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Michal-NovomestskyMichal-Novomestsky
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refactor: re-ran notebook
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‎notebooks/INLA_testing.ipynb

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"cell_type": "code",
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"execution_count": 11,
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"id": "e344b7d0-f76e-4a28-9be9-884a2ba1f2c4",
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"metadata": {},
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 17,
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"id": "f1eb9a04-15dd-437d-bcf0-ce369feec912",
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"metadata": {},
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"outputs": [
@@ -624,10 +624,124 @@
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"collected 3 items \u001b[0m\u001b[1m\n",
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"\n",
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"../tests/test_inla.py::test_non_gaussian_latent \u001b[32mPASSED\u001b[0m\u001b[32m [ 33%]\u001b[0m\n",
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"../tests/test_inla.py::test_non_precision_MvNormal \u001b[32mPASSED\u001b[0m\u001b[32m [ 66%]\u001b[0m\n",
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"../tests/test_inla.py::test_3_layer_normal \u001b[32mPASSED\u001b[0m\u001b[32m [100%]\u001b[0m\n",
627+
"../tests/test_inla.py::test_non_precision_MvNormal \u001b[31mFAILED\u001b[0m\u001b[31m [ 66%]\u001b[0m\n",
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"../tests/test_inla.py::test_3_layer_normal \u001b[31mFAILED\u001b[0m\u001b[31m [100%]\u001b[0m\n",
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"\n",
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"\u001b[32m============================== \u001b[32m\u001b[1m3 passed\u001b[0m\u001b[32m in 19.44s\u001b[0m\u001b[32m ==============================\u001b[0m\n"
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"=================================== FAILURES ===================================\n",
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"\u001b[31m\u001b[1m_________________________ test_non_precision_MvNormal __________________________\u001b[0m\n",
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"\n",
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"rng = Generator(PCG64) at 0x7F6BDC40DC40\n",
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"\n",
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" \u001b[0m\u001b[94mdef\u001b[39;49;00m\u001b[90m \u001b[39;49;00m\u001b[92mtest_non_precision_MvNormal\u001b[39;49;00m(rng):\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m \u001b[39;49;00m\u001b[33m\"\"\"\u001b[39;49;00m\n",
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" \u001b[33m INLA should raise an error if trying to use a latent not in precision form\u001b[39;49;00m\n",
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" \u001b[33m \"\"\"\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" n = \u001b[94m10000\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" d = \u001b[94m3\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m\u001b[39;49;00m\n",
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" mu_mu = np.zeros((d,))\u001b[90m\u001b[39;49;00m\n",
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" mu_true = rng.random(d)\u001b[90m\u001b[39;49;00m\n",
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" tau = np.identity(d)\u001b[90m\u001b[39;49;00m\n",
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" cov = np.linalg.inv(tau)\u001b[90m\u001b[39;49;00m\n",
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" y_obs = rng.multivariate_normal(mean=mu_true, cov=cov, size=n)\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m\u001b[39;49;00m\n",
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" \u001b[94mwith\u001b[39;49;00m pm.Model() \u001b[94mas\u001b[39;49;00m model:\u001b[90m\u001b[39;49;00m\n",
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" mu = pm.MvNormal(\u001b[33m\"\u001b[39;49;00m\u001b[33mmu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, mu=mu_mu, tau=tau)\u001b[90m\u001b[39;49;00m\n",
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" x = pm.MvNormal(\u001b[33m\"\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, mu=mu, cov=cov)\u001b[90m\u001b[39;49;00m\n",
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" y = pm.MvNormal(\u001b[33m\"\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, mu=x, tau=tau, observed=y_obs)\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m\u001b[39;49;00m\n",
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" \u001b[94mwith\u001b[39;49;00m pytest.raises(\u001b[96mValueError\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n",
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"> pmx.fit(method=\u001b[33m\"\u001b[39;49;00m\u001b[33mINLA\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, x=x)\u001b[90m\u001b[39;49;00m\n",
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"\n",
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"\u001b[1m\u001b[31m../tests/test_inla.py\u001b[0m:67: \n",
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"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
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"\u001b[1m\u001b[31m../pymc_extras/inference/fit.py\u001b[0m:47: in fit\n",
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" \u001b[0m\u001b[94mreturn\u001b[39;49;00m fit_INLA(**kwargs)\u001b[90m\u001b[39;49;00m\n",
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" ^^^^^^^^^^^^^^^^^^\u001b[90m\u001b[39;49;00m\n",
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"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
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"\n",
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"x = x, temp_kwargs = None, model = None, minimizer_kwargs = None\n",
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"return_latent_posteriors = True, sampler_kwargs = {}\n",
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"\n",
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" \u001b[0m\u001b[94mdef\u001b[39;49;00m\u001b[90m \u001b[39;49;00m\u001b[92mfit_INLA\u001b[39;49;00m(\u001b[90m\u001b[39;49;00m\n",
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" x: TensorVariable,\u001b[90m\u001b[39;49;00m\n",
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" temp_kwargs=\u001b[94mNone\u001b[39;49;00m, \u001b[90m# TODO REMOVE. DEBUGGING TOOL\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" model: pm.Model | \u001b[94mNone\u001b[39;49;00m = \u001b[94mNone\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" minimizer_kwargs: \u001b[96mdict\u001b[39;49;00m | \u001b[94mNone\u001b[39;49;00m = \u001b[94mNone\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" return_latent_posteriors: \u001b[96mbool\u001b[39;49;00m = \u001b[94mTrue\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" **sampler_kwargs,\u001b[90m\u001b[39;49;00m\n",
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" ) -> az.InferenceData:\u001b[90m\u001b[39;49;00m\n",
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"> warnings.warn(\u001b[33m'\u001b[39;49;00m\u001b[33mCurrently only valid for a nested normal model. WIP.\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[96mUserWarning\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n",
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"\u001b[1m\u001b[31mE UserWarning: Currently only valid for a nested normal model. WIP.\u001b[0m\n",
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"\n",
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"\u001b[1m\u001b[31m../pymc_extras/inference/inla.py\u001b[0m:151: UserWarning\n",
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"\u001b[31m\u001b[1m_____________________________ test_3_layer_normal ______________________________\u001b[0m\n",
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"\n",
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"rng = Generator(PCG64) at 0x7F6BDC40DC40\n",
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"\n",
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" \u001b[0m\u001b[94mdef\u001b[39;49;00m\u001b[90m \u001b[39;49;00m\u001b[92mtest_3_layer_normal\u001b[39;49;00m(rng):\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m \u001b[39;49;00m\u001b[33m\"\"\"\u001b[39;49;00m\n",
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" \u001b[33m Test INLA against a simple toy problem:\u001b[39;49;00m\n",
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" \u001b[33m\u001b[39;49;00m\n",
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" \u001b[33m mu ~ N(mu_mu, I)\u001b[39;49;00m\n",
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" \u001b[33m x ~ N(mu, I)\u001b[39;49;00m\n",
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" \u001b[33m y ~ N(x, I)\u001b[39;49;00m\n",
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" \u001b[33m\u001b[39;49;00m\n",
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" \u001b[33m The mean of the posterior should be the midpoint between mu_mu and mu_true\u001b[39;49;00m\n",
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" \u001b[33m \"\"\"\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" n = \u001b[94m10000\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" d = \u001b[94m3\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m\u001b[39;49;00m\n",
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" mu_mu = \u001b[94m10\u001b[39;49;00m * rng.random(d)\u001b[90m\u001b[39;49;00m\n",
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" mu_true = rng.random(d)\u001b[90m\u001b[39;49;00m\n",
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" tau = np.identity(d)\u001b[90m\u001b[39;49;00m\n",
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" cov = np.linalg.inv(tau)\u001b[90m\u001b[39;49;00m\n",
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" y_obs = rng.multivariate_normal(mean=mu_true, cov=cov, size=n)\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m\u001b[39;49;00m\n",
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" \u001b[94mwith\u001b[39;49;00m pm.Model() \u001b[94mas\u001b[39;49;00m model:\u001b[90m\u001b[39;49;00m\n",
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" mu = pm.MvNormal(\u001b[33m\"\u001b[39;49;00m\u001b[33mmu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, mu=mu_mu, tau=tau)\u001b[90m\u001b[39;49;00m\n",
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" x = pm.MvNormal(\u001b[33m\"\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, mu=mu, tau=tau)\u001b[90m\u001b[39;49;00m\n",
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" y = pm.MvNormal(\u001b[33m\"\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, mu=x, tau=tau, observed=y_obs)\u001b[90m\u001b[39;49;00m\n",
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" \u001b[90m\u001b[39;49;00m\n",
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"> idata = pmx.fit(\u001b[90m\u001b[39;49;00m\n",
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" method=\u001b[33m\"\u001b[39;49;00m\u001b[33mINLA\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" x=x,\u001b[90m\u001b[39;49;00m\n",
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" temp_kwargs=[n, y_obs], \u001b[90m# TODO REMOVE LATER - DEBUGGING TOOL\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" return_latent_posteriors=\u001b[94mFalse\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" )\u001b[90m\u001b[39;49;00m\n",
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"\n",
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"\u001b[1m\u001b[31m../tests/test_inla.py\u001b[0m:94: \n",
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"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
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"\u001b[1m\u001b[31m../pymc_extras/inference/fit.py\u001b[0m:47: in fit\n",
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" \u001b[0m\u001b[94mreturn\u001b[39;49;00m fit_INLA(**kwargs)\u001b[90m\u001b[39;49;00m\n",
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" ^^^^^^^^^^^^^^^^^^\u001b[90m\u001b[39;49;00m\n",
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"_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n",
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"\n",
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"x = x\n",
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"temp_kwargs = [10000, array([[-0.92256686, 0.34251042, -1.07012389],\n",
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" [ 1.54076702, -0.55042364, 1.023203 ],\n",
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" [ 2.251...37462 , 0.78161016],\n",
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" [ 2.21600752, -0.66114102, -0.10427736],\n",
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" [ 1.37171357, -0.27766388, -0.73127505]])]\n",
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"model = None, minimizer_kwargs = None, return_latent_posteriors = False\n",
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"sampler_kwargs = {}\n",
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"\n",
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" \u001b[0m\u001b[94mdef\u001b[39;49;00m\u001b[90m \u001b[39;49;00m\u001b[92mfit_INLA\u001b[39;49;00m(\u001b[90m\u001b[39;49;00m\n",
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" x: TensorVariable,\u001b[90m\u001b[39;49;00m\n",
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" temp_kwargs=\u001b[94mNone\u001b[39;49;00m, \u001b[90m# TODO REMOVE. DEBUGGING TOOL\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n",
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" model: pm.Model | \u001b[94mNone\u001b[39;49;00m = \u001b[94mNone\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" minimizer_kwargs: \u001b[96mdict\u001b[39;49;00m | \u001b[94mNone\u001b[39;49;00m = \u001b[94mNone\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" return_latent_posteriors: \u001b[96mbool\u001b[39;49;00m = \u001b[94mTrue\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n",
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" **sampler_kwargs,\u001b[90m\u001b[39;49;00m\n",
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" ) -> az.InferenceData:\u001b[90m\u001b[39;49;00m\n",
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"> warnings.warn(\u001b[33m'\u001b[39;49;00m\u001b[33mCurrently only valid for a nested normal model. WIP.\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[96mUserWarning\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n",
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"\u001b[1m\u001b[31mE UserWarning: Currently only valid for a nested normal model. WIP.\u001b[0m\n",
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"\n",
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"\u001b[1m\u001b[31m../pymc_extras/inference/inla.py\u001b[0m:151: UserWarning\n",
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"\u001b[36m\u001b[1m=========================== short test summary info ============================\u001b[0m\n",
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"\u001b[31mFAILED\u001b[0m ../tests/test_inla.py::\u001b[1mtest_non_precision_MvNormal\u001b[0m - UserWarning: Currently only valid for a nested normal model. WIP.\n",
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"\u001b[31mFAILED\u001b[0m ../tests/test_inla.py::\u001b[1mtest_3_layer_normal\u001b[0m - UserWarning: Currently only valid for a nested normal model. WIP.\n",
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"\u001b[31m========================= \u001b[31m\u001b[1m2 failed\u001b[0m, \u001b[32m1 passed\u001b[0m\u001b[31m in 2.37s\u001b[0m\u001b[31m ==========================\u001b[0m\n"
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]
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}
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],

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