Tabular models

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from fastai.tabular import *


    

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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
  

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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]))

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