def test_HawkesSumExpKern_solver_step(self): """...Test HawkesSumExpKern setting of step parameter of solver """ for solver in solvers: if solver in ['bfgs']: msg = '^Solver "%s" has no settable step$' % solver with self.assertWarnsRegex(RuntimeWarning, msg): learner = HawkesSumExpKern( self.decays, solver=solver, step=1, **Test.specific_solver_kwargs(solver)) self.assertIsNone(learner.step) else: learner = HawkesSumExpKern( self.decays, solver=solver, step=self.float_1, **Test.specific_solver_kwargs(solver)) self.assertEqual(learner.step, self.float_1) self.assertEqual(learner._solver_obj.step, self.float_1) learner.step = self.float_2 self.assertEqual(learner.step, self.float_2) self.assertEqual(learner._solver_obj.step, self.float_2) if solver in ['sgd']: msg = '^SGD step needs to be tuned manually$' with self.assertWarnsRegex(RuntimeWarning, msg): learner = HawkesSumExpKern(self.decays, solver='sgd', max_iter=1) learner.fit(self.events, 0.3)
def test_corresponding_simu(self): """...Test that the corresponding simulation object is correctly built """ learner = HawkesSumExpKern(self.decays, max_iter=10) learner.fit(self.events) corresponding_simu = learner._corresponding_simu() np.testing.assert_array_equal(corresponding_simu.decays, learner.decays) np.testing.assert_array_equal(corresponding_simu.baseline, learner.baseline) np.testing.assert_array_equal(corresponding_simu.adjacency, learner.adjacency) learner = HawkesSumExpKern(self.decays, n_baselines=3, period_length=1, max_iter=10) learner.fit(self.events) corresponding_simu = learner._corresponding_simu() np.testing.assert_array_equal(corresponding_simu.decays, learner.decays) np.testing.assert_array_equal(corresponding_simu.baseline, learner.baseline) np.testing.assert_array_equal(corresponding_simu.adjacency, learner.adjacency)
def test_HawkesSumExpKern_fit(self): """...Test HawkesSumExpKern fit with different solvers and penalties """ sto_seed = 179312 n_nodes = 2 events, baseline, adjacency = Test.get_train_data( self.decays, n_nodes=n_nodes, n_decays=self.n_decays) start = 0.01 initial_adjacency_error = \ Test.estimation_error(start * np.ones((n_nodes, n_nodes)), adjacency) for penalty in penalties: for solver in solvers: solver_kwargs = { 'penalty': penalty, 'tol': 1e-10, 'solver': solver, 'verbose': False, 'max_iter': 1000 } if penalty != 'none': solver_kwargs['C'] = 50 if solver in ['sgd', 'svrg']: solver_kwargs['random_state'] = sto_seed # manually set step if solver == 'sgd': solver_kwargs['step'] = 1e-5 elif solver == 'svrg': continue if solver == 'bfgs': # BFGS only accepts ProxZero and ProxL2sq for now if penalty != 'l2': continue if penalty == 'nuclear': # Nuclear penalty only compatible with batch solvers if solver in \ HawkesSumExpKern._solvers_stochastic: continue learner = HawkesSumExpKern(self.decays, **solver_kwargs) learner.fit(events, start=start) adjacency_error = Test.estimation_error( learner.adjacency, adjacency) self.assertLess( adjacency_error, initial_adjacency_error * 0.8, "solver %s with penalty %s " "reached too high baseline error" % (solver, penalty))
def test_sparse(self): """...Test that original coeffs are correctly retrieved when some realizations are empty """ baseline, adjacency, events = self.simulate_sparse_realization() learner = HawkesSumExpKern(self.decays, verbose=False) learner.fit(events) np.testing.assert_array_almost_equal(learner.baseline, baseline, decimal=1) np.testing.assert_array_almost_equal(learner.adjacency, adjacency, decimal=1)
def test_plot_hawkes_kernels(self): """...Test plot_hawkes_history rendering given a fitted Hawkes learner """ decays = np.array([1.5, 3.5]) hawkes_sumexp = HawkesSumExpKern(decays, max_iter=0) # We set some specific coeffs to be free from any future learner # modifications # With 0 iteration and coeffs as start point it should remain there coeffs = np.array( [0.99, 0.99, 0.55, 0.37, 0.39, 0.16, 0.63, 0.49, 0.49, 0.30]) hawkes_sumexp.fit(self.hawkes_simu.timestamps, start=coeffs) n_points = 10 for support in [None, 4]: fig = plot_hawkes_kernels(hawkes_sumexp, hawkes=self.hawkes_simu, show=False, n_points=n_points, support=support) if support is None: max_support = hawkes_sumexp.get_kernel_supports().max() * 1.2 else: max_support = support for i, j in itertools.product(range(self.n_nodes), repeat=2): index = i * self.n_nodes + j ax = fig.axes[index] ax_t_axis, ax_estimated_kernel = ax.lines[0].get_xydata().T t_axis = np.linspace(0, max_support, n_points) np.testing.assert_array_equal(ax_t_axis, t_axis) estimated_kernel = hawkes_sumexp.get_kernel_values( i, j, t_axis) np.testing.assert_array_equal(ax_estimated_kernel, estimated_kernel) _, ax_true_kernel = ax.lines[1].get_xydata().T true_kernel = self.hawkes_simu.kernels[i, j].get_values(t_axis) np.testing.assert_array_equal(ax_true_kernel, true_kernel)
def test_HawkesSumExpKern_score(self): """...Test HawkesSumExpKern score method """ n_nodes = 2 n_realizations = 3 train_events = [[ np.cumsum(np.random.rand(4 + i)) for i in range(n_nodes) ] for _ in range(n_realizations)] test_events = [[ np.cumsum(np.random.rand(4 + i)) for i in range(n_nodes) ] for _ in range(n_realizations)] learner = HawkesSumExpKern(self.decays) msg = '^You must either call `fit` before `score` or provide events$' with self.assertRaisesRegex(ValueError, msg): learner.score() given_baseline = np.random.rand(n_nodes) given_adjacency = np.random.rand(n_nodes, n_nodes, self.n_decays) learner.fit(train_events) train_score_current_coeffs = learner.score() self.assertAlmostEqual(train_score_current_coeffs, 1.684827141) train_score_given_coeffs = learner.score(baseline=given_baseline, adjacency=given_adjacency) self.assertAlmostEqual(train_score_given_coeffs, 1.16247892) test_score_current_coeffs = learner.score(test_events) self.assertAlmostEqual(test_score_current_coeffs, 1.66494295) test_score_given_coeffs = learner.score(test_events, baseline=given_baseline, adjacency=given_adjacency) self.assertAlmostEqual(test_score_given_coeffs, 1.1081362)
def test_HawkesSumExpKern_fit_start(self): """...Test HawkesSumExpKern starting point of fit method """ n_nodes = len(self.events) n_coefs = n_nodes + n_nodes * n_nodes * self.n_decays # Do not step learner = HawkesSumExpKern(self.decays, max_iter=-1) learner.fit(self.events) np.testing.assert_array_equal(learner.coeffs, np.ones(n_coefs)) learner.fit(self.events, start=self.float_1) np.testing.assert_array_equal(learner.coeffs, np.ones(n_coefs) * self.float_1) learner.fit(self.events, start=self.int_1) np.testing.assert_array_equal(learner.coeffs, np.ones(n_coefs) * self.int_1) random_coeffs = np.random.rand(n_coefs) learner.fit(self.events, start=random_coeffs) np.testing.assert_array_equal(learner.coeffs, random_coeffs)
decays = [0.1, 0.5, 1.] baseline = [0.12, 0.07] adjacency = [[[0, .1, .4], [.2, 0., .2]], [[0, 0, 0], [.6, .3, 0]]] hawkes_exp_kernels = SimuHawkesSumExpKernels( adjacency=adjacency, decays=decays, baseline=baseline, end_time=end_time, verbose=False, seed=1039) hawkes_exp_kernels.track_intensity(0.1) hawkes_exp_kernels.simulate() learner = HawkesSumExpKern(decays, penalty='elasticnet', elastic_net_ratio=0.8) learner.fit(hawkes_exp_kernels.timestamps) t_min = 100 t_max = 200 fig, ax_list = plt.subplots(2, 1, figsize=(10, 6)) learner.plot_estimated_intensity(hawkes_exp_kernels.timestamps, t_min=t_min, t_max=t_max, ax=ax_list) plot_point_process(hawkes_exp_kernels, plot_intensity=True, t_min=t_min, t_max=t_max, ax=ax_list) # Enhance plot for ax in ax_list: # Set labels to both plots ax.lines[0].set_label('estimated')
from tick.inference import HawkesSumExpKern end_time = 1000 n_realizations = 10 decays = [.5, 2., 6.] baseline = [0.12, 0.07] adjacency = [[[0, .1, .4], [.2, 0., .2]], [[0, 0, 0], [.6, .3, 0]]] hawkes_exp_kernels = SimuHawkesSumExpKernels(adjacency=adjacency, decays=decays, baseline=baseline, end_time=end_time, verbose=False, seed=1039) multi = SimuHawkesMulti(hawkes_exp_kernels, n_simulations=n_realizations) multi.end_time = [(i + 1) / 10 * end_time for i in range(n_realizations)] multi.simulate() learner = HawkesSumExpKern(decays, penalty='elasticnet', elastic_net_ratio=0.8) learner.fit(multi.timestamps) fig = plot_hawkes_kernels(learner, hawkes=hawkes_exp_kernels, show=False) for ax in fig.axes: ax.set_ylim([0., 1.]) plt.show()