Tabular learner

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

source

TabularLearner


def TabularLearner(
    dls:DataLoaders, # [`DataLoaders`](https://docs.fast.ai/data.core.html#dataloaders) containing fastai or PyTorch [`DataLoader`](https://docs.fast.ai/data.load.html#dataloader)s
    model:Callable, # PyTorch model for training or inference
    loss_func:Callable | None=None, # Loss function. Defaults to `dls` loss
    opt_func:Optimizer | OptimWrapper=Adam, # Optimization function for training
    lr:float | slice=0.001, # Default learning rate
    splitter:Callable=trainable_params, # Split model into parameter groups. Defaults to one parameter group
    cbs:Callback | MutableSequence | None=None, # [`Callback`](https://docs.fast.ai/callback.core.html#callback)s to add to [`Learner`](https://docs.fast.ai/learner.html#learner)
    metrics:Callable | MutableSequence | None=None, # [`Metric`](https://docs.fast.ai/learner.html#metric)s to calculate on validation set
    path:str | Path | None=None, # Parent directory to save, load, and export models. Defaults to `dls` `path`
    model_dir:str | Path='models', # Subdirectory to save and load models
    wd:float | int | None=None, # Default weight decay
    wd_bn_bias:bool=False, # Apply weight decay to normalization and bias parameters
    train_bn:bool=True, # Train frozen normalization layers
    moms:tuple=(0.95, 0.85, 0.95), # Default momentum for schedulers
    default_cbs:bool=True, # Include default [`Callback`](https://docs.fast.ai/callback.core.html#callback)s
):

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.

source

tabular_learner


def tabular_learner(
    dls:TabularDataLoaders, layers:list=None, # Size of the layers generated by [`LinBnDrop`](https://docs.fast.ai/layers.html#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`](https://docs.fast.ai/tabular.model.html#tabular_config)
    n_out:int=None, # Final output size of the model
    y_range:Tuple=None, # Low and high for the final sigmoid function
    loss_func:Callable | None=None, # Loss function. Defaults to `dls` loss
    opt_func:Optimizer | OptimWrapper=Adam, # Optimization function for training
    lr:float | slice=0.001, # Default learning rate
    splitter:Callable=trainable_params, # Split model into parameter groups. Defaults to one parameter group
    cbs:Callback | MutableSequence | None=None, # [`Callback`](https://docs.fast.ai/callback.core.html#callback)s to add to [`Learner`](https://docs.fast.ai/learner.html#learner)
    metrics:Callable | MutableSequence | None=None, # [`Metric`](https://docs.fast.ai/learner.html#metric)s to calculate on validation set
    path:str | Path | None=None, # Parent directory to save, load, and export models. Defaults to `dls` `path`
    model_dir:str | Path='models', # Subdirectory to save and load models
    wd:float | int | None=None, # Default weight decay
    wd_bn_bias:bool=False, # Apply weight decay to normalization and bias parameters
    train_bn:bool=True, # Train frozen normalization layers
    moms:tuple=(0.95, 0.85, 0.95), # Default momentum for schedulers
    default_cbs:bool=True, # Include default [`Callback`](https://docs.fast.ai/callback.core.html#callback)s
):

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

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)

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TabularLearner.predict


def predict(
    row:pd.Series, # Features to be predicted
):

Predict on a single sample

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]))