Add unit test for yli.kaplanmeier

tests/test_kaplanmeier.py

@@ -0,0 +1,58 @@
+#   scipy-yli: Helpful SciPy utilities and recipes
+#   Copyright © 2022–2023  Lee Yingtong Li (RunasSudo)
+#
+#   This program is free software: you can redistribute it and/or modify
+#   it under the terms of the GNU Affero General Public License as published by
+#   the Free Software Foundation, either version 3 of the License, or
+#   (at your option) any later version.
+#
+#   This program is distributed in the hope that it will be useful,
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#   GNU Affero General Public License for more details.
+#
+#   You should have received a copy of the GNU Affero General Public License
+#   along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+from pytest import approx
+
+import pandas as pd
+
+import yli
+
+def test_kaplanmeier_simple():
+	"""Compare yli.kaplanmeier for simple example"""
+	
+	df = pd.DataFrame({
+		'SurvTime': [2, 4, 6, 8],
+		'Status': [True, True, True, True]
+	})
+	
+	xpoints, ypoints, _, _ = yli.survival.calc_survfunc_kaplanmeier(df['SurvTime'], df['Status'], False)
+	
+	assert xpoints[0] == 0
+	assert ypoints[0] == 1
+	
+	assert xpoints[1] == 2
+	assert ypoints[1] == 1
+	
+	assert xpoints[2] == 2
+	assert ypoints[2] == 0.75
+	
+	assert xpoints[3] == 4
+	assert ypoints[3] == 0.75
+	
+	assert xpoints[4] == 4
+	assert ypoints[4] == approx(0.5)
+	
+	assert xpoints[5] == 6
+	assert ypoints[5] == approx(0.5)
+	
+	assert xpoints[6] == 6
+	assert ypoints[6] == approx(0.25)
+	
+	assert xpoints[7] == 8
+	assert ypoints[7] == approx(0.25)
+	
+	assert xpoints[8] == 8
+	assert ypoints[8] == 0

yli/survival.py

@@ -87,6 +87,16 @@ def kaplanmeier(df, time, status, by=None, *, ci=True, transform_x=None, transfo
 	return fig, ax
 
 def plot_survfunc_kaplanmeier(ax, time, status, ci, transform_x=None, transform_y=None):
+	xpoints, ypoints, ypoints0, ypoints1 = calc_survfunc_kaplanmeier(time, status, ci, transform_x, transform_y)
+	
+	handle = ax.plot(xpoints, ypoints)[0]
+	
+	if ci:
+		ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
+	
+	return handle
+
+def calc_survfunc_kaplanmeier(time, status, ci, transform_x=None, transform_y=None):
 	# Estimate the survival function
 	sf = sm.SurvfuncRight(time, status)
 	
@@ -94,7 +104,6 @@ def plot_survfunc_kaplanmeier(ax, time, status, ci, transform_x=None, transform_
 	# np.concatenate(...) to force starting drawing from time 0, survival 100%
 	xpoints = np.concatenate([[0], sf.surv_times]).repeat(2)[1:]
 	ypoints = np.concatenate([[1], sf.surv_prob]).repeat(2)[:-1]
-	handle = ax.plot(xpoints, ypoints)[0]
 	
 	if transform_x:
 		xpoints = transform_x(xpoints)
@@ -116,9 +125,9 @@ def plot_survfunc_kaplanmeier(ax, time, status, ci, transform_x=None, transform_
 			ypoints0 = transform_y(ypoints0)
 			ypoints1 = transform_y(ypoints1)
 		
-		ax.fill_between(xpoints, ypoints0, ypoints1, alpha=0.3, label='_')
+		return xpoints, ypoints, ypoints0, ypoints1
 	
-	return handle
+	return xpoints, ypoints, None, None
 
 def turnbull(df, time_left, time_right, by=None, *, step_loc=0.5, transform_x=None, transform_y=None, nan_policy='warn'):
 	"""