def Test_avgP():
start = time()
collection = "cisi"
q = "1"
model = "Vectorial"
index1 = SimpleIndex("data/" + collection + "/" + collection + ".txt")
index1.indexing()
queries = QueryParser.QueryParser.Parse(
"data/" + collection + "/" + collection + ".qry",
"data/" + collection + "/" + collection + ".rel")
query = queries[q]
# display the content of the query
# print ( Weighter.Ponder2( index1 ).getWeightsForQuery ( query.getText() ))
print("testing ", model, " Model !")
print("collection : ", collection)
m = getattr(IRModel, model)(Weighter.Ponder2(index1))
retrieved_docs = m.getRanking(query.getText(), scoring=False)
avgP = EvalMesure.avgP().evalQuery(retrieved_docs, query)
print(" AvgP : ", avgP)
end = time()
print("executing time : ", end - start)
def testWeighter():
t = time()
index1 = SimpleIndex("data/cacm/ex1.txt")
index1.indexing()
doc = "B" #"1"
stem = "discover" #"class"
query = "hello world discover" #"program algebra "
p = Weighter.Ponder1(index1)
print("Ponder 1 :")
print(p.getWeightsForDoc(doc))
print("--------------------------")
print(p.getWeightsForQuery(query))
print("--------------------------")
print(p.getWeightsForStem(stem))
print("--------------------------")
print("####################################")
p = Weighter.Ponder2(index1)
print("Ponder 2 :")
print(p.getWeightsForDoc(doc))
print("--------------------------")
print(p.getWeightsForQuery(query))
print("--------------------------")
print(p.getWeightsForStem(stem))
print("--------------------------")
print("####################################")
p = Weighter.Ponder3(index1)
print("Ponder 3 :")
print(p.getWeightsForDoc(doc))
print("--------------------------")
print(p.getWeightsForQuery(query))
print("--------------------------")
print(p.getWeightsForStem(stem))
print("--------------------------")
print("####################################")
p = Weighter.Ponder4(index1)
print("Ponder 4 :")
print(p.getWeightsForDoc(doc))
print("--------------------------")
print(p.getWeightsForQuery(query))
print("--------------------------")
print(p.getWeightsForStem(stem))
print("--------------------------")
print("####################################")
p = Weighter.Ponder5(index1)
print("Ponder 5 :")
print(p.getWeightsForDoc(doc))
print("--------------------------")
print(p.getWeightsForQuery(query))
print("--------------------------")
print(p.getWeightsForStem(stem))
print("--------------------------")
print("####################################")
print("\nexecuting time :", round(time() - t, 2), " seconds .")
def testIndex():
t = time()
'''
- Default parameters for the constructor SImpleIndex:
--> lower=True , stemm=True , stopwords=True
- you can change it to test other initial values !
'''
index1 = SimpleIndex("data/cacm/ex1.txt",
stopwords=False,
stemm=True,
lower=True)
index1.indexing()
#params
doc_test = "B"
stem_test = "discover"
print("\nstring of the doc '", doc_test, "' is : ",
index1.getStrDoc(doc_test))
print("\nTFs for document '", doc_test, "' ",
index1.getTfsForDoc(doc_test))
print("\nTFIDFs for document '", doc_test, "' ",
index1.getTfIDFsForDoc(doc_test))
print("#########################################################")
#TERM
print("\nTFs for stem '", stem_test, "' ", index1.getTfsForStem(stem_test))
print("\nIDF for stem '", stem_test, "' ", index1.getIDFforStem(stem_test))
print("\nTFIDFs for stem '", stem_test, "' ",
index1.getTfIDFsForStem(stem_test))
print("\nexecuting time :", round(time() - t, 2), " seconds .")
print(len(index1.index_occ_normalized))
def Test_Recall_Precision_Fmesure():
start = time()
q = "15"
model = "ModeleLangue"
collection = "cisi"
index1 = SimpleIndex("data/" + collection + "/" + collection + ".txt")
index1.indexing()
queries = QueryParser.QueryParser.Parse(
"data/" + collection + "/" + collection + ".qry",
"data/" + collection + "/" + collection + ".rel")
query = queries[q]
# display the content of the query
# print ( Weighter.Ponder2( index1 ).getWeightsForQuery ( query.getText() ))
print("testing ", model, " Model !")
print("collection : ", collection)
m = getattr(IRModel, model)(Weighter.Ponder2(index1))
retrieved_docs = m.getRanking(query.getText(), scoring=False)
print(retrieved_docs[:10])
R = EvalMesure.Recall().evalQuery(retrieved_docs, query)
P = EvalMesure.Precision().evalQuery(retrieved_docs, query)
F = EvalMesure.F_mesure().evalQuery(retrieved_docs, query)
print(" Rappel : ", R)
print(" Precision : ", P)
print(" F_mesure : ", F)
end = time()
print("executing time : ", end - start)
def testOkapiBM25():
index1 = SimpleIndex("data/cisi/cisi.txt")
index1.indexing()
m = IRModel.Okapi(Weighter.Ponder2(index1))
query = "computer algebra "
print("Scores : ", m.getScores(query))
print("Ranking : ", m.getRanking(query))
def testLangModel():
t = time()
index1 = SimpleIndex("data/cacm/cacmShort-good.txt")
index1.indexing()
query = "hello world world world line line algebra "
m = IRModel.ModeleLangue(Weighter.Ponder2(index1))
print("Scores : ", m.getScores(query))
print("Ranking : ", m.getRanking(query))
print("\nexecuting time :", round(time() - t, 2), " seconds .")
def testVectorialModel():
t = time()
index1 = SimpleIndex("data/cacm/ex1.txt")
index1.indexing()
m = IRModel.Vectorial(Weighter.Ponder2(index1), normalize=True)
query = "hello world world world line line algebra "
print("Scores : ", m.getScores(query))
print("Ranking : ", m.getRanking(query))
print("\nexecuting time :", round(time() - t, 2), " seconds .")
def FullPageRank():
start = time()
index1 = SimpleIndex("data/cacm/cacm.txt")
index1.indexing()
m = IRModel.Vectorial(Weighter.Ponder2(index1))
q = "computer coding Algebraic"
'''
default parameters :
d =0.85 , aj=1 , Eps = 0.001 , max_iter = 20 , n=10 , k=5
'''
res = PageRank.page_rank(q, m, index1.getCollection(), Eps=1e-7)
print(res)
end = time()
print("executing time : ", end - start)
def evalModelsTest():
start = time()
collection = "cisi"
index1 = SimpleIndex("data/" + collection + "/" + collection + ".txt")
index1.indexing()
queries = QueryParser.QueryParser.Parse(
"data/" + collection + "/" + collection + ".qry",
"data/" + collection + "/" + collection + ".rel")
m1 = getattr(IRModel, "Vectorial")(Weighter.Ponder2(index1))
m2 = getattr(IRModel, "ModeleLangue")(Weighter.Ponder2(index1))
m3 = getattr(IRModel, "Okapi")(Weighter.Ponder2(index1))
print("collection : ", collection)
# 5 queries
ls = [
queries[k] for k in queries if len(queries[k].get_relevant_doc()) != 0
][:15]
res = evalIRmodel.EvalIRModel.evalModels(models=[m1, m2, m3], queries=ls)
print("\nReporting ")
print("--------------")
print("shape confirmation ", res.shape)
print("\nVectorial :")
print(tabulate(res[0], headers=['Precision', 'Recall', 'F1-score']))
print("Mean :", np.mean(res[0], axis=0), " Standard Deviation : ",
np.std(res[0], axis=0))
print("\nModeleLangue :")
print(tabulate(res[1], headers=['Precision', 'Recall', 'F1-score']))
print("Mean :", np.mean(res[1], axis=0), " Standard Deviation : ",
np.std(res[1], axis=0))
print("\nOkapi BM-25:")
print(tabulate(res[2], headers=['Precision', 'Recall', 'F1-score']))
print("Mean :", np.mean(res[2], axis=0), " Standard Deviation : ",
np.std(res[2], axis=0))
end = time()
print("executing time : ", end - start)
def TuningParams_CrossValidationTest():
start = time()
collection = "cisi"
model = "Okapi"
index1 = SimpleIndex("data/" + collection + "/" + collection + ".txt")
index1.indexing()
queries = QueryParser.QueryParser.Parse(
"data/" + collection + "/" + collection + ".qry",
"data/" + collection + "/" + collection + ".rel")
m = getattr(IRModel, model)(Weighter.Ponder2(index1))
print("testing ", model, " Model !")
print("collection : ", collection)
ls = [
queries[k] for k in queries if len(queries[k].get_relevant_doc()) != 0
]
evalIRmodel.EvalIRModel.tuning_params \
( model=m , metric="MAP",method="cross_validation" ,
queries=ls , grid_param={"k1":np.arange(1.2,1.4,0.1),"b":np.arange(0.8,1.1,0.1)} , param_method=5 )
end = time()
print("executing time : ", end - start)
def Test_NDCG_mean():
start = time()
collection = "cisi"
model = "Vectorial"
index1 = SimpleIndex("data/" + collection + "/" + collection + ".txt")
index1.indexing()
queries = QueryParser.QueryParser.Parse(
"data/" + collection + "/" + collection + ".qry",
"data/" + collection + "/" + collection + ".rel")
m = getattr(IRModel, model)(Weighter.Ponder2(index1))
print("testing ", model, " Model !")
print("collection : ", collection)
ls = [
queries[k] for k in queries if len(queries[k].get_relevant_doc()) != 0
]
ndcg_mean = EvalMesure.NDCG().evaluate(m, ls)
print(" NDCG : ", ndcg_mean)
end = time()
print("executing time : ", end - start)
def testMAP():
start = time()
collection = "cisi"
model = "Okapi"
index1 = SimpleIndex("data/" + collection + "/" + collection + ".txt")
index1.indexing()
queries = QueryParser.QueryParser.Parse(
"data/" + collection + "/" + collection + ".qry",
"data/" + collection + "/" + collection + ".rel")
m = getattr(IRModel, model)(Weighter.Ponder2(index1))
print("testing ", model, " Model !")
print("collection : ", collection)
#get queries that has at least one relevant doc to evaluate it !
ls = [
queries[k] for k in queries if len(queries[k].get_relevant_doc()) != 0
]
MAP = EvalMesure.MAP().evaluate(m, ls)
print(" MAP : ", MAP)
end = time()
print("executing time : ", end - start)
def Exo1PagerankTest():
index1 = SimpleIndex("data/cacm/ex1.txt")
index1.indexing()
m = IRModel.Vectorial(Weighter.Ponder2(index1))
q = "hello hello worlds"
PageRank.page_rank(q, m, index1.getCollection())