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mzarghammzargham
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Hacky reorg for scattered code (#31)
Reorg, refactor and rerun notebooks; updated PR description with links and next steps. Due to the reorg, many of these experiments file references were broken, i fixed the references and reran all of the experiments. I believe one can still read those notes after I merge: #31 (comment)
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‎analysis/Contextualizing Cred.md

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‎experiments/.ipynb_checkpoints/biline-checkpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'page_ranker'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-99a27dde11b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#import json\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#import os\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpage_ranker\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;31m#import import_graph as ig\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0minspect_subgraph\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0misg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'page_ranker'"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import networkx as nx\n",
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"#import json\n",
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"#import os\n",
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"import page_ranker as pr\n",
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"#import import_graph as ig\n",
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"import inspect_subgraph as isg\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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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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"cwd = os.getcwd()\n",
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"\n",
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"dir_as_list = cwd.split('/')\n",
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"base_path=''\n",
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"go = True\n",
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"i=0\n",
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"while go:\n",
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" base_path= base_path+dir_as_list[i]+'/'\n",
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" if dir_as_list[i]=='research':\n",
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" go = False\n",
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" i=i+1\n",
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" \n",
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"rel_path = \"sample-graphs/sourcecred_sourcecred.json\"\n",
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"\n",
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"abs_file_path = os.path.abspath(os.path.join(base_path, rel_path))"
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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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"N =10"
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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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"line = nx.path_graph(N, create_using=nx.MultiDiGraph)\n",
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"edges = line.edges\n",
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"\n",
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"G = nx.MultiDiGraph()\n",
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"for e in edges:\n",
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" G.add_edge(e[0],e[1])\n",
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" G.add_edge(e[1],e[0])\n",
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" \n",
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"nx.set_node_attributes(G,'vanilla', 'type')\n",
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"nx.set_edge_attributes(G,'vanilla', 'type')"
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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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"G.nodes[0]"
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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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"G.edges[(0,1,0)]"
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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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"isg.inspectSubGraph(G, G.nodes, expand=False, verbose=False, label=True,pos = \"kk\")"
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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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"G.edges[(0,1,0)]"
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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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"alpha = .1\n",
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"iterations = 25\n",
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"\n",
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"seed = {n:1/len(G.nodes) for n in G.nodes}\n",
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"#seed = {n:0 for n in G.nodes}\n",
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"#seed[0] =1\n",
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"\n",
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"node_wt_by_type = {'vanilla': 1}\n",
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"edge_wt_by_type = {'vanilla': (1,1/2)}\n",
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"\n",
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"self_loop_wt= .1"
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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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"r, df, g = pr.pageRanker(G,\n",
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" alpha,\n",
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" iterations,\n",
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" seed=seed,\n",
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" initial_value = seed,\n",
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" lazy=True,\n",
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" lazy_decay=True,\n",
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" self_loop_wt=self_loop_wt, \n",
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" node_wt_by_type =node_wt_by_type,\n",
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" edge_wt_by_type=edge_wt_by_type)"
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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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"df.tail(1).T.plot(kind='bar',legend=False, title='PageRank Distribution')"
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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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"df.plot(figsize=(20,10))"
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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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"df.sum(axis=1).plot()"
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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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"ns = [10000*G.nodes[n]['score'] for n in G.nodes]\n",
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"nl = {n:n for n in G.nodes}\n",
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"\n",
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"nx.draw_circular(G, node_size=ns, labels=nl, font_size=8, alpha=.5)"
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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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"nx.get_node_attributes(G,'self_wt')"
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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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"nx.get_edge_attributes(G,'out_weight')"
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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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"nx.get_edge_attributes(G,'in_weight')"
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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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"nx.get_node_attributes(G,'total_wt')"
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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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"all_nbr = {n:list(nx.all_neighbors(G,n)) for n in G.nodes}"
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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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"all_nbr"
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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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"nbr = {n:list(G.neighbors(n)) for n in G.nodes}"
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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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"nbr"
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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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"G.edges"
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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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{
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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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": "3.6.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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