usage: cleanup

usage.ipynb

@@ -18,24 +18,9 @@
     "\n",
     "From there, we'll structure our data with a graph $\\G = (\\V, \\E, A)$ where $\\V$ is the set of $d_x = |\\V|$ vertices, $\\E$ is the set of edges and $A \\in \\R^{d_x \\times d_x}$ is the adjacency matrix. That matrix represents the weight of each edge, i.e. $A_{i,j}$ is the weight of the edge connecting $v_i \\in \\V$ to $v_j \\in \\V$. The weights of that feature graph thus represent pairwise relationships between features $i$ and $j$. We call that regime **signal classification / regression**, as the samples $x_i$ to be classified or regressed are graph signals.\n",
     "\n",
-    "Some applications of that regime:\n",
-    "*  Task classification for task fMRI: the graph is a functional or anatomical connectome. Graph signals are activations measured by fMRI while the subject is performing some task. The goal is to find which task.\n",
-    "* Anomaly detection: given a transportation, energy or communication network and some traffic measures, predict whether something is going wrong.\n",
-    "* Text classification: each document is modeled as a graph signal (bag-of-words or TF-IDF) and each node represents a word. The graph represents the vocabulary, where edge weights indicate the similarity between words."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "Other modelling possibilities include:\n",
     "1. Using a data graph, i.e. an adjacency matrix $A \\in \\R^{n \\times n}$ which represents pairwise relationships between samples $x_i \\in \\R^{d_x}$. The problem is here to predict a graph signal $y \\in \\R^{n \\times d_y}$ given a graph characterized by $A$ and some graph signals $X \\in \\R^{n \\times d_x}$. We call that regime **node classification / regression**, as we classify or regress nodes instead of signals.\n",
-    "    1. Example application: text classification where each document is modeled as a node and each hyperlink or citation is an edge between them. The graph signals may be bag-of-words or TF-IDF representations of the documents.\n",
-    "    2. [Kipf & Weiling (2016)][kipf_weiling] uses a first-order approximation of our spectral graph convolution for semi-supervised node classification.\n",
-    "2. Another problem of interest is whole graph classification, with or without signals on top. We'll call that third regime **graph classification / regression**. The problem here is to classify or regress a whole graph $A_i \\in \\R^{n \\times n}$ (with or without an associated data matrix $X_i \\in \\R^{n \\times d_x}$) into $y_i \\in \\R^{d_y}$. In case we have no signal, we can use a constant vector $X_i = 1_n$ of size $n$.\n",
-    "    1. Example application: predict some characteristic of a chemical compound given its arrangement.\n",
-    "\n",
-    "[kipf_weiling]: https://arxiv.org/abs/1609.02907"
+    "2. Another problem of interest is whole graph classification, with or without signals on top. We'll call that third regime **graph classification / regression**. The problem here is to classify or regress a whole graph $A_i \\in \\R^{n \\times n}$ (with or without an associated data matrix $X_i \\in \\R^{n \\times d_x}$) into $y_i \\in \\R^{d_y}$. In case we have no signal, we can use a constant vector $X_i = 1_n$ of size $n$."
    ]
   },
   {
@@ -46,19 +31,12 @@
    },
    "outputs": [],
    "source": [
-    "#import graph, coarsening, utils\n",
+    "import graph, coarsening, utils\n",
     "%run -n models.ipynb  # import models\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import shutil\n",
-    "%matplotlib inline\n",
-    "\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 1\n",
-    "%aimport graph\n",
-    "%aimport coarsening\n",
-    "%aimport utils"
+    "%matplotlib inline"
    ]
   },
   {