elif args.htru2:
datasets.append(htru2_details)
elif not args.statlog and not args.htru2:
datasets.append(statlog_details)
datasets.append(htru2_details)
experiment_details = []
for ds in datasets:
data = ds['data']
data.load_and_process()
data.build_train_test_split()
data.scale_standard()
experiment_details.append(
experiments.ExperimentDetails(data,
ds['name'],
ds['readable_name'],
threads=threads,
seed=seed))
if args.all or args.benchmark or args.ica or args.pca or args.lda or args.svd or args.rf or args.rp:
if verbose:
logger.info("----------")
logger.info("Running experiments")
timings = {}
if args.benchmark or args.all:
run_experiment(experiment_details, experiments.BenchmarkExperiment,
'Benchmark', args.dim, args.skiprerun, verbose,
timings)
'name':
'cliff_walking',
'readable_name':
'Cliff Walking (4x12)',
}]
experiment_details = []
for env in envs:
env['env'].seed(seed)
logger.info('{}: State space: {}, Action space: {}'.format(
env['readable_name'], env['env'].unwrapped.nS,
env['env'].unwrapped.nA))
experiment_details.append(
experiments.ExperimentDetails(env['env'],
env['name'],
env['readable_name'],
threads=threads,
seed=seed))
if verbose:
logger.info("----------")
print('\n\n')
logger.info("Running experiments")
timings = {}
if args.policy or args.all:
print('\n\n')
run_experiment(experiment_details, experiments.PolicyIterationExperiment, 'PI', verbose, timings, \
MAX_STEPS['pi'], NUM_TRIALS['pi'], theta=PI_THETA)
ds1_data.load_and_process()
ds2_data = loader.PenDigitData(verbose=verbose, seed=seed)
ds2_name = 'pen_digits'
ds2_readable_name = 'Handwritten Digits'
ds2_data.load_and_process()
if verbose:
print("----------")
print("Running experiments")
timings = {}
experiment_details_ds1 = experiments.ExperimentDetails(ds1_data,
ds1_name,
ds1_readable_name,
threads=threads,
seed=seed)
experiment_details_ds2 = experiments.ExperimentDetails(ds2_data,
ds2_name,
ds2_readable_name,
threads=threads,
seed=seed)
if args.ann or args.all:
t = datetime.now()
experiment = experiments.ANNExperiment(experiment_details_ds1,
verbose=verbose)
experiment.perform()
experiment = experiments.ANNExperiment(experiment_details_ds2,
datasets.append(dataset1_details)
elif args.dataset2:
datasets.append(dataset2_details)
elif not args.dataset1 and not args.dataset2:
datasets.append(dataset1_details)
datasets.append(dataset2_details)
experiment_details = []
for ds in datasets:
data = ds['data']
data.load_and_process()
data.build_train_test_split()
data.scale_standard()
experiment_details.append(experiments.ExperimentDetails(
data, ds['name'], ds['readable_name'], ds['best_nn_params'],
threads=threads,
seed=seed
))
if args.all or args.benchmark or args.ica or args.pca or args.lda or args.svd or args.rf or args.rp:
if verbose:
logger.info("----------")
logger.info("Running experiments")
timings = {}
if args.benchmark or args.all:
run_experiment(experiment_details, experiments.BenchmarkExperiment, 'Benchmark', args.dim, args.skiprerun,
verbose, timings)
if args.ica or args.all:
}, {
'env': environments.get_taxi_environment(),
'name': 'taxi',
'readable_name': 'Taxi problem',
'state_to_track': 14
}]
experiment_details = []
for env in envs:
env['env'].seed(seed)
logger.info('{}: State space: {}, Action space: {}'.format(
env['readable_name'], env['env'].unwrapped.nS,
env['env'].unwrapped.nA))
experiment_details.append(
experiments.ExperimentDetails(env['env'], env['name'],
env['readable_name'], seed,
env['state_to_track']))
if verbose:
logger.info("----------")
logger.info("Running experiments")
timings = {}
if args.policy or args.all:
run_experiment(experiment_details,
experiments.PolicyIterationExperiment, 'PI', verbose,
timings)
if args.value or args.all:
run_experiment(experiment_details,
if seed is None:
seed = np.random.randint(0, (2**32) - 1)
logger.info("Seed: {}".format(seed))
logger.info("Available datasets: {}".format(DATASETS.keys()))
logger.info("Selected dataset: {}".format(args.dataset))
timings = {}
data_loader = dataset["loader"](verbose=verbose, seed=seed)
data_loader.set_logger(logger)
data_loader.load_and_process()
data_loader.build_train_test_split()
data_loader.scale_standard()
experiment_details = experiments.ExperimentDetails(
data_loader,
args.dataset,
dataset["readable_name"],
threads=threads,
seed=seed)
if args.ann or args.all:
run_experiment(experiment_details, experiments.ANNExperiment, "ANN",
verbose, timings)
if args.boosting or args.all:
run_experiment(experiment_details, experiments.BoostingExperiment,
"Boosting", verbose, timings)
if args.dt or args.all:
run_experiment(experiment_details, experiments.DTExperiment, "DT",
verbose, timings)
datasets = [ds1_details, ds2_details]
datasets = [enhancer_brain, wine_quality_details]
datasets = [wine_quality_uniq_details]
datasets = [enhancer_brain, wine_quality_uniq_details]
experiment_details = []
for ds in datasets:
data = ds['data']
data.load_and_process()
data.build_train_test_split()
data.scale_standard()
experiment_details.append(
experiments.ExperimentDetails(
data,
ds['name'],
ds['readable_name'],
threads=threads,
seed=seed,
bparams=True, # Turn this to True for best params in each clf
))
if args.knn or args.all:
run_experiment(experiment_details, experiments.KNNExperiment, 'KNN',
verbose, timings)
if args.boosting or args.all:
run_experiment(experiment_details, experiments.BoostingExperiment,
'Boosting', verbose, timings)
if args.ann or args.all:
run_experiment(experiment_details, experiments.ANNExperiment, 'ANN',
verbose, timings)
print("Running experiments")
timings = {}
datasets = [ds1_details, ds2_details]
experiment_details = []
for ds in datasets:
data = ds["data"]
data.load_and_process()
data.build_train_test_split()
data.scale_standard()
experiment_details.append(
experiments.ExperimentDetails(data,
ds["name"],
ds["readable_name"],
threads=threads,
seed=seed))
if args.ann or args.all:
run_experiment(experiment_details, experiments.ANNExperiment, "ANN",
verbose, timings)
if args.boosting or args.all:
run_experiment(
experiment_details,
experiments.BoostingExperiment,
"Boosting",
verbose,
timings,
)
datasets.append(dataset2_details)
elif not args.dataset1 and not args.dataset2:
datasets.append(dataset1_details)
datasets.append(dataset2_details)
experiment_details = []
for ds in datasets:
data = ds["data"]
data.load_and_process()
data.build_train_test_split()
data.scale_standard()
experiment_details.append(
experiments.ExperimentDetails(
data,
ds["name"],
ds["readable_name"],
ds["best_nn_params"],
threads=threads,
seed=seed,
))
if (args.all or args.benchmark or args.ica or args.pca or args.lda
or args.svd or args.rf or args.rp):
if verbose:
logger.info("----------")
logger.info("Running experiments")
timings = {}
if args.benchmark or args.all:
run_experiment(