diff --git a/nbs/common.base_model.ipynb b/nbs/common.base_model.ipynb index 444074b98..ce3ac6601 100644 --- a/nbs/common.base_model.ipynb +++ b/nbs/common.base_model.ipynb @@ -34,7 +34,6 @@ "import random\n", "import warnings\n", "from contextlib import contextmanager\n", - "from copy import deepcopy\n", "from dataclasses import dataclass\n", "\n", "import fsspec\n", @@ -121,10 +120,6 @@ " random_seed,\n", " loss,\n", " valid_loss,\n", - " optimizer,\n", - " optimizer_kwargs,\n", - " lr_scheduler,\n", - " lr_scheduler_kwargs,\n", " futr_exog_list,\n", " hist_exog_list,\n", " stat_exog_list,\n", @@ -150,18 +145,6 @@ " self.train_trajectories = []\n", " self.valid_trajectories = []\n", "\n", - " # Optimization\n", - " if optimizer is not None and not issubclass(optimizer, torch.optim.Optimizer):\n", - " raise TypeError(\"optimizer is not a valid subclass of torch.optim.Optimizer\")\n", - " self.optimizer = optimizer\n", - " self.optimizer_kwargs = optimizer_kwargs if optimizer_kwargs is not None else {}\n", - "\n", - " # lr scheduler\n", - " if lr_scheduler is not None and not issubclass(lr_scheduler, torch.optim.lr_scheduler.LRScheduler):\n", - " raise TypeError(\"lr_scheduler is not a valid subclass of torch.optim.lr_scheduler.LRScheduler\")\n", - " self.lr_scheduler = lr_scheduler\n", - " self.lr_scheduler_kwargs = lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {}\n", - "\n", " # customized by set_configure_optimizers()\n", " self.config_optimizers = None\n", "\n", @@ -412,41 +395,19 @@ "\n", " def configure_optimizers(self):\n", " if self.config_optimizers is not None:\n", + " # return the customized optimizer settings if specified\n", " return self.config_optimizers\n", - " \n", - " if self.optimizer:\n", - " optimizer_signature = inspect.signature(self.optimizer)\n", - " optimizer_kwargs = deepcopy(self.optimizer_kwargs)\n", - " if 'lr' in optimizer_signature.parameters:\n", - " if 'lr' in optimizer_kwargs:\n", - " warnings.warn(\"ignoring learning rate passed in optimizer_kwargs, using the model's learning rate\")\n", - " optimizer_kwargs['lr'] = self.learning_rate\n", - " optimizer = self.optimizer(params=self.parameters(), **optimizer_kwargs)\n", - " else:\n", - " if self.optimizer_kwargs:\n", - " warnings.warn(\n", - " \"ignoring optimizer_kwargs as the optimizer is not specified\"\n", - " )\n", - " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n", " \n", - " lr_scheduler = {'frequency': 1, 'interval': 'step'}\n", - " if self.lr_scheduler:\n", - " lr_scheduler_signature = inspect.signature(self.lr_scheduler)\n", - " lr_scheduler_kwargs = deepcopy(self.lr_scheduler_kwargs)\n", - " if 'optimizer' in lr_scheduler_signature.parameters:\n", - " if 'optimizer' in lr_scheduler_kwargs:\n", - " warnings.warn(\"ignoring optimizer passed in lr_scheduler_kwargs, using the model's optimizer\")\n", - " del lr_scheduler_kwargs['optimizer']\n", - " lr_scheduler['scheduler'] = self.lr_scheduler(optimizer=optimizer, **lr_scheduler_kwargs)\n", - " else:\n", - " if self.lr_scheduler_kwargs:\n", - " warnings.warn(\n", - " \"ignoring lr_scheduler_kwargs as the lr_scheduler is not specified\"\n", - " ) \n", - " lr_scheduler['scheduler'] = torch.optim.lr_scheduler.StepLR(\n", + " # default choice\n", + " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n", + " scheduler = {\n", + " \"scheduler\": torch.optim.lr_scheduler.StepLR(\n", " optimizer=optimizer, step_size=self.lr_decay_steps, gamma=0.5\n", - " )\n", - " return {'optimizer': optimizer, 'lr_scheduler': lr_scheduler}\n", + " ),\n", + " \"frequency\": 1,\n", + " \"interval\": \"step\",\n", + " }\n", + " return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler}\n", "\n", " def set_configure_optimizers(\n", " self, \n", @@ -528,6 +489,22 @@ " model.load_state_dict(content[\"state_dict\"], strict=True)\n", " return model" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "077ea025", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b36e87a", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/nbs/common.base_multivariate.ipynb b/nbs/common.base_multivariate.ipynb index f1321600d..43e31a0ed 100644 --- a/nbs/common.base_multivariate.ipynb +++ b/nbs/common.base_multivariate.ipynb @@ -105,20 +105,12 @@ " drop_last_loader=False,\n", " random_seed=1, \n", " alias=None,\n", - " optimizer=None,\n", - " optimizer_kwargs=None,\n", - " lr_scheduler=None,\n", - " lr_scheduler_kwargs=None,\n", " dataloader_kwargs=None,\n", " **trainer_kwargs):\n", " super().__init__(\n", " random_seed=random_seed,\n", " loss=loss,\n", - " valid_loss=valid_loss,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs, \n", + " valid_loss=valid_loss, \n", " futr_exog_list=futr_exog_list,\n", " hist_exog_list=hist_exog_list,\n", " stat_exog_list=stat_exog_list,\n", diff --git a/nbs/common.base_recurrent.ipynb b/nbs/common.base_recurrent.ipynb index 7b0ed5585..38ac09dba 100644 --- a/nbs/common.base_recurrent.ipynb +++ b/nbs/common.base_recurrent.ipynb @@ -111,20 +111,12 @@ " drop_last_loader=False,\n", " random_seed=1, \n", " alias=None,\n", - " optimizer=None,\n", - " optimizer_kwargs=None,\n", - " lr_scheduler=None,\n", - " lr_scheduler_kwargs=None,\n", " dataloader_kwargs=None,\n", " **trainer_kwargs):\n", " super().__init__(\n", " random_seed=random_seed,\n", " loss=loss,\n", " valid_loss=valid_loss,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " futr_exog_list=futr_exog_list,\n", " hist_exog_list=hist_exog_list,\n", " stat_exog_list=stat_exog_list,\n", diff --git a/nbs/common.base_windows.ipynb b/nbs/common.base_windows.ipynb index 80f12e5f5..ced5a7913 100644 --- a/nbs/common.base_windows.ipynb +++ b/nbs/common.base_windows.ipynb @@ -115,20 +115,12 @@ " drop_last_loader=False,\n", " random_seed=1,\n", " alias=None,\n", - " optimizer=None,\n", - " optimizer_kwargs=None,\n", - " lr_scheduler=None,\n", - " lr_scheduler_kwargs=None,\n", " dataloader_kwargs=None,\n", " **trainer_kwargs):\n", " super().__init__(\n", " random_seed=random_seed,\n", " loss=loss,\n", " valid_loss=valid_loss,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " futr_exog_list=futr_exog_list,\n", " hist_exog_list=hist_exog_list,\n", " stat_exog_list=stat_exog_list,\n", diff --git a/nbs/core.ipynb b/nbs/core.ipynb index e916ef356..3a83b52a8 100644 --- a/nbs/core.ipynb +++ b/nbs/core.ipynb @@ -3172,15 +3172,22 @@ " mean = default_optimizer_predict.loc[:, nf_model.__name__].mean()\n", "\n", " # using a customized optimizer\n", - " params.update({\n", - " \"optimizer\": torch.optim.Adadelta,\n", - " \"optimizer_kwargs\": {\"rho\": 0.45}, \n", - " })\n", + " optimizer = torch.optim.Adadelta(params=models2[0].parameters(), rho=0.75)\n", + " scheduler=torch.optim.lr_scheduler.StepLR(\n", + " optimizer=optimizer, step_size=10e7, gamma=0.5\n", + " )\n", + "\n", " models2 = [nf_model(**params)]\n", + " models2[0].set_configure_optimizers(\n", + " optimizer=optimizer,\n", + " scheduler=scheduler,\n", + " )\n", + "\n", " nf2 = NeuralForecast(models=models2, freq='M')\n", " nf2.fit(AirPassengersPanel_train)\n", " customized_optimizer_predict = nf2.predict()\n", " mean2 = customized_optimizer_predict.loc[:, nf_model.__name__].mean()\n", + "\n", " assert mean2 != mean" ] }, @@ -3194,100 +3201,18 @@ "#| hide\n", "# test that if the user-defined optimizer is not a subclass of torch.optim.optimizer, failed with exception\n", "# tests cover different types of base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", - "test_fail(lambda: NHITS(h=12, input_size=24, max_steps=10, optimizer=torch.nn.Module), contains=\"optimizer is not a valid subclass of torch.optim.Optimizer\")\n", - "test_fail(lambda: RNN(h=12, input_size=24, max_steps=10, optimizer=torch.nn.Module), contains=\"optimizer is not a valid subclass of torch.optim.Optimizer\")\n", - "test_fail(lambda: StemGNN(h=12, input_size=24, max_steps=10, n_series=2, optimizer=torch.nn.Module), contains=\"optimizer is not a valid subclass of torch.optim.Optimizer\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d908240f", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "# test that if we pass \"lr\" parameter, we expect warning and it ignores the passed in 'lr' parameter\n", - "# tests consider models implemented using different base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", "\n", - "for nf_model in [NHITS, RNN, StemGNN]:\n", - " params = {\n", - " \"h\": 12, \n", - " \"input_size\": 24, \n", - " \"max_steps\": 1, \n", - " \"optimizer\": torch.optim.Adadelta, \n", - " \"optimizer_kwargs\": {\"lr\": 0.8, \"rho\": 0.45}\n", - " }\n", + "for model_name in [NHITS, RNN, StemGNN]:\n", + " params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 10}\n", " if nf_model.__name__ == \"StemGNN\":\n", " params.update({\"n_series\": 2})\n", - " models = [nf_model(**params)]\n", - " nf = NeuralForecast(models=models, freq='M')\n", - " with warnings.catch_warnings(record=True) as issued_warnings:\n", - " warnings.simplefilter('always', UserWarning)\n", - " nf.fit(AirPassengersPanel_train)\n", - " assert any(\"ignoring learning rate passed in optimizer_kwargs, using the model's learning rate\" in str(w.message) for w in issued_warnings)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c97858b5-e6a0-4353-a48f-5a5460eb2314", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "# test that if we pass \"optimizer_kwargs\" but not \"optimizer\", we expect a warning\n", - "# tests consider models implemented using different base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", "\n", - "for nf_model in [NHITS, RNN, StemGNN]:\n", - " params = {\n", - " \"h\": 12, \n", - " \"input_size\": 24, \n", - " \"max_steps\": 1,\n", - " \"optimizer_kwargs\": {\"lr\": 0.8, \"rho\": 0.45}\n", - " }\n", - " if nf_model.__name__ == \"StemGNN\":\n", - " params.update({\"n_series\": 2})\n", - " models = [nf_model(**params)]\n", - " nf = NeuralForecast(models=models, freq='M')\n", - " with warnings.catch_warnings(record=True) as issued_warnings:\n", - " warnings.simplefilter('always', UserWarning)\n", - " nf.fit(AirPassengersPanel_train)\n", - " assert any(\"ignoring optimizer_kwargs as the optimizer is not specified\" in str(w.message) for w in issued_warnings)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24142322", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "# test customized lr_scheduler behavior such that the user defined lr_scheduler result should differ from default\n", - "# tests consider models implemented using different base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", - "\n", - "for nf_model in [NHITS, RNN, StemGNN]:\n", - " params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 1}\n", - " if nf_model.__name__ == \"StemGNN\":\n", - " params.update({\"n_series\": 2})\n", - " models = [nf_model(**params)]\n", - " nf = NeuralForecast(models=models, freq='M')\n", - " nf.fit(AirPassengersPanel_train)\n", - " default_optimizer_predict = nf.predict()\n", - " mean = default_optimizer_predict.loc[:, nf_model.__name__].mean()\n", - "\n", - " # using a customized lr_scheduler, default is StepLR\n", - " params.update({\n", - " \"lr_scheduler\": torch.optim.lr_scheduler.ConstantLR,\n", - " \"lr_scheduler_kwargs\": {\"factor\": 0.78}, \n", - " })\n", - " models2 = [nf_model(**params)]\n", - " nf2 = NeuralForecast(models=models2, freq='M')\n", - " nf2.fit(AirPassengersPanel_train)\n", - " customized_optimizer_predict = nf2.predict()\n", - " mean2 = customized_optimizer_predict.loc[:, nf_model.__name__].mean()\n", - " assert mean2 != mean" + " model = model_name(**params) \n", + " optimizer = torch.nn.Module()\n", + " scheduler = torch.optim.lr_scheduler.StepLR(\n", + " optimizer=torch.optim.Adam(model.parameters()), step_size=10e7, gamma=0.5\n", + " ) \n", + " test_fail(lambda: model.set_configure_optimizers(optimizer=optimizer, scheduler=scheduler), contains=\"optimizer is not a valid instance of torch.optim.Optimizer\")\n" ] }, { @@ -3298,68 +3223,16 @@ "outputs": [], "source": [ "#| hide\n", - "# test that if the user-defined lr_scheduler is not a subclass of torch.optim.lr_scheduler, failed with exception\n", + "# test that if the user-defined scheduler is not a subclass of torch.optim.lr_scheduler, failed with exception\n", "# tests cover different types of base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", - "test_fail(lambda: NHITS(h=12, input_size=24, max_steps=10, lr_scheduler=torch.nn.Module), contains=\"lr_scheduler is not a valid subclass of torch.optim.lr_scheduler.LRScheduler\")\n", - "test_fail(lambda: RNN(h=12, input_size=24, max_steps=10, lr_scheduler=torch.nn.Module), contains=\"lr_scheduler is not a valid subclass of torch.optim.lr_scheduler.LRScheduler\")\n", - "test_fail(lambda: StemGNN(h=12, input_size=24, max_steps=10, n_series=2, lr_scheduler=torch.nn.Module), contains=\"lr_scheduler is not a valid subclass of torch.optim.lr_scheduler.LRScheduler\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b1d8bebb", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "# test that if we pass in \"optimizer\" parameter, we expect warning and it ignores them\n", - "# tests consider models implemented using different base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", - "\n", - "for nf_model in [NHITS, RNN, StemGNN]:\n", - " params = {\n", - " \"h\": 12, \n", - " \"input_size\": 24, \n", - " \"max_steps\": 1, \n", - " \"lr_scheduler\": torch.optim.lr_scheduler.ConstantLR, \n", - " \"lr_scheduler_kwargs\": {\"optimizer\": torch.optim.Adadelta, \"factor\": 0.22}\n", - " }\n", - " if nf_model.__name__ == \"StemGNN\":\n", - " params.update({\"n_series\": 2})\n", - " models = [nf_model(**params)]\n", - " nf = NeuralForecast(models=models, freq='M')\n", - " with warnings.catch_warnings(record=True) as issued_warnings:\n", - " warnings.simplefilter('always', UserWarning)\n", - " nf.fit(AirPassengersPanel_train)\n", - " assert any(\"ignoring optimizer passed in lr_scheduler_kwargs, using the model's optimizer\" in str(w.message) for w in issued_warnings)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06febece", - "metadata": {}, - "outputs": [], - "source": [ - "#| hide\n", - "# test that if we pass in \"lr_scheduler_kwargs\" but not \"lr_scheduler\", we expect a warning\n", - "# tests consider models implemented using different base classes such as BaseWindows, BaseRecurrent, BaseMultivariate\n", "\n", - "for nf_model in [NHITS, RNN, StemGNN]:\n", - " params = {\n", - " \"h\": 12, \n", - " \"input_size\": 24, \n", - " \"max_steps\": 1,\n", - " \"lr_scheduler_kwargs\": {\"optimizer\": torch.optim.Adadelta, \"factor\": 0.22}\n", - " }\n", + "for model_name in [NHITS, RNN, StemGNN]:\n", + " params = {\"h\": 12, \"input_size\": 24, \"max_steps\": 10}\n", " if nf_model.__name__ == \"StemGNN\":\n", " params.update({\"n_series\": 2})\n", - " models = [nf_model(**params)]\n", - " nf = NeuralForecast(models=models, freq='M')\n", - " with warnings.catch_warnings(record=True) as issued_warnings:\n", - " warnings.simplefilter('always', UserWarning)\n", - " nf.fit(AirPassengersPanel_train)\n", - " assert any(\"ignoring lr_scheduler_kwargs as the lr_scheduler is not specified\" in str(w.message) for w in issued_warnings)\n" + " model = model_name(**params)\n", + " optimizer = torch.optim.Adam(model.parameters())\n", + " test_fail(lambda: model.set_configure_optimizers(optimizer=optimizer, scheduler=torch.nn.Module), contains=\"scheduler is not a valid instance of torch.optim.lr_scheduler.LRScheduler\")" ] }, { @@ -3493,7 +3366,6 @@ " models[0].set_configure_optimizers(\n", " optimizer=optimizer,\n", " scheduler=scheduler,\n", - "\n", " )\n", " nf2 = NeuralForecast(models=models, freq='M')\n", " nf2.fit(AirPassengersPanel_train)\n", diff --git a/nbs/models.autoformer.ipynb b/nbs/models.autoformer.ipynb index 9c6567f2e..6db6deb68 100644 --- a/nbs/models.autoformer.ipynb +++ b/nbs/models.autoformer.ipynb @@ -458,10 +458,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -508,10 +504,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs=None,\n", " **trainer_kwargs):\n", " super(Autoformer, self).__init__(h=h,\n", @@ -537,10 +529,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.bitcn.ipynb b/nbs/models.bitcn.ipynb index cd78bb194..8e9571de6 100644 --- a/nbs/models.bitcn.ipynb +++ b/nbs/models.bitcn.ipynb @@ -178,10 +178,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -221,10 +217,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs=None,\n", " **trainer_kwargs):\n", " super(BiTCN, self).__init__(\n", @@ -251,10 +243,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs\n", " )\n", diff --git a/nbs/models.deepar.ipynb b/nbs/models.deepar.ipynb index c25e27bf9..1f93be176 100644 --- a/nbs/models.deepar.ipynb +++ b/nbs/models.deepar.ipynb @@ -183,10 +183,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -231,10 +227,6 @@ " random_seed: int = 1,\n", " num_workers_loader = 0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -274,10 +266,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.deepnpts.ipynb b/nbs/models.deepnpts.ipynb index 4f5e7ee9f..da83951b5 100644 --- a/nbs/models.deepnpts.ipynb +++ b/nbs/models.deepnpts.ipynb @@ -121,10 +121,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -166,10 +162,6 @@ " random_seed: int = 1,\n", " num_workers_loader = 0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -206,10 +198,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.dilated_rnn.ipynb b/nbs/models.dilated_rnn.ipynb index 4b3bd374f..7c556be3d 100644 --- a/nbs/models.dilated_rnn.ipynb +++ b/nbs/models.dilated_rnn.ipynb @@ -390,10 +390,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -430,10 +426,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(DilatedRNN, self).__init__(\n", @@ -456,10 +448,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs\n", " )\n", diff --git a/nbs/models.dlinear.ipynb b/nbs/models.dlinear.ipynb index ea1a38a43..57edcc945 100644 --- a/nbs/models.dlinear.ipynb +++ b/nbs/models.dlinear.ipynb @@ -162,10 +162,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -203,10 +199,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs=None,\n", " **trainer_kwargs):\n", " super(DLinear, self).__init__(h=h,\n", @@ -232,10 +224,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.fedformer.ipynb b/nbs/models.fedformer.ipynb index 2268c058d..47a13e205 100644 --- a/nbs/models.fedformer.ipynb +++ b/nbs/models.fedformer.ipynb @@ -451,10 +451,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -500,10 +496,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer=None,\n", - " optimizer_kwargs=None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(FEDformer, self).__init__(h=h,\n", @@ -528,10 +520,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs, \n", " **trainer_kwargs)\n", " # Architecture\n", diff --git a/nbs/models.gru.ipynb b/nbs/models.gru.ipynb index 7f0608a5f..b3210e198 100644 --- a/nbs/models.gru.ipynb +++ b/nbs/models.gru.ipynb @@ -134,10 +134,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -175,10 +171,6 @@ " random_seed=1,\n", " num_workers_loader=0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(GRU, self).__init__(\n", @@ -201,10 +193,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs\n", " )\n", diff --git a/nbs/models.informer.ipynb b/nbs/models.informer.ipynb index c8e30137c..1666abc67 100644 --- a/nbs/models.informer.ipynb +++ b/nbs/models.informer.ipynb @@ -306,10 +306,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -356,10 +352,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(Informer, self).__init__(h=h,\n", @@ -385,10 +377,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.itransformer.ipynb b/nbs/models.itransformer.ipynb index 5e134cfa0..f55a1927b 100644 --- a/nbs/models.itransformer.ipynb +++ b/nbs/models.itransformer.ipynb @@ -228,10 +228,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", " \n", @@ -273,10 +269,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None, \n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " \n", @@ -299,10 +291,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.kan.ipynb b/nbs/models.kan.ipynb index ac7cc5e2b..93aa02fa3 100644 --- a/nbs/models.kan.ipynb +++ b/nbs/models.kan.ipynb @@ -362,8 +362,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -411,8 +409,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " \n", @@ -440,8 +436,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", " dataloader_kwargs = dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.lstm.ipynb b/nbs/models.lstm.ipynb index 3eb469306..464a539bb 100644 --- a/nbs/models.lstm.ipynb +++ b/nbs/models.lstm.ipynb @@ -121,10 +121,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -161,10 +157,6 @@ " random_seed = 1,\n", " num_workers_loader = 0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(LSTM, self).__init__(\n", @@ -187,10 +179,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs\n", " )\n", diff --git a/nbs/models.mlp.ipynb b/nbs/models.mlp.ipynb index 46c09406f..075dd28e1 100644 --- a/nbs/models.mlp.ipynb +++ b/nbs/models.mlp.ipynb @@ -114,10 +114,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -153,10 +149,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -184,10 +176,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.mlpmultivariate.ipynb b/nbs/models.mlpmultivariate.ipynb index 71abdfb04..b6fb8e302 100644 --- a/nbs/models.mlpmultivariate.ipynb +++ b/nbs/models.mlpmultivariate.ipynb @@ -108,10 +108,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -143,10 +139,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -170,10 +162,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.nbeats.ipynb b/nbs/models.nbeats.ipynb index 9504770d5..5d28efdd3 100644 --- a/nbs/models.nbeats.ipynb +++ b/nbs/models.nbeats.ipynb @@ -270,10 +270,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -315,10 +311,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " \n", @@ -348,10 +340,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.nbeatsx.ipynb b/nbs/models.nbeatsx.ipynb index 9952c3cf9..5db08fec5 100644 --- a/nbs/models.nbeatsx.ipynb +++ b/nbs/models.nbeatsx.ipynb @@ -414,10 +414,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -465,10 +461,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs,\n", " ):\n", @@ -502,10 +494,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.nhits.ipynb b/nbs/models.nhits.ipynb index e844f4660..9b214ce62 100644 --- a/nbs/models.nhits.ipynb +++ b/nbs/models.nhits.ipynb @@ -303,10 +303,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -354,10 +350,6 @@ " random_seed: int = 1,\n", " num_workers_loader = 0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -385,10 +377,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.nlinear.ipynb b/nbs/models.nlinear.ipynb index b55d42204..1b922b883 100644 --- a/nbs/models.nlinear.ipynb +++ b/nbs/models.nlinear.ipynb @@ -102,10 +102,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -142,10 +138,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(NLinear, self).__init__(h=h,\n", @@ -171,10 +163,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.patchtst.ipynb b/nbs/models.patchtst.ipynb index 31064cc24..1088bc6d4 100644 --- a/nbs/models.patchtst.ipynb +++ b/nbs/models.patchtst.ipynb @@ -662,10 +662,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -719,10 +715,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(PatchTST, self).__init__(h=h,\n", @@ -748,10 +740,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs) \n", "\n", diff --git a/nbs/models.rmok.ipynb b/nbs/models.rmok.ipynb index 017477c13..6245d0eb7 100644 --- a/nbs/models.rmok.ipynb +++ b/nbs/models.rmok.ipynb @@ -359,10 +359,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -401,10 +397,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " \n", @@ -427,10 +419,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.rnn.ipynb b/nbs/models.rnn.ipynb index f5e1a67b9..bd856c014 100644 --- a/nbs/models.rnn.ipynb +++ b/nbs/models.rnn.ipynb @@ -125,10 +125,6 @@ " `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br>\n", " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", "\n", @@ -168,10 +164,6 @@ " random_seed=1,\n", " num_workers_loader=0,\n", " drop_last_loader=False,\n", - " optimizer=None,\n", - " optimizer_kwargs=None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None, \n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " super(RNN, self).__init__(\n", @@ -194,10 +186,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs\n", " )\n", diff --git a/nbs/models.softs.ipynb b/nbs/models.softs.ipynb index 978f3c2c2..05d30886f 100644 --- a/nbs/models.softs.ipynb +++ b/nbs/models.softs.ipynb @@ -200,10 +200,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", " \n", @@ -243,10 +239,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None, \n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " \n", @@ -269,10 +261,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.stemgnn.ipynb b/nbs/models.stemgnn.ipynb index b2222fc1c..54aad7471 100644 --- a/nbs/models.stemgnn.ipynb +++ b/nbs/models.stemgnn.ipynb @@ -204,10 +204,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -241,10 +237,6 @@ " random_seed: int = 1,\n", " num_workers_loader = 0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -268,10 +260,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " # Quick fix for now, fix the model later.\n", diff --git a/nbs/models.tcn.ipynb b/nbs/models.tcn.ipynb index dee324513..25b6085de 100644 --- a/nbs/models.tcn.ipynb +++ b/nbs/models.tcn.ipynb @@ -126,10 +126,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", " \"\"\"\n", @@ -166,10 +162,6 @@ " random_seed: int = 1,\n", " num_workers_loader = 0,\n", " drop_last_loader = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None, \n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " super(TCN, self).__init__(\n", @@ -192,10 +184,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs = dataloader_kwargs,\n", " **trainer_kwargs\n", " )\n", diff --git a/nbs/models.tft.ipynb b/nbs/models.tft.ipynb index bae287acf..6ded2b3bb 100644 --- a/nbs/models.tft.ipynb +++ b/nbs/models.tft.ipynb @@ -696,10 +696,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -744,10 +740,6 @@ " num_workers_loader=0,\n", " drop_last_loader=False,\n", " random_seed: int = 1,\n", - " optimizer=None,\n", - " optimizer_kwargs=None,\n", - " lr_scheduler=None,\n", - " lr_scheduler_kwargs=None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs,\n", " ):\n", @@ -776,10 +768,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs,\n", " )\n", diff --git a/nbs/models.tide.ipynb b/nbs/models.tide.ipynb index 6a16d2b2b..f635beec0 100644 --- a/nbs/models.tide.ipynb +++ b/nbs/models.tide.ipynb @@ -167,10 +167,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -216,10 +212,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -248,10 +240,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs\n", " ) \n", diff --git a/nbs/models.timellm.ipynb b/nbs/models.timellm.ipynb index 67f4a03d1..a05c33156 100755 --- a/nbs/models.timellm.ipynb +++ b/nbs/models.timellm.ipynb @@ -291,10 +291,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> \n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -348,10 +344,6 @@ " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", " random_seed: int = 1,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(TimeLLM, self).__init__(h=h,\n", @@ -376,10 +368,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.timemixer.ipynb b/nbs/models.timemixer.ipynb index 9bfdd9cc5..207d44b29 100644 --- a/nbs/models.timemixer.ipynb +++ b/nbs/models.timemixer.ipynb @@ -360,10 +360,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -410,10 +406,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None, \n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " \n", @@ -436,10 +428,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " \n", diff --git a/nbs/models.timesnet.ipynb b/nbs/models.timesnet.ipynb index 37e5d46e4..98eefe2f6 100644 --- a/nbs/models.timesnet.ipynb +++ b/nbs/models.timesnet.ipynb @@ -263,12 +263,6 @@ " Workers to be used by `TimeSeriesDataLoader`.\n", " drop_last_loader : bool (default=False)\n", " If True `TimeSeriesDataLoader` drops last non-full batch.\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional (default=None)\n", - " User specified optimizer instead of the default choice (Adam).\n", - " `optimizer_kwargs`: dict, optional (defualt=None)\n", - " List of parameters used by the user specified `optimizer`.\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional (default=None)\n", " List of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " **trainer_kwargs\n", @@ -314,10 +308,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None, \n", " dataloader_kwargs = None, \n", " **trainer_kwargs):\n", " super(TimesNet, self).__init__(h=h,\n", @@ -343,11 +333,7 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs, \n", - " dataloader_kwargs=dataloader_kwargs, \n", + " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", " # Architecture\n", diff --git a/nbs/models.tsmixer.ipynb b/nbs/models.tsmixer.ipynb index 94a9e4125..c255c233c 100644 --- a/nbs/models.tsmixer.ipynb +++ b/nbs/models.tsmixer.ipynb @@ -250,10 +250,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -291,10 +287,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -318,10 +310,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/nbs/models.tsmixerx.ipynb b/nbs/models.tsmixerx.ipynb index cb0ba72b6..d1f220823 100644 --- a/nbs/models.tsmixerx.ipynb +++ b/nbs/models.tsmixerx.ipynb @@ -274,10 +274,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> \n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> \n", "\n", @@ -315,10 +311,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", "\n", @@ -342,10 +334,6 @@ " random_seed=random_seed,\n", " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", " # Reversible InstanceNormalization layer\n", diff --git a/nbs/models.vanillatransformer.ipynb b/nbs/models.vanillatransformer.ipynb index b76cc9ba2..56cb5e33b 100644 --- a/nbs/models.vanillatransformer.ipynb +++ b/nbs/models.vanillatransformer.ipynb @@ -198,10 +198,6 @@ " `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>\n", " `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>\n", " `alias`: str, optional, Custom name of the model.<br>\n", - " `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br>\n", - " `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br>\n", - " `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br>\n", - " `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br>\n", " `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br>\n", " `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>\n", "\n", @@ -245,10 +241,6 @@ " random_seed: int = 1,\n", " num_workers_loader: int = 0,\n", " drop_last_loader: bool = False,\n", - " optimizer = None,\n", - " optimizer_kwargs = None,\n", - " lr_scheduler = None,\n", - " lr_scheduler_kwargs = None,\n", " dataloader_kwargs = None,\n", " **trainer_kwargs):\n", " super(VanillaTransformer, self).__init__(h=h,\n", @@ -273,10 +265,6 @@ " num_workers_loader=num_workers_loader,\n", " drop_last_loader=drop_last_loader,\n", " random_seed=random_seed,\n", - " optimizer=optimizer,\n", - " optimizer_kwargs=optimizer_kwargs,\n", - " lr_scheduler=lr_scheduler,\n", - " lr_scheduler_kwargs=lr_scheduler_kwargs,\n", " dataloader_kwargs=dataloader_kwargs,\n", " **trainer_kwargs)\n", "\n", diff --git a/neuralforecast/common/_base_model.py b/neuralforecast/common/_base_model.py index 59f7d4a14..e6e7db0b6 100644 --- a/neuralforecast/common/_base_model.py +++ b/neuralforecast/common/_base_model.py @@ -8,7 +8,6 @@ import random import warnings from contextlib import contextmanager -from copy import deepcopy from dataclasses import dataclass import fsspec @@ -72,10 +71,6 @@ def __init__( random_seed, loss, valid_loss, - optimizer, - optimizer_kwargs, - lr_scheduler, - lr_scheduler_kwargs, futr_exog_list, hist_exog_list, stat_exog_list, @@ -101,26 +96,6 @@ def __init__( self.train_trajectories = [] self.valid_trajectories = [] - # Optimization - if optimizer is not None and not issubclass(optimizer, torch.optim.Optimizer): - raise TypeError( - "optimizer is not a valid subclass of torch.optim.Optimizer" - ) - self.optimizer = optimizer - self.optimizer_kwargs = optimizer_kwargs if optimizer_kwargs is not None else {} - - # lr scheduler - if lr_scheduler is not None and not issubclass( - lr_scheduler, torch.optim.lr_scheduler.LRScheduler - ): - raise TypeError( - "lr_scheduler is not a valid subclass of torch.optim.lr_scheduler.LRScheduler" - ) - self.lr_scheduler = lr_scheduler - self.lr_scheduler_kwargs = ( - lr_scheduler_kwargs if lr_scheduler_kwargs is not None else {} - ) - # customized by set_configure_optimizers() self.config_optimizers = None @@ -389,47 +364,19 @@ def on_fit_start(self): def configure_optimizers(self): if self.config_optimizers is not None: + # return the customized optimizer settings if specified return self.config_optimizers - if self.optimizer: - optimizer_signature = inspect.signature(self.optimizer) - optimizer_kwargs = deepcopy(self.optimizer_kwargs) - if "lr" in optimizer_signature.parameters: - if "lr" in optimizer_kwargs: - warnings.warn( - "ignoring learning rate passed in optimizer_kwargs, using the model's learning rate" - ) - optimizer_kwargs["lr"] = self.learning_rate - optimizer = self.optimizer(params=self.parameters(), **optimizer_kwargs) - else: - if self.optimizer_kwargs: - warnings.warn( - "ignoring optimizer_kwargs as the optimizer is not specified" - ) - optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) - - lr_scheduler = {"frequency": 1, "interval": "step"} - if self.lr_scheduler: - lr_scheduler_signature = inspect.signature(self.lr_scheduler) - lr_scheduler_kwargs = deepcopy(self.lr_scheduler_kwargs) - if "optimizer" in lr_scheduler_signature.parameters: - if "optimizer" in lr_scheduler_kwargs: - warnings.warn( - "ignoring optimizer passed in lr_scheduler_kwargs, using the model's optimizer" - ) - del lr_scheduler_kwargs["optimizer"] - lr_scheduler["scheduler"] = self.lr_scheduler( - optimizer=optimizer, **lr_scheduler_kwargs - ) - else: - if self.lr_scheduler_kwargs: - warnings.warn( - "ignoring lr_scheduler_kwargs as the lr_scheduler is not specified" - ) - lr_scheduler["scheduler"] = torch.optim.lr_scheduler.StepLR( + # default choice + optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) + scheduler = { + "scheduler": torch.optim.lr_scheduler.StepLR( optimizer=optimizer, step_size=self.lr_decay_steps, gamma=0.5 - ) - return {"optimizer": optimizer, "lr_scheduler": lr_scheduler} + ), + "frequency": 1, + "interval": "step", + } + return {"optimizer": optimizer, "lr_scheduler": scheduler} def set_configure_optimizers( self, diff --git a/neuralforecast/common/_base_multivariate.py b/neuralforecast/common/_base_multivariate.py index 0fdc3b94d..5acdf75eb 100644 --- a/neuralforecast/common/_base_multivariate.py +++ b/neuralforecast/common/_base_multivariate.py @@ -50,10 +50,6 @@ def __init__( drop_last_loader=False, random_seed=1, alias=None, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -61,10 +57,6 @@ def __init__( random_seed=random_seed, loss=loss, valid_loss=valid_loss, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, futr_exog_list=futr_exog_list, hist_exog_list=hist_exog_list, stat_exog_list=stat_exog_list, diff --git a/neuralforecast/common/_base_recurrent.py b/neuralforecast/common/_base_recurrent.py index 0479996c1..604eaddb8 100644 --- a/neuralforecast/common/_base_recurrent.py +++ b/neuralforecast/common/_base_recurrent.py @@ -50,10 +50,6 @@ def __init__( drop_last_loader=False, random_seed=1, alias=None, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -61,10 +57,6 @@ def __init__( random_seed=random_seed, loss=loss, valid_loss=valid_loss, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, futr_exog_list=futr_exog_list, hist_exog_list=hist_exog_list, stat_exog_list=stat_exog_list, diff --git a/neuralforecast/common/_base_windows.py b/neuralforecast/common/_base_windows.py index dd4a4c869..f83936fcb 100644 --- a/neuralforecast/common/_base_windows.py +++ b/neuralforecast/common/_base_windows.py @@ -53,10 +53,6 @@ def __init__( drop_last_loader=False, random_seed=1, alias=None, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -64,10 +60,6 @@ def __init__( random_seed=random_seed, loss=loss, valid_loss=valid_loss, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, futr_exog_list=futr_exog_list, hist_exog_list=hist_exog_list, stat_exog_list=stat_exog_list, diff --git a/neuralforecast/models/autoformer.py b/neuralforecast/models/autoformer.py index 815e57bc2..ffa081907 100644 --- a/neuralforecast/models/autoformer.py +++ b/neuralforecast/models/autoformer.py @@ -442,10 +442,6 @@ class Autoformer(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -494,10 +490,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -525,10 +517,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/bitcn.py b/neuralforecast/models/bitcn.py index 53a775838..ed48fa5e0 100644 --- a/neuralforecast/models/bitcn.py +++ b/neuralforecast/models/bitcn.py @@ -116,10 +116,6 @@ class BiTCN(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -161,10 +157,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -192,10 +184,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/deepar.py b/neuralforecast/models/deepar.py index 3d2a2fd94..06e0860c2 100644 --- a/neuralforecast/models/deepar.py +++ b/neuralforecast/models/deepar.py @@ -87,10 +87,6 @@ class DeepAR(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -139,10 +135,6 @@ def __init__( random_seed: int = 1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -188,10 +180,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/deepnpts.py b/neuralforecast/models/deepnpts.py index f958e71be..8ba95a2f8 100644 --- a/neuralforecast/models/deepnpts.py +++ b/neuralforecast/models/deepnpts.py @@ -49,10 +49,6 @@ class DeepNPTS(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -96,10 +92,6 @@ def __init__( random_seed: int = 1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -142,10 +134,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/dilated_rnn.py b/neuralforecast/models/dilated_rnn.py index d56cc5f08..a56d3ed0c 100644 --- a/neuralforecast/models/dilated_rnn.py +++ b/neuralforecast/models/dilated_rnn.py @@ -317,10 +317,6 @@ class DilatedRNN(BaseRecurrent): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -359,10 +355,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -386,10 +378,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/dlinear.py b/neuralforecast/models/dlinear.py index 17965c869..c0ba3773c 100644 --- a/neuralforecast/models/dlinear.py +++ b/neuralforecast/models/dlinear.py @@ -75,10 +75,6 @@ class DLinear(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -118,10 +114,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -149,10 +141,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/fedformer.py b/neuralforecast/models/fedformer.py index 89e2fe3ef..2073fde45 100644 --- a/neuralforecast/models/fedformer.py +++ b/neuralforecast/models/fedformer.py @@ -440,10 +440,6 @@ class FEDformer(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -491,10 +487,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -521,10 +513,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/gru.py b/neuralforecast/models/gru.py index 9a6d92325..da24a52e7 100644 --- a/neuralforecast/models/gru.py +++ b/neuralforecast/models/gru.py @@ -52,10 +52,6 @@ class GRU(BaseRecurrent): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -95,10 +91,6 @@ def __init__( random_seed=1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -122,10 +114,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/informer.py b/neuralforecast/models/informer.py index 8b115cebd..82ad48f55 100644 --- a/neuralforecast/models/informer.py +++ b/neuralforecast/models/informer.py @@ -226,10 +226,6 @@ class Informer(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -278,10 +274,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -309,10 +301,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/itransformer.py b/neuralforecast/models/itransformer.py index 9e577a71d..b651ca730 100644 --- a/neuralforecast/models/itransformer.py +++ b/neuralforecast/models/itransformer.py @@ -134,10 +134,6 @@ class iTransformer(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -180,10 +176,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -208,10 +200,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/kan.py b/neuralforecast/models/kan.py index 29d7b1d00..74ea0b099 100644 --- a/neuralforecast/models/kan.py +++ b/neuralforecast/models/kan.py @@ -284,8 +284,6 @@ class KAN(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -334,8 +332,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -365,8 +361,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/lstm.py b/neuralforecast/models/lstm.py index e89db3628..2f1e832e1 100644 --- a/neuralforecast/models/lstm.py +++ b/neuralforecast/models/lstm.py @@ -50,10 +50,6 @@ class LSTM(BaseRecurrent): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -92,10 +88,6 @@ def __init__( random_seed=1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -119,10 +111,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/mlp.py b/neuralforecast/models/mlp.py index 0794ac7c3..40cc8ce31 100644 --- a/neuralforecast/models/mlp.py +++ b/neuralforecast/models/mlp.py @@ -49,10 +49,6 @@ class MLP(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -90,10 +86,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -123,10 +115,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/mlpmultivariate.py b/neuralforecast/models/mlpmultivariate.py index 7554bb44d..b25e6d2e7 100644 --- a/neuralforecast/models/mlpmultivariate.py +++ b/neuralforecast/models/mlpmultivariate.py @@ -43,10 +43,6 @@ class MLPMultivariate(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -80,10 +76,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -109,10 +101,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/nbeats.py b/neuralforecast/models/nbeats.py index 02280fb79..0957abffc 100644 --- a/neuralforecast/models/nbeats.py +++ b/neuralforecast/models/nbeats.py @@ -228,10 +228,6 @@ class NBEATS(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -275,10 +271,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -310,10 +302,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/nbeatsx.py b/neuralforecast/models/nbeatsx.py index 811392a66..4fb461db2 100644 --- a/neuralforecast/models/nbeatsx.py +++ b/neuralforecast/models/nbeatsx.py @@ -315,10 +315,6 @@ class NBEATSx(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -366,10 +362,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -404,10 +396,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/nhits.py b/neuralforecast/models/nhits.py index ce5caeaaa..1d1bb9dd1 100644 --- a/neuralforecast/models/nhits.py +++ b/neuralforecast/models/nhits.py @@ -226,10 +226,6 @@ class NHITS(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -279,10 +275,6 @@ def __init__( random_seed: int = 1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -312,10 +304,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/nlinear.py b/neuralforecast/models/nlinear.py index 4909ddbd3..3480fc48c 100644 --- a/neuralforecast/models/nlinear.py +++ b/neuralforecast/models/nlinear.py @@ -39,10 +39,6 @@ class NLinear(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -81,10 +77,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -112,10 +104,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/patchtst.py b/neuralforecast/models/patchtst.py index 0b2029fd4..3d92a532d 100644 --- a/neuralforecast/models/patchtst.py +++ b/neuralforecast/models/patchtst.py @@ -836,10 +836,6 @@ class PatchTST(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -895,10 +891,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -926,10 +918,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/rmok.py b/neuralforecast/models/rmok.py index 35db80aca..4061f36c8 100644 --- a/neuralforecast/models/rmok.py +++ b/neuralforecast/models/rmok.py @@ -284,10 +284,6 @@ class RMoK(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -327,10 +323,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -355,10 +347,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/rnn.py b/neuralforecast/models/rnn.py index f5d60f42a..f950c5d99 100644 --- a/neuralforecast/models/rnn.py +++ b/neuralforecast/models/rnn.py @@ -50,10 +50,6 @@ class RNN(BaseRecurrent): `random_seed`: int=1, random_seed for pytorch initializer and numpy generators.<br> `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `alias`: str, optional, Custom name of the model.<br> @@ -95,10 +91,6 @@ def __init__( random_seed=1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -122,10 +114,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/softs.py b/neuralforecast/models/softs.py index cb425200a..6112c3d80 100644 --- a/neuralforecast/models/softs.py +++ b/neuralforecast/models/softs.py @@ -109,10 +109,6 @@ class SOFTS(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -153,10 +149,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -181,10 +173,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/stemgnn.py b/neuralforecast/models/stemgnn.py index 85a014e65..4fa2ccf40 100644 --- a/neuralforecast/models/stemgnn.py +++ b/neuralforecast/models/stemgnn.py @@ -169,10 +169,6 @@ class StemGNN(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -208,10 +204,6 @@ def __init__( random_seed: int = 1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -237,10 +229,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/tcn.py b/neuralforecast/models/tcn.py index fd900512c..fdbd1cdd1 100644 --- a/neuralforecast/models/tcn.py +++ b/neuralforecast/models/tcn.py @@ -47,10 +47,6 @@ class TCN(BaseRecurrent): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> """ @@ -89,10 +85,6 @@ def __init__( random_seed: int = 1, num_workers_loader=0, drop_last_loader=False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -116,10 +108,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/tft.py b/neuralforecast/models/tft.py index f96d5646b..faadec9d5 100644 --- a/neuralforecast/models/tft.py +++ b/neuralforecast/models/tft.py @@ -457,10 +457,6 @@ class TFT(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -505,10 +501,6 @@ def __init__( num_workers_loader=0, drop_last_loader=False, random_seed: int = 1, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -537,10 +529,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/tide.py b/neuralforecast/models/tide.py index ec98c2b13..257972570 100644 --- a/neuralforecast/models/tide.py +++ b/neuralforecast/models/tide.py @@ -81,10 +81,6 @@ class TiDE(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -132,10 +128,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -165,10 +157,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/timellm.py b/neuralforecast/models/timellm.py index aa9276f72..93bd52c84 100644 --- a/neuralforecast/models/timellm.py +++ b/neuralforecast/models/timellm.py @@ -214,10 +214,6 @@ class TimeLLM(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -272,10 +268,6 @@ def __init__( num_workers_loader: int = 0, drop_last_loader: bool = False, random_seed: int = 1, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -302,10 +294,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/timemixer.py b/neuralforecast/models/timemixer.py index 5585539bd..57e081ea5 100644 --- a/neuralforecast/models/timemixer.py +++ b/neuralforecast/models/timemixer.py @@ -285,10 +285,6 @@ class TimeMixer(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -336,10 +332,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -364,10 +356,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, ) diff --git a/neuralforecast/models/timesnet.py b/neuralforecast/models/timesnet.py index aab548382..87ed9ca56 100644 --- a/neuralforecast/models/timesnet.py +++ b/neuralforecast/models/timesnet.py @@ -182,12 +182,6 @@ class TimesNet(BaseWindows): Workers to be used by `TimeSeriesDataLoader`. drop_last_loader : bool (default=False) If True `TimeSeriesDataLoader` drops last non-full batch. - `optimizer`: Subclass of 'torch.optim.Optimizer', optional (default=None) - User specified optimizer instead of the default choice (Adam). - `optimizer_kwargs`: dict, optional (defualt=None) - List of parameters used by the user specified `optimizer`. - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional (default=None) List of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> **trainer_kwargs @@ -235,10 +229,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -266,10 +256,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/tsmixer.py b/neuralforecast/models/tsmixer.py index 0d68e1e4c..23a3e4b99 100644 --- a/neuralforecast/models/tsmixer.py +++ b/neuralforecast/models/tsmixer.py @@ -160,10 +160,6 @@ class TSMixer(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -203,10 +199,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -232,10 +224,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/tsmixerx.py b/neuralforecast/models/tsmixerx.py index 24897d442..b8fed092f 100644 --- a/neuralforecast/models/tsmixerx.py +++ b/neuralforecast/models/tsmixerx.py @@ -188,10 +188,6 @@ class TSMixerx(BaseMultivariate): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -231,10 +227,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs ): @@ -260,10 +252,6 @@ def __init__( random_seed=random_seed, num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs ) diff --git a/neuralforecast/models/vanillatransformer.py b/neuralforecast/models/vanillatransformer.py index 69fcc9c4d..c41eec20b 100644 --- a/neuralforecast/models/vanillatransformer.py +++ b/neuralforecast/models/vanillatransformer.py @@ -117,10 +117,6 @@ class VanillaTransformer(BaseWindows): `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br> `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br> `alias`: str, optional, Custom name of the model.<br> - `optimizer`: Subclass of 'torch.optim.Optimizer', optional, user specified optimizer instead of the default choice (Adam).<br> - `optimizer_kwargs`: dict, optional, list of parameters used by the user specified `optimizer`.<br> - `lr_scheduler`: Subclass of 'torch.optim.lr_scheduler.LRScheduler', optional, user specified lr_scheduler instead of the default choice (StepLR).<br> - `lr_scheduler_kwargs`: dict, optional, list of parameters used by the user specified `lr_scheduler`.<br> `dataloader_kwargs`: dict, optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`. <br> `**trainer_kwargs`: int, keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br> @@ -166,10 +162,6 @@ def __init__( random_seed: int = 1, num_workers_loader: int = 0, drop_last_loader: bool = False, - optimizer=None, - optimizer_kwargs=None, - lr_scheduler=None, - lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs, ): @@ -196,10 +188,6 @@ def __init__( num_workers_loader=num_workers_loader, drop_last_loader=drop_last_loader, random_seed=random_seed, - optimizer=optimizer, - optimizer_kwargs=optimizer_kwargs, - lr_scheduler=lr_scheduler, - lr_scheduler_kwargs=lr_scheduler_kwargs, dataloader_kwargs=dataloader_kwargs, **trainer_kwargs, )