rasbt--deeplearning-models
wehub-resource-sync
83 行
2.5 KiB
Python
83 行
2.5 KiB
Python
import numpy as np
|
|
import os
|
|
from packaging import version
|
|
import pandas as pd
|
|
import sys
|
|
import tarfile
|
|
import time
|
|
from tqdm import tqdm
|
|
import urllib.request
|
|
|
|
|
|
def download_data():
|
|
|
|
source = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
|
|
target = "aclImdb_v1.tar.gz"
|
|
|
|
if os.path.exists(target):
|
|
return
|
|
|
|
def reporthook(count, block_size, total_size):
|
|
global start_time
|
|
if count == 0:
|
|
start_time = time.time()
|
|
return
|
|
duration = time.time() - start_time
|
|
progress_size = int(count * block_size)
|
|
speed = progress_size / (1024.0**2 * duration)
|
|
percent = count * block_size * 100.0 / total_size
|
|
|
|
sys.stdout.write(
|
|
f"\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB "
|
|
f"| {speed:.2f} MB/s | {duration:.2f} sec elapsed"
|
|
)
|
|
sys.stdout.flush()
|
|
|
|
if not os.path.isdir("aclImdb") and not os.path.isfile("aclImdb_v1.tar.gz"):
|
|
urllib.request.urlretrieve(source, target, reporthook)
|
|
|
|
|
|
def prepare_data():
|
|
if os.path.exists("train.csv"):
|
|
return
|
|
|
|
target = "aclImdb_v1.tar.gz"
|
|
basepath = "aclImdb"
|
|
|
|
if not os.path.isdir(basepath):
|
|
|
|
with tarfile.open(target, "r:gz") as tar:
|
|
tar.extractall()
|
|
|
|
labels = {"pos": 1, "neg": 0}
|
|
|
|
df = pd.DataFrame()
|
|
|
|
with tqdm(total=50000) as pbar:
|
|
for s in ("test", "train"):
|
|
for l in ("pos", "neg"):
|
|
path = os.path.join(basepath, s, l)
|
|
for file in sorted(os.listdir(path)):
|
|
with open(os.path.join(path, file), "r", encoding="utf-8") as infile:
|
|
txt = infile.read()
|
|
|
|
if version.parse(pd.__version__) >= version.parse("1.3.2"):
|
|
x = pd.DataFrame(
|
|
[[txt, labels[l]]], columns=["review", "sentiment"]
|
|
)
|
|
df = pd.concat([df, x], ignore_index=False)
|
|
|
|
else:
|
|
df = df.append([[txt, labels[l]]], ignore_index=True)
|
|
pbar.update()
|
|
df.columns = ["text", "label"]
|
|
np.random.seed(0)
|
|
df = df.reindex(np.random.permutation(df.index))
|
|
|
|
df_train = df.iloc[:35_000]
|
|
df_val = df.iloc[35_000:40_000]
|
|
df_test = df.iloc[40_000:]
|
|
|
|
df_train.to_csv("train.csv", index=False, encoding="utf-8")
|
|
df_val.to_csv("validation.csv", index=False, encoding="utf-8")
|
|
df_test.to_csv("test.csv", index=False, encoding="utf-8") |