from fastai.tabular import *  # Quick accesss to tabular functionality

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')

df['salary'].unique()

# function import
from fastai.utils.mem import *

# other function teset
gpu_with_max_free_mem()

# test reduce_mem_usage(df)

df.head()

dep_var = 'salary'
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [FillMissing, Categorify, Normalize]

test = TabularList.from_df(df.iloc[800:1000].copy(), path=path, cat_names=cat_names, cont_names=cont_names)

data = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)
                           .split_by_idx(list(range(800,1000)))
                           .label_from_df(cols=dep_var)
                           .add_test(test)
                           .databunch())

data.show_batch(rows=10)

learn = tabular_learner(data, layers=[200,100], metrics=accuracy)
learn.fit(1, 1e-2)

row = df.iloc[0]

learn.predict(row)