myfm.utils.benchmark_data.MovieLens1MDataManager¶

class myfm.utils.benchmark_data.MovieLens1MDataManager(zippath: Optional[pathlib.Path] = None)[source]¶

Bases: myfm.utils.benchmark_data.loader_base.MovieLensBase

__init__(zippath: Optional[pathlib.Path] = None)¶

Methods

`__init__`([zippath])
`load_rating_all`()	Read all (1M) interactions.
`load_rating_kfold_split`(K, fold[, random_state])	Load the entire dataset and split it into train/test set.

Attributes

`DEFAULT_PATH`
`DOWNLOAD_URL`

load_rating_all() → pandas.core.frame.DataFrame[source]¶

Read all (1M) interactions.

Returns: Movielens 1M rating dataframe.
Return type: pd.DataFrame

load_rating_kfold_split(K: int, fold: int, random_state: Optional[int] = 0) → Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame]¶

Load the entire dataset and split it into train/test set. K-fold

Parameters

K (int) – K in the K-fold splitting scheme.
fold (int) – fold index.
random_state (Union[np.RandomState, int, None], optional) – Controlls random state of the split.

Returns

train and test dataframes.

Return type

Tuple[pd.DataFrame, pd.DataFrame]

Raises

ValueError – When 0 <= fold < K is not met.