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run_local_test.py
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run_local_test.py
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################################################################################
# Name: Run Local Test Tool
# Author: Zhengying Liu
# Created on: 20 Sep 2018
# Update time: 5 May 2019
# Usage: python run_local_test.py -dataset_dir=<dataset_dir> -code_dir=<code_dir>
VERISION = "v20190505"
DESCRIPTION =\
"""This script allows participants to run local test of their method within the
downloaded starting kit folder (and avoid using submission quota on CodaLab). To
do this, run:
```
python run_local_test.py -dataset_dir=./AutoDL_sample_data/miniciao -code_dir=./AutoDL_sample_code_submission/
```
in the starting kit directory. If you want to test the performance of a
different algorithm on a different dataset, please specify them using respective
arguments.
If you want to use default folders (i.e. those in above command line), simply
run
```
python run_local_test.py
```
"""
# ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS".
# ISABELLE GUYON, CHALEARN, AND/OR OTHER ORGANIZERS OR CODE AUTHORS DISCLAIM
# ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE
# WARRANTY OF NON-INFRINGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS.
# IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL,
# INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN
# CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS,
# PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE.
################################################################################
# Verbosity level of logging.
# Can be: NOTSET, DEBUG, INFO, WARNING, ERROR, CRITICAL
verbosity_level = 'INFO'
import logging
import os
import tensorflow as tf
import time
import shutil # for deleting a whole directory
import webbrowser
from multiprocessing import Process
logging.basicConfig(
level=getattr(logging, verbosity_level),
format='%(asctime)s %(levelname)s %(filename)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S'
)
def _HERE(*args):
h = os.path.dirname(os.path.realpath(__file__))
return os.path.join(h, *args)
def get_path_to_ingestion_program(starting_kit_dir):
return os.path.join(starting_kit_dir,
'AutoDL_ingestion_program', 'ingestion.py')
def get_path_to_scoring_program(starting_kit_dir):
return os.path.join(starting_kit_dir,
'AutoDL_scoring_program', 'score.py')
def remove_dir(output_dir):
"""Remove the directory `output_dir`.
This aims to clean existing output of last run of local test.
"""
if os.path.isdir(output_dir):
logging.info("Cleaning existing output directory of last run: {}"\
.format(output_dir))
shutil.rmtree(output_dir)
def get_basename(path):
if len(path) == 0:
return ""
if path[-1] == os.sep:
path = path[:-1]
return path.split(os.sep)[-1]
def run_baseline(dataset_dir, code_dir, time_budget=1200):
logging.info("#"*50)
logging.info("Begin running local test using")
logging.info("code_dir = {}".format(get_basename(code_dir)))
logging.info("dataset_dir = {}".format(get_basename(dataset_dir)))
logging.info("#"*50)
# Current directory containing this script
starting_kit_dir = os.path.dirname(os.path.realpath(__file__))
path_ingestion = get_path_to_ingestion_program(starting_kit_dir)
path_scoring = get_path_to_scoring_program(starting_kit_dir)
# Run ingestion and scoring at the same time
command_ingestion =\
"python {} --dataset_dir={} --code_dir={} --time_budget={}"\
.format(path_ingestion, dataset_dir, code_dir, time_budget)
command_scoring =\
'python {} --solution_dir={}'\
.format(path_scoring, dataset_dir)
def run_ingestion():
exit_code = os.system(command_ingestion)
assert exit_code == 0
def run_scoring():
exit_code = os.system(command_scoring)
assert exit_code == 0
ingestion_process = Process(name='ingestion', target=run_ingestion)
scoring_process = Process(name='scoring', target=run_scoring)
ingestion_output_dir = os.path.join(starting_kit_dir,
'AutoDL_sample_result_submission')
score_dir = os.path.join(starting_kit_dir,
'AutoDL_scoring_output')
remove_dir(ingestion_output_dir)
remove_dir(score_dir)
ingestion_process.start()
scoring_process.start()
detailed_results_page = os.path.join(starting_kit_dir,
'AutoDL_scoring_output',
'detailed_results.html')
detailed_results_page = os.path.abspath(detailed_results_page)
# Open detailed results page in a browser
time.sleep(2)
for i in range(30):
if os.path.isfile(detailed_results_page):
webbrowser.open('file://'+detailed_results_page, new=2)
break
time.sleep(1)
ingestion_process.join()
scoring_process.join()
if not ingestion_process.exitcode == 0:
logging.info("Some error occurred in ingestion program.")
if not scoring_process.exitcode == 0:
raise Exception("Some error occurred in scoring program.")
if __name__ == '__main__':
default_starting_kit_dir = _HERE()
# The default dataset is 'miniciao' under the folder AutoDL_sample_data/
default_dataset_dir = os.path.join(default_starting_kit_dir,
'AutoDL_sample_data', 'miniciao')
default_code_dir = os.path.join(default_starting_kit_dir,
'AutoDL_sample_code_submission')
default_time_budget = 1200
tf.flags.DEFINE_string('dataset_dir', default_dataset_dir,
"Directory containing the content (e.g. adult.data/ + "
"adult.solution) of an AutoDL dataset. Specify this "
"argument if you want to test on a different dataset.")
tf.flags.DEFINE_string('code_dir', default_code_dir,
"Directory containing a `model.py` file. Specify this "
"argument if you want to test on a different algorithm."
)
tf.flags.DEFINE_float('time_budget', default_time_budget,
"Time budget for running ingestion " +
"(training + prediction)."
)
FLAGS = tf.flags.FLAGS
dataset_dir = FLAGS.dataset_dir
code_dir = FLAGS.code_dir
time_budget = FLAGS.time_budget
run_baseline(dataset_dir, code_dir, time_budget)