dodox/CaliGraph
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smashing 'deees bugs

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Dominik Moritz Roth 2022-03-07 17:56:59 +01:00
parent 9a02bdc2a8
commit cbb884b377
2 changed files with 7 additions and 6 deletions
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2
.gitignore vendored
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@ -2,5 +2,5 @@ __pycache__
*.html *.html
.venv .venv
neuralWeights.json neuralWeights.json
neuralWeights.json.bak neuralWeights.json.*
.imgLinkCache.json .imgLinkCache.json

11
caliGraph.py
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@ -435,8 +435,9 @@ def scoreUnread(G, globMu, globStd):
score = 0 score = 0
nb = dict(neuralBins) nb = dict(neuralBins)
act = {} act = {}
jig = {}
for b in nb: for b in nb:
act[b] = sum(nb[b])/len(nb[b]) act[b], jig[b] = norm.fit(nb[b])
score += act[b] * getWeightForType(b) score += act[b] * getWeightForType(b)
score /= sum([abs(getWeightForType(b)) for b in nb]) score /= sum([abs(getWeightForType(b)) for b in nb])
node['score'] = math.tanh(score/10)*10 node['score'] = math.tanh(score/10)*10
@ -1200,7 +1201,7 @@ def findNewBooks(G, books, mu, num=-1, minRecSco=5):
# while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1) # while batchSize is implemented, we only get a good gonvergence when we disable it (batchSize=-1)
# but might be necessary to enable later for a larger libary for better training performance... # but might be necessary to enable later for a larger libary for better training performance...
# maybe try again for 128 books? # maybe try again for 128 books?
def evaluateFitness(books, batchSize=16, debugPrint=False, runPagerank=True): def evaluateFitness(books, batchSize=16, debugPrint=False, calcPagerank=True):
global weights global weights
G = buildBookGraph(books) G = buildBookGraph(books)
graphAddAuthors(G, books) graphAddAuthors(G, books)
@ -1208,7 +1209,7 @@ def evaluateFitness(books, batchSize=16, debugPrint=False, runPagerank=True):
graphAddTopLists(G, books) graphAddTopLists(G, books)
graphAddSeries(G, books) graphAddSeries(G, books)
graphAddTags(G, books) graphAddTags(G, books)
if runPagerank: if calcPagerank:
runPagerank(G) runPagerank(G)
ratedBooks = [n for n in list(G.nodes) if 'rating' in G.nodes[n] and G.nodes[n]['rating'] != None] ratedBooks = [n for n in list(G.nodes) if 'rating' in G.nodes[n] and G.nodes[n]['rating'] != None]
@ -1259,7 +1260,7 @@ def train(initGamma, full=True, noPagerank=False):
gamma = initGamma gamma = initGamma
books = loadBooksFromDB() books = loadBooksFromDB()
bestWeights = copy.copy(weights) bestWeights = copy.copy(weights)
mse, gradient = evaluateFitness(books, runPagerank=not noPagerank) mse, gradient = evaluateFitness(books, calcPagerank=not noPagerank)
delta = math.sqrt(sum(gradient[g]**2 for g in gradient)/len(gradient)) delta = math.sqrt(sum(gradient[g]**2 for g in gradient)/len(gradient))
best_mse = mse best_mse = mse
stagLen = 0 stagLen = 0
@ -1277,7 +1278,7 @@ def train(initGamma, full=True, noPagerank=False):
weights[wt] += gamma*gradient[wt]/math.sqrt(delta) weights[wt] += gamma*gradient[wt]/math.sqrt(delta)
#else: #else:
# del weights[wt] # del weights[wt]
mse, gradient = evaluateFitness(books, runPagerank=not noPagerank) mse, gradient = evaluateFitness(books, calcPagerank=not noPagerank)
if mse < last_mse: if mse < last_mse:
gamma = gamma*1.25 gamma = gamma*1.25
else: else: