[Minor] Structure component configurations to be more consistent

docs/source/code/forecaster.rst

@@ -6,6 +6,7 @@ Core Module Documentation
    :maxdepth: 1
 
    configure.py <configure>
+   configure_components.py <configure_components>
    df_utils.py <df_utils>
    event_utils.py <event_utils>
    plot_forecast_plotly.py <plot_forecast_plotly>
@@ -14,6 +15,7 @@ Core Module Documentation
    plot_model_parameters_matplotlib.py <plot_model_parameters_matplotlib>
    time_dataset.py <time_dataset>
    time_net.py <time_net>
+   utils_time_dataset.py <utils_time_dataset>
    utils.py <utils>
 
 .. automodule:: neuralprophet.forecaster

docs/source/code/index.rst

@@ -6,6 +6,7 @@ Code Documentation
 
    forecaster.py (NeuralProphet) <forecaster>
    configure.py <configure>
+   configure_components.py <configure_components>
    time_dataset.py <time_dataset>
    time_net.py <time_net>
    torch_prophet.py <torch_prophet>
@@ -23,6 +24,7 @@ Code Documentation
    plot_model_parameters_plotly.py <plot_model_parameters_plotly>
    plot_model_parameters_matplotlib.py <plot_model_parameters_matplotlib>
    utils.py <utils>
+   utils_time_dataset.py <utils_time_dataset>
    df_utils.py <df_utils>
    hdays_utils.py <hdays_utils>
    plot_utils.py <plot_utils>

docs/source/how-to-guides/feature-guides/collect_predictions.ipynb

@@ -34,7 +34,7 @@
    "source": [
     "if \"google.colab\" in str(get_ipython()):\n",
     "    # uninstall preinstalled packages from Colab to avoid conflicts\n",
-    "    !pip uninstall -y torch notebook notebook_shim tensorflow tensorflow-datasets prophet torchaudio torchdata torchtext torchvision \n",
+    "    !pip uninstall -y torch notebook notebook_shim tensorflow tensorflow-datasets prophet torchaudio torchdata torchtext torchvision\n",
     "    !pip install git+https://github.com/ourownstory/neural_prophet.git # may take a while\n",
     "    #!pip install neuralprophet # much faster, but may not have the latest upgrades/bugfixes\n",
     "\n",
@@ -787,7 +787,7 @@
    "outputs": [],
    "source": [
     "df = pd.read_csv(data_location + \"air_passengers.csv\")\n",
-    "future = m.make_future_dataframe(df, periods=3)  # periods=m.n_forecasts, n_historic_predictions=False"
+    "future = m.make_future_dataframe(df, periods=3)  # periods=m.config_model.n_forecasts, n_historic_predictions=False"
    ]
   },
   {

docs/source/how-to-guides/feature-guides/global_local_modeling_fut_regr.ipynb

@@ -344,62 +344,6 @@
     "df_local = df_local[[\"ds\", \"y\"]]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# import neuralprophet.configure as configure"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# from dataclasses import dataclass, field\n",
-    "# from typing import Callable, List, Optional\n",
-    "# from typing import OrderedDict as OrderedDictType\n",
-    "# from typing import Type, Union\n",
-    "# from collections import OrderedDict\n",
-    "\n",
-    "# @dataclass\n",
-    "# class Regressor:\n",
-    "#     reg_lambda: Optional[float]\n",
-    "#     normalize: str\n",
-    "#     mode: str\n",
-    "\n",
-    "# @dataclass\n",
-    "# class ConfigFutureRegressors:\n",
-    "#     model: str\n",
-    "#     regressors: OrderedDict = field(init=False)  # contains SeasonConfig objects\n",
-    "\n",
-    "#     def __post_init__(self):\n",
-    "#         self.regressors = OrderedDictType[str, Regressor]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ConfigFutureRegressors(\n",
-    "#             model='linear',\n",
-    "#         ) # Opti"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# m.config_regressors"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

docs/zh/自回归.md

@@ -65,7 +65,7 @@ m = NeuralProphet(
     weekly_seasonality=False,
     daily_seasonality=False
 )
-m.highlight_nth_step_ahead_of_each_forecast(step_number=m.n_forecasts)
+m.highlight_nth_step_ahead_of_each_forecast(step_number=m.config_model.n_forecasts)
 ```
 
 您可以指定任何小于或等于`n_forecasts`的值到`step_number`参数。一旦你这样做，指标将看起来像下面。

neuralprophet/components/future_regressors/neural_nets.py

@@ -21,14 +21,14 @@ def __init__(self, config, id_list, quantiles, n_forecasts, device, config_trend
         if self.regressors_dims is not None:
             # Regresors params
             self.regressor_nets = nn.ModuleDict({})
-            self.regressors_layers = config.regressors_layers
+            self.layers = config.layers
             # one net per regressor. to be adapted to combined network
             for regressor in self.regressors_dims.keys():
                 # Nets for both additive and multiplicative regressors
                 regressor_net = nn.ModuleList()
                 # This will be later 1 + static covariates
                 d_inputs = 1
-                for d_hidden_i in self.regressors_layers:
+                for d_hidden_i in self.layers:
                     regressor_net.append(nn.Linear(d_inputs, d_hidden_i, bias=True))
                     d_inputs = d_hidden_i
                 # final layer has input size d_inputs and output size equal to no. of quantiles
@@ -77,7 +77,7 @@ def regressor(self, regressor_input, name):
                 Forecast component of dims (batch, n_forecasts, num_quantiles)
         """
         x = regressor_input
-        for i in range(len(self.regressors_layers) + 1):
+        for i in range(len(self.layers) + 1):
             if i > 0:
                 x = nn.functional.relu(x)
             x = self.regressor_nets[name][i](x)

neuralprophet/components/future_regressors/shared_neural_nets.py

@@ -21,14 +21,14 @@ def __init__(self, config, id_list, quantiles, n_forecasts, device, config_trend
         if self.regressors_dims is not None:
             # Regresors params
             self.regressor_nets = nn.ModuleDict({})
-            self.regressors_layers = config.regressors_layers
+            self.layers = config.layers
             # Combined network
             for net_i, size_i in Counter([x["mode"] for x in self.regressors_dims.values()]).items():
                 # Nets for both additive and multiplicative regressors
                 regressor_net = nn.ModuleList()
                 # This will be later size_i(1 + static covariates)
                 d_inputs = size_i
-                for d_hidden_i in self.regressors_layers:
+                for d_hidden_i in self.layers:
                     regressor_net.append(nn.Linear(d_inputs, d_hidden_i, bias=True))
                     d_inputs = d_hidden_i
                 # final layer has input size d_inputs and output size equal to  no. of quantiles
@@ -79,7 +79,7 @@ def regressors(self, regressor_inputs, mode):
                 Forecast component of dims (batch, n_forecasts, num_quantiles)
         """
         x = regressor_inputs
-        for i in range(len(self.regressors_layers) + 1):
+        for i in range(len(self.layers) + 1):
             if i > 0:
                 x = nn.functional.relu(x)
             x = self.regressor_nets[mode][i](x)

neuralprophet/components/future_regressors/shared_neural_nets_coef.py

@@ -21,14 +21,14 @@ def __init__(self, config, id_list, quantiles, n_forecasts, device, config_trend
         if self.regressors_dims is not None:
             # Regresors params
             self.regressor_nets = nn.ModuleDict({})
-            self.regressors_layers = config.regressors_layers
+            self.layers = config.layers
             # Combined network
             for net_i, size_i in Counter([x["mode"] for x in self.regressors_dims.values()]).items():
                 # Nets for both additive and multiplicative regressors
                 regressor_net = nn.ModuleList()
                 # This will be later size_i(1 + static covariates)
                 d_inputs = size_i
-                for d_hidden_i in self.regressors_layers:
+                for d_hidden_i in self.layers:
                     regressor_net.append(nn.Linear(d_inputs, d_hidden_i, bias=True))
                     d_inputs = d_hidden_i
                 # final layer has input size d_inputs and output size equal to no. of quantiles
@@ -80,7 +80,7 @@ def regressors(self, regressor_inputs, mode):
                 Forecast component of dims (batch, n_forecasts, num_quantiles)
         """
         x = regressor_inputs
-        for i in range(len(self.regressors_layers) + 1):
+        for i in range(len(self.layers) + 1):
             if i > 0:
                 x = nn.functional.relu(x)
             x = self.regressor_nets[mode][i](x)

neuralprophet/components/router.py

@@ -20,7 +20,7 @@ def get_trend(config, n_forecasts, quantiles, id_list, num_trends_modelled, devi
 
     Parameters
         ----------
-            config : configure.Trend
+            config : configure_components.Trend
             n_forecasts : int
                 number of steps to forecast. Aka number of model outputs
             quantiles : list