def test_set_model(self):
"""...Test set_model of saga, should only accept childs of
ModelGeneralizedLinear"""
# We try to pass a ModelCoxRegPartialLik which is not a generalized
# linear model to SAGA to check that the error is raised
msg = '^SAGA accepts only childs of `ModelGeneralizedLinear`$'
with self.assertRaisesRegex(ValueError, msg):
w = weights_sparse_gauss(n_weights=2, nnz=0)
X, T, C = SimuCoxReg(w).simulate()
model = ModelCoxRegPartialLik().fit(X, T, C)
SAGA().set_model(model)
msg = '^SAGA accepts only childs of `ModelGeneralizedLinear`$'
with self.assertRaisesRegex(RuntimeError, msg):
w = weights_sparse_gauss(n_weights=2, nnz=0)
X, T, C = SimuCoxReg(w).simulate()
model = ModelCoxRegPartialLik().fit(X, T, C)
saga = SAGA()
saga._solver.set_model(model._model)
def test_coxreg_serialize_and_compare(self):
"""Test serialization (cereal/pickle) of Cox Regression."""
np.random.seed(123)
n_samples, n_features = 100, 5
w0 = np.random.randn(n_features)
features, times, censoring = SimuCoxReg(
w0, n_samples=n_samples, verbose=False, seed=1234).simulate()
model = ModelCoxRegPartialLik()
model.fit(features, times, censoring)
pickled = pickle.loads(pickle.dumps(model))
self.assertTrue(model._model.compare(pickled._model))
def test_ModelCoxRegPartialLik(self):
"""...Numerical consistency check of loss and gradient for Cox Regression
"""
np.random.seed(123)
n_samples, n_features = 100, 5
w0 = np.random.randn(n_features)
features, times, censoring = SimuCoxReg(
w0, n_samples=n_samples, verbose=False, seed=1234).simulate()
model = ModelCoxRegPartialLik()
model.fit(features, times, censoring)
model_spars = ModelCoxRegPartialLik()
model_spars.fit(csr_matrix(features), times, censoring)
self.run_test_for_glm(model, model_spars)
def test_SimuCoxReg(self):
"""...Test simulation of a Cox Regression
"""
# Simulate a Cox model with specific seed
n_samples = 10
n_features = 3
idx = np.arange(n_features)
# Parameters of the Cox simu
coeffs = np.exp(-idx / 10.)
coeffs[::2] *= -1
seed = 123
simu = SimuCoxReg(coeffs,
n_samples=n_samples,
seed=seed,
verbose=False)
features_, times_, censoring_ = simu.simulate()
times = np.array([
1.5022119, 5.93102441, 6.82837051, 0.50940341, 0.14859682,
30.22922996, 3.54945974, 0.8671229, 1.4228358, 0.11483298
])
censoring = np.array([1, 0, 1, 1, 1, 1, 1, 1, 0, 1], dtype=np.ushort)
features = np.array([[1.4912667, 0.80881799, 0.26977298],
[1.23227551, 0.50697013, 1.9409132],
[1.8891494, 1.49834791, 2.41445794],
[0.19431319, 0.80245126, 1.02577552],
[-1.61687582, -1.08411865, -0.83438387],
[2.30419894, -0.68987056, -0.39750262],
[-0.28826405, -1.23635074, -0.76124386],
[-1.32869473, -1.8752391, -0.182537],
[0.79464218, 0.65055633, 1.57572506],
[0.71524202, 1.66759831, 0.88679047]])
np.testing.assert_almost_equal(features, features_)
np.testing.assert_almost_equal(times, times_)
np.testing.assert_almost_equal(censoring, censoring_)
def get_train_data(n_features=10, n_samples=10000, nnz=3, seed=12):
np.random.seed(seed)
coeffs0 = weights_sparse_gauss(n_features, nnz=nnz)
features, times, censoring = SimuCoxReg(coeffs0,
verbose=False).simulate()
return features, times, censoring
==============================
Cox regression data simulation
==============================
Generates Cox Regression realization given a weight vector
"""
import matplotlib.pyplot as plt
import numpy as np
from tick.survival import SimuCoxReg
n_samples = 150
weights = np.array([0.3, 1.2])
seed = 123
simu_coxreg = SimuCoxReg(weights, n_samples=n_samples, seed=123, verbose=False)
X, T, C = simu_coxreg.simulate()
plt.figure(figsize=(6, 4))
plt.scatter(*X[C == 0].T,
c=T[C == 0],
cmap='RdBu',
marker="x",
label="censoring")
plt.scatter(*X[C == 1].T,
c=T[C == 1],
cmap='RdBu',
marker="o",
label="failure")
plt.colorbar()