test: tests for efron loss with edge cases

survhive/tests/test_efron.py

@@ -0,0 +1,91 @@
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+import pytest
+from scipy.optimize import check_grad
+from test_data_gen_final import numpy_test_data_1d, numpy_test_data_2d
+
+from survhive.loss import efron_negative_likelihood
+
+def efron_calculation(linear_predictor, time, event):
+    """TODO:Efron loss Moeschberger page 259."""
+    numerator = []
+    denominator = []
+    n_samples = len(linear_predictor)
+
+    for idx, t in enumerate(np.unique(time[event.astype(bool)])):
+        numerator.append(np.exp(np.sum(np.where(t==time, linear_predictor, 0))))
+
+    riskset = (np.outer(time,time)<=np.square(time)).astype(int)
+
+    linear_predictor_exp = np.exp(linear_predictor)
+    riskset = riskset*linear_predictor_exp
+    uni, idx, counts = np.unique(time[event.astype(bool)], return_index=True, return_counts=True)
+    denominator = np.sum(riskset[event.astype(bool)], axis=1)[idx]
+    return -np.log(np.prod(numerator/(denominator**counts)))/n_samples
+
+class TestEfronLoss:
+
+    def test_default(self):
+        linear_predictor, time, event = numpy_test_data_1d("default")
+
+        efron_formula_computation = efron_calculation(linear_predictor, time, event)
+        efron_loss = efron_negative_likelihood(linear_predictor, time, event)
+
+        assert np.allclose(efron_loss,efron_formula_computation, atol=1e-2), f"Computed efron loss is {efron_loss} but formula yields {efron_formula_computation} for default data."
+
+
+    def test_first_five_zero(self):
+
+        linear_predictor, time, event = numpy_test_data_1d("first_five_zero")
+
+        efron_formula_computation = efron_calculation(linear_predictor, time, event)
+        efron_loss = efron_negative_likelihood(linear_predictor, time, event)
+
+        assert np.allclose(efron_loss,efron_formula_computation, atol=1e-2), f"Computed efron loss is {efron_loss} but formula yields {efron_formula_computation} for edge case: first five zero events."
+
+    def test_last_five_zero(self):
+
+        linear_predictor, time, event = numpy_test_data_1d("last_five_zero")
+
+        efron_formula_computation = efron_calculation(linear_predictor, time, event)
+        efron_loss = efron_negative_likelihood(linear_predictor, time, event)
+
+        assert np.allclose(efron_loss,efron_formula_computation, atol=1e-2), f"Computed efron loss is {efron_loss} but formula yields {efron_formula_computation} for edge case: last five zero events."
+
+    def test_high_event_ratio(self):
+
+        linear_predictor, time, event = numpy_test_data_1d("high_event_ratio")
+
+        efron_formula_computation = efron_calculation(linear_predictor, time, event)
+        efron_loss = efron_negative_likelihood(linear_predictor, time, event)
+
+        assert np.allclose(efron_loss,efron_formula_computation, atol=1e-2), f"Computed efron loss is {efron_loss} but formula yields {efron_formula_computation} for edge case: high event ratio."
+
+    def test_low_event_ratio(self):
+
+        linear_predictor, time, event = numpy_test_data_1d("low_event_ratio")
+
+        efron_formula_computation = efron_calculation(linear_predictor, time, event)
+        efron_loss = efron_negative_likelihood(linear_predictor, time, event)
+
+        assert np.allclose(efron_loss,efron_formula_computation, atol=1e-2), f"Computed efron loss is {efron_loss} but formula yields {efron_formula_computation} for edge case: low event ratio."
+
+    def test_all_events(self):
+
+        linear_predictor, time, event = numpy_test_data_1d("all_events")
+
+        efron_formula_computation = efron_calculation(linear_predictor, time, event)
+        efron_loss = efron_negative_likelihood(linear_predictor, time, event)
+
+        assert np.allclose(efron_loss,efron_formula_computation, atol=1e-2), f"Computed efron loss is {efron_loss} but formula yields {efron_formula_computation} for edge case: all(100%) events."
+
+    def test_no_events(self):
+
+        linear_predictor, time, event = numpy_test_data_1d("no_events")
+
+        with pytest.raises(RuntimeError) as excinfo:
+            efron_negative_likelihood(linear_predictor, time, event)
+        assert "No events detected!" in str(excinfo.value), f"Events detected in data. Check data or the function <efron_negative_likelihood> to make sure data is processed correctly."