Time Series Tabular Data

Main Tabular functions used throughout the library. This is helpful when you have additional time series data like metadata, time series features, etc.

---

source

get_tabular_ds

 get_tabular_ds (df, procs=[<class 'fastai.tabular.core.Categorify'>,
                 <class 'fastai.tabular.core.FillMissing'>, <class
                 'fastai.data.transforms.Normalize'>], cat_names=None,
                 cont_names=None, y_names=None, groupby=None,
                 y_block=None, splits=None, do_setup=True, inplace=False,
                 reduce_memory=True, device=None)

---

source

get_tabular_dls

 get_tabular_dls (df, procs=[<class 'fastai.tabular.core.Categorify'>,
                  <class 'fastai.tabular.core.FillMissing'>, <class
                  'fastai.data.transforms.Normalize'>], cat_names=None,
                  cont_names=None, y_names=None, bs=64, y_block=None,
                  splits=None, do_setup=True, inplace=False,
                  reduce_memory=True, device=None,
                  path:Union[str,pathlib.Path]='.')

---

source

preprocess_df

 preprocess_df (df, procs=[<class 'fastai.tabular.core.Categorify'>,
                <class 'fastai.tabular.core.FillMissing'>, <class
                'fastai.data.transforms.Normalize'>], cat_names=None,
                cont_names=None, y_names=None, sample_col=None,
                reduce_memory=True)

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
# df['salary'] = np.random.rand(len(df)) # uncomment to simulate a cont dependent variable

cat_names = ['workclass', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
             'capital-gain', 'capital-loss', 'native-country']
cont_names = ['age', 'fnlwgt', 'hours-per-week']
target = ['salary']
splits = RandomSplitter()(range_of(df))

dls = get_tabular_dls(df, cat_names=cat_names, cont_names=cont_names, y_names='salary', splits=splits, bs=512, device=device)
dls.show_batch()

	workclass	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	native-country	age	fnlwgt	hours-per-week	salary
0	Private	Some-college	10.0	Divorced	Exec-managerial	Not-in-family	White	Male	0	0	United-States	48.000000	190072.000005	50.000000	>=50k
1	Self-emp-not-inc	Some-college	10.0	Married-civ-spouse	Sales	Husband	White	Male	0	0	United-States	72.000001	284120.002964	40.000000	<50k
2	Private	Some-college	10.0	Married-civ-spouse	Protective-serv	Husband	Black	Male	0	0	United-States	72.000001	53684.002497	40.000000	<50k
3	Self-emp-inc	Some-college	10.0	Married-civ-spouse	Farming-fishing	Husband	White	Male	0	0	United-States	47.000000	337049.998875	40.000000	<50k
4	Private	HS-grad	9.0	Divorced	Craft-repair	Not-in-family	White	Male	0	0	United-States	46.000000	207677.000707	30.000000	<50k
5	Private	5th-6th	3.0	Divorced	Priv-house-serv	Unmarried	White	Female	0	0	Mexico	45.000000	265082.999142	35.000000	<50k
6	Private	Assoc-acdm	12.0	Never-married	Other-service	Not-in-family	White	Female	0	0	United-States	28.000000	150296.001328	79.999999	<50k
7	Private	HS-grad	9.0	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	United-States	50.000000	94080.999353	40.000000	>=50k
8	Private	Assoc-voc	11.0	Married-civ-spouse	Exec-managerial	Husband	White	Male	0	0	Germany	58.000000	235624.000302	40.000000	>=50k
9	Private	HS-grad	9.0	Never-married	Other-service	Unmarried	Black	Female	0	0	Japan	29.000000	419721.008996	40.000000	<50k

metrics = mae if dls.c == 1 else accuracy
learn = tabular_learner(dls, layers=[200, 100], y_range=None, metrics=metrics)
learn.fit(1, 1e-2)

epoch	train_loss	valid_loss	accuracy	time
0	0.349525	0.288922	0.866093	00:05

learn.dls.one_batch()

(tensor([[  5,  12,   9,  ...,   1,   1,  21],
         [  1,  10,  13,  ...,   1,   1,   3],
         [  5,   4,   2,  ...,   1,   1,   6],
         ...,
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         [  3,  10,  13,  ...,   1,   1,  40],
         [  5,  12,   9,  ..., 116,   1,  40]]),
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         [-0.9913, -1.4041, -0.0347],
         [-0.1129,  0.4583, -0.0347],
         ...,
         [-1.5769, -0.1989,  0.3712],
         [ 0.4727, -1.4400,  0.3712],
         [ 1.5708, -0.2222, -0.0347]]),
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learn.model

TabularModel(
  (embeds): ModuleList(
    (0): Embedding(10, 6)
    (1): Embedding(17, 8)
    (2): Embedding(17, 8)
    (3): Embedding(8, 5)
    (4): Embedding(16, 8)
    (5): Embedding(7, 5)
    (6): Embedding(6, 4)
    (7): Embedding(3, 3)
    (8): Embedding(117, 23)
    (9): Embedding(90, 20)
    (10): Embedding(43, 13)
  )
  (emb_drop): Dropout(p=0.0, inplace=False)
  (bn_cont): BatchNorm1d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (layers): Sequential(
    (0): LinBnDrop(
      (0): Linear(in_features=106, out_features=200, bias=False)
      (1): ReLU(inplace=True)
      (2): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (1): LinBnDrop(
      (0): Linear(in_features=200, out_features=100, bias=False)
      (1): ReLU(inplace=True)
      (2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (2): LinBnDrop(
      (0): Linear(in_features=100, out_features=2, bias=True)
    )
  )
)

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
cat_names = ['workclass', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',
             'capital-gain', 'capital-loss', 'native-country']
cont_names = ['age', 'fnlwgt', 'hours-per-week']
target = ['salary']
df, procs = preprocess_df(df, procs=[Categorify, FillMissing, Normalize], cat_names=cat_names, cont_names=cont_names, y_names=target, 
                          sample_col=None, reduce_memory=True)
df.head()

	workclass	education	education-num	marital-status	occupation	relationship	race	sex	capital-gain	capital-loss	native-country	age	fnlwgt	hours-per-week	salary
0	5	8	12	3	0	6	5	1	1	48	40	0.763796	-0.838084	-0.035429	1
1	5	13	14	1	5	2	5	2	101	1	40	0.397233	0.444987	0.369519	1
2	5	12	0	1	0	5	3	1	1	1	40	-0.042642	-0.886734	-0.683348	0
3	6	15	15	3	11	1	2	2	1	1	40	-0.042642	-0.728873	-0.035429	1
4	7	6	0	3	9	6	3	1	1	1	40	0.250608	-1.018314	0.774468	0

procs.classes, procs.means, procs.stds

({'workclass': ['#na#', ' ?', ' Federal-gov', ' Local-gov', ' Never-worked', ' Private', ' Self-emp-inc', ' Self-emp-not-inc', ' State-gov', ' Without-pay'],
  'education': ['#na#', ' 10th', ' 11th', ' 12th', ' 1st-4th', ' 5th-6th', ' 7th-8th', ' 9th', ' Assoc-acdm', ' Assoc-voc', ' Bachelors', ' Doctorate', ' HS-grad', ' Masters', ' Preschool', ' Prof-school', ' Some-college'],
  'education-num': ['#na#', 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0],
  'marital-status': ['#na#', ' Divorced', ' Married-AF-spouse', ' Married-civ-spouse', ' Married-spouse-absent', ' Never-married', ' Separated', ' Widowed'],
  'occupation': ['#na#', ' ?', ' Adm-clerical', ' Armed-Forces', ' Craft-repair', ' Exec-managerial', ' Farming-fishing', ' Handlers-cleaners', ' Machine-op-inspct', ' Other-service', ' Priv-house-serv', ' Prof-specialty', ' Protective-serv', ' Sales', ' Tech-support', ' Transport-moving'],
  'relationship': ['#na#', ' Husband', ' Not-in-family', ' Other-relative', ' Own-child', ' Unmarried', ' Wife'],
  'race': ['#na#', ' Amer-Indian-Eskimo', ' Asian-Pac-Islander', ' Black', ' Other', ' White'],
  'sex': ['#na#', ' Female', ' Male'],
  'capital-gain': ['#na#', 0, 114, 401, 594, 914, 991, 1055, 1086, 1111, 1151, 1173, 1409, 1424, 1455, 1471, 1506, 1639, 1797, 1831, 1848, 2009, 2036, 2050, 2062, 2105, 2174, 2176, 2202, 2228, 2290, 2329, 2346, 2354, 2387, 2407, 2414, 2463, 2538, 2580, 2597, 2635, 2653, 2829, 2885, 2907, 2936, 2961, 2964, 2977, 2993, 3103, 3137, 3273, 3325, 3411, 3418, 3432, 3456, 3464, 3471, 3674, 3781, 3818, 3887, 3908, 3942, 4064, 4101, 4386, 4416, 4508, 4650, 4687, 4787, 4865, 4931, 4934, 5013, 5060, 5178, 5455, 5556, 5721, 6097, 6360, 6418, 6497, 6514, 6723, 6767, 6849, 7298, 7430, 7443, 7688, 7896, 7978, 8614, 9386, 9562, 10520, 10566, 10605, 11678, 13550, 14084, 14344, 15020, 15024, 15831, 18481, 20051, 22040, 25124, 25236, 27828, 34095, 41310, 99999],
  'capital-loss': ['#na#', 0, 155, 213, 323, 419, 625, 653, 810, 880, 974, 1092, 1138, 1258, 1340, 1380, 1408, 1411, 1485, 1504, 1539, 1564, 1573, 1579, 1590, 1594, 1602, 1617, 1628, 1648, 1651, 1668, 1669, 1672, 1719, 1721, 1726, 1735, 1740, 1741, 1755, 1762, 1816, 1825, 1844, 1848, 1876, 1887, 1902, 1944, 1974, 1977, 1980, 2001, 2002, 2042, 2051, 2057, 2080, 2129, 2149, 2163, 2174, 2179, 2201, 2205, 2206, 2231, 2238, 2246, 2258, 2267, 2282, 2339, 2352, 2377, 2392, 2415, 2444, 2457, 2467, 2472, 2489, 2547, 2559, 2603, 2754, 2824, 3004, 3683, 3770, 3900, 4356],
  'native-country': ['#na#', ' ?', ' Cambodia', ' Canada', ' China', ' Columbia', ' Cuba', ' Dominican-Republic', ' Ecuador', ' El-Salvador', ' England', ' France', ' Germany', ' Greece', ' Guatemala', ' Haiti', ' Holand-Netherlands', ' Honduras', ' Hong', ' Hungary', ' India', ' Iran', ' Ireland', ' Italy', ' Jamaica', ' Japan', ' Laos', ' Mexico', ' Nicaragua', ' Outlying-US(Guam-USVI-etc)', ' Peru', ' Philippines', ' Poland', ' Portugal', ' Puerto-Rico', ' Scotland', ' South', ' Taiwan', ' Thailand', ' Trinadad&Tobago', ' United-States', ' Vietnam', ' Yugoslavia']},
 {'age': 38.58164675532078,
  'fnlwgt': 189778.36651208502,
  'hours-per-week': 40.437455852092995},
 {'age': 13.640223192304274,
  'fnlwgt': 105548.3568809908,
  'hours-per-week': 12.347239175707989})