This module defines the main class to handle tabular data in the fastai library: TabularDataset. 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.
from fastai.gen_doc.nbdoc import *
from fastai.tabular import *
from fastai import *
show_doc(TabularDataBunch, doc_string=False)
class TabularDataBunch[source]
TabularDataBunch(train_dl:DataLoader,valid_dl:DataLoader,test_dl:Optional[DataLoader]=None,device:device=None,tfms:Optional[Collection[Callable]]=None,path:PathOrStr='.',collate_fn:Callable='data_collate') ::DataBunch
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()
| age | workclass | fnlwgt | education | education-num | marital-status | occupation | relationship | race | sex | capital-gain | capital-loss | hours-per-week | native-country | >=50k | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 49 | Private | 101320 | Assoc-acdm | 12.0 | Married-civ-spouse | NaN | Wife | White | Female | 0 | 1902 | 40 | United-States | 1 |
| 1 | 44 | Private | 236746 | Masters | 14.0 | Divorced | Exec-managerial | Not-in-family | White | Male | 10520 | 0 | 45 | United-States | 1 |
| 2 | 38 | Private | 96185 | HS-grad | NaN | Divorced | NaN | Unmarried | Black | Female | 0 | 0 | 32 | United-States | 0 |
| 3 | 38 | Self-emp-inc | 112847 | Prof-school | 15.0 | Married-civ-spouse | Prof-specialty | Husband | Asian-Pac-Islander | Male | 0 | 0 | 40 | United-States | 1 |
| 4 | 42 | Self-emp-not-inc | 82297 | 7th-8th | NaN | Married-civ-spouse | Other-service | Wife | Black | Female | 0 | 0 | 50 | United-States | 0 |
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']
dep_var = '>=50k'
show_doc(TabularDataBunch.from_df, doc_string=False)
from_df[source]
from_df(path,df:DataFrame,dep_var:str,valid_idx:Collection[int],procs:Optional[Collection[TabularProc]]=None,cat_names:OptStrList=None,cont_names:OptStrList=None,classes:Collection=None,kwargs) →DataBunch
Creates a DataBunch in path from train_df, valid_df and optionally test_df. 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 TabularTransform in tfms 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. You can pass the stats to use for that step. If log_output is True, the dependant variable is replaced by its log.
Note that the transforms should be passed as Callable: the actual initialization with cat_names and cont_names is done inside.
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.
show_doc(tabular_learner)
tabular_learner[source]
tabular_learner(data:DataBunch,layers:Collection[int],emb_szs:Dict[str,int]=None,metrics=None,ps:Collection[float]=None,emb_drop:float=0.0,y_range:OptRange=None,use_bn:bool=True,kwargs)
Get a Learner using data, with metrics, including a TabularModel created using the remaining params.
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.
show_doc(TabularList)
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.
show_doc(TabularList.from_df)
from_df[source]
from_df(df:DataFrame,cat_names:OptStrList=None,cont_names:OptStrList=None,procs=None,kwargs) →ItemList
Get the list of inputs in the col of path/csv_name.
show_doc(TabularList.get_emb_szs)
get_emb_szs[source]
get_emb_szs(sz_dict)
Return the default embedding sizes suitable for this data or takes the ones in sz_dict.
show_doc(TabularList.show_xys)
show_doc(TabularList.show_xyzs)
show_doc(TabularLine, doc_string=False)
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.
show_doc(TabularProcessor)
class TabularProcessor[source]
TabularProcessor(ds:ItemBase=None,procs=None) ::PreProcessor
Create a PreProcessor from procs.
show_doc(TabularProcessor.process_one)
process_one[source]
process_one(item)
show_doc(TabularList.new)
new[source]
new(items:Iterator,kwargs) →TabularList
show_doc(TabularList.get)
get[source]
get(o)
show_doc(TabularProcessor.process)
process[source]
process(ds)
show_doc(TabularList.reconstruct)