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post_training.py
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274 lines (232 loc) · 8.32 KB
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import os
import ast
import pickle
import argparse
import itertools
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.optimizers.legacy import Adam
from deephyper.gnn_uq.load_data import load_data
from deephyper.gnn_uq.gnn_model import (
RegressionUQSpace,
nll,
)
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
def get_dir(default_path, provided_path):
if provided_path:
if not os.path.exists(provided_path):
os.makedirs(provided_path)
return provided_path
else:
if not os.path.exists(default_path):
os.makedirs(default_path)
return default_path
def post_train(args, arch, name):
ROOT_DIR = get_dir("./autognnuq/", args.ROOT_DIR)
POST_DIR = get_dir("./autognnuq/", args.POST_DIR)
DATA_DIR = get_dir("./autognnuq/data/", args.DATA_DIR)
SPLIT_TYPE = args.SPLIT_TYPE
seed = int(args.seed)
dataset = args.dataset
bs = int(args.batch_size)
lr = float(args.learning_rate)
epoch = int(args.epoch)
mode = args.mode
if mode == "normal":
POST_MODEL_DIR = os.path.join(
POST_DIR,
f"NEW_POST_MODEL/post_model_{dataset}_random_{seed}_split_{SPLIT_TYPE}/",
)
POST_RESULT_DIR = os.path.join(
POST_DIR,
f"NEW_POST_RESULT/post_result_{dataset}_random_{seed}_split_{SPLIT_TYPE}/",
)
elif mode == "simple":
POST_MODEL_DIR = os.path.join(
POST_DIR,
f"NEW_SIMPLE_MODEL/simplepost_model_{dataset}_random_{seed}_split_{SPLIT_TYPE}/",
)
POST_RESULT_DIR = os.path.join(
POST_DIR,
f"NEW_SIMPLE_RESULT/simplepost_model_{dataset}_random_{seed}_split_{SPLIT_TYPE}/",
)
elif mode == "random":
POST_MODEL_DIR = os.path.join(
POST_DIR,
f"NEW_POST_MODEL_RANDOM/post_model_{dataset}_random_{seed}_split_{SPLIT_TYPE}/",
)
POST_RESULT_DIR = os.path.join(
POST_DIR,
f"NEW_POST_RANDOM_RESULT/post_result_{dataset}_random_{seed}_split_{SPLIT_TYPE}/",
)
POST_MODEL_H5 = os.path.join(
POST_MODEL_DIR,
f"best_{name}.h5",
)
POST_RESULT_PICKLE = os.path.join(
POST_RESULT_DIR,
f"test_{name}.pickle",
)
POST_RESULT_VAL_PICKLE = os.path.join(
POST_RESULT_DIR,
f"val_{name}.pickle",
)
if not os.path.exists(POST_MODEL_DIR):
os.makedirs(POST_MODEL_DIR)
if not os.path.exists(POST_RESULT_DIR):
os.makedirs(POST_RESULT_DIR)
if not os.path.exists(POST_RESULT_PICKLE):
tf.keras.backend.clear_session()
if SPLIT_TYPE == "811":
sizes = (0.8, 0.1, 0.1)
elif SPLIT_TYPE == "523":
sizes = (0.5, 0.2, 0.3)
(x_train, y_train), (x_valid, y_valid), (x_test, y_test), (mean, std) = (
load_data(
DATA_DIR=DATA_DIR,
dataset=dataset,
test=1,
split_type="random",
seed=seed,
sizes=sizes,
)
)
# turn str of architecture choice to a list
arch = ast.literal_eval(arch)
input_shape = [item.shape[1:] for item in x_train]
output_shape = y_train.shape[1:]
shapes = dict(input_shape=input_shape, output_shape=output_shape)
space = RegressionUQSpace(**shapes).build()
model = space.sample(choice=arch)
print(model.summary())
model.compile(loss=nll, optimizer=Adam(learning_rate=lr))
cp = ModelCheckpoint(
POST_MODEL_H5,
monitor="val_loss",
verbose=2,
save_best_only=True,
save_weights_only=True,
mode="min",
)
es = EarlyStopping(monitor="val_loss", mode="min", patience=200)
history = model.fit(
x_train,
y_train,
batch_size=bs,
epochs=epoch,
callbacks=[cp, es],
validation_data=(x_valid, y_valid),
verbose=2,
).history
# make prediction
model.load_weights(POST_MODEL_H5)
y_test = y_test.squeeze()
y_preds = []
y_uncs = []
batch = int(np.ceil(len(x_test[0]) / 128))
for i in range(batch):
x_test_ = [x_test[j][i * 128 : (i + 1) * 128] for j in range(len(x_test))]
y_dist_ = model(x_test_)
y_preds.append(y_dist_.loc.numpy())
y_uncs.append(y_dist_.scale.numpy())
y_pred = np.concatenate(y_preds, axis=0).squeeze()
y_unc = np.concatenate(y_uncs, axis=0).squeeze()
with open(POST_RESULT_PICKLE, "wb") as handle:
pickle.dump(y_test, handle)
pickle.dump(y_pred, handle)
pickle.dump(y_unc, handle)
pickle.dump(history, handle)
y_valid = y_valid.squeeze()
y_val_preds = []
y_val_uncs = []
batch = int(np.ceil(len(x_valid[0]) / 128))
for i in range(batch):
x_valid_ = [
x_valid[j][i * 128 : (i + 1) * 128] for j in range(len(x_valid))
]
y_dist_ = model(x_valid_)
y_val_preds.append(y_dist_.loc.numpy())
y_val_uncs.append(y_dist_.scale.numpy())
y_val_pred = np.concatenate(y_val_preds, axis=0).squeeze()
y_val_unc = np.concatenate(y_val_uncs, axis=0).squeeze()
with open(POST_RESULT_VAL_PICKLE, "wb") as handle:
pickle.dump(y_valid, handle)
pickle.dump(y_val_pred, handle)
pickle.dump(y_val_unc, handle)
def main():
parser = argparse.ArgumentParser(description="Neural architecture search.")
parser.add_argument(
"--ROOT_DIR", type=str, help="Root directory", default="./autognnuq/"
)
parser.add_argument(
"--POST_DIR", type=str, help="Post training directory", default="./autognnuq/"
)
parser.add_argument(
"--DATA_DIR", type=str, help="Data directory", default="./autognnuq/data/"
)
parser.add_argument(
"--SPLIT_TYPE", type=str, help="Split ratio 811 or 523", default="523"
)
parser.add_argument("--seed", type=int, help="Random seed data split", default=0)
parser.add_argument(
"--dataset",
type=str,
help="lipo, delaney, qm7, freesolv, qm9",
default="delaney",
)
parser.add_argument("--batch_size", type=int, help="Batch size", default=128)
parser.add_argument(
"--learning_rate", type=float, help="Learning rate", default=1e-3
)
parser.add_argument(
"--epoch", type=int, help="Number of search epochs", default=1000
)
parser.add_argument(
"--mode",
type=str,
help="Training mode [normal, simple, ranodm]",
default="normal",
)
args = parser.parse_args()
ROOT_DIR = get_dir("./autognnuq/", args.ROOT_DIR)
POST_DIR = get_dir("./autognnuq/", args.POST_DIR)
DATA_DIR = get_dir("./autognnuq/data/", args.DATA_DIR)
SPLIT_TYPE = args.SPLIT_TYPE
seed = int(args.seed)
dataset = args.dataset
bs = int(args.batch_size)
lr = float(args.learning_rate)
epoch = int(args.epoch)
mode = args.mode
print(f"# ROOT DIR : {ROOT_DIR}")
print(f"# DATA DIR : {DATA_DIR}")
print(f"# POST DIR : {POST_DIR}")
print(f"# dataset : {dataset}")
print(f"# split ratio : {SPLIT_TYPE}")
print(f"# random seed : {seed}")
print(f"# batch size : {bs}")
print(f"# learning rate: {lr}")
print(f"# epoch : {epoch}")
print(f"# mode : {mode}")
MODEL_DIR = os.path.join(
ROOT_DIR, f"NEW_RE_{dataset}_random_{seed}_split_{SPLIT_TYPE}/save/model/"
)
arch_path = MODEL_DIR.split("save")[0] + "results.csv"
df = pd.read_csv(arch_path)
loss_min = []
arch_min = []
id_min = []
for i in range(len(df)):
loss_min_ = np.argsort(df["objective"])[::-1].values[i]
arch_min_ = df["p:arch_seq"][loss_min_]
id_min_ = df["job_id"][loss_min_]
if not any(np.array_equal(arch_min_, x) for x in arch_min):
loss_min.append(loss_min_)
arch_min.append(arch_min_)
id_min.append(id_min_)
for i in range(10):
print(f"Model {i + 1} started... previous loss {df['objective'][loss_min_]}")
post_train(args, arch_min[i], id_min[i])
if __name__ == "__main__":
main()