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Runbook for ECIR 2019 axiomatic semantic term matching paper (#535)

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Peilin-Yang authored and lintool committed Jan 12, 2019
1 parent 7c0eee7 commit 7b41ae38dc3a1a429cff0142530ea63d49937367
@@ -17,4 +17,11 @@ log.*
*.log
out.*
runs.regression/
runs.jdiq2018/
runs.jdiq2018/
# automatically generated by ECIR2019_axiomatic scripts
src/main/resources/topics-and-qrels/qrels.cw09.all.txt
src/main/resources/topics-and-qrels/qrels.cw12.all.txt
src/main/resources/topics-and-qrels/qrels.disk12.all.txt
src/main/resources/topics-and-qrels/qrels.gov2.all.txt
src/main/resources/topics-and-qrels/qrels.mb11.all.txt
src/main/resources/topics-and-qrels/qrels.mb13.all.txt
@@ -0,0 +1,23 @@
### Requirements

Python>=2.6 or Python>=3.5
`pip install -r src/main/python/requirements.txt`

### Run the Parameter Sensitivity (Fig 1,2,3 in the paper)

*** Users will need to change the index path at `src/main/resources/fine_tuning/collections.yaml`
(the program will go through the `index_roots` and concatenate with collection's `index_path`. the first match will be the index path)


```
python src/main/python/ecir2019_axiomatic/run_batch.py --collection disk12 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection robust04 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection robust05 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection core17 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection wt10g --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection gov2 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection cw09b --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection cw12b13 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection mb11 --models bm25 ql f2exp --n 32 --run --plot
python src/main/python/ecir2019_axiomatic/run_batch.py --collection mb13 --models bm25 ql f2exp --n 32 --run --plot
```
@@ -0,0 +1 @@

@@ -0,0 +1,94 @@
# -*- coding: utf-8 -*-
#
# Anserini: A toolkit for reproducible information retrieval research built on Lucene
#
# 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.

import os
import json
import ast
from operator import itemgetter
from inspect import currentframe, getframeinfo
import logging

logging.basicConfig()

class Effectiveness(object):
"""Handles the effectiveness.
For example, get all the effectiveness of one method (has multiple parameters).
When constructing, pass the index path."""
def __init__(self, index_path):
self.logger = logging.getLogger('effectiveness.Effectiveness')
self.index_path = os.path.abspath(index_path)
if not os.path.exists(self.index_path):
frameinfo = getframeinfo(currentframe())
self.logger.error(frameinfo.filename, frameinfo.lineno)
self.logger.error('[Effectiveness Constructor]:Please provide a valid index path - ' + self.index_path)
exit(1)

self.run_files_root = 'run_files'
self.eval_files_root = 'eval_files'
self.effectiveness_root = 'effectiveness_files'

def output_effectiveness(self, output_root):
if not os.path.exists(os.path.join(output_root, self.effectiveness_root)):
os.makedirs(os.path.join(output_root, self.effectiveness_root))
all_params = []
all_results = {}
for metric_dir in os.listdir(os.path.join(output_root, self.eval_files_root)):
for fn in os.listdir(os.path.join(output_root, self.eval_files_root, metric_dir)):
if len(fn.split('_')) == 3:
basemodel, model, model_params = fn.split('_')
elif len(fn.split('_')) == 2:
basemodel, model = fn.split('_')
eval_res = self.read_eval_file(os.path.join(output_root, self.eval_files_root, metric_dir, fn))
for metric in eval_res:
if metric not in all_results:
all_results[metric] = {}
if basemodel not in all_results[metric]:
all_results[metric][basemodel] = []
all_results[metric][basemodel].append(eval_res[metric]['all'])

for metric in all_results:
with open(os.path.join(output_root, self.effectiveness_root, 'axiom_paras_sensitivity_%s.csv' % metric), 'w') as f:
for basemodel in all_results[metric]:
all_results[metric][basemodel].sort(key = itemgetter(0))
for ele in all_results[metric][basemodel]:
f.write('%s,%.1f,%.4f\n' % (basemodel, ele[0], ele[1]))

def read_eval_file(self, fn):
"""return {qid: {metric: [(value, para), ...]}}"""
split_fn = os.path.basename(fn).split('_')
params = split_fn[-1] if len(split_fn) == 3 else ''
res = {}
with open(fn) as _in:
for line in _in:
line = line.strip()
if line:
row = line.split()
metric = row[0]
qid = row[1]
try:
value = ast.literal_eval(row[2])
except:
continue
if metric not in res:
res[metric] = {}
for param in params.split(','):
if 'axiom.beta' in param:
beta = float(param.split(':')[1])
res[metric][qid] = (beta, value)
if split_fn[1] == 'baseline': # baseline
res[metric][qid] = (-1, value)
return res
@@ -0,0 +1,80 @@
# -*- coding: utf-8 -*-
#
# Anserini: A toolkit for reproducible information retrieval research built on Lucene
#
# 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.

import os
from inspect import currentframe, getframeinfo
from subprocess import Popen, PIPE
import logging

logging.basicConfig()
class Evaluation(object):
"""Get the evaluation of a corpus for a result."""
def __init__(self, index_path):
self.logger = logging.getLogger('evalation.Evaluation')
self.index_path = os.path.abspath(index_path)
if not os.path.exists(self.index_path):
frameinfo = getframeinfo(currentframe())
self.logger.error(frameinfo.filename, frameinfo.lineno)
self.logger.error('[Search Constructor]:Please provide a valid index path - ' + self.index_path)
exit(1)

self.run_files_root = 'run_files'
self.eval_files_root = 'eval_files'

def gen_batch_eval_params(self, output_root, metric):
if not os.path.exists(os.path.join(output_root, self.eval_files_root, metric)):
os.makedirs(os.path.join(output_root, self.eval_files_root, metric))
all_params = []
for fn in os.listdir(os.path.join(output_root, self.run_files_root)):
if not os.path.exists( os.path.join(output_root, self.eval_files_root, metric, fn) ):
all_params.append((
os.path.join(output_root, self.run_files_root, fn),
os.path.join(output_root, self.eval_files_root, metric, fn)
))
return all_params


@classmethod
def output_all_evaluations(self, qrel_programs, qrel_file_path, result_file_path, output_path):
"""Returns various effectiveness figures.
@Return: a dict of all performances
"""
for i, qrel_program in enumerate(qrel_programs):
process = Popen(' '.join([qrel_program, qrel_file_path, result_file_path]), shell=True, stdout=PIPE)
stdout, stderr = process.communicate()
if process.returncode == 0:
try:
if i == 0:
o = open( output_path, 'w')
else:
o = open( output_path, 'a')
if 'trec_eval' in qrel_program:
o.write(stdout)
elif 'gdeval' in qrel_program:
for line in stdout.split('\n')[1:-1]:
line = line.strip()
if line:
row = line.split(',')
qid = row[-3]
ndcg20 = row[-2]
err20 = row[-1]
o.write('ndcg20\t%s\t%s\n' % (qid if qid != 'amean' else 'all', ndcg20))
o.write('err20\t%s\t%s\n' % (qid if qid != 'amean' else 'all', err20))
finally:
o.close()
else:
logger.error('ERROR when running the evaluation for:' + result_file_path)
@@ -0,0 +1,91 @@
# -*- coding: utf-8 -*-
#
# Anserini: A toolkit for reproducible information retrieval research built on Lucene
#
# 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.

from __future__ import print_function
import os, sys
import csv
import logging
from operator import itemgetter
import matplotlib

# https://stackoverflow.com/questions/37604289/tkinter-tclerror-no-display-name-and-no-display-environment-variable
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
matplotlib.use('Agg')

import matplotlib.pyplot as plt

plt.style.use('ggplot')

logging.basicConfig()

class Plots(object):
def __init__(self):
self.logger = logging.getLogger('plot.Plots')
self.run_files_root = 'run_files'
self.eval_files_root = 'eval_files'
self.effectiveness_root = 'effectiveness_files'
self.plots_root = 'plots'

def read_data(self, fn):
all_results = {}
with open(fn) as f:
r = csv.reader(f)
for row in r:
model, beta, score = row
if model not in all_results:
all_results[model] = []
all_results[model].append((float(beta), float(score)))
return all_results

def plot_params_sensitivity(self, collection, output_root):
if not os.path.exists(os.path.join(output_root, self.plots_root)):
os.makedirs(os.path.join(output_root, self.plots_root))
title_mappings = {
'disk12': 'Disk 1 & 2',
'robust04': 'Disks 4 & 5',
'robust05': 'AQUAINT',
'core17': 'New York Times',
'core18': 'Washington Post',
'wt10g': 'WT10g',
'gov2': 'Gov2',
'cw09b': 'ClueWeb09b',
'cw12b13': 'ClueWeb12-B13',
'cw12': 'ClueWeb12',
'mb11': 'Tweets 2011',
'mb13': 'Tweets 2013'
}

for fn in os.listdir(os.path.join(output_root, self.effectiveness_root)):
all_results = self.read_data(os.path.join(output_root, self.effectiveness_root, fn))
ls = ['-', '--', ':']
colors = ['r', 'g', 'b']
fig, ax = plt.subplots(1, 1, figsize=(6, 4))
for (model, linestyle, color) in zip(sorted(all_results), ls, colors):
all_results[model].sort(key = itemgetter(0))
x = [float(ele[0]) for ele in all_results[model] if ele[0] > 0]
y = [float(ele[1]) for ele in all_results[model] if ele[0] > 0]
ax.plot(x, y, linestyle=linestyle, marker='o', ms=5, label=model.upper()+'+Ax', color=color)
baseline = [float(ele[1]) for ele in all_results[model] if ele[0] < 0]
if len(baseline) == 1:
ax.axhline(baseline[0], linestyle=linestyle, color=color, label=model.upper())
ax.grid(True)
ax.set_title(collection if collection not in title_mappings else title_mappings[collection])
ax.set_xlabel(r'$\beta$')
ax.set_ylabel('MAP' if not 'cw' in collection else 'NDCG@20')
ax.legend(loc=4)
output_fn = os.path.join(output_root, self.plots_root, 'params_sensitivity_{}.eps'.format(collection))
plt.savefig(output_fn, bbox_inches='tight', format='eps')
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