fetch_openml: Add an option to which returns a DataFrame

fetch_openml currently rejects STRING-valued attributes and ordinal-encodes all NOMINAL attributes, in order to return an array or sparse matrix of floats by default.

We should have a parameter that instead returns a DataFrame of features as the ‘data’ entry in the returned Bunch. This would (by default) keep nominals as pd.Categorical and strings as objects. Columns would have names determined from the ARFF attribute names / OpenML metadata. Perhaps we would also set the DataFrame’s index corresponding to the is_row_identifier attribute in OpenML.

See #10733 for the general issue of an API for returning DataFrames in sklearn.datasets.

[amueller]

Btw, #12502 is somewhat related.

[rth]

Just to give some feedback on this as a user. Tried to load https://www.openml.org/d/1461 which is heterogeneous dataset with fetch_openml. It can be represented nicely with a DataFrame and can be read from the orignal csv with one line of pd.read_csv.

When using fetch_openml function, categorical features are encoded as ordinals and than cast to float (since we want an array). From my perspective on this dataset, this prevent one from doing anything useful with it. The python-openml package also doesn’t support loading data as DataFrame until https://github.com/openml/openml-python/pull/548 is merged.

In terms of usability of OpenML datasets returning DataFrames would be really nice.