The function to immediately get a `Learner` ready to train for tabular data

The main function you probably want to use in this module is tabular_learner. It will automatically create a TabularModel suitable for your data and infer the right loss function. See the tabular tutorial for an example of use in context.

Main functions

class TabularLearner[source]

TabularLearner(dls, model, loss_func=None, opt_func=Adam, lr=0.001, splitter=trainable_params, cbs=None, metrics=None, path=None, model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95, 0.85, 0.95)) :: Learner

Learner for tabular data

It works exactly as a normal Learner, the only difference is that it implements a predict method specific to work on a row of data.

tabular_learner[source]

tabular_learner(dls:TabularDataLoaders, layers:list=None, emb_szs:list=None, config:dict=None, n_out:int=None, y_range:(float, float)=None, loss_func=None, opt_func=Adam, lr=0.001, splitter=trainable_params, cbs=None, metrics=None, path=None, model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95, 0.85, 0.95))

Get a Learner using dls, with metrics, including a TabularModel created using the remaining params.

Type Default Details
layers list None Size of the layers generated by LinBnDrop
emb_szs list None Tuples of n_unique, embedding_size for all categorical features
config dict None Config params for TabularModel from tabular_config
n_out int None Final output size of the model
y_range (float, float) None Low and high for the final sigmoid function
Valid Keyword Arguments
dls TabularDataLoaders Argument passed to Learner.__init__
loss_func None Argument passed to Learner.__init__
opt_func function Adam Argument passed to Learner.__init__
lr float 0.001 Argument passed to Learner.__init__
splitter function trainable_params Argument passed to Learner.__init__
cbs None Argument passed to Learner.__init__
metrics None Argument passed to Learner.__init__
path None Argument passed to Learner.__init__
model_dir str models Argument passed to Learner.__init__
wd None Argument passed to Learner.__init__
wd_bn_bias bool False Argument passed to Learner.__init__
train_bn bool True Argument passed to Learner.__init__
moms tuple (0.95, 0.85, 0.95) Argument passed to Learner.__init__

If your data was built with fastai, you probably won't need to pass anything to emb_szs unless you want to change the default of the library (produced by get_emb_sz), same for n_out which should be automatically inferred. layers will default to [200,100] and is passed to TabularModel along with the config.

Use tabular_config to create a config and customize the model used. There is just easy access to y_range because this argument is often used.

All the other arguments are passed to Learner.

path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']
cont_names = ['age', 'fnlwgt', 'education-num']
procs = [Categorify, FillMissing, Normalize]
dls = TabularDataLoaders.from_df(df, path, procs=procs, cat_names=cat_names, cont_names=cont_names, 
                                 y_names="salary", valid_idx=list(range(800,1000)), bs=64)
learn = tabular_learner(dls)

TabularLearner.predict[source]

TabularLearner.predict(row:pd.Series)

Predict on a single sample

Type Default Details
row pd.Series Features to be predicted

We can pass in an individual row of data into our TabularLearner's predict method. It's output is slightly different from the other predict methods, as this one will always return the input as well:

row, clas, probs = learn.predict(df.iloc[0])
row.show()
workclass education marital-status occupation relationship race education-num_na age fnlwgt education-num salary
0 Private Assoc-acdm Married-civ-spouse #na# Wife White False 49.0 101320.001685 12.0 <50k
clas, probs
(tensor(0), tensor([0.5264, 0.4736]))