def test_ranking_short():
parsed = None
file = "data/cacmShort.txt"
for path in ["./", "../"]:
try:
parsed = myParser.buildDocCollectionSimple(path + file, ".T")
break
except FileNotFoundError:
pass
assert parsed
indexer = indexerSimple.IndexerSimple(parsed.docs)
requete = "home computer microphotographi"
models = [weighter.c1, weighter.c2, weighter.c3, weighter.c4, weighter.c5]
models = [clas(indexer) for clas in models]
models = [vectoriel.Vectoriel(indexer, weight, False) for weight in models]
models.append(jelinekMercer.JelinekMercer(indexer, 1))
models.append(okapiBM25.OkapiBM25(indexer, 1.2, .75))
rankings = [m.getRanking(requete) for m in models]
# 5 docs ont un score non nul qqsoit modèle
for ranking in rankings:
assert len(ranking) == 5
# ordre des résultats
for ranking in rankings[:-1]:
assert [x[0] for x in ranking] == ["7", "6", "4", "2", "10"]
assert [x[0] for x in rankings[-1]] == ["6", "7", "4", "10", "2"]
def optimisationOkapi(debut1, fin1, nbPoint1, debut2, fin2, nbPoint2, donnees,
labels):
rangeK = np.linspace(debut1, fin1, nbPoint1)
rangeB = np.linspace(debut2, fin2, nbPoint2)
m = okapiBM25.OkapiBM25()
mapKB = []
param = []
for k1 in rangeK:
for b in rangeB:
s = 0
for k in range(len(donnees)):
predictionModele = m.getRanking(donnees[k], [k1, b])
s += avgPrec(predictionModele, labels[k])
mapKB.append(s / len(donnees))
param.append([k1, b])
leMeilleurKBDuMonde = param[np.argmax(mapKB)]
def test_ranking_long():
parsed = None
file = "data/cacm/cacm.txt"
for path in ["./", "../"]:
try:
parsed = myParser.buildDocCollectionSimple(path + file, ".T")
break
except FileNotFoundError:
pass
assert parsed
indexer = indexerSimple.IndexerSimple(parsed.docs)
requete = "home computer microphotographi"
models = [weighter.c1, weighter.c2, weighter.c3, weighter.c4, weighter.c5]
models = [clas(indexer) for clas in models]
models = [vectoriel.Vectoriel(indexer, weight, False) for weight in models]
models.append(jelinekMercer.JelinekMercer(indexer, .2))
models.append(okapiBM25.OkapiBM25(indexer, 1.2, .75))
rankings = [m.getRanking(requete) for m in models]
#modèle O rang 0 à un score de 2
assert rankings[0][0][1] == 2
#modèle O rang 9 à un score de 1
assert rankings[0][9][1] == 1
#modèle 1
assert rankings[1][0][1] == 2
assert rankings[1][9][1] == 1
#meilleur docs
assert rankings[0][0][0] == "80"
assert rankings[1][0][0] == "80"
assert rankings[2][0][0] == "3646"
assert rankings[3][0][0] == "3646"
assert rankings[4][0][0] == "80"
assert rankings[5][0][0] == "866"
assert rankings[6][0][0] == "3156"
def testLong():
parsedQuery = None
parsedText = None
file = "data/cisi/cisi"
for path in ["./", "../"]:
try:
parsedQuery = queryParser.parse(path + file)
parsedText = myParser.buildDocCollectionSimple(
path + file + ".txt", ".W")
break
except FileNotFoundError:
pass
assert parsedQuery
assert parsedText
assert len(parsedQuery.queries) == 112
assert len(parsedQuery.queries["1"].pertient_list_id) == 46
#nombre de requêtes ayant au moins un document pertinent
assert sum(
len(q.pertient_list_id) > 0
for q in parsedQuery.queries.values()) == 76
print("calcul indexer")
indexer = indexerSimple.IndexerSimple(parsedText.docs)
models = [weighter.c1, weighter.c2, weighter.c3, weighter.c4, weighter.c5]
models = [clas(indexer) for clas in models]
models = [vectoriel.Vectoriel(indexer, weight, False) for weight in models]
jelinek = jelinekMercer.JelinekMercer(indexer)
models.append(jelinek)
okapi = okapiBM25.OkapiBM25(indexer)
models.append(okapi)
data_fit = [q.T for q in parsedQuery.queries.values()]
labels = [q.pertient_list_id for q in parsedQuery.queries.values()]
print("fit")
# jelinek.fit(np.linspace(0, 2, 2), data_fit, labels)
# okapi.fit((np.linspace(0, 2, 2), np.linspace(0, 2, 2)), data_fit, labels)
# train test
print(len(data_fit))
n = 100
jelinek.fit(np.linspace(.2, .7, 3), data_fit[:n], labels[:n])
okapi.fit((np.linspace(0, 2, 2), np.linspace(0, 2, 2)), data_fit[:n],
labels[:n])
for i in range(len(models)):
models.append(pagerank.PagerankMarcheAlea(indexer, models[i]))
models[-2].fit(np.linspace(.2, .7, 3), data_fit[:n], labels[:n])
print("précisions")
for m in models:
pred = [m.getRanking(d) for d in data_fit[n:]]
avgPrec = 0
for p, l in zip(pred, labels[n:]):
avgPrec += m.avgPrec(p, l)
print(m, avgPrec / len(pred))
def eval():
parsedQuery = None
parsedText = None
file = "data/cisi/cisi"
for path in ["./", "../"]:
try:
parsedQuery = queryParser.parse(path + file)
parsedText = myParser.buildDocCollectionSimple(path + file + ".txt", ".W")
break
except FileNotFoundError:
pass
indexer = indexerSimple.IndexerSimple(parsedText.docs)
models = [weighter.c1, weighter.c2, weighter.c3, weighter.c4, weighter.c5]
models = [clas(indexer) for clas in models]
models = [vectoriel.Vectoriel(indexer, weight, False) for weight in models]
jelinek = jelinekMercer.JelinekMercer(indexer)
models.append(jelinek)
okapi = okapiBM25.OkapiBM25(indexer)
models.append(okapi)
data_fit = [q.T for q in parsedQuery.queries.values()]
labels = [q.pertient_list_id for q in parsedQuery.queries.values()]
print("fit")
# jelinek.fit(np.linspace(0, 2, 2), data_fit, labels)
# okapi.fit((np.linspace(0, 2, 2), np.linspace(0, 2, 2)), data_fit, labels)
for i in range(len(models)):
models.append(pagerank.PagerankMarcheAlea(indexer, models[i]))
k = 9
metrics = [
averagePrecision.AveragePrecision(),
precisionAtK.PrecisionAtK(k),
fMesureK.FMesureK(1, k),
rappelAtK.RappelAtK(k),
NDCG.NDCG(k),
reciprocalRank.ReciprocalRank()]
perf = []
print(models)
print(metrics)
for i, model in enumerate(models):
print(i,"/", len(models))
perf.append([])
pred = [model.getRanking(data_fit[k]) for k in range(len(data_fit))]
for metric in metrics:
score, std = metric.eval_list_query(pred, labels)
perf[-1].append(score)
print([round(x, 4) for x in perf[-1]])
import matplotlib.pyplot as plt
plt.imshow(perf)
plt.colorbar()
plt.xlabel("Metrique")
plt.ylabel("Modèle")
plt.figtext(0,0,"Metriques : 0 : averagePrecision,1 : precisionAtK,2 : fMesureK,3 : rappelAtK,4 : NDCG,5 : reciprocalRank;Modèles : 0-4: Vectoriel, 5 : jelinekMercer,6 : okapiBM25, 7-12 : avec pagerank")
plt.show()