Classification and survival

The classification metrics come from scikit-learn and the survival estimate from lifelines; depictr redraws both under the shared theme. The survival figure adds a number-at-risk table beneath the curves in one call.

import numpy as np

import depictr as dp

ct = dp.clinical_trial()
score = 1 / (1 + np.exp(-ct["biomarker"]))  # a probability-like score

An ROC curve with the area under the curve.

p = dp.roc_curve_plot(ct["adverse_event"], score)
p

A calibration (reliability) curve.

p = dp.calibration_plot(ct["adverse_event"], score)
p

A confusion-matrix heatmap, normalised by the true class.

p = dp.confusion_matrix_plot(ct["adverse_event"], (score > 0.12).astype(int),
                             normalise="true")
p

Kaplan-Meier curves with a log-rank test and a number-at-risk table. The legend sits in the empty top-right the descending curves leave behind.

p = dp.survival_plot(ct["time"], ct["event"], group=ct["arm"],
                     risk_table=True, legend_inside=True, title="Survival by arm")
p