Plotting analysis#

class ablator.analysis.plot.main.PlotAnalysis(results: DataFrame | Results, categorical_attributes: list[str] | None = None, numerical_attributes: list[str] | None = None, optim_metrics: dict[str, ablator.config.mp.Optim] | None = None, save_dir: str | None = None, cache=False)[source]

Bases: Analysis

Class for plotting experiment results. You can use this class and Results class to visualize the relationship between any hyperparameter that you run ablation study on with the result metrics. This valuable insight offers an intuitive understanding of how these parameters may influence your model’s performance.

Plots supported are linear plots for numerical data and violin plots for categorical data.

Examples

  • Data frame to be used:

>>> df2 = pd.DataFrame({'val_accuracy': np.random.uniform(0.8,0.9,10),
...       'train_config.optimizer_config.arguments.lr': np.random.uniform(0.001, 0.1,10),
...       "index": range(10),
...       "path": range(10)})

  • Creating dictionaries that map the configuration parameters [categorical + numerical] to custom labels for plots:

>>> numerical_name_remap = {
...             "train_config.optimizer_config.arguments.lr": "Learning Rate",
...         }
...     categorical_name_remap = {}
...     attribute_name_remap = {**categorical_name_remap, **numerical_name_remap}

  • Initalize the PlotAnalysis and plot the figures:

>>> analysis = PlotAnalysis(
...     df,
...     save_dir="./plots",
...     cache=True,
...     optim_metrics={"val_accuracy": Optim.max},
...     numerical_attributes=list(numerical_name_remap.keys()),
...     categorical_attributes=list(categorical_name_remap.keys()),
... )
>>> analysis.make_figures(
...    metric_name_remap={
...        "val_accuracy": "Validation Accuracy",
...    },
...    attribute_name_remap= attribute_name_remap
... )

The directory "plots" contains all the plots of the HPO experiments