scikit-survival 0.22.0 released

I am pleased to announce the release of scikit-survival 0.22.0. The highlights for this release include

• Compatibility with scikit-learn 1.3.
• Missing value support for SurvivalTree.
• A reduced memory mode for RandomSurvivalForest, ExtraSurvivalTrees, and SurvivalTree.
• Support for predict_cumulative_hazard_function() and predict_survival_function() in Stacking.

Missing Values Support in SurvivalTree

Based on the missing value support in scikit-learn 1.3, a SurvivalTree can now deal with missing values if it is fit with splitter='best'.

If the training data contained no missing values, then during prediction missing values are mapped to the child node with the most samples:

X, y = load_veterans_lung_cancer()
X_train = np.asarray(X.loc[:, ["Karnofsky_score"]], dtype=np.float32)

est = SurvivalTree(max_depth=1)
est.fit(X_train, y)

X_test = np.array([[np.nan]])
surv_fn = est.predict_survival_function(X_test, return_array=True)

mask = X_train[:, 0] > est.tree_.threshold[0]
km_x, km_y = kaplan_meier_estimator(
  y[mask]["Status"], y[mask]["Survival_in_days"]
)

plt.step(km_x, km_y, where="post", linewidth=5)
plt.step(
  est.unique_times_, surv_fn[0], where="post", linewidth=3, linestyle="dotted"
)
plt.ylim(0, 1)

If a tree is fit to training data with missing values, the splitter will evaluate each split with all samples with missing values going to the left or the right child node.

X, y = load_veterans_lung_cancer()
X_train = np.asarray(X.loc[:, ["Age_in_years"]], dtype=np.float32)
X_train[-50:, :] = np.nan

est = SurvivalTree(max_depth=1)
est.fit(X_train, y)

X_test = np.array([[np.nan]])
surv_fn = est.predict_survival_function(X_test, return_array=True)

mask = X_train[:, 0] > est.tree_.threshold[0]
mask |= np.isnan(X_train[:, 0])
km_x, km_y = kaplan_meier_estimator(
  y[mask]["Status"], y[mask]["Survival_in_days"]
)

plt.step(km_x, km_y, where="post", linewidth=5)
plt.step(
  est.unique_times_, surv_fn[0], where="post", linewidth=3, linestyle="dotted"
)
plt.ylim(0, 1)

These rules are identical to those of scikit-learn’s missing value support.

Low-memory Mode for SurvivalTree and RandomSurvivalForest

The last release already saw performance improvments to SurvivalTree and RandomSurvivalForest. This release adds the low_memory option to RandomSurvivalForest, ExtraSurvivalTrees, and SurvivalTree.

If low-memory mode is disabled, which is the default, calling predict on a sample will require memory in the order of unique event times in the training data, because the cumulative hazard function is computed as an intermediate value. If low-memory mode is enabled, then the risk score is computed directly, without computing the cumulative hazard function. However, low-memory mode disables using predict_cumulative_hazard_function and predict_survival_function.

Install

Pre-built conda packages are available for Linux, macOS, and Windows, either

via pip:

pip install scikit-survival

or via conda

 conda install -c sebp scikit-survival