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test: tests for breslow loss with edge cases
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import os | ||
import sys | ||
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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 | ||
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from survhive.loss import ( | ||
# aft_negative_likelihood, | ||
breslow_negative_likelihood, | ||
# efron_negative_likelihood, | ||
# eh_negative_likelihood, | ||
) | ||
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def breslow_calculation(linear_predictor, time, event): | ||
"""Breslow loss Moeschberger page 259.""" | ||
numerator = [] | ||
denominator = [] | ||
n_samples = len(linear_predictor) | ||
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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 | ||
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class TestBreslowLoss: | ||
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def test_default(self): | ||
linear_predictor, time, event = numpy_test_data_1d("default") | ||
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breslow_formula_computation = breslow_calculation(linear_predictor, time, event) | ||
breslow_loss = breslow_negative_likelihood(linear_predictor, time, event) | ||
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assert np.allclose(breslow_loss,breslow_formula_computation, atol=1e-2), f"Computed Breslow loss is {breslow_loss} but formula yields {breslow_formula_computation} for default data." | ||
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def test_first_five_zero(self): | ||
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linear_predictor, time, event = numpy_test_data_1d("first_five_zero") | ||
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breslow_formula_computation = breslow_calculation(linear_predictor, time, event) | ||
breslow_loss = breslow_negative_likelihood(linear_predictor, time, event) | ||
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assert np.allclose(breslow_loss,breslow_formula_computation, atol=1e-2), f"Computed Breslow loss is {breslow_loss} but formula yields {breslow_formula_computation} for edge case: first five zero events." | ||
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def test_last_five_zero(self): | ||
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linear_predictor, time, event = numpy_test_data_1d("last_five_zero") | ||
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breslow_formula_computation = breslow_calculation(linear_predictor, time, event) | ||
breslow_loss = breslow_negative_likelihood(linear_predictor, time, event) | ||
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assert np.allclose(breslow_loss,breslow_formula_computation, atol=1e-2), f"Computed Breslow loss is {breslow_loss} but formula yields {breslow_formula_computation} for edge case: last five zero events." | ||
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def test_high_event_ratio(self): | ||
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linear_predictor, time, event = numpy_test_data_1d("high_event_ratio") | ||
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breslow_formula_computation = breslow_calculation(linear_predictor, time, event) | ||
breslow_loss = breslow_negative_likelihood(linear_predictor, time, event) | ||
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assert np.allclose(breslow_loss,breslow_formula_computation, atol=1e-2), f"Computed Breslow loss is {breslow_loss} but formula yields {breslow_formula_computation} for edge case: high event ratio." | ||
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def test_low_event_ratio(self): | ||
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linear_predictor, time, event = numpy_test_data_1d("low_event_ratio") | ||
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breslow_formula_computation = breslow_calculation(linear_predictor, time, event) | ||
breslow_loss = breslow_negative_likelihood(linear_predictor, time, event) | ||
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assert np.allclose(breslow_loss,breslow_formula_computation, atol=1e-2), f"Computed Breslow loss is {breslow_loss} but formula yields {breslow_formula_computation} for edge case: low event ratio." | ||
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def test_all_events(self): | ||
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linear_predictor, time, event = numpy_test_data_1d("all_events") | ||
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breslow_formula_computation = breslow_calculation(linear_predictor, time, event) | ||
breslow_loss = breslow_negative_likelihood(linear_predictor, time, event) | ||
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assert np.allclose(breslow_loss,breslow_formula_computation, atol=1e-2), f"Computed Breslow loss is {breslow_loss} but formula yields {breslow_formula_computation} for edge case: all(100%) events." | ||
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def test_no_events(self): | ||
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linear_predictor, time, event = numpy_test_data_1d("no_events") | ||
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with pytest.raises(RuntimeError) as excinfo: | ||
breslow_negative_likelihood(linear_predictor, time, event) | ||
assert "No events detected!" in str(excinfo.value), f"Events detected in data. Check data or the function <breslow_negative_likelihood> to make sure data is processed correctly." |