deepcave.plugins.hyperparameter.importances¶
# Importances
This module provides a plugin for the visualization of the importances.
Provided utilities include getting input and output layout (filtered or non-filtered), processing the data and loading the outputs. Also provides a matplotlib version.
- ## Classes
Importances: This class provides a plugin for the visualization of the importances.
Classes
Provide a plugin for the visualization of the importances. |
- class deepcave.plugins.hyperparameter.importances.Importances[source]¶
Bases:
StaticPluginProvide a plugin for the visualization of the importances.
Provided utilities include getting input/output layout, data processing and loading outputs. Also provides a matplotlib version.
- static get_filter_layout(register)[source]¶
Get the layout for the filter block.
- Parameters:
register (Callable) – Method to register (user) variables. The register_input function is located in the Plugin superclass.
- Returns:
Layout for the filter block.
- Return type:
List[html.Div]
- static get_input_layout(register)[source]¶
Get the layout for the input block.
- Parameters:
register (Callable) – Method to register (user) variables. The register_input function is located in the Plugin superclass.
- Returns:
Layout for the input block.
- Return type:
List[Any]
- static get_mpl_output_layout(register)[source]¶
Get the layout for the matplotlib output block.
- Parameters:
register (Callable) – Method to register outputs. The register_input function is located in the Plugin superclass.
- Returns:
The layout for the matplotlib output block.
- Return type:
html.Img
- static get_output_layout(register)[source]¶
Get the layout for the output block.
- Parameters:
register (Callable) – Method to register outputs. The register_input function is located in the Plugin superclass.
- Returns:
Layout for the output block.
- Return type:
dcc.Graph
- load_dependency_inputs(run, _, inputs)[source]¶
Work like ‘load_inputs’ but called after inputs have changed.
Note
Only the changes have to be returned. The returned dictionary will be merged with the inputs.
- Parameters:
run – The selected run.
inputs (Dict[str, Any]) – Current content of the inputs.
- Returns:
A dictionary with the changes.
- Return type:
Dict[str, Any]
- load_inputs()[source]¶
Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.
This method is necessary to pre-load contents for the inputs. If the plugin is called for the first time, or there are no results in the cache, the plugin gets its content from this method.
- Returns:
Content to be filled.
- Return type:
Dict[str, Dict[str, Any]]
- static load_mpl_outputs(run, inputs, outputs)[source]¶
Read the raw data and prepare it for the layout.
- Parameters:
run – The selected run.
inputs (Dict[str, Any]) – Input and filter values from the user.
outputs – Raw output from the run.
- Return type:
The rendered matplotlib figure of the importances.
- static load_outputs(run, inputs, outputs)[source]¶
Read in raw data and prepare for layout.
Note
The passed inputs are cleaned and therefore differ compared to ‘load_inputs’ or ‘load_dependency_inputs’. Please see ‘_clean_inputs’ for more information.
- Parameters:
run – The selected run.
inputs – Input and filter values from the user.
outputs – Raw output from the run.
- Returns:
The figure of the importances.
- Return type:
go.figure
- static process(run, inputs)[source]¶
Return raw data based on the run and input data.
Warning
The returned data must be JSON serializable.
Note
The passed inputs are cleaned and therefore differ compared to ‘load_inputs’ or ‘load_dependency_inputs’. Please see ‘_clean_inputs’ for more information.
- Parameters:
run (AbstractRun) – The run to process.
inputs (Dict[str, Any]) – The input data.
- Returns:
A serialzied dictionary.
- Return type:
Dict[str, Any]
- Raises:
RuntimeError – If the number of trees is not specified. If the method is not found.