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fix example
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‎Example.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Embedding Methods and Datasets "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"graphwave\n",
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"degree2\n",
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"drne\n",
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"node2vec\n",
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"degree\n",
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"role2vec\n",
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"line\n",
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"degree1\n",
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"struc2vec\n",
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"xnetmf\n",
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"multilens\n",
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"segk\n",
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"riwalk\n"
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]
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}
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],
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"source": [
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"from semb.methods import load as load_method\n",
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"from semb.methods import get_method_ids\n",
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"for mid in get_method_ids():\n",
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" print(mid)\n",
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" load_method(mid)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"airports\n"
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]
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}
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],
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"source": [
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"from semb.datasets import load as load_dataset\n",
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"from semb.datasets import get_dataset_ids\n",
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"for did in get_dataset_ids():\n",
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" print(did)\n",
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" load_dataset(did)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Get Embedding Result Using struc2vec"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"rm /Users/mark/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/methods/struc2vec/pickles/weights_distances-layer-*.pickle\n"
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]
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}
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],
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"source": [
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"# Define a hyper-class to load the embedding method\n",
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"EmbMethodClass = load_method(\"struc2vec\")\n",
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"\n",
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"# Get airports datasets\n",
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"AirportDataProvider = load_dataset(\"airports\")\n",
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"airport_datasets = AirportDataProvider().get_datasets()\n",
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"brazil_airport_graph = AirportDataProvider().load_dataset(airport_datasets[0])\n",
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"\n",
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"# Call the embedding method with the graph for initialization\n",
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"struc2vec = EmbMethodClass(brazil_airport_graph, \n",
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" num_walks=10, \n",
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" walk_length=80, \n",
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" window_size=10, \n",
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" dim=128, \n",
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" opt1=True, opt2=True, opt3=True, until_layer=2)\n",
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"struc2vec.train()\n",
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"\n",
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"# Get the embedding result with the get_embeddings() method,\n",
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"# The return type is a dictionary with key as node_id and value as the embedding\n",
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"dict_struc2vec_emb = struc2vec.get_embeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'dim': 128,\n",
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" 'walk_length': 80,\n",
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" 'num_walks': 10,\n",
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" 'window_size': 10,\n",
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" 'until_layer': None,\n",
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" 'iter': 5,\n",
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" 'workers': 1,\n",
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" 'weighted': False,\n",
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" 'directed': False,\n",
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" 'opt1': False,\n",
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" 'opt2': False,\n",
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" 'opt3': False}"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# This shows the tunable parameters for the certain embedding method\n",
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"EmbMethodClass.__PARAMS__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Evaluation Library and Perform Evaluation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"from semb.evaluations.classification import *\n",
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"from semb.evaluations.clustering import *\n",
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"from semb.evaluations.utils import *"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Perform Classification"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Read in 131 node labels.\n",
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">>> Label 0 appears 32 times\n",
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">>> Label 1 appears 32 times\n",
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">>> Label 3 appears 35 times\n",
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">>> Label 2 appears 32 times\n"
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]
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}
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],
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"source": [
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"# Read the label file with the get_label(fn) function\n",
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"dict_labels = get_label(\"./sample-data/labels/airport_Brazil_label.txt\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'overall': {'accuracy': {'mean': 0.7633, 'std': 0.0787},\n",
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" 'f1_macro': {'mean': 0.7548, 'std': 0.0765},\n",
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" 'f1_micro': {'mean': 0.7633, 'std': 0.0787},\n",
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" 'auc_micro': {'mean': 0.9182, 'std': 0.0327},\n",
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" 'auc_macro': {'mean': 0.9224, 'std': 0.0301}},\n",
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" 'detailed': {0: {'accuracy': 0.7778,\n",
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" 'f1_macro': 0.7515,\n",
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" 'f1_micro': 0.7778,\n",
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" 'auc_micro': 0.9204,\n",
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" 'auc_macro': 0.9298},\n",
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" 1: {'accuracy': 0.6154,\n",
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" 'f1_macro': 0.6209,\n",
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" 'f1_micro': 0.6154,\n",
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" 'auc_micro': 0.858,\n",
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" 'auc_macro': 0.866},\n",
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" 2: {'accuracy': 0.7692,\n",
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" 'f1_macro': 0.7448,\n",
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" 'f1_micro': 0.7692,\n",
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" 'auc_micro': 0.9413,\n",
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" 'auc_macro': 0.926},\n",
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" 3: {'accuracy': 0.8462,\n",
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" 'f1_macro': 0.8421,\n",
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" 'f1_micro': 0.8462,\n",
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" 'auc_micro': 0.9527,\n",
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" 'auc_macro': 0.9561},\n",
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" 4: {'accuracy': 0.8077,\n",
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" 'f1_macro': 0.8148,\n",
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" 'f1_micro': 0.8077,\n",
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" 'auc_micro': 0.9186,\n",
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" 'auc_macro': 0.9339}}}"
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]
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},
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"execution_count": 31,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"perform_classification(dict_struc2vec_emb, dict_labels)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Perform Clustering"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
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" FutureWarning)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'overall': {'purity': [0.6412213740458015], 'nmi': [0.4771373196787525]}}"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"perform_clustering(dict_struc2vec_emb, dict_labels)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Perform Centrality Correlation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from semb.evaluations.centrality_correlation import *\n",
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"centrality_correlation(brazil_airport_graph, \n",
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" dict_struc2vec_emb, \n",
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" centrality='clustering_coeff', \n",
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" similarity='euclidean')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "SEMB",
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"language": "python",
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"name": "semb"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

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