File size: 4,630 Bytes
4c3c1d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import copy
import torch
from torch import nn
from torch.optim import Adam
from tqdm import tqdm
from data import get_loaders_n_gram
from methods import MLP
def train(loader_train, loader_dev, model, device, optimizer, n_epochs):
acc_best = 0
model_best = None
criterion = nn.CrossEntropyLoss()
bar_epochs = tqdm(range(n_epochs), leave=False)
for epoch in bar_epochs:
# train
bar_epoch = tqdm(loader_train, disable=True, leave=False)
model.train()
for x, y in bar_epoch:
x = x.to(device)
y = y.to(device)
y_out = model(x)
loss = criterion(y_out, y.type(torch.LongTensor))
loss.backward()
optimizer.step()
loss_iter = loss.item()
bar_epoch.set_postfix({"loss": loss_iter})
bar_epoch.close()
bar_dev = tqdm(loader_dev, disable=True, leave=False)
model.eval()
# val
ys_pred, ys_true = [], []
with torch.no_grad():
for x, y in bar_dev:
x = x.to(device)
y = y.to(device)
y_out = model(x)
y_pred = torch.argmax(y_out, axis=1)
ys_pred.append(y_pred.cpu())
ys_true.append(y.cpu())
bar_dev.close()
ys_pred = torch.cat(ys_pred)
ys_true = torch.cat(ys_true)
acc = (ys_pred == ys_true).float().mean()
acc = acc.item() * 100
if acc > acc_best:
acc_best = acc
model_best = copy.deepcopy(model)
bar_epochs.set_postfix({"acc_best": acc_best})
return model_best
def test(loader_test, model, device):
model.eval()
ys_pred, ys_true = [], []
bar_test = tqdm(loader_test, leave=False)
with torch.no_grad():
for x, y in bar_test:
x = x.to(device)
y = y.to(device)
y_pred = model(x)
y_pred = torch.argmax(y_pred, axis=1)
ys_pred.append(y_pred.cpu())
ys_true.append(y.cpu())
bar_test.close()
ys_pred = torch.cat(ys_pred)
ys_true = torch.cat(ys_true)
return ys_pred, ys_true
def run(
csv_file,
seed,
n=5,
topk=1000,
ratio_dev=0.1,
ratio_test=0.1,
batch_size=32,
size_hidden=None,
dropout=0.1,
n_epochs=50,
lr=3e-4,
weight_decay=0,
):
# data settings
ratio_dev = ratio_dev
ratio_test = ratio_test
batch_size = batch_size
n = n
data = get_loaders_n_gram(
csv_file,
n=n,
topk=topk,
ratio_dev=ratio_dev,
ratio_test=ratio_test,
seed=seed,
batch_size=batch_size,
)
size_x = data["sizes"]["x"]
size_y = data["sizes"]["y"]
loader_train = data["loaders"]["train"]
loader_dev = data["loaders"]["dev"]
loader_test = data["loaders"]["test"]
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# model settings
if size_hidden is None:
size_hidden = [size_x // 2, size_x // 4]
size_hidden = [size_x] + size_hidden
dropout = dropout
model = MLP(
size_in=size_x,
size_out=size_y,
size_hidden=size_hidden,
dropout=dropout,
)
model = model.to(device)
# training settings
n_epochs = n_epochs
lr = lr
weight_decay = weight_decay
optimizer = Adam(
model.parameters(),
lr=lr,
weight_decay=weight_decay,
)
# train
model_best = train(loader_train, loader_dev, model, device, optimizer, n_epochs)
return test(loader_test, model_best, device)
if __name__ == "__main__":
# data dir
csv_file = "./_DATA/all_chem_df.csv"
# number of trials
n_trials = 5
seeds = list(range(n_trials))
# data settings
topk = 1000
ratio_dev = 0.1
ratio_test = 0.2
batch_size = 32
# model settings
n = 5
dropout = 0.1
size_hidden = [512, 256, 128, 32]
# training settings
n_epochs = 200
lr = 3e-5
weight_decay = 0
for seed in seeds:
y_pred, y_true = run(
csv_file,
seed,
n,
topk,
ratio_dev,
ratio_test,
batch_size,
size_hidden,
dropout,
n_epochs,
lr,
)
log_file = f"./scores/MLP/{seed}-seed--{n}-gram--topk-{topk}--lr-{lr}.csv"
with open(log_file, "a") as f:
f.write("pred,true\n")
for p, t in zip(y_pred, y_true):
f.write(f"{p},{t}\n")
|