Source code for secml.figure._plots.c_plot_sec_eval

.. module:: CPlotSecEval
   :synopsis: Classifier Security Evaluation plots.

.. moduleauthor:: Marco Melis <>
.. moduleauthor:: Ambra Demontis <>

from secml.figure._plots import CPlot
from import CMetric
from secml.array import CArray

def _cmpt_sec_eval_curve(sec_eval_data, metric, label=1):
    """Compute metric for each security evaluation parameter.

    sec_eval_data : CSecEvalData
        Security Evaluation data object.
    metric : CMetric
        Metric object.
    label : int, optional
        Label wrt the metric should be computed. Default 1.

    perf = CArray.zeros(shape=(sec_eval_data.param_values.size,))
    for k in range(sec_eval_data.param_values.size):

        scores = sec_eval_data.scores[k][:, label].ravel()
        y_pred = sec_eval_data.Y_pred[k].ravel()

        metric_val = metric.performance_score(
            y_true=sec_eval_data.Y, y_pred=y_pred, score=scores)

        perf[k] = metric_val

    return perf

[docs]class CPlotSecEval(CPlot): """Plots Classifier Security Evaluation results. This class creates a figure plotting in a fancy and standard style data from `.CSecEvalData` class. Custom plotting parameters can be specified. Currently parameters default: - `show_legend`: True. Set False to hide `show_legend` on next plot. - grid: True. See Also -------- .CPlot : basic subplot functions. .CFigure : creates and handle figures. """
[docs] def apply_params_sec_eval(self): """Apply defined parameters to active subplot.""" fig_legend = self.get_legend() if self.show_legend is not False and fig_legend is not None: fig_legend.set_visible(True) self.grid(grid_on=True)
[docs] def plot_sec_eval(self, sec_eval_data, metric='accuracy', mean=False, percentage=False, show_average=False, label=None, linestyle='-', color=None, marker=None, metric_args=()): """Plot the Security Evaluation Curve using desired metric. Parameters ---------- sec_eval_data : CSecEvalData or list A single CSecEvalData object or a list with multiple repetitions. metric : str or CMetric, optional Metric to be evaluated. Default 'accuracy'. mean : bool, optional If True, the mean of all sec eval repetitions will be computed. Default False.. percentage : bool, optional If True, values will be displayed in percentage. Default False. show_average : bool, optional If True, the average along the sec eval parameters will be shown in legend. Default False. label : str, optional Label of the sec eval curve. Default None. linestyle : str, optional Style of the curve. Default '-'. color : str or None, optional Color of the curve. If None (default) the plot engine will chose. marker : str or None, optional Style of the markers. Default None. metric_args Any other argument for the metric. """ metric = CMetric.create(metric, *metric_args) if not isinstance(sec_eval_data, list): sec_eval_data = [sec_eval_data] n_sec_eval = len(sec_eval_data) n_param_val = sec_eval_data[0].param_values.size perf = CArray.zeros((n_sec_eval, n_param_val)) for i in range(n_sec_eval): if sec_eval_data[i].param_values.size != n_param_val: raise ValueError("the number of sec eval parameters changed!") perf[i, :] = _cmpt_sec_eval_curve(sec_eval_data[i], metric) if mean is True: perf_std = perf.std(axis=0, keepdims=False) perf = perf.mean(axis=0, keepdims=False) else: if len(sec_eval_data) > 1: raise ValueError("if `mean` is False, " "only one sec eval data should be passed") perf = perf.ravel() if percentage is True: perf *= 100 if mean is True: perf_std *= 100 if show_average is True: auc_val = perf.mean() if label is None: label = "err: {:.2f}".format(auc_val) else: label += ", err: {:.2f}".format(auc_val) # This is done here to make 'markevery' work correctly self.xticks(sec_eval_data[0].param_values) self.plot(sec_eval_data[0].param_values, perf, label=label, linestyle=linestyle, color=color, marker=marker, markevery=self.get_xticks_idx(sec_eval_data[0].param_values)) if mean is True: std_up = perf + perf_std std_down = perf - perf_std std_down[std_down < 0.0] = 0.0 if percentage is True: std_up[std_up > 100] = 100 else: std_up[std_up > 1.0] = 1.0 self.fill_between(sec_eval_data[0].param_values, std_up, std_down, interpolate=False, alpha=0.2, facecolor=color, linestyle='None') if self._xlabel is None: self.xlabel(sec_eval_data[0].param_name) if self._ylabel is None: self.ylabel(metric.class_type.capitalize()) self.legend(loc='best', labelspacing=0.4, handletextpad=0.3, edgecolor='k') self.title("Security Evaluation Curve") self.apply_params_sec_eval()