Docs cleanup

docs/doc_yamls/run_pipeline_architecture.py

@@ -9,16 +9,6 @@ def example_pipeline(architecture, optimizer, learning_rate):
 
     # E.g., in shape = (N, 3, 32, 32) => out shape = (N, 10)
     model = architecture.to_pytorch()
-    model = nn.Sequential(
-        nn.Conv2d(in_channels, base_channels, 3, padding=1, bias=False),
-        nn.BatchNorm2d(base_channels),
-        model,
-        nn.BatchNorm2d(base_channels * out_channels_factor),
-        nn.ReLU(inplace=True),
-        nn.AdaptiveAvgPool2d(1),
-        nn.Flatten(),
-        nn.Linear(base_channels * out_channels_factor, n_classes),
-    )
     training_loss = train_model(model, optimizer, learning_rate)
     evaluation_loss = evaluate_model(model)
     return {"loss": evaluation_loss, "training_loss": training_loss}

docs/reference/analyse.md

@@ -84,7 +84,7 @@ The `run_status.csv` provides general run details, such as the number of sampled
 ## TensorBoard Integration
 [TensorBoard](https://www.tensorflow.org/tensorboard) serves as a valuable tool for visualizing machine learning experiments,
 offering the ability to observe losses and metrics throughout the model training process.
-In NePS, we use this to show metrics of configurations during training in addition to comparisons to different hyperparameters used in the search for better diagnosis of the model.
+In NePS, we use this to show metrics of configurations during training in addition to comparisons of different hyperparameters used in the search for better diagnosis of the model.
 
 ### Logging Things
 

docs/reference/declarative_usage.md

@@ -44,12 +44,7 @@ and its location, enabling more flexible project structures.
     ```python
     --8<-- "docs/doc_yamls/run_pipeline_extended.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    # No need to define run_pipeline here. NePS loads it directly from the specified path.
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
+
 
 #### Comprehensive YAML Configuration Template
 This example showcases a more comprehensive YAML configuration, which includes not only the essential parameters
@@ -62,12 +57,6 @@ but also advanced settings for more complex setups.
     ```python
     --8<-- "docs/doc_yamls/run_pipeline_extended.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    # Executes the configuration specified in your YAML file
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
 
 The `searcher` key used in the YAML configuration corresponds to the same keys used for selecting an optimizer directly
 through `neps.run`. For a detailed list of integrated optimizers, see [here](optimizers.md#list-available-searchers)
@@ -93,11 +82,6 @@ Customize an internal NePS optimizer by specifying its parameters directly under
     ```python
     --8<-- "docs/doc_yamls/run_pipeline.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
 
 For detailed information about the available optimizers and their parameters, please visit the [optimizer page](optimizers.md#list-available-searching-algorithms)
 
@@ -116,11 +100,6 @@ Simplify experiments with multiple optimizer settings by outsourcing the optimiz
     ```python
     --8<-- "docs/doc_yamls/run_pipeline.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
 
 ### Handling Large Search Spaces
 Manage large search spaces by outsourcing the pipeline space configuration in a separate YAML file or for keeping track
@@ -138,12 +117,6 @@ of your experiments.
     ```python
     --8<-- "docs/doc_yamls/run_pipeline_big_search_space.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
-
 
 ### Using Architecture Search Spaces
 Since the option for defining the search space via YAML is limited to HPO, grammar-based search spaces or architecture
@@ -161,11 +134,6 @@ search spaces must be loaded via a dictionary, which is then referenced in the `
     ```python
     --8<-- "docs/doc_yamls/run_pipeline_architecture.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
 
 
 ### Integrating Custom Optimizers
@@ -181,13 +149,6 @@ Note: You can still overwrite arguments via searcher_kwargs of `neps.run` like f
     ```python
     --8<-- "docs/doc_yamls/run_pipeline.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    neps.run(run_args="path/to/your/config.yaml")
-    ```
-
-
 
 ### Adding Custom Hooks to Your Configuration
 Define hooks in your YAML configuration to extend the functionality of your experiment.
@@ -199,8 +160,3 @@ Define hooks in your YAML configuration to extend the functionality of your expe
     ```python
     --8<-- "docs/doc_yamls/run_pipeline_extended.py"
     ```
-=== "run_neps.py"
-    ```python
-    import neps
-    neps.run(run_args="path/to/your/config.yaml")
-    ```

docs/reference/pipeline_space.md

@@ -54,11 +54,11 @@ neps.run(.., pipeline_space=pipeline_space)
 
 
 ## Using your knowledge, providing a Prior
-When optimizing, you can provide your own knowledge using the parameters `default=`.
-By indicating a `default=` we take this to be your user prior,
+When optimizing, you can provide your own knowledge using the parameter `default`.
+By indicating a `default` we take this to be your user prior,
 **your knowledge about where a good value for this parameter lies**.
 
-You can also specify a `default_confidence=` to indicate how strongly you want NePS,
+You can also specify a `default_confidence` to indicate how strongly you want NePS,
 to focus on these, one of either `"low"`, `"medium"`, or `"high"`.
 
 Currently the two major algorithms that exploit this in NePS are `PriorBand`
@@ -77,14 +77,14 @@ neps.run(
     }
 )
 ```
-!!! warning "Must set `default=` for all parameters, if any"
+!!! warning "Must set `default` for all parameters, if any"
 
-    If you specify `default=` for one parameter, you must do so for all your variables.
+    If you specify `default` for one parameter, you must do so for all your variables.
     This will be improved in future versions.
 
 !!! warning "Interaction with `is_fidelity`"
 
-    If you specify `is_fidelity=True` for one parameter, the `default=` and `default_confidence=` are ignored.
+    If you specify `is_fidelity=True` for one parameter, the `default` and `default_confidence` are ignored.
     This will be dissallowed in future versions.
 
 ## Defining a pipeline space using YAML
@@ -131,7 +131,7 @@ If none of these hold, an error will be raised.
 
 ## Using ConfigSpace
 
-For users familiar with the [`ConfigSpace`](https://automl.github.io/ConfigSpace/main/) library,
+For users familiar with the [`ConfigSpace`](https://automl.github.io/ConfigSpace/latest/) library,
 can also define the `pipeline_space` through `ConfigurationSpace()`
 
 ```python

neps/search_spaces/hyperparameters/categorical.py

@@ -72,7 +72,7 @@ def __init__(
             default: default value for the hyperparameter, must be in `choices=`
                 if provided.
             default_confidence: confidence score for the default value, used when
-                condsider prior based optimization.
+                considering prior based optimization.
         """
         choices = list(choices)
         if len(choices) <= 1:

neps/search_spaces/search_space.py

@@ -272,7 +272,7 @@ def has_fidelity(self) -> bool:
     def compute_prior(self, *, log: bool = False, ignore_fidelity: bool = False) -> float:
         """Compute the prior probability of the search space.
 
-        This is better know as the `pdf` of the configuraiton in the search space, or a
+        This is better know as the `pdf` of the configuration in the search space, or a
         relative measure of how likely this configuration is under the search space.
 
         Args:
@@ -480,7 +480,7 @@ def get_normalized_hp_categories(
         *,
         ignore_fidelity: bool = False,
     ) -> dict[Literal["continuous", "categorical", "graphs"], list[Any]]:
-        """Get the normalized values for each hyperparameter in the configuraiton.
+        """Get the normalized values for each hyperparameter in the configuration.
 
         Args:
             ignore_fidelity: Whether to ignore the fidelity parameter when getting the