Interactive Topic Map

├── modeling ├── __init__.py ├── questions.txt.gz ├── HOWTO ├── stopwords.list ├── convert_corpus.py ├── build_graph.py └── graph.json ├── interface ├── data │ └── graph.json ├── HOWTO ├── index.html ├── css │ └── style.css └── js │ ├── color.min.js │ ├── topicGraph.js │ └── underscore-min.js └── README.md /modeling/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /interface/data/graph.json: -------------------------------------------------------------------------------- 1 | ../../modeling/graph.json -------------------------------------------------------------------------------- /modeling/questions.txt.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sronnqvist/topicMap/HEAD/modeling/questions.txt.gz -------------------------------------------------------------------------------- /interface/HOWTO: -------------------------------------------------------------------------------- 1 | Run local web server: python -m SimpleHTTPServer 2 | Open http://127.0.0.1:8000/ in browser. Best performance in Chrome. 3 | Folder "data" contains example data, may be replaced by output JSON-file from modeling scripts. 4 | 5 | -------------------------------------------------------------------------------- /modeling/HOWTO: -------------------------------------------------------------------------------- 1 | Input data: corpus as plain text file containing one document per line 2 | 3 | Run: 4 | python convert_corpus.py 5 | python build_graph.py 6 | 7 | Further configuration in source code, modeling parameters, pre-processing, etc. 8 | 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /interface/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | Interactive Topic Map 6 | 7 | 8 | 12 |

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Selected

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17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | -------------------------------------------------------------------------------- /modeling/stopwords.list: -------------------------------------------------------------------------------- 1 | -- 2 | a 3 | about 4 | all 5 | also 6 | an 7 | and 8 | any 9 | are 10 | as 11 | at 12 | b 13 | be 14 | been 15 | by 16 | for 17 | from 18 | has 19 | have 20 | he 21 | her 22 | his 23 | http 24 | i 25 | if 26 | in 27 | inc 28 | is 29 | its 30 | may 31 | more 32 | not 33 | of 34 | on 35 | one 36 | or 37 | other 38 | our 39 | per 40 | plc 41 | s 42 | 's 43 | she 44 | than 45 | that 46 | the 47 | their 48 | these 49 | this 50 | to 51 | up 52 | was 53 | we 54 | when 55 | which 56 | will 57 | with 58 | said 59 | it 60 | after 61 | but 62 | were 63 | would 64 | had 65 | who 66 | they 67 | can 68 | you 69 | over 70 | de 71 | two 72 | most 73 | into 74 | them 75 | what 76 | like 77 | now 78 | your 79 | three 80 | no 81 | so 82 | only 83 | where 84 | how 85 | some 86 | 't 87 | while 88 | through 89 | just 90 | such 91 | further 92 | there 93 | since 94 | last 95 | before 96 | during 97 | both 98 | no location 99 | could 100 | each 101 | those 102 | should 103 | please 104 | within 105 | out 106 | under 107 | made 108 | make 109 | do 110 | us 111 | then 112 | upon 113 | wherein 114 | because 115 | still 116 | very 117 | many 118 | among 119 | against 120 | include 121 | including 122 | based 123 | used 124 | using 125 | around 126 | across 127 | 128 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # topicMap 2 | 3 | Exploratory topic modeling with distributional semantics and interactive visualization 4 | 5 | 6 | By: Samuel Rönnqvist (sronnqvi@abo.fi) 7 | Release 0.1 8 | License: Creative Commons, Attribution-ShareAlike 4.0 International 9 | 10 | --- --- --- 11 | 12 | For academic purposes, please cite: 13 | 14 | Samuel Rönnqvist. 2015. Exploratory topic modeling with distributional semantics. In Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings. 15 | 16 | ``` 17 | @Inbook{Ronnqvist2015topicMap, author="R{\"o}nnqvist, Samuel", editor="Fromont, Elisa 18 | and De Bie, Tijl and van Leeuwen, Matthijs", title="Exploratory Topic Modeling with Distributional Semantics", bookTitle="Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne. France, October 22 -24, 2015. Proceedings", 19 | year="2015", publisher="Springer International Publishing", address="Cham", pages="241--252", isbn="978-3-319-24465-5", doi="10.1007/978-3-319-24465-5_21", url="http://dx.doi.org/10.1007/978-3-319-24465-5_21"} 20 | 21 | ``` 22 | 23 | --- --- --- 24 | 25 | The code base consists of two parts: 26 | 27 | 1. Python scripts for: 28 | - converting corpus, training word vectors 29 | - constructing topic map 30 | 31 | 2. JavaScript-based visualization of topic map 32 | 33 | 34 | -------------------------------------------------------------------------------- /interface/css/style.css: -------------------------------------------------------------------------------- 1 | /* topicGraph - (c) 2014 Samuel Ronnqvist - sronnqvi@abo.fi */ 2 | 3 | .overlay { 4 | fill: none; 5 | pointer-events: all; 6 | } 7 | 8 | .gnode { 9 | font-family: sans-serif; 10 | font-size: 9pt; 11 | fill: black; 12 | cursor: pointer; 13 | } 14 | 15 | .node { 16 | stroke-width: 1.5px; 17 | fill: #15181f; 18 | } 19 | 20 | .link { 21 | stroke-width: 2.2px; 22 | } 23 | 24 | .graph{ 25 | margin-left: auto; 26 | margin-right: auto; 27 | } 28 | 29 | body{ 30 | margin: 0px; 31 | background-color: #15181f; 32 | background-color: #fff; 33 | } 34 | 35 | .info{ 36 | position: fixed; 37 | top: 150px; 38 | left: 0px; 39 | width: 200px; 40 | background-color: #000; 41 | opacity: 1; 42 | color: #000; 43 | font-family: sans-serif; 44 | font-size: 10pt; 45 | padding: 8px; 46 | display: none; 47 | padding: 10px; 48 | border-top: 1px solid #333; 49 | border-right: 1px solid #333; 50 | border-bottom: 1px solid #333; 51 | border-bottom-right-radius: 10px; 52 | border-top-right-radius: 10px; 53 | font-size: 9pt; 54 | color: #666; 55 | } 56 | 57 | .info h4{ 58 | margin-top: 3px; 59 | } 60 | 61 | .header{ 62 | position: absolute; 63 | top: 0px; 64 | left: 0px; 65 | width: 165px; 66 | padding: 10px; 67 | padding-top: 0px; 68 | padding-bottom: 0px; 69 | border-right: 1px solid #333; 70 | border-bottom: 1px solid #333; 71 | border-bottom-right-radius: 10px; 72 | background-color: #0b0e14; 73 | font-family: sans-serif; 74 | font-size: 9pt; 75 | color: #666; 76 | display: none; 77 | } 78 | 79 | .header h1{ 80 | font-size: 12pt; 81 | } 82 | 83 | .controls{ 84 | position: fixed; 85 | top: 5px; 86 | right: 5px; 87 | } 88 | .controls input{ 89 | width: 25px; 90 | } -------------------------------------------------------------------------------- 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f.RGBtoHSL(this.rgbData())};d.rgb=function(){return"rgb("+this.rgbData().join(",")+")"};d.rgba=function(){return"rgba("+this.channels.join(",")+")"};d.hsl=function(){var h=this.hslData();return"hsl("+h[0]*360+","+(h[1]*100)+"%,"+(h[2]*100)+"%)"};d.hsla=function(){var h=this.hslData();return"hsla("+h[0]*360+","+(h[1]*100)+"%,"+(h[2]*100)+"%,"+this.channels[3]+")"};return f}()); 3 | -------------------------------------------------------------------------------- /modeling/convert_corpus.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import sys, re 4 | from gensim import corpora, models 5 | import unidecode 6 | import logging 7 | import numpy 8 | import collections 9 | import time 10 | 11 | 12 | class MyDocs(object): 13 | def __init__(self, filename): 14 | self.filename = filename 15 | def __iter__(self): 16 | for line in open(self.filename): 17 | line = unidecode.unidecode(line.decode('utf-8')) 18 | line = line.replace('`','\'').replace('\'', ' \' ') 19 | line = line.replace('/',' / ') 20 | line = re.sub("\d+([\-,\.]\d+)+", "000", line) 21 | line = re.sub("\-{2,}", "", line) 22 | line = re.sub("(^\-|\-$)", "", line) 23 | line = re.sub("(\s\-|\-\s)", " ", line) 24 | line = line.replace(":", " : ") 25 | line = line.replace(",", " , ") 26 | line = filter(lambda x: x.isalpha() or x.isdigit() or x in ".- ", line) 27 | line = re.sub("\.\s", " ", line) 28 | line = line.split() 29 | line = map(lambda x: x.lower(), line) 30 | yield line 31 | 32 | logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) 33 | 34 | print "Reading corpus..." 35 | texts = MyDocs(sys.argv[1]) 36 | texts = [text for text in texts] 37 | 38 | print "Detecting phrases..." 39 | bigram = models.Phrases(texts) 40 | models.phrases.prune_vocab(bigram.vocab, numpy.percentile(bigram.vocab.values(), 90)) 41 | 42 | texts = [[token.replace('_', ' ') for token in bigram[text]] for text in texts] 43 | 44 | # Generate gensim dictionary 45 | print "Building dictionary..." 46 | dictionary = corpora.Dictionary(texts) 47 | 48 | # Save dictionary 49 | if len(sys.argv) > 2: 50 | dictionary.save(sys.argv[2]+".dict") 51 | else: 52 | print "Usage: %s " % sys.argv[0] 53 | exit() 54 | 55 | # Save converted corpus 56 | print "Building BOWs..." 57 | corpus = [dictionary.doc2bow(text) for text in texts] 58 | corpora.MmCorpus.serialize(sys.argv[2]+".mm", corpus) 59 | 60 | print "Building word vectors..." 61 | 62 | evaluate = False 63 | 64 | # Modeling parameters: 65 | # size = vector size, V 66 | # window = context size, C 67 | # iter = epochs, E 68 | 69 | for size in [85]: 70 | for window in [15]: 71 | start = time.time() 72 | print "size:",size, ", win:",window 73 | iter = 10 74 | logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) 75 | model = models.Word2Vec(texts, min_count=1, workers=4, size=size, iter=iter, window=window) 76 | for handler in logging.root.handlers[:]: 77 | logging.root.removeHandler(handler) 78 | print "size:",size, ", win:",window, ", time:",time.time()-start 79 | if evaluate: 80 | logging.basicConfig(format='size:%d,win:%d,iter:%d' % (size, window, iter) + ' %(message)s', level=logging.INFO, filename="accuracy.log", filemode='a') 81 | ac = model.accuracy("questions.txt") 82 | for handler in logging.root.handlers[:]: 83 | logging.root.removeHandler(handler) 84 | model.save(sys.argv[2]+".w2v") 85 | 86 | -------------------------------------------------------------------------------- /modeling/build_graph.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python 2 | 3 | import sys, collections, math 4 | from gensim import corpora, models 5 | import networkx as nx 6 | import matplotlib.pyplot as plt 7 | import pylab 8 | import json 9 | import numpy 10 | import logging 11 | 12 | # Network construction parameters 13 | # number of most frequent terms, N 14 | topn = 500 15 | # threshold percentile, P 16 | th = 98.5 17 | # max links per node, L 18 | node_lim = 12 19 | 20 | logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) 21 | 22 | if len(sys.argv) < 2: 23 | print "Usage: %s " % sys.argv[0] 24 | exit() 25 | 26 | # Load files 27 | dictionary = corpora.Dictionary.load(sys.argv[1]+".dict") 28 | corpus = corpora.MmCorpus(sys.argv[1]+".mm") 29 | 30 | stopwords = set(map(str.strip, open("stopwords.list").read().split('\n'))) 31 | 32 | print "Loading semantic model..." 33 | sem_model = models.Word2Vec.load("%s.w2v" % sys.argv[1]) 34 | 35 | termcntr = collections.defaultdict(lambda:0) 36 | 37 | for doc in corpus: 38 | for term, freq in doc: 39 | termcntr[term] += freq 40 | 41 | termtot = float(sum(termcntr.values())) 42 | 43 | revdict = dict([(b, a) for a, b in dictionary.items()]) 44 | 45 | top_terms = [(dictionary[x[0]], x[1]/termtot) for x in sorted(termcntr.items(), key=lambda x: x[1])[-1*topn:] 46 | if dictionary[x[0]] not in stopwords and len(dictionary[x[0]]) > 1 and 47 | not dictionary[x[0]].isdigit() and x[1] > 2] 48 | 49 | 50 | visdict = set() 51 | g = nx.Graph() 52 | 53 | bucket = [] 54 | terms = [t for t, w in top_terms] 55 | for t_i, t1 in enumerate(terms): 56 | g.add_node(t1) 57 | queue = [] 58 | for t2 in terms[(t_i+1):]: 59 | try: 60 | sim = sem_model.similarity(t1, t2) 61 | except KeyError: 62 | sim = 0 63 | if sim > 0.0: 64 | #print t1, t2, sim 65 | queue.append((sim, t2)) 66 | #for tx, w in [x for x in sem_model.most_similar([t1,t2], topn=20) if termcntr[revdict[x[0]]] > termcntr[revdict[top_terms[0][0]]] and x[1] > 0.6]: 67 | # g.add_edge(t1 +'+'+t2, tx, {'w': w}) 68 | bucket.append(sim) 69 | print t1 70 | for sim, t2 in sorted(queue)[-50:]: 71 | #print t2, 72 | g.add_edge(t1, t2, {'w': sim}) 73 | #print 74 | 75 | 76 | limit = numpy.percentile(bucket, th) 77 | 78 | weights = [x for x in bucket if x > limit] 79 | w_max = max(weights) 80 | w_min = min(weights) 81 | normalize = lambda w: (w-w_min)/(w_max-w_min) 82 | 83 | term_weights = collections.defaultdict(lambda: 0) 84 | term_weights.update(dict(top_terms)) 85 | 86 | for node, edges in g.edge.items(): 87 | if len(edges) > node_lim: 88 | for node2, data in sorted(g.edge[node].items(),key=lambda x: x[1]['w'])[:(len(edges)-node_lim)]: 89 | g.remove_edge(node, node2) 90 | 91 | connected_nodes = set(reduce(lambda a,b: a+b, [[x[0], x[1]] for x in g.edges(data=True) if x[2]['w'] > limit])) 92 | 93 | jsondata = {} 94 | jsondata['nodes'] = [{'name': x[0], 95 | 'group': x[1]['group'] if 'group' in x[1] else 0, 96 | 'prop': term_weights[x[0]] 97 | } for x in g.nodes(data=True) if x[0] in connected_nodes] 98 | node_id_lookup = dict(zip([x['name'] for x in jsondata['nodes']], range(len(jsondata['nodes'])))) 99 | jsondata['links'] = [{'source': node_id_lookup[x[0]], 'target': node_id_lookup[x[1]], 'value': normalize(x[2]['w'])} for x in g.edges(data=True) if x[2]['w'] > limit] 100 | 101 | for i, _ in enumerate(jsondata['nodes']): 102 | try: 103 | jsondata['nodes'][i]['name'] = jsondata['nodes'][i]['name'].encode('utf-8') 104 | except: 105 | jsondata['nodes'][i]['name'] = "///" 106 | 107 | open("graph.json","w").write(json.dumps(jsondata)) 108 | 109 | ### TODO: Sem model on corpus to reflext local contexts, focus by most_similar 110 | -------------------------------------------------------------------------------- /interface/js/topicGraph.js: -------------------------------------------------------------------------------- 1 | /* topicGraph - (c) 2014-15 Samuel Ronnqvist - sronnqvi@abo.fi */ 2 | 3 | 4 | 5 | var width = window.innerWidth-4, 6 | height = window.innerHeight-4; 7 | 8 | function color(h){return Color.hsl(h,0.7,0.4)} 9 | 10 | function prob2opacity(p){ return (p)*0.8+0.1; } 11 | 12 | function neighboring(a, b) { 13 | var n = neighbors[a]; 14 | if(a == undefined || b == undefined || n == undefined) return false; 15 | return n.indexOf(b) > -1; 16 | } 17 | 18 | function node_focus(cnode){ 19 | gnodes 20 | .attr("fill",function(o){ 21 | if(neighboring(cnode.index, o.index)){ 22 | if(o.group == 1 && o.index != cnode.index){ 23 | var size = node_weights[[o.name, cnode.name]]*16+7; 24 | return("#000");//#fff"); 25 | } //else return("9pt") 26 | } //else return "9pt"; 27 | }); 28 | 29 | // Change node opacities 30 | gnodes.transition().delay(50).attr("opacity", function(o) { 31 | if(neighboring(cnode.index, o.index)) return "0.9" 32 | else {return "0.2"}; 33 | }).duration(200); 34 | /* 35 | gnodes.selectAll("text").transition().delay(50).attr("font-weight", function(o) { 36 | if(neighboring(cnode.index, o.index)) return "bold"; 37 | else {return "normal"}; 38 | }).duration(200);*/ 39 | 40 | d3.select(this).attr("stroke-opacity","1"); 41 | 42 | // Change link opacities 43 | var retval; 44 | link.transition().delay(50).attr('opacity', function(l) { 45 | if(cnode == l.source){ 46 | retval = 0.8; 47 | } else if(cnode == l.target){ 48 | retval = 0.8; 49 | } else { 50 | retval = 0.2; 51 | } 52 | return retval; 53 | }) 54 | /*.attr('stroke', function(l) { 55 | if(cnode == l.source){ 56 | retval = "#acf"; 57 | } else if(cnode == l.target){ 58 | retval = "#acf"; 59 | } else { 60 | retval = "#666"; 61 | } 62 | return retval; 63 | })*/ 64 | .duration(200); 65 | } 66 | 67 | function link_focus(clink){ 68 | link.attr('opacity', function(l) { 69 | if(clink.source.index != l.source.index && clink.target.index != l.target.index){ 70 | return 0.15; 71 | } else return prob2opacity(l.value); 72 | }); 73 | } 74 | 75 | function unfocus(){ 76 | if(mouseDown) return; 77 | gn = d3.selectAll(".gnode"); 78 | gn.transition() 79 | .attr("opacity","1.0") 80 | .duration(200); 81 | /*gn.selectAll("text") 82 | .attr("font-weight","normal");*/ 83 | 84 | gn.style('font-size','9pt'); 85 | 86 | link.transition() 87 | .attr('opacity', function(l) { 88 | return prob2opacity(l.value); 89 | }) 90 | .duration(200); 91 | } 92 | 93 | function node_select(cnode){ 94 | // Manage selection list 95 | if(cnode.name in selected){ 96 | // Deselect node 97 | delete selected[cnode.name]; 98 | } else { 99 | // Select node 100 | selected[cnode.name] = true; 101 | } 102 | 103 | // Set appearance for selected nodes 104 | for(n in selected){ 105 | nd = d3.select("#node_"+n) 106 | if(n[0] == 'T'){ 107 | // Topic nodes 108 | nd.attr("fill",function(d){ return Color.hsl(d.topic/ntopics, 0.7, 0.2) }); 109 | } else { 110 | // Keyword nodes 111 | nd.select("text") 112 | .style("text-decoration","underline") 113 | .style("font-weight","bold") 114 | //.style("font-color","#eee"); 115 | .style("font-color","#111"); 116 | } 117 | } 118 | 119 | // Set appearance for deselected nodes 120 | var not_selected = {}; 121 | _.filter(_.map(nodes, function(x){ return x.name }), function(x){ return !(x in selected) }).forEach( 122 | function(n){ not_selected[n] = true } 123 | ); 124 | for(n in not_selected){ 125 | nd = d3.select("#node_"+n); 126 | if(n[0] == 'T'){ 127 | // Topic nodes 128 | nd.attr("fill", "black"); 129 | } else { 130 | // Keyword nodes 131 | nd.select("text") 132 | .style("text-decoration","inherit") 133 | .style("font-weight","inherit") 134 | .style("font-color","inherit"); 135 | } 136 | } 137 | 138 | update_panel(); 139 | 140 | } 141 | 142 | function update_panel(){ 143 | var sel = d3.select(".info"); 144 | sel.style("display","block"); 145 | sel.selectAll("p").data([]).exit().remove(); 146 | 147 | if(_.keys(selected).length == 0){ sel.style("display","none"); return } 148 | sel.append("p").text(_.reduce(_.keys(selected), function(a,b){return a+', '+b})); 149 | 150 | sel.append("p").html("Documents"); 151 | var out = "

"+titl+' '+d[1]+"

"); 161 | } 162 | 163 | function link_select(link){ 164 | // Not implemented / needed 165 | } 166 | 167 | function linkStrengthFunc(d){ 168 | return d.value*0.7+0.3; 169 | } 170 | 171 | var force = d3.layout.force() 172 | .charge(-200) 173 | /*.linkDistance(function(d,i) { 174 | termDegree = _.filter(links, function(x){return(x.source.index == i || x.target.index == i)}).length; 175 | return ((180-d.value*100)*Math.pow(termDegree), 1.2); 176 | })*/ 177 | .linkDistance(150) 178 | .size([width, height]) 179 | .gravity(0.025) 180 | .friction(0.9) 181 | .linkStrength(function(d){ 182 | return Math.pow(d.value,2)*1.2; 183 | }); 184 | 185 | var mouseDown = 0; 186 | document.body.onmousedown = function() { 187 | mouseDown = 1; 188 | } 189 | 190 | document.body.onmouseup = function() { 191 | mouseDown = 0; 192 | } 193 | 194 | var nodes, links, node, link, gnodes, neighbors = {}, node_weights = {}, selected = {}, ntopics, node_names; 195 | 196 | var zoomer = d3.behavior.zoom().scaleExtent([-8, 8]).on("zoom", zoom); 197 | 198 | var svg = d3.select("body") 199 | .append("svg") 200 | .attr("id", "svgRoot") 201 | .attr("width", width) 202 | .attr("height", height) 203 | .attr("xmlns", "http://www.w3.org/2000/svg") 204 | .attr("version", 1.1) 205 | .append("g") 206 | .call(zoomer) 207 | .append("g") 208 | .attr("id", "allContent"); 209 | 210 | svg.append("rect") 211 | .attr("class", "overlay") 212 | .attr("width", width*10) 213 | .attr("height", height*10) 214 | .attr("x",-5*width) 215 | .attr("y",-5*height) 216 | .attr("stroke","#aaa") 217 | .attr("fill", "none"); 218 | 219 | function zoom() { 220 | svg.attr("transform", "translate(" + d3.event.translate + ")scale(" + d3.event.scale + ")"); 221 | } 222 | 223 | zoomer.scale(0.2); 224 | zoomer.translate([width*0.4,height*0.4]); 225 | zoomer.event(svg); 226 | 227 | function zoomBy(factor){ 228 | zoomer.scale(zoomer.scale()*factor); 229 | //zoomer.translate([width*0.45,height*0.45]); 230 | zoomer.event(svg); 231 | } 232 | 233 | nodes = []; 234 | links = []; 235 | 236 | var graph = d3.json("data/graph.json", function(error, graph) { 237 | nodes = graph.nodes; 238 | links = graph.links; 239 | 240 | force 241 | .nodes(graph.nodes) 242 | .links(graph.links) 243 | .start(); 244 | 245 | ntopics = _.filter(nodes, function(x){return x.group == 0}).length; 246 | 247 | link = svg.selectAll(".link") 248 | .data(graph.links) 249 | .enter().append("line") 250 | .attr("class", "link") 251 | .attr("stroke-width", function(d) { return 2.2; }) 252 | .attr("stroke", function(d,i) { 253 | return "#024"; 254 | /* //if(d.value > 0.7) return "#24e"; 255 | if(nodes[d.source.index].group == 0) return "#aaa";//"#666";//color(nodes[d.source.index].topic/ntopics) 256 | else if(nodes[d.target.index].group == 0) return "#aaa";//"#666";//color(nodes[d.target.index].topic/ntopics) 257 | else return "#000";//"#fff";*/ 258 | }) 259 | .attr("opacity", function(d) { return prob2opacity(d.value) }); 260 | 261 | var drag = force.drag() 262 | .on("dragstart", dragstarted) 263 | .on("drag", dragged) 264 | .on("dragend", dragended); 265 | 266 | function dragstarted(d) { 267 | d3.event.sourceEvent.stopPropagation(); 268 | //d.fixed = true; 269 | d3.select(this).classed("dragging", true); 270 | } 271 | 272 | function dragged(d) { 273 | d3.select(this).attr("cx", d.x = d3.event.x).attr("cy", d.y = d3.event.y); 274 | } 275 | function dragended(d) { 276 | d3.select(this).classed("dragging", false); 277 | } 278 | 279 | gnodes = svg.selectAll(".gnode") 280 | .data(graph.nodes) 281 | .enter() 282 | .append("g") 283 | .classed("gnode", true) 284 | .attr("id", function(d){ return ("node_"+d.name) }) 285 | .on("mouseenter", node_focus) 286 | .on("mouseleave", unfocus) 287 | .on("dblclick", node_select) 288 | .call(force.drag); 289 | 290 | gnodes.append("circle") 291 | .attr("class", "node") 292 | .attr("r", function(d){ 293 | if(d.group == 0) return Math.pow(d.prop,0.9)*200; //d.prop*200 //50*Math.sqrt(d.prop/3.1415926) 294 | else return 5; 295 | }) 296 | .attr("opacity", function(d){ 297 | if(d.group == 0) return 0.9; 298 | else return 0.3; 299 | }) 300 | .attr("stroke", function(d,i) { 301 | if(d.group == 0) 302 | return color(d.topic/ntopics); 303 | else return "white"; 304 | }) 305 | .attr("stroke-width", "2px"); 306 | 307 | gnodes.append("text") 308 | .attr("text-anchor", "middle") 309 | .attr("dy",".35em") 310 | .text(function(d) { return d.name; }) 311 | .attr("fill", function(d){ 312 | if(d.group==0) 313 | return("#000");//"#e0e0e0");//return(color(d.topic/ntopics)); 314 | else 315 | return("#888"); 316 | }) 317 | .style("font-weight", function(d){ 318 | if(false && d.group==0) 319 | return("bold"); 320 | else return("normal"); 321 | }) 322 | .style("font-size", function(d){ 323 | if(d.group==0){ 324 | console.log(d.prop); 325 | //return((8+Math.log(1+d.prop,2)*6000)+"pt"); 326 | return((Math.log(d.prop)+10)*10+"pt"); 327 | } else { 328 | return("10pt"); 329 | } 330 | }) 331 | .on("mouseenter", function(){ 332 | d3.select(this).attr("fill", "#003")//#fff") 333 | }) 334 | .on("mouseleave", function(){ 335 | d3.select(this).attr("fill", "#000")//#e0e0e0") 336 | }); 337 | 338 | force.on("tick", function() { 339 | link.attr("x1", function(d) { return d.source.x; }) 340 | .attr("y1", function(d) { return d.source.y; }) 341 | .attr("x2", function(d) { return d.target.x; }) 342 | .attr("y2", function(d) { return d.target.y; }); 343 | 344 | gnodes.attr("cx", function(d) { return d.x; }) 345 | .attr("cy", function(d) { return d.y; }); 346 | 347 | gnodes.attr("transform", function(d) { 348 | return 'translate(' + [d.x, d.y] + ')'; 349 | }); 350 | }); 351 | 352 | links.forEach(function(l){ 353 | if(neighbors[l.source.index] == undefined) 354 | neighbors[l.source.index] = [l.source.index, l.target.index]; 355 | else neighbors[l.source.index].push(l.target.index); 356 | if(neighbors[l.target.index] == undefined) 357 | neighbors[l.target.index] = [l.target.index, l.source.index]; 358 | else neighbors[l.target.index].push(l.source.index); 359 | 360 | if(l.source.group == 0){ 361 | var termnode = l.target; 362 | var topicnode = l.source; 363 | } else { 364 | var termnode = l.source; 365 | var topicnode = l.target; 366 | } 367 | node_weights[[termnode.name, topicnode.name]] = l.value; 368 | }); 369 | 370 | node_names = _.map(nodes, function(x){ return x.name }) 371 | 372 | }); 373 | 374 | 375 | setTimeout(function(){ 376 | var header = d3.selectAll(".header"); 377 | header.transition().duration(2000).style("opacity", 0); 378 | header.transition().style("display", "none").delay(2000); 379 | }, 6000); 380 | 381 | setTimeout(function(){ 382 | force.charge(-800).start(); 383 | }, 200); 384 | setTimeout(function(){ 385 | force.charge(-1200).linkStrength(linkStrengthFunc).start(); 386 | }, 600); 387 | 388 | -------------------------------------------------------------------------------- /interface/js/underscore-min.js: 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