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experiment_1_and_2_runner.py
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"""
Runs an active learning simulation for experiment 1 and 2.
Experiment 1: 10 random initial starting sets for aid624173 target for 10 iters. Prune to top-performing hyperparameters.
Experiment 2: 10 random initial starting sets for aid624173 target for 50 iters. Further prune for experiment 3.
Usage:
python experiment_1_and_2_runner.py \
--pipeline_params_json_file=../param_configs/exp_1_and_2_pipeline_config.json \
--hyperparams_json_file=../param_configs/first_pass_hyperparams/middle/batch_size_96/ClusterBasedWCSelector_55.json \
--initial_dataset_file=../datasets/aid624173_cv_96/unlabeled_1338.csv
--iter_max=3 \
--batch_size_index=0 \
--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
import shutil
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('--hyperparams_json_file', action="store", dest="hyperparams_json_file", required=True)
parser.add_argument('--initial_dataset_file', action="store", dest="initial_dataset_file", required=True)
parser.add_argument('--iter_max', type=int, default=10, action="store", dest="iter_max", required=False)
parser.add_argument('--batch_size_index', type=int, default=0, action="store", dest="batch_size_index", required=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')
given_args = parser.parse_args()
pipeline_params_json_file = given_args.pipeline_params_json_file
hyperparams_json_file = given_args.hyperparams_json_file
initial_dataset_file = given_args.initial_dataset_file
iter_max = given_args.iter_max
batch_size_index = given_args.batch_size_index
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(hyperparams_json_file) as f:
next_batch_selector_params = json.load(f)
# setup initial training plate
pathlib.Path(pipeline_config['training_data_params']['data_path_format']).parent.mkdir(parents=True, exist_ok=True)
[os.remove(f) for f in glob.glob(pipeline_config['training_data_params']['data_path_format'].format('*'))]
shutil.copyfile(initial_dataset_file, pipeline_config['training_data_params']['data_path_format'].format(0))
print('Set {} as starting initial set.'.format(initial_dataset_file))
assert pd.read_csv(initial_dataset_file).equals(pd.read_csv(pipeline_config['training_data_params']['data_path_format'].format(0)))
rnd_seed = next_batch_selector_params['rnd_seed']
params_set_results_dir = pipeline_config['common']['params_set_results_dir'].format(next_batch_selector_params['hyperparameter_group'],
next_batch_selector_params['hyperparameter_id'],
initial_dataset_file.split('_')[-1].replace('.csv',''))
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 not os.path.exists(batch_size_results_dir):
pathlib.Path(batch_size_results_dir+'/'+pipeline_config['common']['params_set_config_csv']).parent.mkdir(parents=True, exist_ok=True)
start_iter = len(glob.glob(batch_size_results_dir + '/training_data/iter_*')) - 1
# 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')
start_iter = 0
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, min(start_iter+5, 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)