In [ ]:

from fastai import *          # Quick access to most common functionality
from fastai.tabular import *  # Quick access to tabular functionality

Tabular example¶

Tabular data should be in a Pandas DataFrame.

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path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
train_df, valid_df = df[:-2000].copy(),df[-2000:].copy()
train_df.head()

Out[ ]:

	age	workclass	fnlwgt	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	hours-per-week	native-country	>=50k
0	49	Private	101320	Assoc-acdm	12.0	Married-civ-spouse	NaN	Wife	White	Female	0	1902	40	United-States	1
1	44	Private	236746	Masters	14.0	Divorced	Exec-managerial	Not-in-family	White	Male	10520	0	45	United-States	1
2	38	Private	96185	HS-grad	NaN	Divorced	NaN	Unmarried	Black	Female	0	0	32	United-States	0
3	38	Self-emp-inc	112847	Prof-school	15.0	Married-civ-spouse	Prof-specialty	Husband	Asian-Pac-Islander	Male	0	0	40	United-States	1
4	42	Self-emp-not-inc	82297	7th-8th	NaN	Married-civ-spouse	Other-service	Wife	Black	Female	0	0	50	United-States	0

Convert your DataFrame in to a DataBunch suitable for modeling by calling tabular_data_from_df.

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dep_var = '>=50k'
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']
data = TabularDataBunch.from_df(path, train_df, valid_df, dep_var, 
                                tfms=[FillMissing, Categorify], cat_names=cat_names)

Now you can create a Learner with gen_tabular_dta, and fit your model

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

VBox(children=(HBox(children=(IntProgress(value=0, max=1), HTML(value='0.00% [0/1 00:00<00:00]'))), HTML(value…

Total time: 00:05
epoch  train loss  valid loss  accuracy
0      0.340980    0.330657    0.847000  (00:05)