Tabular data handling¶
This module defines the main class to handle tabular data in the fastai library: TabularDataBunch. As always, there is also a helper function to quickly get your data.
To allow you to easily create a Learner for your data, it provides tabular_learner.
The best way to quickly get your data in a DataBunch suitable for tabular data is to organize it in two (or three) dataframes. One for training, one for validation, and if you have it, one for testing. Here we are interested in a subsample of the adult dataset.
path = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(path/'adult.csv')
valid_idx = range(len(df)-2000, len(df))
df.head()
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']
dep_var = 'salary'
The initialization of TabularDataBunch is the same as DataBunch so you really want to use the factory method instead.
Optionally, use test_df for the test set. The dependent variable is dep_var, while the categorical and continuous variables are in the cat_names columns and cont_names columns respectively. If cont_names is None then we assume all variables that aren't dependent or categorical are continuous. The TabularProcessor in procs are applied to the dataframes as preprocessing, then the categories are replaced by their codes+1 (leaving 0 for nan) and the continuous variables are normalized.
Note that the TabularProcessor should be passed as Callable: the actual initialization with cat_names and cont_names is done during the preprocessing.
procs = [FillMissing, Categorify, Normalize]
data = TabularDataBunch.from_df(path, df, dep_var, valid_idx=valid_idx, procs=procs, cat_names=cat_names)
You can then easily create a Learner for this data with tabular_learner.
emb_szs is a dict mapping categorical column names to embedding sizes; you only need to pass sizes for columns where you want to override the default behaviour of the model.
Basic class to create a list of inputs in items for tabular data. cat_names and cont_names are the names of the categorical and the continuous variables respectively. processor will be applied to the inputs or one will be created from the transforms in procs.
An object that will contain the encoded cats, the continuous variables conts, the classes and the names of the columns. This is the basic input for a dataset dealing with tabular data.
Create a PreProcessor from procs.