Practical Deep Learning for Coders, v3¶

Lesson4_tabular¶

Tabular models¶

Tabular(表格)模型¶

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from fastai.tabular import *

Tabular data should be in a Pandas DataFrame.

Tabular数据是Pandas里的DataFrame。

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

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

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

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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())

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data.show_batch(rows=10)

workclass	education	marital-status	occupation	relationship	race	education-num_na	age	fnlwgt	education-num	target
Private	HS-grad	Never-married	Sales	Not-in-family	White	False	-1.2158	1.1004	-0.4224	<50k
?	HS-grad	Widowed	?	Not-in-family	White	False	1.8627	0.0976	-0.4224	<50k
Self-emp-not-inc	HS-grad	Never-married	Craft-repair	Own-child	Black	False	0.0303	0.2092	-0.4224	<50k
Private	HS-grad	Married-civ-spouse	Protective-serv	Husband	White	False	1.5695	-0.5938	-0.4224	<50k
Private	HS-grad	Married-civ-spouse	Handlers-cleaners	Husband	White	False	-0.9959	-0.0318	-0.4224	<50k
Private	10th	Married-civ-spouse	Farming-fishing	Wife	White	False	-0.7027	0.6071	-1.5958	<50k
Private	HS-grad	Married-civ-spouse	Machine-op-inspct	Husband	White	False	0.1036	-0.0968	-0.4224	<50k
Private	Some-college	Married-civ-spouse	Exec-managerial	Own-child	White	False	-0.7760	-0.6653	-0.0312	>=50k
State-gov	Some-college	Never-married	Tech-support	Own-child	White	False	-0.8493	-1.4959	-0.0312	<50k
Private	11th	Never-married	Machine-op-inspct	Not-in-family	White	False	-1.0692	-0.9516	-1.2046	<50k

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learn = tabular_learner(data, layers=[200,100], metrics=accuracy)

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learn.fit(1, 1e-2)

Total time: 00:03

epoch	train_loss	valid_loss	accuracy
1	0.354604	0.378520	0.820000

Inference 预测¶

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row = df.iloc[0]

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learn.predict(row)

Out[ ]:

(Category >=50k, tensor(1), tensor([0.4402, 0.5598]))