diff --git a/Python/numpyro_hierarchical_forecasting_2.ipynb b/Python/numpyro_hierarchical_forecasting_2.ipynb index 6fa8338..38507aa 100644 --- a/Python/numpyro_hierarchical_forecasting_2.ipynb +++ b/Python/numpyro_hierarchical_forecasting_2.ipynb @@ -100,12 +100,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([50, 50, 78888, 1])\n" + "torch.Size([50, 50, 78888])\n" ] } ], "source": [ - "data = dataset[\"counts\"].permute(1, 2, 0).unsqueeze(-1).log1p()\n", + "data = dataset[\"counts\"].permute(1, 2, 0).log1p()\n", "T = data.shape[-2]\n", "print(data.shape)" ] @@ -123,7 +123,7 @@ "metadata": {}, "outputs": [], "source": [ - "T2 = data.size(-2) # end\n", + "T2 = data.size(-1) # end\n", "T1 = T2 - 24 * 7 * 2 # train/test split\n", "T0 = T1 - 24 * 90 # beginning: train on 90 days of data" ] @@ -137,16 +137,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "y: (50, 50, 2496, 1)\n", - "y_train: (50, 50, 2160, 1)\n", - "y_test: (50, 50, 336, 1)\n" + "y: (50, 50, 2496)\n", + "y_train: (50, 50, 2160)\n", + "y_test: (50, 50, 336)\n" ] } ], "source": [ - "y = jnp.array(data[..., T0:T2, :])\n", - "y_train = jnp.array(data[..., T0:T1, :])\n", - "y_test = jnp.array(data[..., T1:T2, :])\n", + "y = jnp.array(data[..., T0:T2])\n", + "y_train = jnp.array(data[..., T0:T1])\n", + "y_test = jnp.array(data[..., T1:T2])\n", "\n", "print(f\"y: {y.shape}\")\n", "print(f\"y_train: {y_train.shape}\")\n", @@ -159,7 +159,7 @@ "metadata": {}, "outputs": [], "source": [ - "n_stations = y_train.shape[-3]\n", + "n_stations = y_train.shape[-2]\n", "\n", "time = jnp.array(range(T0, T2))\n", "time_train = jnp.array(range(T0, T1))\n", @@ -169,8 +169,8 @@ "t_max_test = time_test.size\n", "\n", "assert time_train.size + time_test.size == time.size\n", - "assert y_train.shape == (n_stations, n_stations, t_max_train, 1)\n", - "assert y_test.shape == (n_stations, n_stations, t_max_test, 1)" + "assert y_train.shape == (n_stations, n_stations, t_max_train)\n", + "assert y_test.shape == (n_stations, n_stations, t_max_test)" ] }, { @@ -346,7 +346,7 @@ { "data": { "text/plain": [ - "(50, 50, 2496, 1)" + "(50, 50, 2496)" ] }, "execution_count": 12, @@ -365,11 +365,11 @@ "outputs": [], "source": [ "def model(\n", - " covariates: Float[Array, \"n_series n_series t_max 1\"],\n", - " y: Float[Array, \"n_series n_series t_max 1\"] | None = None,\n", + " covariates: Float[Array, \"n_series n_series t_max\"],\n", + " y: Float[Array, \"n_series n_series t_max\"] | None = None,\n", ") -> None:\n", " # Get the time and feature dimensions\n", - " n_series, n_series, t_max, _ = covariates.shape\n", + " n_series, n_series, t_max = covariates.shape\n", "\n", " origin_plate = numpyro.plate(\"origin\", n_series, dim=-3)\n", " destin_plate = numpyro.plate(\"destin\", n_series, dim=-2)\n", @@ -411,8 +411,6 @@ " destin_scale = numpyro.sample(\"destin_scale\", dist.LogNormal(-5, 5))\n", " scale = origin_scale + destin_scale\n", "\n", - " scale = jnp.expand_dims(scale, axis=-2)\n", - "\n", " # Repeat the seasonal parameters to match the length of the time series\n", " seasonal = origin_seasonal + destin_seasonal\n", " seasonal_repeat = periodic_repeat_jax(seasonal, t_max, dim=-1)\n", @@ -431,10 +429,8 @@ "\n", " pred_levels = pred_levels.transpose(1, 0)\n", "\n", - " # Compute the mean of the model\n", - " mu = seasonal_repeat + pairwise + pred_levels\n", - "\n", - " mu = mu[..., None]\n", + " # # Compute the mean of the model\n", + " mu = pred_levels + seasonal_repeat + pairwise\n", "\n", " # Sample the observations\n", " with numpyro.handlers.condition(data={\"obs\": y}):\n", @@ -453,6 +449,14 @@ "execution_count": 14, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50, 50, 1)\n", + "(50, 50, 1)\n" + ] + }, { "data": { "image/svg+xml": [ @@ -640,7 +644,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -752,27 +756,50 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50, 50, 1)\n", + "(50, 50, 1)\n", + "(50, 50, 1)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 1000/1000 [00:38<00:00, 26.19it/s, init loss: 3533380864.0000, avg. loss [951-1000]: 18223372.2400]\n" + " 0%| | 0/1000 [00:00" ] @@ -790,7 +817,7 @@ "%%time\n", "\n", "guide = AutoNormal(model)\n", - "optimizer = numpyro.optim.Adam(step_size=0.05)\n", + "optimizer = numpyro.optim.Adam(step_size=0.1)\n", "svi = SVI(model, guide, optimizer, loss=Trace_ELBO())\n", "num_steps = 1000\n", "\n", @@ -842,29 +869,18 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(100, 50, 50, 2160, 1)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "(50, 50, 1)\n", + "(50, 50, 1)\n" + ] } ], - "source": [ - "{k: v for k, v in posterior(rng_subkey, covariates_train).items()}[\"obs\"].shape\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], "source": [ "rng_key, rng_subkey = random.split(rng_key)\n", "\n", @@ -872,846 +888,25 @@ " posterior_predictive={\n", " k: v[None, ...] for k, v in posterior(rng_subkey, covariates_train).items()\n", " },\n", - " coords={\"time_train\": time_train, \"n_series\": jnp.arange(n_stations)},\n", - " dims={\"obs\": [\"n_series\", \"n_series\", \"time_train\",]},\n", + " coords={\n", + " \"time_train\": time_train,\n", + " \"n_series_origin\": jnp.arange(n_stations),\n", + " \"n_series_destin\": jnp.arange(n_stations),\n", + " },\n", + " dims={\"obs\": [\"n_series_origin\", \"n_series_destin\", \"time_train\"]},\n", ")\n", "\n", - "# idata_test = az.from_dict(\n", - "# posterior_predictive={k: v for k, v in posterior(rng_subkey, covariates).items()},\n", - "# # coords={\"time\": time, \"n_series\": jnp.arange(n_stations)},\n", - "# # dims={\"obs\": [\"n_series\", \"n_series\", \"time\"]},\n", - "# )" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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      <xarray.Dataset> Size: 2GB\n",
-       "Dimensions:    (chain: 1, draw: 100, obs_dim_0: 50, obs_dim_1: 50,\n",
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\n", - " " - ], - "text/plain": [ - "Inference data with groups:\n", - "\t> posterior_predictive" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "idata_train" + "idata_test = az.from_dict(\n", + " posterior_predictive={\n", + " k: v[None, ...] for k, v in posterior(rng_subkey, covariates).items()\n", + " },\n", + " coords={\n", + " \"time\": time,\n", + " \"n_series_origin\": jnp.arange(n_stations),\n", + " \"n_series_destin\": jnp.arange(n_stations),\n", + " },\n", + " dims={\"obs\": [\"n_series_origin\", \"n_series_destin\", \"time\"]},\n", + ")" ] }, { @@ -1723,9 +918,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeCheckError", + "evalue": "Type-check error whilst checking the parameters of __main__.crps.\nThe problem arose whilst typechecking parameter 'truth'.\nActual value: f32[50,50,2160]\nExpected type: .\n----------------------\nCalled with parameters: {'truth': f32[50,50,2160], 'pred': f32[100,50,50,2160], 'sample_weight': None}\nParameter annotations: (truth: Float[Array, 't_max n_series'], pred: Float[Array, 'n_samples t_max n_series'], sample_weight: Float[Array, 't_max'] | None = None) -> Any.\n", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mBeartypeCallHintParamViolation\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/jaxtyping/_decorator.py:412\u001b[0m, in \u001b[0;36mjaxtyped..wrapped_fn_impl\u001b[0;34m(args, kwargs, bound, memos)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 412\u001b[0m \u001b[43mparam_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m AnnotationError:\n", + "File \u001b[0;32m<@beartype(__main__.crps) at 0x3c2680040>:31\u001b[0m, in \u001b[0;36mcrps\u001b[0;34m(__beartype_object_16908407488, __beartype_get_violation, __beartype_conf, __beartype_object_16908409216, __beartype_object_17320097920, __beartype_check_meta, __beartype_func, *args, **kwargs)\u001b[0m\n", + "\u001b[0;31mBeartypeCallHintParamViolation\u001b[0m: Function __main__.crps() parameter truth=\"Array([[[0. , 0. , 0. , ..., 2.0794415, 1.3862944,\n 0.6931472],\n...)\" violates type hint , as this array has 3 dimensions, not the 2 expected by the type hint.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mBeartypeCallHintParamViolation\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/jaxtyping/_decorator.py:769\u001b[0m, in \u001b[0;36m_get_problem_arg\u001b[0;34m(param_signature, args, kwargs, arguments, module, typechecker)\u001b[0m\n\u001b[1;32m 768\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 769\u001b[0m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 770\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m<@beartype(__main__.check_single_arg) at 0x3c2680900>:29\u001b[0m, in \u001b[0;36mcheck_single_arg\u001b[0;34m(__beartype_object_16908407488, __beartype_get_violation, __beartype_conf, __beartype_check_meta, __beartype_func, *args, **kwargs)\u001b[0m\n", + "\u001b[0;31mBeartypeCallHintParamViolation\u001b[0m: Function __main__.check_single_arg() parameter truth=\"Array([[[0. , 0. , 0. , ..., 2.0794415, 1.3862944,\n 0.6931472],\n...)\" violates type hint , as this array has 3 dimensions, not the 2 expected by the type hint.", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mTypeCheckError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/jaxtyping/_decorator.py:417\u001b[0m, in \u001b[0;36mjaxtyped..wrapped_fn_impl\u001b[0;34m(args, kwargs, bound, memos)\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 417\u001b[0m argmsg \u001b[38;5;241m=\u001b[39m \u001b[43m_get_problem_arg\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 418\u001b[0m \u001b[43m \u001b[49m\u001b[43mparam_signature\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 419\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 420\u001b[0m \u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 421\u001b[0m \u001b[43m \u001b[49m\u001b[43mbound\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marguments\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 422\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 423\u001b[0m \u001b[43m \u001b[49m\u001b[43mtypechecker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 424\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 425\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m TypeCheckError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/jaxtyping/_decorator.py:772\u001b[0m, in \u001b[0;36m_get_problem_arg\u001b[0;34m(param_signature, args, kwargs, arguments, module, typechecker)\u001b[0m\n\u001b[1;32m 771\u001b[0m keep_value \u001b[38;5;241m=\u001b[39m _pformat(arguments[keep_name], short_self\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m--> 772\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TypeCheckError(\n\u001b[1;32m 773\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mThe problem arose whilst typechecking parameter \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkeep_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 774\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mActual value: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkeep_value\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 775\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected type: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkeep_annotation\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 776\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 777\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 778\u001b[0m \u001b[38;5;66;03m# Could not localise the problem to a single argument -- probably due to\u001b[39;00m\n\u001b[1;32m 779\u001b[0m \u001b[38;5;66;03m# e.g. a mismatched typevar, which each individual argument is okay with.\u001b[39;00m\n", + "\u001b[0;31mTypeCheckError\u001b[0m: \nThe problem arose whilst typechecking parameter 'truth'.\nActual value: f32[50,50,2160]\nExpected type: .", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mTypeCheckError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[31], line 27\u001b[0m\n\u001b[1;32m 23\u001b[0m per_obs_crps \u001b[38;5;241m=\u001b[39m absolute_error \u001b[38;5;241m-\u001b[39m jnp\u001b[38;5;241m.\u001b[39msum(diff \u001b[38;5;241m*\u001b[39m weight, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m) \u001b[38;5;241m/\u001b[39m num_samples\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m jnp\u001b[38;5;241m.\u001b[39maverage(per_obs_crps, weights\u001b[38;5;241m=\u001b[39msample_weight)\n\u001b[0;32m---> 27\u001b[0m crps_train \u001b[38;5;241m=\u001b[39m \u001b[43mcrps\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 28\u001b[0m \u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 29\u001b[0m \u001b[43m \u001b[49m\u001b[43mjnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43midata_train\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mposterior_predictive\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 30\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 32\u001b[0m crps_test \u001b[38;5;241m=\u001b[39m crps(\n\u001b[1;32m 33\u001b[0m y_test,\n\u001b[1;32m 34\u001b[0m jnp\u001b[38;5;241m.\u001b[39marray(\n\u001b[1;32m 35\u001b[0m idata_test[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39msel(chain\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39msel(time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mslice\u001b[39m(T1, T2))\n\u001b[1;32m 36\u001b[0m ),\n\u001b[1;32m 37\u001b[0m )\n", + " \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/jaxtyping/_decorator.py:446\u001b[0m, in \u001b[0;36mjaxtyped..wrapped_fn_impl\u001b[0;34m(args, kwargs, bound, memos)\u001b[0m\n\u001b[1;32m 444\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TypeCheckError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 445\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 446\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m TypeCheckError(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \u001b[38;5;66;03m# Actually call the function.\u001b[39;00m\n\u001b[1;32m 449\u001b[0m out \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "\u001b[0;31mTypeCheckError\u001b[0m: Type-check error whilst checking the parameters of __main__.crps.\nThe problem arose whilst typechecking parameter 'truth'.\nActual value: f32[50,50,2160]\nExpected type: .\n----------------------\nCalled with parameters: {'truth': f32[50,50,2160], 'pred': f32[100,50,50,2160], 'sample_weight': None}\nParameter annotations: (truth: Float[Array, 't_max n_series'], pred: Float[Array, 'n_samples t_max n_series'], sample_weight: Float[Array, 't_max'] | None = None) -> Any.\n" + ] + } + ], "source": [ "def crps(\n", " truth: Float[Array, \"t_max n_series\"],\n", @@ -1775,9 +999,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "\"'n_series' is not a valid dimension or coordinate for Dataset with dimensions FrozenMappingWarningOnValuesAccess({'chain': 1, 'draw': 100, 'n_series_origin': 50, 'n_series_destin': 50, 'time_train': 2160})\"", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[32], line 10\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(axes):\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, hdi_prob \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m([\u001b[38;5;241m0.94\u001b[39m, \u001b[38;5;241m0.5\u001b[39m]):\n\u001b[1;32m 8\u001b[0m az\u001b[38;5;241m.\u001b[39mplot_hdi(\n\u001b[1;32m 9\u001b[0m time_train[time_train \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m T1 \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m24\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m7\u001b[39m],\n\u001b[0;32m---> 10\u001b[0m \u001b[43midata_train\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mposterior_predictive\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mobs\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_series\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mi\u001b[49m\u001b[43m)\u001b[49m[\n\u001b[1;32m 11\u001b[0m :, :, time_train \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m T1 \u001b[38;5;241m-\u001b[39m 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\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhdi_prob\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.0f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m% HDI (train)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 18\u001b[0m },\n\u001b[1;32m 19\u001b[0m smooth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 20\u001b[0m ax\u001b[38;5;241m=\u001b[39max,\n\u001b[1;32m 21\u001b[0m )\n\u001b[1;32m 22\u001b[0m az\u001b[38;5;241m.\u001b[39mplot_hdi(\n\u001b[1;32m 23\u001b[0m time[time \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m T1],\n\u001b[1;32m 24\u001b[0m idata_test[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mposterior_predictive\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobs\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39msel(n_series\u001b[38;5;241m=\u001b[39mi)[:, :, time \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m T1],\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 32\u001b[0m ax\u001b[38;5;241m=\u001b[39max,\n\u001b[1;32m 33\u001b[0m )\n\u001b[1;32m 34\u001b[0m ax\u001b[38;5;241m.\u001b[39maxvline(christmas_index, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mC2\u001b[39m\u001b[38;5;124m\"\u001b[39m, lw\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, label\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChristmas\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/xarray/core/dataarray.py:1670\u001b[0m, in \u001b[0;36mDataArray.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 1554\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msel\u001b[39m(\n\u001b[1;32m 1555\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 1556\u001b[0m indexers: Mapping[Any, Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1560\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mindexers_kwargs: Any,\n\u001b[1;32m 1561\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[1;32m 1562\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new DataArray whose data is given by selecting index\u001b[39;00m\n\u001b[1;32m 1563\u001b[0m \u001b[38;5;124;03m labels along the specified dimension(s).\u001b[39;00m\n\u001b[1;32m 1564\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1668\u001b[0m \u001b[38;5;124;03m Dimensions without coordinates: points\u001b[39;00m\n\u001b[1;32m 1669\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1670\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_temp_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1671\u001b[0m \u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1672\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1673\u001b[0m \u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1674\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1675\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mindexers_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1676\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1677\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/xarray/core/dataset.py:3184\u001b[0m, in \u001b[0;36mDataset.sel\u001b[0;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 3116\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns a new dataset with each array indexed by tick labels\u001b[39;00m\n\u001b[1;32m 3117\u001b[0m \u001b[38;5;124;03malong the specified dimension(s).\u001b[39;00m\n\u001b[1;32m 3118\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3181\u001b[0m \n\u001b[1;32m 3182\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3183\u001b[0m indexers \u001b[38;5;241m=\u001b[39m either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msel\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 3184\u001b[0m query_results \u001b[38;5;241m=\u001b[39m \u001b[43mmap_index_queries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3185\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[1;32m 3186\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[1;32m 3189\u001b[0m no_scalar_variables \u001b[38;5;241m=\u001b[39m {}\n", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/xarray/core/indexing.py:185\u001b[0m, in \u001b[0;36mmap_index_queries\u001b[0;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[1;32m 182\u001b[0m options \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmethod\u001b[39m\u001b[38;5;124m\"\u001b[39m: method, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtolerance\u001b[39m\u001b[38;5;124m\"\u001b[39m: tolerance}\n\u001b[1;32m 184\u001b[0m indexers \u001b[38;5;241m=\u001b[39m either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmap_index_queries\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 185\u001b[0m grouped_indexers \u001b[38;5;241m=\u001b[39m \u001b[43mgroup_indexers_by_index\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 187\u001b[0m results \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m index, labels \u001b[38;5;129;01min\u001b[39;00m grouped_indexers:\n", + "File \u001b[0;32m~/Documents/website_projects/.pixi/envs/default/lib/python3.11/site-packages/xarray/core/indexing.py:146\u001b[0m, in \u001b[0;36mgroup_indexers_by_index\u001b[0;34m(obj, indexers, options)\u001b[0m\n\u001b[1;32m 144\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mno index found for coordinate \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m key \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mdims:\n\u001b[0;32m--> 146\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[1;32m 147\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m is not a valid dimension or coordinate for \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 148\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mobj\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with dimensions \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mobj\u001b[38;5;241m.\u001b[39mdims\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 149\u001b[0m )\n\u001b[1;32m 150\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(options):\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 152\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot supply selection options \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moptions\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m for dimension \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 153\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthat has no associated coordinate or index\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 154\u001b[0m )\n", + "\u001b[0;31mKeyError\u001b[0m: \"'n_series' is not a valid dimension or coordinate for Dataset with dimensions FrozenMappingWarningOnValuesAccess({'chain': 1, 'draw': 100, 'n_series_origin': 50, 'n_series_destin': 50, 'time_train': 2160})\"" + ] + }, + { + "data": { + "image/png": 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