Bug Fix

examples/rsf_brier_calibration.py

@@ -0,0 +1,48 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Mar 20 00:15:45 2020
+ Example using brier-score and  calibration plot with random survival forrest
+@author: Fabian
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import OrdinalEncoder
+
+from sksurv.datasets import load_gbsg2
+from sksurv.preprocessing import OneHotEncoder
+from sksurv.ensemble import RandomSurvivalForest
+
+from sksurv.metrics import brier_score, integrated_brier_score, calibration_curve
+
+if __name__ == '__main__':
+
+    X, y = load_gbsg2()
+
+    grade_str = X.loc[:, "tgrade"].astype(object).values[:, np.newaxis]
+    grade_num = OrdinalEncoder(categories=[["'I'", "'II'", "'III'"]]).fit_transform(grade_str)
+
+    X_no_grade = X.drop("tgrade", axis=1)
+    Xt = OneHotEncoder().fit_transform(X_no_grade)
+    Xt = np.column_stack((Xt.values, grade_num))
+
+    feature_names = X_no_grade.columns.tolist() + ["tgrade"]
+
+    random_state = 20
+
+    X_train, X_test, y_train, y_test = train_test_split(
+    Xt, y, test_size=0.25, random_state=random_state)
+
+    rsf = RandomSurvivalForest(n_estimators=1000,
+                           min_samples_split=10,
+                           min_samples_leaf=15,
+                           max_features="sqrt",
+                           n_jobs=-1,
+                           random_state=random_state)
+    rsf.fit(X_train, y_train)
+
+    SurvivalFunction = rsf.predict_survival_function(X_test)
+    bs=brier_score(y_train,y_test,SurvivalFunction,rsf.event_times_)

sksurv/metrics.py

@@ -765,7 +765,7 @@ def brier_score(survival_train,survival_test, estimate, times,
         is_control = (T > t)
 
         # get survival function S(t) by interpolating the Survival function
-        S=numpy.interpolate(Survival,times,t)
+        S=numpy.interp(Survival,times,t)
         S2=numpy.multiply(S,S)
         omS2=numpy.multiply(1 - S,1 - S)
 
@@ -972,7 +972,7 @@ def calibration_curve(survival_train,survival_test, estimate, times,
         test_time_traintest=test_time
         test_event_traintest=test_event
     # interpolate predicted survival at fu_time.
-    pred_surv = numpy.interpolate(estimate, times, fu_time)
+    pred_surv = numpy.interp(estimate, times, fu_time)
 
     # sort by pred_surv in ascending order
     order=numpy.argsort(pred_surv)