Hi,
I am trying cross validation on my dataset following this example - Am I doing k-fold cross validation right? . But I get this warning -
UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use
zero_divisionparameter to control this behavior.
So I think that not all the labels are shown to the model. I am not sure if my cross validation code is correct. Could anyone help me in correcting this code.
Thankyou in advance.
train_df, test_df = train_test_split(df, train_size= 0.8, test_size=0.2)
X = train_df['filename'].to_numpy()
y = train_df['label'].to_numpy()
def get_x(train_df): return train_df['filename']
def get_y(train_df): return train_df['label']
def get_test_x(test_df): return test_df['filename']
def get_test_y(test_df): return test_df['label']
folds = 5
skf = StratifiedKFold(n_splits=folds, shuffle=True)
batch_size = 16
for train_index, val_index in skf.split(train_df.index, y):
train_block = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_x=get_x,
get_y=get_y,
splitter=IndexSplitter(val_index), # added val_index,
item_tfms=[Resize((600,1000), method = ResizeMethod.Pad, pad_mode='border')],
batch_tfms=[Normalize.from_stats(*imagenet_stats)]
)
test_block = DataBlock(
blocks=(ImageBlock, CategoryBlock),
get_x=get_test_x,
get_y=get_test_y,
item_tfms=[Resize((600,1000), method = ResizeMethod.Pad, pad_mode='border')],
batch_tfms=[Normalize.from_stats(*imagenet_stats)]
)
train_dl = train_block.dataloaders(train_df, bs=batch_size)
test_dl = test_block.dataloaders(test_df, bs=batch_size)
learn = Learner(train_dl, xresnet34(n_out=24), model_dir=model_dir, metrics=[accuracy, Precision(average='weighted'), Recall(average='weighted')])
learn.model.cuda()
learn.fit_one_cycle(5)
val = learn.validate()
learn.dls.valid = test_dl.valid
test = learn.validate()
print('done, appending results.. \n')
val_pct.append(val)
test_pct.append(test)