def main():
testBooks = {
'Agata_Christie': 'Tajemnica_Wawrzynow.txt',
'Janusz_A_Zajdel': 'Awaria.txt',
'Paulo_Coelho': 'Alchemik.txt',
'George_Orwell': 'Orwell_George_-_Rok_1984.txt',
'Sapkowski_Andrzej': 'Pani_Jeziora.txt',
'Andre_Norton': 'Andre_Norton_-_Prekursorka.txt',
'Dick_Philip_K': 'Dick_Philip_K_-_Kolonia.txt',
'Gordon_R_Dickson': 'Gordon_R_Dickson_-_Nekromanta.txt',
'Lem_Stanislaw': 'Lem_Stanislaw_-_Bajki_robotow.txt',
'Terry_Pratchett': 'Terry_Pratchett_-_Ruchome_Obrazki.txt',
}
firstN = 10
scores = []
for name, testBook in testBooks.items():
pFindBest = partial(findBest, tuple(testBooks.values()), testBook)
result = LocalCache.load(f'{name}.scoreResult', pFindBest)
score = list(OrderedDict(result).keys()).index(name)
scores.append(score)
print(f"Author: {name} has score: {score} for book: {testBook}")
print(
f"First {firstN} best results: \n{pprint.pformat(result[:firstN])}\n\n"
)
else:
print(f"scores: {scores}")
print(
f"Average score: {sum(scores)/len(testBooks)}, std: {np.std(scores)}"
)
def getTfIdfModel() -> TfidfModel:
def _tfIdf():
loader = getLoaderPrimaryForm()
tfidf = TfidfModel(loader)
tfidf.calcVector()
return tfidf
return LocalCache.load("tfidf", _tfIdf)
def getGraph(degree) -> GraphModel:
def _getGraph():
loader = getLoaderPrimaryForm()
g = GraphModel(loader, degree)
g.processGraphs(slice(51555))
return g
return LocalCache.load(f'graph{degree}', _getGraph)
def getLoaderPrimaryForm() -> Loader:
return LocalCache.load('loaderPrimaryForm',
lambda: Loader(primaryForm=getPrimaryForm()))
def getPrimaryForm() -> PrimaryForm:
return LocalCache.load('primaryForm', lambda: PrimaryForm(primaryFormPath))
