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Creato con Colaboratory
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@nickprock
nickprock committed Feb 14, 2022
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "HITS.ipynb",
"private_outputs": true,
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyN4Xg++97xwPc2Nr8C+c6hx",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/nickprock/influencer/blob/master/notebook/HITS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"source": [
"import numpy as np\n",
"import torch"
],
"metadata": {
"id": "Yl-axB2vbPSR"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install git+https://github.com/nickprock/influencer.git"
],
"metadata": {
"id": "wSSHSYpDbUYB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import influencer\n",
"influencer.__version__"
],
"metadata": {
"id": "QzAd2MnIbnqB"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install --upgrade jax jaxlib"
],
"metadata": {
"id": "4017MOi1o0UU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from influencer.centrality import hits as npHITS\n",
"from influencer.torch_centrality import hits as torchHITS"
],
"metadata": {
"id": "JcdonChabqGi"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"torch.cuda.is_available()"
],
"metadata": {
"id": "TQe7eU-pUXcU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# lazy_cerntrality version\n",
"\n",
"import jax.numpy as jnp\n",
"from jax import jit\n",
"\n",
"def jhits(adjMatrix, p: int = 100):\n",
" n = adjMatrix.shape[0]\n",
" \n",
" a = jnp.ones([1,n])\n",
" h = jnp.ones([1,n])\n",
" \n",
" pa=a\n",
" \n",
" authority = {}\n",
" hub = {}\n",
" \n",
" for k in range(1,p):\n",
" h1 = jnp.dot(adjMatrix, pa.T)/jnp.linalg.norm(jnp.dot(adjMatrix, pa.T))\n",
" a1 = jnp.dot(adjMatrix.T, h1)/jnp.linalg.norm(jnp.dot(adjMatrix.T , h1))\n",
" \n",
" h = jnp.vstack((h,jnp.dot(adjMatrix, a[k-1,:].T)/jnp.linalg.norm(jnp.dot(adjMatrix, a[k-1,:].T))))\n",
" a = jnp.vstack((a,jnp.dot(adjMatrix.T, h[k,:].T)/jnp.linalg.norm(jnp.dot(adjMatrix.T, h[k,:].T))))\n",
" \n",
" pa = a1.T\n",
" \n",
" for i in range(n):\n",
" authority[str(i)] = a[-1,i]\n",
" hub[str(i)] = h[-1,i]\n",
" \n",
" return hub, authority, h, a"
],
"metadata": {
"id": "MfiQGaGVo1lL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"jit_jhits = jit(jhits)"
],
"metadata": {
"id": "RI6BjpI6o1ZL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import time"
],
"metadata": {
"id": "DLRt_OOEUGAP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"np.random.seed(42)\n",
"\n",
"num_nodes = [x for x in range(500,15000, 500)]\n",
"time_np = []\n",
"time_torch = []\n",
"time_torch_cpu = []\n",
"time_jnp = []"
],
"metadata": {
"id": "eaDzCz8cUGk9"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"for N in num_nodes:\n",
" adjM = np.random.rand(N, N)\n",
" adjM[adjM>0.5]=1\n",
" adjM[adjM<=0.5]=0\n",
" start_time1 = time.time()\n",
" _, _,_,_ = npHITS(adjM, p=10)\n",
" exe_time1 = time.time() - start_time1\n",
" MT = torch.from_numpy(adjM).float().to(0)\n",
" start_time2 = time.time()\n",
" _,_,_,_ = torchHITS(MT, p=10)\n",
" exe_time2 = time.time() - start_time2\n",
" MT_cpu = torch.from_numpy(adjM).float()\n",
" start_time3 = time.time()\n",
" _,_,_,_ = torchHITS(MT_cpu, p=10, device='cpu')\n",
" exe_time3 = time.time() - start_time3\n",
" start_time4 = time.time()\n",
" _, _,_,_ = jhits(adjM, p=10)\n",
" exe_time4 = time.time() - start_time4\n",
" time_np.append(exe_time1)\n",
" time_torch.append(exe_time2)\n",
" time_torch_cpu.append(exe_time3)\n",
" time_jnp.append(exe_time4)"
],
"metadata": {
"id": "98GaRZ5QmyYP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt"
],
"metadata": {
"id": "M2fXTQ2UcK0-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(18,10))\n",
"plt.plot(num_nodes,time_np, 'bo')\n",
"plt.plot(num_nodes,time_torch, 'ro')\n",
"plt.plot(num_nodes,time_torch_cpu, 'go')\n",
"plt.plot(num_nodes,time_jnp, 'ko')\n",
"plt.xlabel(\"nodes\")\n",
"plt.ylabel(\"seconds\")\n",
"plt.title(\"HITS algorithm execution time\")\n",
"plt.legend([\"numpy\", \"torch\", \"torch_CPU\", \"JAX\"])\n",
"plt.show()"
],
"metadata": {
"id": "fPsHmIivcMMF"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"plt.figure(figsize=(18,10))\n",
"plt.plot(num_nodes,time_np, 'bo')\n",
"plt.plot(num_nodes,time_torch, 'ro')\n",
"plt.plot(num_nodes,time_torch_cpu, 'go')\n",
"plt.xlabel(\"nodes\")\n",
"plt.ylabel(\"seconds\")\n",
"plt.title(\"HITS algorithm execution time\")\n",
"plt.legend([\"numpy\", \"torch\", \"torch_CPU\"])\n",
"plt.show()"
],
"metadata": {
"id": "WsiYMTzS_NgL"
},
"execution_count": null,
"outputs": []
}
]
}

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