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simulation_runner.py
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"""
Runs an iterative screening simulation.
Usage:
python simulation_runner.py \
--pipeline_params_json_file=../param_configs/general_pipeline_config.json \
--nbs_params_json_file=../param_configs/ClusterBasedWCSelector_params_reduced.json \
--exploration_strategy=weighted \
--iter_max=5 \
--process_num=$process_num \
--batch_size_index=0 \
--rnd_seed=0 \
--no-random_param_sampling \
--no-precompute_dissimilarity_matrix
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import json
import pathlib
import numpy as np
import pandas as pd
import csv
import time
import os
from active_learning_dd.active_learning_dd import get_next_batch
from active_learning_dd.database_loaders.prepare_loader import prepare_loader
from active_learning_dd.utils.data_utils import get_duplicate_smiles_in1d
from active_learning_dd.utils.generate_dissimilarity_matrix import compute_dissimilarity_matrix
from simulation_utils import *
if __name__ == '__main__':
# read args
parser = argparse.ArgumentParser()
parser.add_argument('--pipeline_params_json_file', action="store", dest="pipeline_params_json_file", required=True)
parser.add_argument('--nbs_params_json_file', action="store", dest="nbs_params_json_file", required=True)
parser.add_argument('--exploration_strategy', action="store", dest="exploration_strategy", required=True)
parser.add_argument('--iter_max', type=int, default=10, action="store", dest="iter_max", required=True)
parser.add_argument('--process_num', type=int, default=0, action="store", dest="process_num", required=True)
parser.add_argument('--batch_size_index', type=int, default=0, action="store", dest="batch_size_index", required=True)
parser.add_argument('--rnd_seed', type=int, default=0, action="store", dest="rnd_seed", required=True)
parser.add_argument('--random_param_sampling', dest='random_param_sampling', action='store_true')
parser.add_argument('--no-random_param_sampling', dest='random_param_sampling', action='store_false')
parser.add_argument('--precompute_dissimilarity_matrix', dest='precompute_dissimilarity_matrix', action='store_true')
parser.add_argument('--no-precompute_dissimilarity_matrix', dest='precompute_dissimilarity_matrix', action='store_false')
parser.set_defaults(random_param_sampling=True)
given_args = parser.parse_args()
pipeline_params_json_file = given_args.pipeline_params_json_file
nbs_params_json_file = given_args.nbs_params_json_file
exploration_strategy = given_args.exploration_strategy
iter_max = given_args.iter_max
process_num = given_args.process_num
batch_size_index = given_args.batch_size_index
rnd_seed = given_args.rnd_seed
random_param_sampling = given_args.random_param_sampling
precompute_dissimilarity_matrix = given_args.precompute_dissimilarity_matrix
start_iter = 0
# load param json configs
with open(pipeline_params_json_file) as f:
pipeline_config = json.load(f)
with open(nbs_params_json_file) as f:
nbs_config = json.load(f)
param_sampling_str = {True: 'random', False: 'distributive'}
param_sampling_str = param_sampling_str[random_param_sampling]
print(param_sampling_str)
next_batch_selector_params = get_param_from_dist(nbs_config, rnd_seed=rnd_seed,
use_uniform=random_param_sampling,
exploration_strategy=exploration_strategy)
params_set_results_dir = pipeline_config['common']['params_set_results_dir'].format(param_sampling_str, exploration_strategy,
next_batch_selector_params['class'], process_num)
params_set_config_csv = params_set_results_dir+'/'+pipeline_config['common']['params_set_config_csv']
pathlib.Path(params_set_config_csv).parent.mkdir(parents=True, exist_ok=True)
with open(params_set_config_csv,'w') as f:
csv_w = csv.writer(f)
csv_w.writerow(list(next_batch_selector_params.keys()) + ['rnd_seed'])
csv_w.writerow(list(next_batch_selector_params.values()) + [rnd_seed])
# run this param set for each batch size
batch_size_list = next_batch_selector_params["batch_size"]
batch_size = batch_size_list[batch_size_index]
print('---------------------------------------------------------------')
print('Starting AL pipeline with batch_size: {}'.format(batch_size))
next_batch_selector_params["batch_size"] = batch_size
batch_size_results_dir = params_set_results_dir + pipeline_config['common']['batch_size_results_dir'].format(batch_size)
if os.path.exists(batch_size_results_dir):
start_iter = len(glob.glob(batch_size_results_dir + '/iter_*')) - 1
else:
pathlib.Path(batch_size_results_dir+'/'+pipeline_config['common']['params_set_config_csv']).parent.mkdir(parents=True, exist_ok=True)
# modify location of training data to be able to continue jobs
if not os.path.exists(batch_size_results_dir + '/training_data/'):
import shutil
shutil.copytree(pathlib.Path(pipeline_config['training_data_params']['data_path_format']).parent,
batch_size_results_dir + '/training_data')
pipeline_config['training_data_params']['data_path_format'] = batch_size_results_dir + '/training_data/iter_{}.csv'
with open(batch_size_results_dir+'/'+pipeline_config['common']['params_set_config_csv'],'w') as f:
csv_w = csv.writer(f)
csv_w.writerow(list(next_batch_selector_params.keys()) + ['rnd_seed'])
csv_w.writerow(list(next_batch_selector_params.values()) + [rnd_seed])
try:
pipeline_config['common']['dissimilarity_memmap_filename']
except:
pipeline_config['common']['dissimilarity_memmap_filename'] = None
if precompute_dissimilarity_matrix:
if pipeline_config['common']['dissimilarity_memmap_filename'] is None:
pipeline_config['common']['dissimilarity_memmap_filename'] = '../datasets/dissimilarity_matrix.dat'
compute_dissimilarity_matrix(csv_file_or_dir=pipeline_config['unlabeled_data_params']['data_path_format'],
output_dir=pipeline_config['common']['dissimilarity_memmap_filename'])
# run iterations for this simulation
for iter_num in range(start_iter, iter_max):
iter_start_time = time.time()
print('---------------------------------------------------------------')
print('Processing iteration number: {}...'.format(iter_num))
#### Run single iteration of active learning pipeline ####
selection_start_time = time.time()
exploitation_df, exploration_df, exploitation_array, exploration_array = get_next_batch(training_loader_params=pipeline_config['training_data_params'],
unlabeled_loader_params=pipeline_config['unlabeled_data_params'],
model_params=pipeline_config['model'],
task_names=pipeline_config['common']['task_names'],
next_batch_selector_params=next_batch_selector_params,
dissimilarity_memmap_filename=pipeline_config['common']['dissimilarity_memmap_filename'])
selection_end_time = time.time()
total_selection_time = selection_end_time - selection_start_time
#### Evaluation ####
# save results
print('Evaluating selected batch...')
eval_start_time = time.time()
evaluate_selected_batch(exploitation_df, exploration_df,
exploitation_array, exploration_array,
batch_size_results_dir,
pipeline_config,
iter_num,
batch_size,
total_selection_time,
add_mean_medians=False)
eval_end_time = time.time()
print('Time it took to evaluate batch {} seconds.'.format(eval_end_time-eval_start_time))
if exploitation_df is not None or exploration_df is not None:
# finally save the exploitation, exploration dataframes to training data directory for next iteration
pd.concat([exploitation_df, exploration_df]).to_csv(pipeline_config['training_data_params']['data_path_format'].format(iter_num+1),
index=False)
iter_end_time = time.time()
print('Finished processing iteration {}. Took {} seconds.'.format(iter_num, iter_end_time-iter_start_time))
# terminate if both exploitation and exploration df are None
if exploitation_df is None and exploration_df is None:
print('Both exploitation and exploration selections are empty. Terminating program.')
break
# summarize the evaluation results into a single csv file
summarize_simulation(batch_size_results_dir,
pipeline_config)