Updated jupyter notebook demo titles

Author: andrewherren

demo/notebooks/causal_inference.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Causal Inference Demo Notebook"
+    "# Causal Inference"
    ]
   },
   {

demo/notebooks/causal_inference_feature_subsets.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Causal Inference with Feature Subsets Demo Notebook\n",
+    "# Causal Inference with Feature Subsets\n",
     "\n",
     "This is a duplicate of the main causal inference demo which shows how a user might decide to use only a subset of covariates in the treatment effect forest. \n",
     "Why might we want to do that? Well, in many cases it is plausible that some covariates (for example age, income, etc...) influence the outcome of interest \n",

demo/notebooks/heteroskedastic_supervised_learning.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Supervised Learning with Heteroskedasticity Demo Notebook"
+    "# Heteroskedastic Supervised Learning"
    ]
   },
   {

demo/notebooks/multivariate_treatment_causal_inference.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Causal Inference with Multivariate Treatments Demo Notebook"
+    "# Multivariate Treatment Causal Inference"
    ]
   },
   {

demo/notebooks/prototype_interface.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Demo of the `StochTree` Prototype Interface"
+    "# Low-Level Interface"
    ]
   },
   {

demo/notebooks/serialization.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Serialization Demo Notebook"
+    "# Model Serialization"
    ]
   },
   {
@@ -29,6 +29,7 @@
    "source": [
     "import json\n",
     "import numpy as np\n",
+    "import os\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
@@ -321,6 +322,22 @@
     "plt.axline((0, 0), slope=1, color=\"black\", linestyle=(0, (3,3)))\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Clean up JSON file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.remove('bart.json')"
+   ]
   }
  ],
  "metadata": {

demo/notebooks/supervised_learning.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Supervised Learning Demo Notebook"
+    "# Supervised Learning"
    ]
   },
   {

demo/notebooks/tree_inspection.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Deeper Dive on Fitted Forests in StochTree\n",
+    "# Internal Tree Inspection\n",
     "\n",
     "While out of sample evaluation and MCMC diagnostics on parametric BART components (i.e. $\\sigma^2$, the global error variance) are helpful, it's important to be able to inspect the trees in a BART / BCF model (or a custom tree ensemble model). This vignette walks through some of the features `stochtree` provides to query and understand the forests / trees in a model."
    ]