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train.py
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257 lines (196 loc) · 8.32 KB
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import argparse
import pandas as pd
import os
import numpy as np
import sklearn.model_selection
import torch
import json
import sys
import copy
from sklearn.linear_model import LinearRegression
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.utils.data import TensorDataset, DataLoader
from sklearn.preprocessing import normalize, StandardScaler
from sklearn.metrics import mean_squared_error, mean_absolute_error
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--features_dir", dest='features_dir', type=str)
parser.add_argument("--output_dir", dest='output_dir', type=str)
parser.add_argument("--prog_list", dest='prog_list', type=str)
parser.add_argument("--epochs", default=-1, type=int)
parser.add_argument("--device", default='cpu', type=str)
parser.add_argument("--batch_size", dest='batch_size', default=4096, type=int)
parser.add_argument("--hidden_dim", dest='hidden_dim', default=64, type=int)
parser.add_argument("--model", dest='model', default='nn', type=str)
args = parser.parse_args()
args.device = 'cpu' if args.device == 'cpu' else f'cuda:{get_free_gpu()}'
return args
def get_free_gpu():
"""
:return: index of gpu with maximum free memory
"""
os.system('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free >tmp')
memory_available = [int(x.split()[2]) for x in open('tmp', 'r').readlines()]
return np.argmax(memory_available)
def get_data(args):
DATA_DIR = args.features_dir
def get_data_selector(func_selector, acc_df):
for prog in progs:
if not os.path.exists(os.path.join(DATA_DIR, func_selector, prog)):
continue
for f in os.listdir(os.path.join(DATA_DIR, func_selector, prog)):
df = pd.read_csv(os.path.join(DATA_DIR, func_selector, prog, f))
if acc_df is None:
acc_df = df
else:
acc_df = pd.concat([acc_df, df])
print(f"Finish collecting data with {func_selector}", flush=True)
return acc_df
target_col = 'reward'
feature_cols = ['stack', 'successor', 'testCase', 'coverageByBranch', 'coverageByPath', 'depth', 'cpicnt', 'icnt',
'covNew', 'subpath1', 'subpath2', 'subpath4', 'subpath8']
progs = []
with open(args.prog_list, 'r') as f:
for line in f:
progs.append(line[:-1])
total_df = None
for selector in os.listdir(DATA_DIR):
if selector.endswith("jlearch"):
continue
total_df = get_data_selector(selector, total_df)
"""
In jlearch directory we store features, which was collected by selectors using models,
which was trained on previous iterations.
We choose only features, that was collected by previous iteration of current model.
"""
jlearch_dir = os.path.join(DATA_DIR, "jlearch")
if os.path.exists(jlearch_dir):
for selector in os.listdir(jlearch_dir):
if not selector.startswith(args.model):
continue
total_df = get_data_selector(selector, total_df)
y = np.expand_dims(total_df[target_col].values, axis=1)
x = total_df[feature_cols].values
x = x.astype(float)
return x, y
def dump_scaler(scaler, args):
"""
Dump scaler in such format:
- first line is mean vector values separated by comma
- second line is variance vector values separated by comma
"""
with open(os.path.join(args.output_dir, 'scaler.txt'), 'w') as f:
for array in np.vstack((scaler.mean_, scaler.scale_)).tolist():
for item in array[:-1]:
f.write("%s," % item)
f.write("%s" % array[-1])
f.write("\n")
class NeuralNetwork(torch.nn.Module):
def __init__(self, hidden_dim=64):
super(NeuralNetwork, self).__init__()
self.nn = torch.nn.Sequential(
torch.nn.Linear(13, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, hidden_dim),
torch.nn.ReLU(),
torch.nn.Linear(hidden_dim, 1)
)
self.scaler = StandardScaler()
def forward(self, x):
out = self.nn(x)
return out
def do_train(self, x, y, args):
x = copy.deepcopy(x)
y = copy.deepcopy(y)
x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(x, y, test_size=0.2)
self.scaler = self.scaler.fit(x_train)
x_train = self.scaler.transform(x_train)
x_test = self.scaler.transform(x_test)
learning_rate = 1e-3
epochs = sys.maxsize if args.epochs == -1 else args.epochs
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(self.parameters(), lr=learning_rate, weight_decay=1e-5)
scheduler = ReduceLROnPlateau(optimizer, 'min', patience=3, verbose=True)
tensor_x = torch.Tensor(x_train)
tensor_y = torch.Tensor(y_train)
tensor_x_test = torch.Tensor(x_test)
tensor_y_test = torch.Tensor(y_test)
train_dataloader = DataLoader(TensorDataset(tensor_x, tensor_y), batch_size=args.batch_size)
test_dataloader = DataLoader(TensorDataset(tensor_x_test, tensor_y_test), batch_size=args.batch_size)
for epoch in range(epochs):
if optimizer.param_groups[0]['lr'] <= 1e-6:
break
train_loss = train_epoch(self, train_dataloader, criterion, optimizer, args.device)
val_loss, val_mae = eval_epoch(self, test_dataloader, criterion, args.device)
scheduler.step(val_loss)
print('epoch {}, train_loss {}, val_loss {}, val_mae {}'.format(epoch, train_loss, val_loss, val_mae),
flush=True)
def dump(self, args):
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
dump_scaler(self.scaler, args)
nn = {
"linearLayers": [],
"activationLayers": ["reLU", "reLU", "reLU"],
"biases": [],
}
state_dict = self.cpu().state_dict()
for i in [0, 2, 4]:
nn["linearLayers"] += [state_dict[f'nn.{i}.weight'].numpy().tolist()]
nn["biases"] += [state_dict[f'nn.{i}.bias'].numpy().tolist()]
with open(os.path.join(args.output_dir, 'nn.json'), 'w') as f:
json.dump(nn, f, indent=4)
class Linear(torch.nn.Module):
def __init__(self):
super(Linear, self).__init__()
self.model = LinearRegression()
self.scaler = StandardScaler()
def do_train(self, x, y, args):
x = copy.deepcopy(x)
y = copy.deepcopy(y)
self.scaler = self.scaler.fit(x)
self.model.fit(self.scaler.transform(x), y)
def dump(self, args):
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
dump_scaler(self.scaler, args)
with open(os.path.join(args.output_dir, 'linear.txt'), 'w') as f:
f.write(f"{','.join(map(str, self.model.coef_[0]))},{self.model.intercept_[0]}\n")
def train_epoch(model, data_loader, loss_fn, optimizer, device):
model.train(True)
running_loss = 0.0
processed_data = 0
for batch_x, batch_y in data_loader:
batch_x = batch_x.to(device=device)
batch_y = batch_y.to(device=device)
optimizer.zero_grad()
outputs = model(batch_x)
loss = loss_fn(outputs, batch_y)
running_loss += loss.detach().cpu().item()
processed_data += batch_x.shape[0]
loss.backward()
optimizer.step()
return running_loss / processed_data
def eval_epoch(model, data_loader, loss_fn, device):
model.eval()
running_loss = 0.0
running_ae = 0.0
processed_data = 0
for batch_x, batch_y in data_loader:
batch_x = batch_x.to(device=device)
batch_y = batch_y.to(device=device)
outputs = model(batch_x)
loss = loss_fn(outputs, batch_y)
running_loss += loss.detach().cpu().item()
processed_data += batch_x.shape[0]
running_ae += torch.sum(torch.abs(outputs - batch_y))
return running_loss / processed_data, running_ae / processed_data
def main():
args = get_args()
model = NeuralNetwork(hidden_dim=args.hidden_dim) if ("nn" in args.model) else Linear()
model = model.to(device=args.device)
x, y = get_data(args)
model.do_train(x, y, args)
model.dump(args)
if __name__ == "__main__":
main()
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