updated examples with proper paths

nestordemeure · Mar 21, 2020 · bb6c792 · bb6c792
1 parent 81d22a2
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diff --git a/examples/1_basic_usage.ipynb b/examples/1_basic_usage.ipynb
@@ -63,22 +63,29 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true
+   },
    "source": [
     "## Regression"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
    "source": [
     "### Problem definition"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'salary']\n",
@@ -89,7 +96,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "data = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
@@ -100,38 +109,49 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
    "source": [
     "### Model"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Creates a gaussian process model:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "learn = tabularGP_learner(data)"
    ]
   },
   {
    "cell_type": "raw",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Trains the model:"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -193,14 +213,19 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true,
+    "hidden": true
+   },
    "source": [
     "### Uncertainty"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Gaussian processes produce a mean (the usual output) and a standard deviation (modelizing the uncertainty on the result).\n",
     "Here they are stored respectively in the index 0 and 1 of the last dimenssion of the tensor outputed by the model:"
@@ -209,7 +234,9 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -235,15 +262,19 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true
+   },
    "source": [
     "### Problem definition"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
@@ -254,7 +285,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "data = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)\n",
@@ -265,22 +298,28 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true
+   },
    "source": [
     "### Model"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Creates a gaussian process model (notice that nothing is doen to indicate that this is a classification problem):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [],
    "source": [
     "learn = tabularGP_learner(data)"
@@ -289,7 +328,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "data": {
@@ -351,7 +392,9 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "heading_collapsed": true
+   },
    "source": [
     "### Uncertainty"
    ]
@@ -360,24 +403,29 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
+    "hidden": true,
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "from loss_functions import gp_softmax"
+    "from tabularGP.loss_functions import gp_softmax"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "Classification models also have a standard deviation but, following the pytorch convention, the output is a raw logit and not a genuine probability (hence the means might not sum to one):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -397,15 +445,19 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "source": [
     "The proper way to get probabilities is to apply `gp_softmax` to your raw output (as you would apply a `softmax` to a traditional classification output):"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "hidden": true
+   },
    "outputs": [
     {
      "name": "stdout",