fixing parameters (#3602)

Co-authored-by: James Maslek <jmaslek11@gmail.com>

openbb_terminal/forecast/brnn_view.py

@@ -33,7 +33,7 @@ def display_brnn_forecast(
     batch_size: int = 32,
     n_epochs: int = 100,
     learning_rate: float = 1e-3,
-    model_save_name: str = "rnn_model",
+    model_save_name: str = "brnn_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
     export: str = "",

openbb_terminal/forecast/forecast_controller.py

@@ -3032,7 +3032,7 @@ class ForecastController(BaseController):
             "--layer_widths",
             dest="layer_widths",
             type=check_positive,
-            default=3,
+            default=512,
             help="The number of neurons in each layer",
         )
         parser.add_argument(
@@ -3056,7 +3056,7 @@ class ForecastController(BaseController):
             "--max_pool_1d",
             action="store_true",
             dest="maxpool1d",
-            default=False,
+            default=True,
             help="Whether to use max_pool_1d or AvgPool1d",
         )
         if other_args and "-" not in other_args[0][0]:
@@ -3068,7 +3068,7 @@ class ForecastController(BaseController):
             target_dataset=True,
             n_days=True,
             force_reset=True,
-            model_save_name="tft_model",
+            model_save_name="nhits_model",
             train_split=True,
             dropout=0.1,
             input_chunk_length=True,

openbb_terminal/forecast/nhits_model.py

@@ -40,7 +40,7 @@ def get_nhits_data(
     batch_size: int = 32,
     n_epochs: int = 100,
     learning_rate: float = 1e-3,
-    model_save_name: str = "brnn_model",
+    model_save_name: str = "nhits_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
 ) -> Tuple[

openbb_terminal/forecast/nhits_view.py

@@ -39,7 +39,7 @@ def display_nhits_forecast(
     batch_size: int = 32,
     n_epochs: int = 100,
     learning_rate: float = 1e-3,
-    model_save_name: str = "rnn_model",
+    model_save_name: str = "nhits_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
     export: str = "",

openbb_terminal/forecast/tcn_model.py

@@ -27,12 +27,12 @@ def get_tcn_data(
     input_chunk_length: int = 14,
     output_chunk_length: int = 5,
     dropout: float = 0.1,
-    num_filters: int = 6,
+    num_filters: int = 3,
     weight_norm: bool = True,
     dilation_base: int = 2,
-    n_epochs: int = 100,
+    n_epochs: int = 300,
     learning_rate: float = 1e-3,
-    batch_size: int = 800,
+    batch_size: int = 32,
     model_save_name: str = "tcn_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,
@@ -95,7 +95,6 @@ def get_tcn_data(
         Mean average precision error,
         Best TCN Model.
     """
-
     # TODO Check if torch GPU AVAILABLE
 
     use_scalers = True

openbb_terminal/forecast/tcn_view.py

@@ -28,12 +28,12 @@ def display_tcn_forecast(
     input_chunk_length: int = 14,
     output_chunk_length: int = 5,
     dropout: float = 0.1,
-    num_filters: int = 6,
+    num_filters: int = 3,
     weight_norm: bool = True,
     dilation_base: int = 2,
-    n_epochs: int = 100,
+    n_epochs: int = 300,
     learning_rate: float = 1e-3,
-    batch_size: int = 800,
+    batch_size: int = 32,
     model_save_name: str = "tcn_model",
     force_reset: bool = True,
     save_checkpoints: bool = True,

openbb_terminal/forecast/trans_model.py

@@ -34,7 +34,7 @@ def get_trans_data(
     activation: str = "relu",
     dropout: float = 0.0,
     batch_size: int = 32,
-    n_epochs: int = 100,
+    n_epochs: int = 300,
     learning_rate: float = 1e-3,
     model_save_name: str = "trans_model",
     force_reset: bool = True,
@@ -103,7 +103,6 @@ def get_trans_data(
         Mean average precision error,
         Best transformer Model.
     """
-
     # TODO Check if torch GPU AVAILABLE
 
     use_scalers = True

openbb_terminal/forecast/trans_view.py

@@ -33,9 +33,9 @@ def display_trans_forecast(
     num_decoder_layers: int = 3,
     dim_feedforward: int = 512,
     activation: str = "relu",
-    dropout: float = 0.1,
-    batch_size: int = 16,
-    n_epochs: int = 100,
+    dropout: float = 0.0,
+    batch_size: int = 32,
+    n_epochs: int = 300,
     learning_rate: float = 1e-3,
     model_save_name: str = "trans_model",
     force_reset: bool = True,

website/content/terminal/reference/forecast/nhits.md

@@ -22,9 +22,9 @@ nhits [--num-stacks NUM_STACKS] [--num-blocks NUM_BLOCKS] [--num-layers NUM_LAYE
 | num_stacks | The number of stacks that make up the model | 3 | True | None |
 | num_blocks | The number of blocks making up every stack | 1 | True | None |
 | num_layers | The number of fully connected layers | 2 | True | None |
-| layer_widths | The number of neurons in each layer | 3 | True | None |
+| layer_widths | The number of neurons in each layer | 512 | True | None |
 | activation | The desired activation | ReLU | True | ReLU, RReLU, PReLU, Softplus, Tanh, SELU, LeakyReLU, Sigmoid |
-| maxpool1d | Whether to use max_pool_1d or AvgPool1d | False | True | None |
+| maxpool1d | Whether to use max_pool_1d or AvgPool1d | True | True | None |
 | past_covariates | Past covariates(columns/features) in same dataset. Comma separated. | None | True | None |
 | all_past_covariates | Adds all rows as past covariates except for date and the target column. | False | True | None |
 | naive | Show the naive baseline for a model. | False | True | None |
@@ -36,7 +36,7 @@ nhits [--num-stacks NUM_STACKS] [--num-blocks NUM_BLOCKS] [--num-layers NUM_LAYE
 | output_chunk_length | The length of the forecast of the model. | 5 | True | None |
 | force_reset | If set to True, any previously-existing model with the same name will be reset (all checkpoints will be discarded). | True | True | None |
 | save_checkpoints | Whether to automatically save the untrained model and checkpoints. | True | True | None |
-| model_save_name | Name of the model to save. | tft_model | True | None |
+| model_save_name | Name of the model to save. | nhits_model | True | None |
 | n_epochs | Number of epochs over which to train the model. | 300 | True | None |
 | dropout | Fraction of neurons affected by Dropout, from 0 to 1. | 0.1 | True | None |
 | batch_size | Number of time series (input and output) used in each training pass | 32 | True | None |