def test_TA(self):
apc.bootstrap_forecast(apc.loss_TA(), B=10)
apc.bootstrap_forecast(apc.loss_TA(), B=10, quantiles=[0.9])
apc.bootstrap_forecast(apc.loss_TA(), B=10, adj_residuals=False)
f1 = apc.bootstrap_forecast(apc.loss_TA(), B=10, seed=1)
f2 = apc.bootstrap_forecast(apc.loss_TA(), B=10, seed=1)
for key in f1.keys():
self.assertTrue(np.allclose(f1[key].values, f2[key].values))
def test_TA_odp(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit('od_poisson_response', 'AC')
model.simulate(repetitions=10)
model.simulate(repetitions=10, sigma2=10)
model.simulate(repetitions=10, od_poisson_dgp='neg_binomial')
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.plot_data_sums()
model.plot_data_sums(logy=True)
for simplify_ranges in ('start', 'mean', 'end', False):
model.plot_data_sums(simplify_ranges)
plt.close('all')
def test_TA(self):
r = apc.r_test(apc.loss_TA(),
'od_poisson_response',
'Ad',
R_stat='ql',
R_dist='ls',
data_format='CL')
self.assertEqual(round(r['R_stat'], 5), 73.16521)
self.assertEqual(round(r['p_value'], 5), 0.78526)
self.assertEqual(round(r['power_at_R'], 5), 0.99896)
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.plot_data_heatmaps()
for simplify_ranges in ('start', 'mean', 'end', False):
model.plot_data_heatmaps(simplify_ranges)
plt.close('all')
for space in ('AC', 'AP', 'PA', 'PC', 'CA', 'CP'):
model.plot_data_heatmaps(space=space)
plt.close('all')
model.plot_data_heatmaps(vmin=20)
def test_TA_odp(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit(family='od_poisson_response', predictor='AC')
self.assertAlmostEqual(model.deviance, 1903014.004, 3)
self.assertTrue(
np.allclose(
model.parameters.sum().values,
np.array([10.99021854, 6.56495772, 0.37934664, 8.3525477])))
self.assertAlmostEqual(model.fitted_values.sum(), 34358090.000, 3)
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit('od_poisson_response', 'Ad')
model.forecast()
model.plot_forecast()
model.plot_forecast(ic=True)
model.plot_forecast('Age', aggregate=True)
model.plot_forecast('Cohort')
model.plot_forecast(from_to=(8, 13))
plt.close('all')
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit('od_poisson_response', 'Ad')
model.plot_fit()
for style in ('detrend', 'sum_sum'):
model.plot_fit(style)
plt.close('all')
for sr in ('start', 'mean', 'end', False):
model.plot_fit(simplify_ranges=sr)
plt.close('all')
model.plot_fit(around_coef=False)
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit('od_poisson_response', 'AC')
for sr in ('start', 'mean', 'end', False):
model.plot_residuals(simplify_ranges=sr)
plt.close('all')
for sp in ('AC', 'AP', 'PA', 'PC', 'CA', 'CP'):
model.plot_data_heatmaps(space=sp)
plt.close('all')
for r in ('anscombe', 'deviance', 'pearson', 'response'):
model.plot_residuals(kind=r)
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.plot_data_within()
for simplify_ranges in ('start', 'mean', 'end', False):
model.plot_data_within(simplify_ranges=simplify_ranges)
plt.close('all')
for aggregate in ('mean', 'sum'):
model.plot_data_within(aggregate=aggregate)
plt.close('all')
model.plot_data_within(logy=True)
model.plot_data_within(n_groups='all')
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA())
self.assertEqual(model.data_format, 'CA')
self.assertEqual(model.I, 10)
self.assertEqual(model.J, 10)
self.assertEqual(model.K, 10)
self.assertEqual(model.L, 0)
self.assertEqual(model.n, 55)
self.assertEqual(model.time_adjust, 1)
self.assertAlmostEqual(model.data_vector.sum()['response'], 34358090.0,
3)
def test_TA_odp(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit('od_poisson_response', 'AC')
model.identify()
self.assertTrue(
np.allclose(
model.parameters_adhoc.sum().values,
np.array([19.93493555, 12.35339911, 23.27262888,
12.98041616])))
self.assertTrue(
np.allclose(
model.identify('sum_sum', attach_to_self=False).sum().values,
np.array([-40.35513352, 19.04311385, -62.3882387,
8.99524907])))
def test_TA(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit('od_poisson_response', 'AC')
model.forecast()
self.assertTrue(np.allclose(
model.forecasts['Period'].iloc[1:,:].sum().values,
np.array([
13454319.7860026 , 3264385.92189263, 2168550.32629664,
2371474.89252264, 527736.31618658, 15678560.7866679 ,
17716020.93110489, 18965575.06511772, 21401447.92641674
])
)
)
self.assertTrue(np.allclose(
model.forecasts['Age'].iloc[1:,:].sum().values,
np.array([
17824052.09094379, 5446971.56429764, 2703382.56455396,
4610723.51207401, 699136.02022292, 21535432.35438537,
24935149.09611903, 27020161.71376697, 31084672.01592125
])
)
)
self.assertTrue(np.allclose(
model.forecasts['Cohort'].iloc[1:,:].sum().values,
np.array([
18586221.79737546, 5480908.10291801, 2617925.92215297,
4585571.92139958, 729031.5957391 , 22320725.25838809,
25741623.42490841, 27839626.40066406, 31929460.02408297
])
)
)
self.assertTrue(np.allclose(
model.forecasts['Total'].sum().values,
np.array([
18680855.61192424, 2952921.04370912, 993729.36519777,
2682411.51615798, 732743.54115671, 20692875.08949177,
22535935.034052 , 23666265.45020602, 25869724.35611854
])
)
)
def test_TA_odp(self):
model = apc.Model()
model.data_from_df(apc.loss_TA(), data_format='CL')
model.fit(family='od_poisson_response', predictor='AC')
model.fit_table()
dev_table_Ad = model.fit_table(reference_predictor='Ad',
attach_to_self=False)
self.assertTrue(
np.allclose(
model.deviance_table.sum().values,
np.array([
6.14205640e+07, 4.26000000e+02, 0.00000000e+00,
4.42934379e+07, 1.02000000e+02, 6.42698087e+01,
8.77350747e-01
])))
self.assertTrue(
np.allclose(
dev_table_Ad.sum().values,
np.array([
3.34371797e+07, 2.48000000e+02, 0.00000000e+00,
2.20883978e+07, 2.80000000e+01, 5.10659264e+01,
5.28199268e-02
])))
