Remove old interface and deprecate the arguments

nbdev_clean --clear_all

remove unnecessary changes

Author: JQGoh

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",

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",

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",

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",

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",