict = field(init=False)  # contains RegressorConfig objects
 
     def __post_init__(self):
         self.regressors = None
 
 
 @dataclass