`rlberry.manager`.plot_smoothed_curves¶

rlberry.manager.plot_smoothed_curves(data, x, y, smoothing_bandwidth=None, ax=None, error_representation='ci', n_boot=2500, level=0.9, show=True, savefig_fname=None, linestyles=False)[source]¶

Plot the performances contained in the data (see data parameter to learn what format it should be).

If there are several simulations, a confidence interval is plotted.

In all cases a smoothing is performed.

Parameters:

data: a pandas dataframe

data must contain the columns “name”, “n_simu”, an x column and a y column.

“n_simu” contain the simulation number (e.g. the seed) for which the raw is computed (beginning at 0 until the total number of seeds).
“name” is the name of the algorithm for which the raw is computed.
x column is named according to x parameter and contain values to have in x axis.
y column is named according to y parameter and contain values to have in y axis.

smoothing_bandwidth: float or array of floats or None

How to choose the bandwidth parameter. If float, then smoothing_bandwidth is used directly as a bandwidth and if is an array, a parameter search using smoothing_bandwidth is used if None, a parameter search from a range of 20 possible values choosen by heuristics is performed.

ax: matplotlib axis or None, default=None

Matplotlib axis on which we plot. If None, create one. Can be used to customize the plot.

error_representation: str in {“cb”, “raw_curves”, “ci”, “pi”}

How to represent multiple simulations. The “ci” and “pi” do not take into account the need for simultaneous inference, it is then harder to draw conclusion from them than with “cb” but they are the most widely used.

“cb” is a confidence band on the mean curve using functional data analysis (band in which the mean curve is with probability larger than 1-level). Method from [1], using scikit-fda [2] library.
“raw curves” is a plot of the raw curves.
“pi” is a plot of a non-simultaneous prediction interval with gaussian model around the mean smoothed curve (e.g. we do curve plus/minus gaussian quantile times std).
“ci” is a confidence interval with gaussian model around the mean smoothed curve (e.g. we do curve plus/minus gaussian quantile times std divided by sqrt of number of seeds).

n_boot: int, default=2500,

Number of bootstrap evaluations used for confidence interval estimation. Only used if error_representation = “ci”.

level: float, default=0.95,

Level of the confidence (or prediction) interval. Only used if error_representation is not “raw_curves”.

show: bool, default=True

If true, calls plt.show().

savefig_fname: str (Optional)

Name of the figure in which the plot is saved with figure.savefig. If None, the figure is not saved.

linestyles: boolean, default=False

Whether to use different linestyles for each curve.

Examples

>>> import pandas as pd
>>> from rlberry.manager import plot_smoothed_curve
>>>  df = pd.DataFrame(
    {"name": ["a", "a", "a"], "x": [1, 2, 3], "y": [3, 4, 5], "n_simu": [0, 0, 0]}
)
>>> plot_smoothed_curve(df, "x", "y")

rlberry.manager.plot_smoothed_curves¶

`rlberry.manager`.plot_smoothed_curves¶