def calculate(pred_relevance, split_idx):
rel_query_model = pred_relevance
print type(rel_query_model)
print rel_query_model.shape.eval()
with open("query_model.pkl", "rb") as file: query_model = Pickle.load(file)[:split_idx]
with open("query_list.pkl", "rb") as file: query_list = Pickle.load(file)[:split_idx]
with open("doc_model.pkl", "rb") as file: doc_model = Pickle.load(file)
with open("doc_list.pkl", "rb") as file: doc_list = Pickle.load(file)
#with open("relevance_model_RM.pkl", "rb") as file : rel_query_model = Pickle.load(file)
#with open("query_relevance_model_RLE.pkl", "rb") as file : rel_query_model = Pickle.load(file)
background_model = ProcDoc.read_background_dict()
qry_eval = evaluate.evaluate_model(True)
''' document smoothing '''
for doc_idx in range(doc_model.shape[0]):
doc_vec = doc_model[doc_idx]
doc_model[doc_idx] = (1 - doc_lambda) * doc_vec + doc_lambda * background_model
mAP_list = []
query_rel_list = []
query_bg_list = []
doc_model = np.log(doc_model)
doc_model = doc_model
for rel_qry_lambda in np.linspace(0, 1., num=11):
''' query smoothing '''
with open("query_model.pkl", "rb") as file: query_model = Pickle.load(file)[:split_idx]
X = T.matrix()
Y = (1- rel_qry_lambda)*X + rel_qry_lambda * rel_query_model
f = theano.function([X], Y)
query_model = f(query_model)
result = np.argsort(-np.dot(query_model, doc_model.T), axis = 1)
query_docs_ranking = {}
''' speedup '''
for q_idx in range(len(query_list)):
docs_ranking = []
for doc_idx in result[q_idx]:
docs_ranking.append(doc_list[doc_idx])
query_docs_ranking[query_list[q_idx]] = docs_ranking
''' query
for query_key, query_vec in zip(query_list, query_model):
print len(query_docs_ranking.keys())
query_result = np.argsort(-(query_vec * doc_model).sum(axis = 1))
docs_ranking = []
for doc_`idx in query_result:
docs_ranking.append(doc_list[doc_idx])
query_docs_ranking[query_key] = docs_ranking
mAP = eval.mean_average_precision(query_docs_ranking)
print mAP, qry_lambda, rel_qry_lambda
'''
mAP = qry_eval.mean_average_precision(query_docs_ranking)
mAP_list.append(mAP)
return max(mAP_list)
def __init__(self, query_model):
self.query_model = copy.deepcopy(query_model)
self.vocabulary_size = 51253
smoothing = 0.1
with open("test_query_list.pkl", "rb") as file:
self.query_list = pickle.load(file)
with open("doc_list.pkl", "rb") as file:
self.doc_list = pickle.load(file)
with open("doc_model.pkl", "rb") as file:
doc_model = pickle.load(file)
self.background_model = ProcDoc.read_background_dict()
''' smoothing '''
for d_idx, doc_vec in enumerate(doc_model):
doc_model[d_idx] = (
1 - smoothing) * doc_vec + smoothing * self.background_model
self.doc_model = doc_model
def __init__(self, query_model, isSpoken=False):
smoothing = 0.0
with open("test_query_list.pkl", "rb") as file:
self.query_list = pickle.load(file)
with open("doc_list.pkl", "rb") as file:
self.doc_list = pickle.load(file)
if isSpoken:
with open("doc_model_wc_s.pkl", "rb") as file:
doc_model = pickle.load(file)
else:
with open("doc_model_wc.pkl", "rb") as file:
doc_model = pickle.load(file)
background_model = ProcDoc.read_background_dict()
self.query_model = copy.deepcopy(query_model)
self.vocabulary_size = 51253
self.doc_model = copy.deepcopy(doc_model)
self.doc_model = doc_model
self.background_model = background_model
doc_lambda = 0.9
#remove_list = ["update_embedded_query_expansion_ci.pkl", "update_embedded_query_expansion_qi.pkl", "collection_embedded.pkl", "query_embedded.pkl", "collection_total_similarity.pkl"]
remove_list = []
document_path = "../Corpus/TDT2/SPLIT_DOC_WDID_NEW"
query_path = "../Corpus/TDT2/QUERY_WDID_NEW_middle"
word_emb_path = "data/word2vec_dict.pkl"
relevance_path = "../Corpus/TDT2/AssessmentTrainSet/AssessmentTrainSet.txt"
# document model
data = ProcDoc.read_file(document_path)
doc_wordcount = ProcDoc.doc_preprocess(data)
doc_unigram = ProcDoc.unigram(dict(doc_wordcount))
doc_mdl, doc_IDs = ProcDoc.dict2np(doc_unigram)
# background_model
background_model = ProcDoc.read_background_dict()
background_model_np = ProcDoc.read_background_np()
# document smoothing
for doc_idx in xrange(doc_mdl.shape[0]):
doc_vec = doc_mdl[doc_idx]
doc_mdl[doc_idx] = (
1 - doc_lambda) * doc_vec + doc_lambda * background_model_np
# general model
collection = {}
collection_total_similarity = {}
for key, value in doc_wordcount.items():
for word, count in value.items():
if word in collection:
collection[word] += count
print 'Building a model whose optimizer=adadelta, activation function=softmax'
filepath = "Epochs/with_gamma_single_TF_Spk"
filename = []
#doc_nn.load_weights("NN_Model/DSSM_WEIGHTS_TD_54_double_gamma_shuffle.h5", by_name=True)
emb_model = load_model("NN_Model/RLE_SWLM_E_S.h5")
with open("model/UM/test_query_model_short.pkl", "rb") as file:query_model = Pickle.load(file)
with open("model/UM/test_query_list_short.pkl", "rb") as file:query_list = Pickle.load(file)
with open("model/test_query_model_short.pkl", "rb") as file:query_TF = Pickle.load(file)
with open("model/log_doc_model_s.pkl", "rb") as file:doc_model = Pickle.load(file)
with open("model/doc_list_s.pkl", "rb") as file:doc_list = Pickle.load(file)
bg_md = ProcDoc.read_background_dict()
query_model = 0.5 * query_model + 0.5 *np.array(emb_model.predict_on_batch(query_model))
query_model *= query_TF.sum(axis = 1).reshape(16, 1) * 10
#doc_model = doc_model
max_mAP = ["", 0]
evl = evaluate.evaluate_model(False)
for dir_item in os.listdir(filepath):
# join dir path and file name
dir_item_path = os.path.join(filepath, dir_item)
# check whether a file exists before read
if os.path.isfile(dir_item_path):
dir_item_path = "Epochs/with_gamma_single_TF_Spk/54_log_gamma_shuffle_spk_weights-30-0.11.hdf5"
#if dir_item_path.find("log") == -1: continue
