我正在参加APTOS 2019 kaggle比赛,并试图进行5折合奏,但是我在正确实施StratifiedKFold时遇到问题。
我已经尝试过搜索Fastai讨论,但没有任何解决方案。
我正在使用fastai库,并且有一个预先训练的模型。
def get_df():
base_image_dir = os.path.join('..', 'input/aptos2019-blindness-
detection/')
train_dir = os.path.join(base_image_dir,'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x:
os.path.join(train_dir,'{}.png'.format(x)))
df = df.drop(columns=['id_code'])
df = df.sample(frac=1).reset_index(drop=True) #shuffle dataframe
test_df = pd.read_csv('../input/aptos2019-blindness-
detection/sample_submission.csv')
return df, test_df
df, test_df = get_df()
random_state = np.random.seed(2019)
skf = StratifiedKFold(n_splits=5, random_state=random_state, shuffle=True)
X = df['path']
y = df['diagnosis']
#getting the splits
for train_index, test_index in skf.split(X, y):
print('##')
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
train = X_train, y_train
test = X_test, y_test
train_list = [list(x) for x in train]
test_list = [list(x) for x in test]
data = (ImageList.from_df(df=df,path='./',cols='path')
.split_by_rand_pct(0.2)
.label_from_df(cols='diagnosis',label_cls=FloatList)
.transform(tfms,size=sz,resize_method=ResizeMethod.SQUISH,padding_mode='zeros')
.databunch(bs=bs,num_workers=4)
.normalize(imagenet_stats)
)
learn = Learner(data,
md_ef,
metrics = [qk],
model_dir="models").to_fp16()
learn.data.add_test(ImageList.from_df(test_df,
'../input/aptos2019-blindness-detection',
folder='test_images',
suffix='.png'))
我想使用从skf.split获得的折叠来训练我的模型,但是我不确定该怎么做。
最佳答案
有两种方法可以做到这一点。
将'split_by_idxs'与索引一起使用
data = (ImageList.from_df(df=df,path='./',cols='path')
.split_by_idxs(train_idx=train_index, valid_idx=test_index)
.label_from_df(cols='diagnosis',label_cls=FloatList)
.transform(tfms,size=sz,resize_method=ResizeMethod.SQUISH,padding_mode='zeros')
.databunch(bs=bs,num_workers=4)
.normalize(imagenet_stats)
)
使用“ split_by_list”
il = ImageList.from_df(df=df,path='./',cols='path')
data = (il.split_by_list(train=il[train_index], valid=il[test_index])
.label_from_df(cols='diagnosis',label_cls=FloatList)
.transform(tfms,size=sz,resize_method=ResizeMethod.SQUISH,padding_mode='zeros')
.databunch(bs=bs,num_workers=4)
.normalize(imagenet_stats)
)