Added a CLI-Interface and 'analyze' command

main.py

@@ -306,7 +306,7 @@ def scoreUnread(G, globMu, globStd, errorFac=0.6):
                     node['score'] = None
 
 
-def printBestList(G, num=25):
+def printBestList(G, num=-1):
     bestlist = []
     for n in list(G.nodes):
         node = G.nodes[n]
@@ -317,7 +317,7 @@ def printBestList(G, num=25):
     for i, book in enumerate(bestlist):
         print("["+str(i+1).zfill(int(math.log10(num)+1))+"] "+book['title'] +
               " ("+" & ".join(book['authors'])+"): {:.5f}".format(book['score']))
-        if i == num-1:
+        if num!=-1 and i == num-1:
             break
 
 
@@ -475,12 +475,8 @@ def genScores(G, books):
     return globMu, globStd
 
 
-def recommendNBooks(n):
+def recommendNBooks(G, mu, std, n):
-    G, books = buildFullGraph()
-    mu, std = genScores(G, books)
-
     removeRestOfSeries(G)
-    removePriv(G)
     removeBad(G, mu-std-1.5)
     removeKeepBest(G, int(n*2) + 5, maxDistForRead=1.5)
     removeEdge(G)
@@ -501,15 +497,7 @@ def recommendNBooks(n):
     scaleOpinionsByRating(G)
     addScoreToLabels(G)
 
-    printBestList(G, num=n)
+def fullGraph(G):
-    genAndShowHTML(G, True)
-
-
-def fullGraph():
-    G, books = buildFullGraph()
-    mu, std = genScores(G, books)
-
-    removePriv(G)
     removeEdge(G)
     removeHighSpanTags(G, 7)
     removeDangling(G, alsoBooks=False)
@@ -521,19 +509,14 @@ def fullGraph():
     scaleOpinionsByRating(G)
     addScoreToLabels(G)
 
-    printBestList(G, num=100)
-    genAndShowHTML(G)
 
-
+def readBooksAnalysis(G, minRating=0, showAllTags=True, removeUnconnected=False):
-def readBooksAnalysis():
-    G, books = buildFullGraph()
-    mu, std = genScores(G, books)
-
-    removePriv(G)
     removeUnread(G)
-    removeEdge(G)
+    removeBad(G, minRating)
+    if not showAllTags:
+        removeEdge(G)
     removeHighSpanTags(G, 15)
-    removeDangling(G, alsoBooks=False)
+    removeDangling(G, alsoBooks=removeUnconnected)
     removeTopLists(G)
     pruneTags(G, 8)
 
@@ -541,9 +524,125 @@ def readBooksAnalysis():
     scaleOpinionsByRating(G)
     addScoreToLabels(G)
 
-    printBestList(G, num=100)
-    genAndShowHTML(G)
 
+def analyze(G, type_name, name, dist=2.7):
+    from fuzzywuzzy import fuzz
+    type_ident = type_name[0]
+    full_name = type_ident + "/" + name
+    bestRatio, match, n = 0, None, 0
+    for ni in list(G.nodes):
+        node = G.nodes[ni]
+        if node['t'] == type_name or type_name=="any":
+            if name==node['label'] or full_name==node['label']:
+                match, n = node, ni
+                break
+            ratio = fuzz.ratio(node['label'], name)
+            if ratio > bestRatio:
+                bestRatio, match, n = ratio, node, ni
+    menge = set()
+    pruneDist(G, match, n, dist, menge)
+    for n in list(G.nodes):
+        if n not in menge:
+            G.remove_node(n)
+    removeHighSpanTags(G, 12)
+    if dist > 1:
+        removeDangling(G, True)
+
+    scaleBooksByRating(G)
+    scaleOpinionsByRating(G)
+    match['value'] += 100
+    addScoreToLabels(G)
+
+def pruneDist(G, node, n, dist, menge, firstEdge=False):
+    if dist <= 0:
+        return
+    dist -= 1
+    if menge==set():
+        firstEdge=True
+    menge.add(n)
+    if node['t'] in ['tag']:
+        if firstEdge:
+            dist-=0.1
+        else:
+            return
+    bestlist = []
+    keeplist = []
+    for m in list(G.adj[n]):
+        book = G.nodes[m]
+        if book['t'] not in ['topList']:
+            if 'score' in book and book['score'] != None:
+                bestlist.append(book)
+            elif 'rating' in book and book['rating'] != None:
+                keeplist.append(book)
+            else:
+                book['score'] = 0
+                bestlist.append(book)
+    bestlist.sort(key=lambda node: node['score'], reverse=True)
+    toKeep = min(int(dist*10), math.ceil(len(bestlist) * dist - len(keeplist)*0.5))
+    if toKeep <= 0:
+        keeplist.sort(key=lambda node: node['rating'], reverse=True)
+        keeplist = keeplist[:min(int(dist*10), int(len(keeplist) * dist))]
+        bestlist = []
+    else:
+        bestlist = bestlist[:toKeep]
+
+    for m in list(G.adj[n]):
+        node = G.nodes[m]
+        if node in bestlist or node in keeplist:
+            pruneDist(G, node, m, dist, menge, firstEdge=firstEdge)
+
+def cliInterface():
+    import argparse
+
+    parser = argparse.ArgumentParser(description='TODO: Write Description.')
+    parser.add_argument('--keep-priv', action="store_true")
+    parser.add_argument('--remove-read', action="store_true")
+    parser.add_argument('--remove-unread', action="store_true")
+    parser.add_argument('--no-web', action="store_true")
+    parser.add_argument('--no-list', action="store_true")
+    cmds = parser.add_subparsers(required=True, dest='cmd')
+
+    p_rec = cmds.add_parser('recommend', description="TODO", aliases=['rec'])
+    p_rec.add_argument('-n', type=int, default=25, help='number of books to recommend')
+
+    p_read = cmds.add_parser('read', description="TODO", aliases=[])
+    p_read.add_argument('--min-rating', type=int, default=0)
+    p_read.add_argument('--all-tags', action="store_true")
+    p_read.add_argument('--only-connected', action="store_true")
+
+    p_show = cmds.add_parser('analyze', description="TODO", aliases=[])
+    p_show.add_argument('type', choices=['any', 'book', 'recommender', 'author', 'series'])
+    p_show.add_argument('name', type=str)
+    p_show.add_argument('-d', type=float, default=2.7, help='depth of expansion')
+
+    p_full = cmds.add_parser('full', description="TODO", aliases=[])
+
+    args = parser.parse_args()
+
+    G, books = buildFullGraph()
+    mu, std = genScores(G, books)
+    if not args.keep_priv:
+        removePriv(G)
+    if args.remove_read:
+        removeRead(G)
+    elif args.remove_unread:
+        removeUnread(G)
+
+    if args.cmd=="recommend":
+        recommendNBooks(G, mu, std, args.n)
+    elif args.cmd=="read":
+        readBooksAnalysis(G, args.min_rating, args.all_tags, args.only_connected)
+    elif args.cmd=="analyze":
+        analyze(G, args.type, args.name, args.d)
+    elif args.cmd=="full":
+        fullGraph(G)
+    else:
+        raise Exception("Bad")
+
+    if not args.no_list:
+        printBestList(G)
+    if not args.no_web:
+        genAndShowHTML(G)
 
 if __name__ == "__main__":
-    recommendNBooks(45)
+    cliInterface()