update versions, run through tutorials

docs/tutorials/initial_values.ipynb

@@ -9,26 +9,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Setting PyTensor floatX type to float32.\n",
-      "Setting \"jax_enable_x64\" to False. If this is not intended, please set `jax` to False.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "import warnings\n",
+    "\n",
     "import numpy as np\n",
     "\n",
     "import hssm\n",
     "\n",
-    "hssm.set_floatX(\"float32\")\n",
-    "import warnings\n",
-    "\n",
     "warnings.filterwarnings(\"ignore\")"
    ]
   },
@@ -69,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -85,14 +75,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'z_interval__': array(0., dtype=float32), 'a_interval__': array(-1.1175871e-07, dtype=float32), 't_log__': array(0.6931472, dtype=float32), 'v': array(0., dtype=float32)}\n"
+      "Model initialized successfully.\n"
      ]
     }
    ],
@@ -101,6 +91,7 @@
     "    data=cav_data,\n",
     "    model=\"ddm\",\n",
     "    loglik_kind=\"approx_differentiable\",\n",
+    "    model_config={\"backend\": \"pytensor\"},\n",
     ")"
    ]
   },
@@ -113,19 +104,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'z': array(0.5, dtype=float32),\n",
-       " 'a': array(1.5, dtype=float32),\n",
-       " 't': array(0.025, dtype=float32),\n",
-       " 'v': array(0., dtype=float32)}"
+       "{'a': array(1.5), 'z': array(0.5), 't': array(0.025), 'v': array(0.)}"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -149,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,94 +149,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Only 10 samples per chain. Reliable r-hat and ESS diagnostics require longer chains for accurate estimate.\n"
+      "Using default initvals. \n",
+      "\n"
      ]
     },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b83aab5a5b545c69100111c82f52123",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a73c530664cf480aa8fdd770c87611f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5bfd44ee15814452bc334013fb08ace3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30db31b9dc60490390fd583306a5247d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "There were 40 divergences after tuning. Increase `target_accept` or reparameterize.\n",
-      "The number of samples is too small to check convergence reliably.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CLEANING RESULTS\n",
-      "MAIN CLEANUP LOOP\n",
-      "RUNNING COMPONENT <bambi.backend.model_components.DistributionalComponent object at 0x2a253b7d0>\n",
-      "PERFORMING PREDICTION\n"
+      "Auto-assigning NUTS sampler...\n",
+      "Initializing NUTS using adapt_diag...\n",
+      "Multiprocess sampling (4 chains in 4 jobs)\n",
+      "NUTS: [a, z, t, v]\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4ca23b047ac7493989f9d17dc7260684",
+       "model_id": "e3e5c918329f4fcdbdb91096727351e3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -281,10 +206,20 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sampling 4 chains for 100 tune and 100 draw iterations (400 + 400 draws total) took 34 seconds.\n",
+      "The rhat statistic is larger than 1.01 for some parameters. This indicates problems during sampling. See https://arxiv.org/abs/1903.08008 for details\n",
+      "The effective sample size per chain is smaller than 100 for some parameters.  A higher number is needed for reliable rhat and ess computation. See https://arxiv.org/abs/1903.08008 for details\n",
+      "100%|██████████| 400/400 [00:09<00:00, 43.43it/s]\n"
+     ]
     }
    ],
    "source": [
-    "idata = model.sample(draws=10, tune=1, initvals=my_initvals)"
+    "idata = model.sample(draws=100, tune=100, sampler=\"mcmc\")  # initvals=my_initvals)"
    ]
   },
   {