Merge pull request #12 from srlearn/notebook_badges

📝 Add `binder` and `colab` badges in notebooks
srlearn · Aug 27, 2021 · 275ec0e · 275ec0e
2 parents c853ef5 + 059c52c
commit 275ec0e
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diff --git a/docs/notebooks/00_loading_and_fetching.ipynb b/docs/notebooks/00_loading_and_fetching.ipynb
@@ -6,8 +6,25 @@
    "source": [
     "# Fetching and Loading Data\n",
     "\n",
-    "This is a short tutorial showing how to fetch and load data in context.\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srlearn/relational-datasets/blob/main/docs/notebooks/00_loading_and_fetching.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/srlearn/relational-datasets/HEAD?filepath=docs%2Fnotebooks%2F00_loading_and_fetching.ipynb)\n",
     "\n",
+    "This is a short tutorial showing how to fetch and load data in context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install relational-datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## `fetch()`\n",
     "\n",
     "`fetch()` always returns the path to a specific version of a dataset.\n",
@@ -96,7 +113,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

diff --git a/docs/notebooks/01_converting_propositional_datasets.ipynb b/docs/notebooks/01_converting_propositional_datasets.ipynb
@@ -6,13 +6,25 @@
    "source": [
     "# Converting machine learning benchmark datasets\n",
     "\n",
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srlearn/relational-datasets/blob/main/docs/notebooks/01_converting_propositional_datasets.ipynb)\n",
+    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/srlearn/relational-datasets/HEAD?filepath=docs%2Fnotebooks%2F01_converting_propositional_datasets.ipynb)\n",
+    "\n",
     "[Alexander L. Hayes](https://hayesall.com): *Ph.D. Student, Indiana University*.\n",
     "\n",
     "**Abstract**: Most benchmark machine learning datasets have a *vector-based representation*, where we have a single type of object (people, images, houses) and we learn an *attribute* of those objects (disease risk, cat/dog, median price). This tutorial bridges the gap between vector-based machine learning and relational machine learning, and shows how to view the former in terms of the latter.\n",
     "\n",
     "Examples in this notebook are provided as documentation, and are available under the terms of the Apache 2.0 License."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install numpy relational-datasets"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -389,6 +401,15 @@
     "Here we: (**1**) load the data and class labels, (**2**) split into training and test sets, (**3**) bin the continuous features to discrete, and (**4**) convert to the relational format."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install scikit-learn"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -678,7 +699,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,