Base - Systematica - Blockforce Capital

`resolve_optuna_suggest`

resolve_optuna_suggest(
    space: Dict[str, Any],
    trial: optuna.trial._trial.Trial,
) ‑> Dict[str, Any]

Recursively resolve optuna suggest calls in a nested dict/list structure. Parameters:

Name	Type	Default	Description
`space`	`tp.Kwargs`	`--`	The search space to resolve.
`trial`	`optuna.trial.Trial`	`--`	The current optuna trial.

Returns:

Type	Description
`tp.Kwargs`	The resolved search space.

`resolve_optuna_metric_output`

resolve_optuna_metric_output(
    metric: Any,
    debug: bool = False,
) ‑> int | float | Tuple[int | float, ...]

Resolves the output metric from a portfolio pipeline into a float or tuple of floats. This function checks for NaN values in the metric and ensures the output is in a format suitable for Optuna optimization. It supports metrics that are floats, lists, pandas DataFrames, or pandas Series. Parameters:

Name	Type	Default	Description
`metric`	`Any`	`--`	The metric returned by the portfolio pipeline. It can be a float, list, pandas DataFrame, or pandas Series.
`debug`	`bool`	`is`	`True` Debug mode turned on if `True`. WHen turned on, model will raise an error and break. Otherwise, uses `optuna.TrialPruned` and silently continue the process. Defaults to False.

Returns:

Type	Description
`int \| float \| Tuple[int \| float, ...]`	The resolved metric as a single float or a tuple of floats. If the input is a DataFrame, Series, or list, it is flattened and converted to a tuple of floats.

Raises:

Type	Description
`optuna.TrialPruned`	If the metric contains `NaN` values, is empty, or is `None`, the trial is pruned.
`ValueError`	If the metric contains `NaN` values, is empty, or is `None`, and `debug=True`.

Examples:

>>> resolve_metric_output(1.5)
# 1.5
>>> resolve_metric_output([1.2, 3.4, 5.6])
# (1.2, 3.4, 5.6)
>>> resolve_metric_output(pd.Series([1.0, 2.0, np.nan]))
# optuna.TrialPruned  # Raises because of NaN values
>>> resolve_metric_output(np.nan, debug=True)

`BaseTracker`

BaseTracker(
    init_kwargs: Dict[str, Any] = _Nothing.NOTHING,
    log_kwargs: Dict[str, Any] = _Nothing.NOTHING,
    add_configs: Dict[str, Any] = _Nothing.NOTHING,
)

Abstract base class for trackers. Method generated by attrs for class BaseTracker.

Ancestors

abc.ABC

Descendants

systematica.tuners.neptune_ai.tracker.NeptuneOptunaTracker
systematica.tuners.sqlite.tracker.SQLiteOptunaTracker

Instance variables

tags: Set: Metadata tags.
add_configs: Dict[str, Any]: Additional config logs to store.
init_kwargs: Dict[str, Any]: Additional arguments.
log_kwargs: Dict[str, Any]: Additional logging arguments.

Methods

`run_context`

run_context(
    self,
    **kwargs,
) ‑> Any

Context manager to initialize run. Parameters:

Name	Type	Default	Description
`kwargs`	`tp.Kwargs`	`--`	Additional arguments.

Yields:

Type	Description
`tp.Any`	The initialized object.

`run_study`

run_study(
    self,
    objective: Callable,
    create_study_kwargs: Dict[str, Any],
    optimize_kwargs: Dict[str, Any],
) ‑> Any

Run study. Parameters:

Name	Type	Default	Description
`objective`	`tp.Callable`	`--`	The objective function to be optimized.
`create_study_kwargs`	`tp.Kwargs`	`--`	Keyword arguments for creating the study.
`optimize_kwargs`	`tp.Kwargs`	`--`	Keyword arguments for the optimization process.

Returns:

Type	Description
`tp.Any`	The study object containing optimization results.

`BaseCustomNeptuneCallback`

BaseCustomNeptuneCallback(
    run: neptune.metadata_containers.run.Run,
    **kwargs,
)

Abstract base class for custom Neptune.ai tracker callback. This class extends Neptune’s callback functionality to track Optuna trials and log them to Neptune.ai. Initialize the Neptune custom callback. Parameters:

Name	Type	Default	Description
`run`	`neptune.Run`	`--`	Neptune run instance for logging.
`kwargs`	`tp.Kwargs`	`--`	Additional keyword arguments passed to parent class.

Ancestors

abc.ABC
systematica.utils.neptune_ai.NeptuneCallback

Descendants

systematica.tuners.neptune_ai.callbacks.CustomCallback

`BaseTrialSelector`

BaseTrialSelector(
    name=None,
)

Abstract class for hyperparameter selection. This is the base class that defines the interface for all parameter selectors. Child classes must implement the run method. Method generated by attrs for class BaseTrialSelector.

Ancestors

abc.ABC

Descendants

systematica.tuners.trial_selectors.AUCMaximization
systematica.tuners.trial_selectors.BestMetricPoint
systematica.tuners.trial_selectors.ClosestIdealPoint
systematica.tuners.trial_selectors.DiversitySampling
systematica.tuners.trial_selectors.EfficientFrontierProjection
systematica.tuners.trial_selectors.ElbowPoint
systematica.tuners.trial_selectors.EpsilonConstraint
systematica.tuners.trial_selectors.HullExtremePoint
systematica.tuners.trial_selectors.HullMidPoint
systematica.tuners.trial_selectors.HullPoint
systematica.tuners.trial_selectors.HypervolumeContribution
systematica.tuners.trial_selectors.MaxMetricPoint
systematica.tuners.trial_selectors.MinMetricPoint
systematica.tuners.trial_selectors.Preference
systematica.tuners.trial_selectors.Random
systematica.tuners.trial_selectors.RegretMinimization
systematica.tuners.trial_selectors.RiskAwareUtility

Instance variables

name: Name of the selector, used for identification.

Methods

`run`

run(
    self,
    df: pandas.core.frame.DataFrame,
) ‑> pandas.core.series.Series | pandas.core.frame.DataFrame

Execute the selection algorithm. Parameters:

Name	Type	Default	Description
`df`	`pandas.DataFrame`	`--`	DataFrame containing parameters to select from.

Returns:

Type	Description
`pd.Series or pd.DataFrame`	The selected parameters or None if no parameters can be selected.

`BaseAnalyzer`

BaseAnalyzer(
    project: str,
    api_token: str = None,
)

A class for analyzing experiment. Abstract methods from BaseAnalyzer:

run_context
fetch_trials
fetch_feature_config

Method generated by attrs for class BaseAnalyzer.

Ancestors

abc.ABC

Descendants

systematica.tuners.base.BaseOptunaAnalyzer
systematica.tuners.base.BaseOptunaAnalyzer

Static methods

`combine_trials`

combine_trials(
    trials: Dict[str, Any],
    metrics: List[str] = None,
) ‑> pandas.core.frame.DataFrame

Combine trials into a pd.DataFrame object. Parameters:

Name	Type	Default	Description
`trials`	`tp.Kwargs`	`--`	Dictionary containing trial data.
`metric`	`tp.List[str]`	`None`	List of metrics to fall back to if values is not in the trials. If `None`, a single ‘metrics’ column with NaN is added when ‘values’ is missing. Defaults to `None`.

Returns:

Type	Description
`pd.DataFrame`	DataFrame containing combined trial data.

Instance variables

api_token: str: API token if needed, e.g. “your_api_token”.
project: str: Project name.

Methods

`run_context`

run_context(
    self,
    **kwargs,
) ‑> Any

Context manager to initialize run. Parameters:

Name	Type	Default	Description
`kwargs`	`tp.Kwargs`	`--`	Additional arguments.

Yields:

Type	Description
`tp.Any`	The initialized object.

`fetch_trials`

fetch_trials(
    self,
    metadata_tag: str,
    run_ids: Tuple[str],
    **kwargs,
) ‑> pandas.core.frame.DataFrame

Fetch metadata and return as a DataFrame. Parameters:

Name	Type	Default	Description
`metadata_tag`	`str`	`--`	The metadata field to fetch.
`run_ids`	`tp.Tuple[str]`	`--`	The ID(s) to fetch.
`kwargs`	`tp.Kwargs`	`--`	Other key-word arguments.

Returns:

Type	Description
`pd.DataFrame`	DataFrame containing the fetched metadata.

`fetch_feature_config`

fetch_feature_config(
    self,
    run,
    run_id: str,
) ‑> Dict[str, Any]

Fetch feature config from Neptune. Parameters:

Name	Type	Default	Description
`run`	`neptune.Run`	`--`	The ID of the Neptune run to fetch.
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Raises:

Type	Description
`SystematicaError`	No metadata found for run_id.

Returns:

Type	Description
`tp.Kwargs`	metadata params.

`get_trial_params`

get_trial_params(
    self,
    run_id: str,
) ‑> pandas.core.frame.DataFrame

Get trial parameters. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`pd.DataFrame`	`pd.DataFrame` with parameters as index and `run_ids` as columns.

`clear_cache`

clear_cache(
    self,
) ‑> None

Clear cache. Returns:

Type	Description
`None`

`get_best_metrics`

get_best_metrics(
    self,
    run_ids: str | List[str],
) ‑> pandas.core.frame.DataFrame

Get best metrics. Uses fetch_trials. Parameters:

Name	Type	Default	Description
`run_ids`	`str`	`--`	The ID(s) to fetch.

Returns:

Type	Description
`pd.DataFrame`	DataFrame with metrics as index and run_ids as columns.

`get_best_params`

get_best_params(
    self,
    run_ids: str | List[str],
) ‑> pandas.core.frame.DataFrame

Get best parameters. Uses fetch_trials. Parameters:

Name	Type	Default	Description
`run_ids`	`str`	`--`	The ID(s) to fetch.

Returns:

Type	Description
`pd.DataFrame`	DataFrame with parameters as index and run_ids as columns.

`get_best_trials`

get_best_trials(
    self,
    run_ids: str | List[str],
) ‑> pandas.core.frame.DataFrame

Get the best trials. Uses fetch_trials. Parameters:

Name	Type	Default	Description
`run_ids`	`str`	`--`	The ID(s) to fetch.

Returns:

Type	Description
`pd.DataFrame`	Trial data, including ‘run_id’ and ‘number’.

`get_all_trials`

get_all_trials(
    self,
    run_ids: str | List[str],
    **kwargs,
) ‑> pandas.core.frame.DataFrame

Get all trials. Uses fetch_trials. Parameters:

Name	Type	Default	Description
`run_ids`	`str`	`--`	The ID(s) to fetch.
`kwargs`	`tp.Kwargs`	`--`	Additional key-word arguments.

Returns:

Type	Description
`pd.DataFrame`	All trial data, including ‘run_id’ and ‘number’.

`get_metrics`

get_metrics(
    self,
    run_id: str,
) ‑> List[str]

List metric names. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`list of str`	List of metric names.

`get_params`

get_params(
    self,
    run_id: str,
) ‑> List[str]

List parameter names. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`list of str`	List of parameter names.

`get_all_params`

get_all_params(
    self,
    run_id: str,
) ‑> List[str]

List parameter names. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`list of str`	List of parameter names.

`get_all_combination`

get_all_combination(
    self,
    run_id: str,
) ‑> List[str]

Generate combinations of metric and parameter names. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`list of tuple`	List of (metric, param) tuples.

`get_param_combination`

get_param_combination(
    self,
    run_id: str,
) ‑> List[str]

Generate permutations of parameter names. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`list of tuple`	List of (metric, param) tuples.

`BaseOptunaObjective`

BaseOptunaObjective(
    validate_model: bool = True,
    debug: bool = True,
)

Base class for Optuna objectives. Method generated by attrs for class BaseOptunaObjective.

Ancestors

abc.ABC

Descendants

systematica.tuners.optuna_.objectives.ObjectiveFullHistory
systematica.tuners.optuna_.objectives.ObjectiveRollingWalkForward

Static methods

`optuna_optimize_kwargs`

optuna_optimize_kwargs(
    feature: ~Feature,
) ‑> Dict[str, Any]

Configure optimization parameters.

Instance variables

debug: bool: Flag to enable debug mode for detailed error messages.
validate_model: bool: Flag to validate model signals before running the backtest.

Methods

`compute`

compute(
    model,
    search: dict,
    **feature,
) ‑> int | float | Tuple[int | float, ...]

Compute the objective value. Parameters:

Name	Type	Default	Description
`model`	`tp.Any`	`--`	The model to be evaluated.
`search`	`dict`	`--`	The hyperparameter search space. **feature : tp.Kwargs Additional feature configuration parameters.

Returns:

Type	Description
`int \| float \| tp.Tuple[int \| float, ...]`	The computed objective value.

`get_objective`

get_objective(
    self,
    model,
    feature: ~Feature,
    search_space: dict,
) ‑> Callable

Create a callable for the Optuna trial. Parameters:

Name	Type	Default	Description
`model`	`tp.Any`	`--`	The model to be evaluated.
`feature`	`FeatureT`	`--`	The feature configuration.
`search_space`	`dict`	`--`	The hyperparameter search space.

Returns:

Type	Description
`tp.Callable`	The Optuna trial function.

`run_study`

run_study(
    self,
    model: Any,
    feature: ~Feature,
    search_space: Dict[str, Any],
    callbacks: Iterable = None,
) ‑> optuna.study.study.Study

Run optuna study or tracker. Parameters:

Name	Type	Default	Description
`model`	`tp.Any`	`--`	The model to be evaluated.
`feature`	`FeatureT`	`--`	The feature configuration.
`search_space`	`dict`	`--`	The hyperparameter search space.
`callbacks`	`tp.Iterable`	`None`	List of callback functions that are invoked at the end of each trial. Each function must accept two parameters with the following types in this order: Study and FrozenTrial.

Returns:

Type	Description
`study : optuna.Study`	Optuna Study.

`BaseOptunaAnalyzer`

BaseOptunaAnalyzer(
    project: str,
    api_token: str = None,
)

A class for analyzing optuna experiment. Abstract methods from BaseAnalyzer:

run_context
fetch_trials
fetch_feature_config

Abstract methods from BaseOptunaAnalyzer:

fetch_optuna_study

Method generated by attrs for class BaseOptunaAnalyzer.

Ancestors

systematica.tuners.base.BaseAnalyzer
abc.ABC

Descendants

systematica.tuners.neptune_ai.analyzer.NeptuneOptunaAnalyzer
systematica.tuners.neptune_ai.analyzer.NeptuneOptunaAnalyzer
systematica.tuners.sqlite.analyzer.SQLiteOptunaAnalyzer
systematica.tuners.sqlite.analyzer.SQLiteOptunaAnalyzer

Methods

`fetch_optuna_study`

fetch_optuna_study(
    self,
    run_id: str,
) ‑> optuna.study.study.Study

Loads Optuna study. Loading mechanics depend on the study storage type used during the run. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`optuna.Study`	The Optuna study object.

`get_optuna_trials`

get_optuna_trials(
    self,
    run_id: str,
) ‑> pandas.core.frame.DataFrame

Export optuna trials as a pandas DataFrame. The DataFrame provides various features to analyze studies. It is also useful to draw a histogram of objective values and to export trials as a CSV file. If there are no trials, an empty pd.DataFrame is returned. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`pd.DataFrame`	The Optuna study object.

`get_optuna_best_trials`

get_optuna_best_trials(
    self,
    run_id: str,
) ‑> pandas.core.frame.DataFrame

Export optuna best trials as a pandas DataFrame. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID to fetch.

Returns:

Type	Description
`pd.DataFrame`	The Optuna study object.

`stats`

stats(
    self,
    run_id: str,
) ‑> pandas.core.series.Series

Compute statistics from a Neptune run. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`pd.Series`	Placeholder return value.

`run_composer`

run_composer(
    self,
    run_id: str,
    trial_selector: str | int | systematica.tuners.base.BaseTrialSelector = None,
    loader: vectorbtpro.data.base.Data | Callable = None,
    loader_kwargs: Dict[str, Any] = None,
    column_stack: bool = False,
    group_by: bool = True,
) ‑> systematica.portfolio.analyzer.PortfolioAnalyzer

Run portfolio analytics from run_id. New generation logging is supported. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`trial_selector`	`str`	`None`	Custom parameter selection. If None, retrieve `best/params` from neptune. if int, retrieve trial number. If `BaseTrialSelector`, retrieve params based on algorithm. Defaults to None.
`loader`	`vbt.Data`	`None`	Data loader instance. If None, defaults to `load_clean_data`. Defaults to None.
`loader_kwargs`	`tp.Kwargs`	`None`	Additional loader parameters. Note that timeframe, start and end are already fetched from the neptune pipeline field. Defaults to None.
`column_stack`	`bool`	`False`	Compute `vbt.PF.column_stack` to combine portfolio when several trial numbers are passed. Default to False.
`group_by`	`bool`	`True`	Group strategy when `column_stack` is True. If False, compute individual backtests. Combined all strategy into a single run otherwise. Defaults to True.

Returns:

Type	Description
`PortfolioAnalyzer`	Class with inherited `PortfolioAnalizer`.

`plot_pareto_front`

plot_pareto_front(
    self,
    run_id: str,
    metrics: List[str] = None,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot pareto front. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`metrics`	`tp.List[str]`	`None`	Metrics to display. If `None`, All metrics are displayed. Defaults to `None`.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_contour`

plot_contour(
    self,
    run_id: str,
    params: List[str],
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the parameter relationship as contour plot in a study. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`params`	`tp.List[str]`	`--`	Parameter list to visualize.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_param_importances`

plot_param_importances(
    self,
    run_id: str,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot parameter importance.

An importance evaluator object that specifies which algorithm to base the importance assessment on, defaults to FanovaImportanceEvaluator.

Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_edf`

plot_edf(
    self,
    run_id: str,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the objective value EDF (empirical distribution function) of a study. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_optimization_history`

plot_optimization_history(
    self,
    run_id: str,
    error_bar: bool = False,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot optimization history of all trials in a study. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`error_bar`	`bool`	`False`	A flag to show the error bar.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_parallel_coordinate`

plot_parallel_coordinate(
    self,
    run_id: str,
    metric: str,
    params: List[str] = None,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the high-dimensional parameter relationships in a study. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`metric`	`str`	`--`	Target metric.
`params`	`tp.List[str]`	`None`	Parameter list to visualize. if None, the default is all parameters.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_rank`

plot_rank(
    self,
    run_id: str,
    params: List[str] = None,
    n_columns: int = 4,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot parameter relations as scatter plots with colors indicating ranks of target value.

Trials missing the specified parameters will not be plotted.

Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`params`	`tp.List[str]`	`None`	Parameter list to visualize. if None, the default is all parameters.
`n_columns`	`int`	`4`	When n_params is more than 3, the number of columns in the grid. The total number of subplots will be split into `n_columns` columns. layout_kwargs, tp.Kwargs Additional layout parameters.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_slice`

plot_slice(
    self,
    run_id: str,
    params: List[str] = None,
    n_columns: int = 4,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the parameter relationship as a slice plot in a study. Trials missing the specified parameters will not be plotted.

The slice plot is useful for visualizing the relationship between hyperparameters and the objective function values.It can help you:

Identify Parameter Sensitivity: Shows which hyperparameters have a strong influence on performance.
Detect Bad Regions: Highlights ranges where performance is consistently poor.
Spot Non-linear Patterns: You might notice trends.
Debug Optimization: If you see no pattern or strange clumping, it might indicate:
- A bad search space.
- Problems with the objective function.
Guide Future Searches: Helps you refine the search space for better performance in future runs.

Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`params`	`tp.List[str]`	`None`	Parameter list to visualize. If None, the default is all parameters.
`n_columns`	`int`	`4`	When n_params is more than 3, the number of columns in the grid. The total number of subplots will be split into `n_columns` columns.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout parameters.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_hypervolume_history`

plot_hypervolume_history(
    self,
    run_id: str,
    reference_point: Sequence[float] = None,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot hypervolume history of all trials in a study. This function is only applicable for multi-objective optimization studies. It computes the hypervolume of the Pareto front at each trial and plots the hypervolume history. The hypervolume is a measure of the volume of the dominated region in the objective space, defined by the reference point. The higher the hypervolume, the better the Pareto front.

Study must be multi-objective. For single-objective optimization, please use plot_optimization_history instead.

Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`reference_point`	`tp.Sequence[float]`	`None`	A reference point to use for hypervolume computation. The dimension of the reference point must be the same as the number of objectives. If None, takes the min of each metrics to ensure the reference point is lower than all Pareto solutions and defines the dominated region and create measurable volume. Important note, the default value implied all objectives are minimized. Optuna transforms the objective values and the reference point using `study.directions`. Defaults to `None`.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_timeline`

plot_timeline(
    self,
    run_id: str,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the timeline of a study. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_intermediate_values`

plot_intermediate_values(
    self,
    run_id: str,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot intermediate values of all trials in a study. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_terminator_improvement`

plot_terminator_improvement(
    self,
    run_id: str,
    plot_error: bool = False,
    min_n_trials: int = 20,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot the potentials for future objective improvement. This function does not support multi-objective optimization study.

This function visualizes the objective improvement potentials, evaluated with improvement_evaluator. It helps to determine whether we should continue the optimization or not. You can also plot the error evaluated with error_evaluator if the plot_error argument is set to True. Note that this function may take some time to compute the improvement potentials. The improvement_evaluator defaults to RegretBoundEvaluator and the error_evaluator to CrossValidationErrorEvaluator.

Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`plot_error`	`bool`	`False`	A flag to show the error. If it is set to `True`, errors evaluated by error_evaluator are also plotted as line graph. Defaults to `False`.
`min_n_trials`	`int`	`20`	The minimum number of trials before termination is considered. Terminator improvements for trials below this value are shown in a lighter color. Defaults to `20`.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_params_correlation`

plot_params_correlation(
    self,
    run_id,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot parameters correlation matrix. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_metrics_correlation`

plot_metrics_correlation(
    self,
    run_id,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot metrics correlation matrix. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_metrics`

plot_metrics(
    self,
    run_id: str,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot a histogram matrix of metrics for a Neptune run. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout arguments for the plot.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_params`

plot_params(
    self,
    run_id: str,
    n_columns: int = 3,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot a scatter matrix of parameters for a Neptune run. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`n_columns`	`int`	`3`	Number of columns in the scatter matrix. Defaults to 3.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout arguments for the plot.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_density_heatmap`

plot_density_heatmap(
    self,
    run_id: str,
    metric: str,
    param: str,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot a density heatmap for a metric and parameter. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`metric`	`str`	`--`	The metric to plot.
`param`	`str`	`--`	The parameter to plot.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional arguments for the plot.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_density_contour`

plot_density_contour(
    self,
    run_id: str,
    metric: str,
    param: str,
    colorscale: List[str | Tuple[float]] = None,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot a density contour for a metric and parameter. Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`metric`	`str`	`--`	The metric to plot.
`param`	`str`	`--`	The parameter to plot.
`colorscale`	`list`	`--`	The colorscale for the plot.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional arguments for the plot.

Returns:

Type	Description
`vbt.FigureWidget`	The generated plot.

`plot_frontier`

plot_frontier(
    self,
    run_id: str,
    x: str,
    y: str,
    list_trial_selectors: List[systematica.tuners.base.BaseTrialSelector] = None,
    remove_negative_values: bool = False,
    **layout_kwargs,
) ‑> vectorbtpro.utils.figure.FigureWidget

Plot an efficient frontier.

This function plots the efficient frontier of a study, which is a graphical representation of the trade-off between risk and return. The efficient frontier is a curve that shows the optimal risk-return combinations for a given set of trials. The points on the curve represent the best possible trade-offs between risk and return, while points below the curve are suboptimal:

x is generally a risk metric (e.g., volatility), while y is a performance measure (e.g., returns).
The risk-adjusted optimized point is calculated as: risk_adj = (returns - risk_free_rate) / risks

Parameters:

Name	Type	Default	Description
`run_id`	`str`	`--`	The ID of the Neptune run to fetch.
`x`	`str`	`--`	X axis. Must be a metric from neptune run.
`y`	`str`	`--`	Y axis. Must be a metric from neptune run.
`list_trial_selectors`	`tp.List[BaseTrialSelector]`	`None`	Trial selector points added to the charts. Defaults to `None`.
`risk_free_rate`	`float`	`0.0`	Risk-free rate for volatility calculation. Defaults to 0.0.
`layout_kwargs`	`tp.Kwargs`	`--`	Additional layout parameters.

Returns:

Type	Description
`vbt.FigureWidget`	The generated efficient frontier plot with CML.

Overview

Neptune Ai

Optuna

Sqlite

​resolve_optuna_suggest

​resolve_optuna_metric_output

​BaseTracker

​Ancestors

​Descendants

​Instance variables

​Methods

​run_context

​run_study

​BaseCustomNeptuneCallback

​Ancestors

​Descendants

​BaseTrialSelector

​Ancestors

​Descendants

​Instance variables

​Methods

​run

​BaseAnalyzer

​Ancestors

​Descendants

​Static methods

​combine_trials

​Instance variables

​Methods

​run_context

​fetch_trials

​fetch_feature_config

​get_trial_params

​clear_cache

​get_best_metrics

​get_best_params

​get_best_trials

​get_all_trials

​get_metrics

​get_params

​get_all_params

​get_all_combination

​get_param_combination

​BaseOptunaObjective

​Ancestors

​Descendants

​Static methods

​optuna_optimize_kwargs

​Instance variables

​Methods

​compute

​get_objective

​run_study

​BaseOptunaAnalyzer

​Ancestors

​Descendants

​Methods

​fetch_optuna_study

​get_optuna_trials

​get_optuna_best_trials

​stats

​run_composer

​plot_pareto_front

​plot_contour

​plot_param_importances

​plot_edf

​plot_optimization_history

​plot_parallel_coordinate

​plot_rank

​plot_slice

​plot_hypervolume_history

​plot_timeline

​plot_intermediate_values

​plot_terminator_improvement

​plot_params_correlation

​plot_metrics_correlation

​plot_metrics

​plot_params

​plot_density_heatmap

​plot_density_contour

`resolve_optuna_suggest`

`resolve_optuna_metric_output`

`BaseTracker`

Ancestors

Descendants

Instance variables

Methods

`run_context`

`run_study`

`BaseCustomNeptuneCallback`

Ancestors

Descendants

`BaseTrialSelector`

Ancestors

Descendants

Instance variables

Methods

`run`

`BaseAnalyzer`

Ancestors

Descendants

Static methods

`combine_trials`

Instance variables

Methods

`run_context`

`fetch_trials`

`fetch_feature_config`

`get_trial_params`

`clear_cache`

`get_best_metrics`

`get_best_params`

`get_best_trials`

`get_all_trials`

`get_metrics`

`get_params`

`get_all_params`

`get_all_combination`

`get_param_combination`

`BaseOptunaObjective`

Ancestors

Descendants

Static methods

`optuna_optimize_kwargs`

Instance variables

Methods

`compute`

`get_objective`

`run_study`

`BaseOptunaAnalyzer`

Ancestors

Descendants

Methods

`fetch_optuna_study`

`get_optuna_trials`

`get_optuna_best_trials`

`stats`

`run_composer`

`plot_pareto_front`

`plot_contour`

`plot_param_importances`

`plot_edf`

`plot_optimization_history`

`plot_parallel_coordinate`

`plot_rank`

`plot_slice`

`plot_hypervolume_history`

`plot_timeline`

`plot_intermediate_values`

`plot_terminator_improvement`

`plot_params_correlation`

`plot_metrics_correlation`

`plot_metrics`

`plot_params`

`plot_density_heatmap`

`plot_density_contour`

`plot_frontier`