I encounter this issue when doing my research. My fastai version is 1.0.42. I replicate a similar scenario using the code from https://docs.fast.ai/tabular.data.html
import fastai
from fastai.tabular import *
path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
# captical-loss is a numerical columns
df = df[["salary", "capital-loss"]]
dep_var = 'salary'
procs = [FillMissing, Categorify, Normalize]
val_idx = range(len(df) - 2000, len(df))
data = TabularDataBunch.from_df(path, df, dep_var, valid_idx=val_idx, procs=procs)
learn = tabular_learner(data, layers=[200,100], metrics=accuracy)
learn.export()
load_learner(path)
the load_learner throws an error as:
Exception reporting mode: Plain
Traceback (most recent call last):
File "<ipython-input-35-524bf55a7a03>", line 2, in <module>
loaded_learn = load_learner(path)
File "/home/aurora/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/basic_train.py", line 471, in load_learner
src = LabelLists.load_state(path, state.pop('data'))
File "/home/aurora/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/data_block.py", line 507, in load_state
train_ds = LabelList.load_state(path, state)
File "/home/aurora/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/data_block.py", line 605, in load_state
res = cls(x, y, tfms=state['tfms'], tfm_y=state['tfm_y'], **state['tfmargs']).process()
File "/home/aurora/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/data_block.py", line 627, in process
self.x.process(xp)
File "/home/aurora/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/data_block.py", line 68, in process
for p in self.processor: p.process(self)
File "/home/aurora/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/tabular/data.py", line 59, in process
ds.classes,ds.cat_names,ds.cont_names = self.classes,self.cat_names,self.cont_names
AttributeError: 'TabularProcessor' object has no attribute 'classes'
I didn’t find this issue when using the whole Adult_sample dataset.
One different between these two is in the classes and y.classes attribution:
Using the full dataset
Using only “salary”, “capital-loss” columns
This is my first pose 
I hope I am not bothering anyone.