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main_tableGAN.py
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main_tableGAN.py
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"""CLI."""
import os
import argparse
import pickle
import datetime
from timeit import default_timer as timer
import pandas as pd
import numpy as np
import wandb
from synthetizers.TableGAN.tableGAN import TableGAN
from evaluation.eval import eval_synthetic_data, sdv_eval_synthetic_data, constraints_sat_check
from evaluation.reeval_final import prepare_gen_data
from utils import set_seed, read_csv, _load_json
# wandb.log({'accuracy': train_acc, 'loss': train_loss})
# wandb.config.dropout = 0.2
# wandb.alert(title="Low accuracy", text=f"Accuracy {acc} is below threshold {thresh}")
# https://docs.wandb.ai/guides/data-and-model-versioning/dataset-versioning?_gl=1*1la1mgf*_ga*MTMwNzYxOTUyOC4xNjU1MzA5NTE0*_ga_JH1SJHJQXJ*MTY3OTY3MTkyNC4xOC4xLjE2Nzk2NzI0MjguMTMuMC4w
# https://wandb.ai/dpaiton/splitting-tabular-data/reports/Tabular-Data-Versioning-and-Deduplication-with-Weights-Biases--VmlldzoxNDIzOTA1?_gl=1*1p4t0h4*_ga*MTMwNzYxOTUyOC4xNjU1MzA5NTE0*_ga_JH1SJHJQXJ*MTY3OTY3MTkyNC4xOC4xLjE2Nzk2NzI0MTUuMjYuMC4w
# https://docs.wandb.ai/guides/data-vis/tables-quickstart
DATETIME = datetime.datetime.now()
def _parse_args():
parser = argparse.ArgumentParser(description='CTGAN Command Line Interface')
parser.add_argument("--seed", default=7, type=int)
parser.add_argument("--use_only_target_original_dtype", action='store_true')
parser.add_argument("--label_ordering", default='random', choices=['random', 'corr', 'kde'])
parser.add_argument("--wandb_project", default='c-dgm_test', type=str)
parser.add_argument("--wandb_mode", default="online", type=str, choices=['online', 'disabled', 'offline'])
parser.add_argument('-e', '--epochs', default=300, type=int,
help='Number of training epochs')
parser.add_argument('-n', '--num-samples', type=int,
help='Number of rows to sample. Defaults to the training data size')
parser.add_argument("--save_every_n_epochs", default=5, type=int)
parser.add_argument('--random_dim', type=int, default=100,
help='')
parser.add_argument('--num_channels', type=int, default=64,
help='Dimension of each generator layer. '
'Comma separated integers with no whitespaces.')
parser.add_argument('--l2scale', type=float, default=1e-5, help='')
parser.add_argument('--lr', type=float, default=2e-4, help='learning rate')
parser.add_argument('--optimiser', type=str, default="adam", choices=['adam','rmsprop','sgd'], help='')
parser.add_argument('--batch_size', type=int, default=500,
help='Batch size. Must be an even number.')
parser.add_argument('--save', default=None, type=str,
help='A filename to save the trained synthesizer.')
parser.add_argument('--load', default=None, type=str,
help='A filename to load a trained synthesizer.')
parser.add_argument("use_case", type=str, choices=["url","wids","botnet","lcld","heloc","news","faults"])
parser.add_argument("--version", type=str, default='unconstrained', choices=['unconstrained','constrained', "postprocessing"],
help='Version of training. Correct values are unconstrained, constrained and postprocessing')
parser.add_argument('--skip_evaluation', action='store_true')
parser.add_argument('--runtime_evaluation_only', action='store_true')
return parser.parse_args()
def main():
"""CLI."""
args = _parse_args()
set_seed(args.seed)
exp_id = f"{args.version}_{args.label_ordering}_{args.seed}_{args.optimiser}_{args.epochs}_{args.batch_size}_{args.random_dim}_{args.num_channels}_{DATETIME:%d-%m-%y--%H-%M-%S}"
path = f"outputs/TableGAN_out/{args.use_case}/{args.version}/{exp_id}"
args.exp_path = path
os.makedirs(path)
######################################################################
wandb_run = wandb.init(project=args.wandb_project, id=exp_id, reinit=True, mode=args.wandb_mode)
for k,v in args._get_kwargs():
wandb_run.config[k] = v
######################################################################
######################################################################
args.constraints_file = f'./data/{args.use_case}/{args.use_case}_constraints.txt'
######################################################################
dataset_info = _load_json("datasets_info.json")[args.use_case]
print(dataset_info)
######################################################################
X_train, (cat_cols, cat_idx), (roundable_idx, round_digits) = read_csv(f"data/{args.use_case}/train_data.csv", args.use_case, dataset_info["manual_inspection_categorical_cols_idx"])
X_test = pd.read_csv(f"data/{args.use_case}/test_data.csv")
X_val = pd.read_csv(f"data/{args.use_case}/val_data.csv")
columns = X_train.columns.values.tolist()
args.train_data_cols = columns
args.dtypes = X_train.dtypes
if args.load:
model = TableGAN.load(args.load)
else:
model = TableGAN(X_test,
random_dim=args.random_dim, num_channels=args.num_channels,
l2scale=args.l2scale, batch_size=args.batch_size,
epochs=args.epochs, path=path, bin_cols_idx=cat_idx, version=args.version)
model.fit(args, X_train, cat_idx)
if args.save is not None:
model.save(args.save)
if args.use_case == "botnet" or args.use_case == "lcld":
X_train = pd.read_csv(f"data/{args.use_case}/tiny/train_data.csv")
X_test = pd.read_csv(f"data/{args.use_case}/tiny/test_data.csv")
X_val = pd.read_csv(f"data/{args.use_case}/tiny/val_data.csv")
num_sampling_rounds = 5
if args.runtime_evaluation_only:
size = 1000
runs = []
for i in range(num_sampling_rounds):
start = timer()
sampled_data, unconstrained_output = model.sample_unconstrained(size, X_train.shape[1])
end = timer()
runtime = end - start
runs.append(runtime)
runtime_df = pd.DataFrame(list(zip([np.mean(runtime)],[np.std(runtime)])), columns=["Mean", "Std"])
wandb.log({'Runtime/Sampling': runtime_df})
else:
gen_data = [[], [], []]
unconstrained_generated_data = [[], [], []]
constrained_unrounded_gen_data = [[], [], []]
sizes = [X_train.shape[0], X_val.shape[0], X_test.shape[0]]
for r in range(num_sampling_rounds):
for i in range (len(sizes)):
sampled_data, unconstrained_output = model.sample_unconstrained(sizes[i], X_train.shape[1])
unconstrained_generated_data[i].append(unconstrained_output)
constrained_unrounded_output = sampled_data
constrained_unrounded_output = pd.DataFrame(constrained_unrounded_output, columns=columns)
constrained_unrounded_output = constrained_unrounded_output.astype(float)
target_col = columns[-1]
constrained_unrounded_output[target_col] = constrained_unrounded_output[target_col].astype(X_train.dtypes[-1])
constrained_unrounded_gen_data[i].append(constrained_unrounded_output)
# sampled_data = pd.DataFrame(sampled_data, columns=columns)
# sampled_data.iloc[:, roundable_idx] = sampled_data.iloc[:, roundable_idx].round(round_digits) # NOTE: this shouldn't be after the constraints have been applied! (fixed by removing constr correction from sample fc, and adding it below here)
# sampled_data = sampled_data.astype(X_train.dtypes)
gen_data[i].append(constrained_unrounded_output)
real_data = {"train":X_train, "val":X_val, "test":X_test}
generated_data = {"train":gen_data[0], "val":gen_data[1], "test":gen_data[2]}
with open(f'{path}/generated_data.pkl', 'wb') as f:
pickle.dump(generated_data, f)
with open(f'{path}/unconstrained_generated_data.pkl', 'wb') as f:
pickle.dump(unconstrained_generated_data, f)
if not args.skip_evaluation:
wandb.finish()
######################################################################
args.real_data_partition = 'test'
args.model_type = 'tablegan'
args.wandb_project = f"evaluation_{args.model_type}_{args.use_case}"
wandb_run = wandb.init(project=args.wandb_project, id=exp_id)
for k, v in args._get_kwargs():
wandb_run.config[k] = v
######################################################################
args.round_before_cons = False
args.round_after_cons = False
args.postprocessing = False
if args.version != 'unconstrained':
args.version = args.label_ordering
generated_data, unrounded_generated_data = prepare_gen_data(args, unconstrained_generated_data, roundable_idx, round_digits, columns, X_train)
# if args.seed < 3:
constraints_sat_check(args, real_data, unrounded_generated_data, log_wandb=True)
sdv_eval_synthetic_data(args, args.use_case, real_data, generated_data, columns,
problem_type=dataset_info["problem_type"],
target_utility=dataset_info["target_col"], target_detection="", log_wandb=True,
wandb_run=wandb_run)
print('Using evaluators with the following specs', dataset_info["problem_type"], dataset_info["target_size"],
dataset_info["target_col"])
eval_synthetic_data(args, args.use_case, real_data, generated_data, columns,
problem_type=dataset_info["problem_type"], target_utility=dataset_info["target_col"],
target_utility_size=dataset_info["target_size"], target_detection="", log_wandb=True,
wandb_run=wandb_run, unrounded_generated_data_for_cons_sat=unrounded_generated_data)
# if args.seed < 3:
# constraints_sat_check(args, real_data, generated_data, log_wandb=True)
# sdv_eval_synthetic_data(args, args.use_case, real_data, generated_data, columns, problem_type=dataset_info["problem_type"], target_utility=dataset_info["target_col"], target_detection="", log_wandb=True, wandb_run=wandb_run)
# eval_synthetic_data(args, args.use_case, real_data, generated_data, columns, problem_type=dataset_info["problem_type"], target_utility=dataset_info["target_col"], target_utility_size=dataset_info["target_size"], target_detection="", log_wandb=True, wandb_run=wandb_run)
if __name__ == '__main__':
main()