def _get_npi(times):
X, y = load_gbsg2()
grade = X.loc[:, "tgrade"].map({"I": 1, "II": 2, "III": 3}).astype(int)
NPI = 0.2 * X.loc[:, "tsize"] / 10 + 1 + grade
NPI[NPI < 3.4] = 1.0
NPI[(NPI >= 3.4) & (NPI <= 5.4)] = 2.0
NPI[NPI > 5.4] = 3.0
preds = numpy.empty((X.shape[0], len(times)), dtype=float)
for j, ts in enumerate(times):
survs = {}
for i in NPI.unique():
idx = numpy.flatnonzero(NPI == i)
yi = y[idx]
t, s = kaplan_meier_estimator(yi["cens"], yi["time"])
if t[-1] < ts and s[-1] == 0.0:
survs[i] = 0.0
else:
fn = StepFunction(t, s)
survs[i] = fn(ts)
preds[:, j] = NPI.map(survs).values
return preds, y
def test_brier_coxph():
X, y = load_gbsg2()
X.loc[:, "tgrade"] = X.loc[:, "tgrade"].map(len).astype(int)
Xt = OneHotEncoder().fit_transform(X)
est = CoxPHSurvivalAnalysis(ties="efron").fit(Xt, y)
survs = est.predict_survival_function(Xt)
preds = [fn(1825) for fn in survs]
_, score = brier_score(y, y, preds, 1825)
assert round(abs(score[0] - 0.208817407492645), 5) == 0
def gbsg2():
x, y = load_gbsg2()
x = encode_categorical(x)
return x.values, y
def test_load_gbsg2():
x, y = sdata.load_gbsg2()
assert x.shape == (686, 8)
assert y.shape == (686,)
assert_structured_array_dtype(y, 'cens', 'time', 299)
@author: petch
"""
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
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,
def setUp(self):
x, y = load_gbsg2()
self.x = encode_categorical(x)
self.y = y
