# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
MQ2007 dataset
MQ2007 is a query set from Million Query track of TREC 2007. There are about 1700 queries in it with labeled documents. In MQ2007, the 5-fold cross
validation strategy is adopted and the 5-fold partitions are included in the package. In each fold, there are three subsets for learning: training set,
validation set and testing set.
MQ2007 dataset from website
http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar and parse training set and test set into paddle reader creators
"""
from __future__ import print_function
import os
import functools
from .common import download
import numpy as np
# URL = "http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar"
URL = "http://www.bigdatalab.ac.cn/benchmark/upload/download_source/7b6dbbe2-842c-11e4-a536-bcaec51b9163_MQ2007.rar"
MD5 = "7be1640ae95c6408dab0ae7207bdc706"
def __initialize_meta_info__():
"""
download and extract the MQ2007 dataset
"""
import rarfile
fn = fetch()
rar = rarfile.RarFile(fn)
dirpath = os.path.dirname(fn)
rar.extractall(path=dirpath)
return dirpath
class Query(object):
"""
queries used for learning to rank algorithms. It is created from relevance scores, query-document feature vectors
Parameters:
----------
query_id : int
query_id in dataset, mapping from query to relevance documents
relevance_score : int
relevance score of query and document pair
feature_vector : array, dense feature
feature in vector format
description : string
comment section in query doc pair data
"""
def __init__(self,
query_id=-1,
relevance_score=-1,
feature_vector=None,
description=""):
self.query_id = query_id
self.relevance_score = relevance_score
if feature_vector is None:
self.feature_vector = []
else:
self.feature_vector = feature_vector
self.description = description
def __str__(self):
string = "%s %s %s" % (str(self.relevance_score), str(self.query_id),
" ".join(str(f) for f in self.feature_vector))
return string
# @classmethod
def _parse_(self, text):
"""
parse line into Query
"""
comment_position = text.find('#')
line = text[:comment_position].strip()
self.description = text[comment_position + 1:].strip()
parts = line.split()
if len(parts) != 48:
sys.stdout.write("expect 48 space split parts, get %d" %
(len(parts)))
return None
# format : 0 qid:10 1:0.000272 2:0.000000 ....
self.relevance_score = int(parts[0])
self.query_id = int(parts[1].split(':')[1])
for p in parts[2:]:
pair = p.split(':')
self.feature_vector.append(float(pair[1]))
return self
class QueryList(object):
"""
group query into list, every item in list is a Query
"""
def __init__(self, querylist=None):
self.query_id = -1
if querylist is None:
self.querylist = []
else:
self.querylist = querylist
for query in self.querylist:
if self.query_id == -1:
self.query_id = query.query_id
else:
if self.query_id != query.query_id:
raise ValueError("query in list must be same query_id")
def __iter__(self):
for query in self.querylist:
yield query
def __len__(self):
return len(self.querylist)
def __getitem__(self, i):
return self.querylist[i]
def _correct_ranking_(self):
if self.querylist is None:
return
self.querylist.sort(key=lambda x: x.relevance_score, reverse=True)
def _add_query(self, query):
if self.query_id == -1:
self.query_id = query.query_id
else:
if self.query_id != query.query_id:
raise ValueError("query in list must be same query_id")
self.querylist.append(query)
def gen_plain_txt(querylist):
"""
gen plain text in list for other usage
Paramters:
--------
querylist : querylist, one query match many document pairs in list, see QueryList
return :
------
query_id : np.array, shape=(samples_num, )
label : np.array, shape=(samples_num, )
querylist : np.array, shape=(samples_num, feature_dimension)
"""
if not isinstance(querylist, QueryList):
querylist = QueryList(querylist)
querylist._correct_ranking_()
for query in querylist:
yield querylist.query_id, query.relevance_score, np.array(
query.feature_vector)
def gen_point(querylist):
"""
gen item in list for point-wise learning to rank algorithm
Paramters:
--------
querylist : querylist, one query match many document pairs in list, see QueryList
return :
------
label : np.array, shape=(samples_num, )
querylist : np.array, shape=(samples_num, feature_dimension)
"""
if not isinstance(querylist, QueryList):
querylist = QueryList(querylist)
querylist._correct_ranking_()
for query in querylist:
yield query.relevance_score, np.array(query.feature_vector)
def gen_pair(querylist, partial_order="full"):
"""
gen pair for pair-wise learning to rank algorithm
Paramters:
--------
querylist : querylist, one query match many document pairs in list, see QueryList
pairtial_order : "full" or "neighbour"
there is redundant in all possible pair combinations, which can be simplified
gen pairs for neighbour items or the full partial order pairs
return :
------
label : np.array, shape=(1)
query_left : np.array, shape=(1, feature_dimension)
query_right : same as left
"""
if not isinstance(querylist, QueryList):
querylist = QueryList(querylist)
querylist._correct_ranking_()
labels = []
docpairs = []
# C(n,2)
for i in range(len(querylist)):
query_left = querylist[i]
for j in range(i + 1, len(querylist)):
query_right = querylist[j]
if query_left.relevance_score > query_right.relevance_score:
labels.append([1])
docpairs.append([
np.array(query_left.feature_vector),
np.array(query_right.feature_vector)
])
elif query_left.relevance_score < query_right.relevance_score:
labels.append([1])
docpairs.append([
np.array(query_right.feature_vector),
np.array(query_left.feature_vector)
])
for label, pair in zip(labels, docpairs):
yield np.array(label), pair[0], pair[1]
def gen_list(querylist):
"""
gen item in list for list-wise learning to rank algorithm
Paramters:
--------
querylist : querylist, one query match many document pairs in list, see QueryList
return :
------
label : np.array, shape=(samples_num, )
querylist : np.array, shape=(samples_num, feature_dimension)
"""
if not isinstance(querylist, QueryList):
querylist = QueryList(querylist)
querylist._correct_ranking_()
relevance_score_list = [[query.relevance_score] for query in querylist]
feature_vector_list = [query.feature_vector for query in querylist]
yield np.array(relevance_score_list), np.array(feature_vector_list)
def query_filter(querylists):
"""
filter query get only document with label 0.
label 0, 1, 2 means the relevance score document with query
parameters :
querylist : QueyList list
return :
querylist : QueyList list
"""
filter_query = []
for querylist in querylists:
relevance_score_list = [query.relevance_score for query in querylist]
if sum(relevance_score_list) != .0:
filter_query.append(querylist)
return filter_query
def load_from_text(filepath, shuffle=False, fill_missing=-1):
"""
parse data file into queries
"""
prev_query_id = -1
querylists = []
querylist = None
fn = __initialize_meta_info__()
with open(os.path.join(fn, filepath)) as f:
for line in f:
query = Query()
query = query._parse_(line)
if query == None:
continue
if query.query_id != prev_query_id:
if querylist is not None:
querylists.append(querylist)
querylist = QueryList()
prev_query_id = query.query_id
querylist._add_query(query)
if querylist is not None:
querylists.append(querylist)
return querylists
def __reader__(filepath, format="pairwise", shuffle=False, fill_missing=-1):
"""
Parameters
--------
filename : string
fill_missing : fill the missing value. default in MQ2007 is -1
Returns
------
yield
label query_left, query_right # format = "pairwise"
label querylist # format = "listwise"
"""
querylists = query_filter(
load_from_text(
filepath, shuffle=shuffle, fill_missing=fill_missing))
for querylist in querylists:
if format == "plain_txt":
yield next(gen_plain_txt(querylist))
elif format == "pointwise":
yield next(gen_point(querylist))
elif format == "pairwise":
for pair in gen_pair(querylist):
yield pair
elif format == "listwise":
yield next(gen_list(querylist))
train = functools.partial(__reader__, filepath="MQ2007/MQ2007/Fold1/train.txt")
test = functools.partial(__reader__, filepath="MQ2007/MQ2007/Fold1/test.txt")
def fetch():
return download(URL, "MQ2007", MD5)
if __name__ == "__main__":
fetch()
mytest = functools.partial(
__reader__, filepath="MQ2007/MQ2007/Fold1/sample", format="listwise")
for label, query in mytest():
print(label, query)