ddop.datasets.load_yaz

ddop.datasets.load_yaz(include_date=False, one_hot_encoding=False, label_encoding=False, return_X_y=False)

Load and return the YAZ dataset

Yaz is a fast casual restaurant in Stuttgart providing good service and food at short waiting times. The dataset contains the demand for the main ingredients at YAZ. Moreover, it stores a number of demand features. A description of targets and features is given below.

Dataset Characteristics:

Number of Instances

765

Number of Targets

7

Number of Features

12

Target Information

  • ‘calamari’ the demand for calamari

  • ‘fish’ the demand for fish

  • ‘shrimp’ the demand for shrimps

  • ‘chicken’ the demand for chicken

  • ‘koefte’ the demand for koefte

  • ‘lamb’ the demand for lamb

  • ‘steak’ the demand for steak

Feature Information

  • ‘date’ the date,

  • ‘weekday’ the day of the week,

  • ‘month’ the month of the year,

  • ‘year’ the year,

  • ‘is_holiday’ whether or not it is a national holiday,

  • ‘is_closed’ whether or not the restaurant is closed,

  • ‘weekend’ whether or not it is weekend,

  • ‘wind’ the wind force,

  • ‘clouds’ the cloudiness degree,

  • ‘rain’ the amount of rain,

  • ‘sunshine’ the sunshine hours,

  • ‘temperature’ the outdoor temperature,

Parameters

  • include_date (bool, default=False) – Whether to include the demand date

  • one_hot_encoding (bool, default=False) – Whether to one hot encode categorical features

  • label_encoding (bool, default=False) – Whether to convert categorical columns (weekday, month, year) to continuous. Will only be applied if one_hot_encoding=False

  • return_X_y (bool, default=False.) – If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

Returns

  • data (sklearn Bunch) – Dictionary-like object, with the following attributes.

    dataPandas DataFrame of shape (765, n_features)

    The data matrix.

    target: Pandas DataFrame of shape (765, n_targets)

    The target values.

    n_features: int

    The number of features included

    n_targets: int

    The number of target variables included

    DESCR: str

    The full description of the dataset.

    data_filename: str

    The path to the location of the data.

    target_filename: str

    The path to the location of the target.

  • (data, target) (tuple if return_X_y is True)

Examples

>>> from ddop.datasets import load_yaz
>>> X, y = load_yaz(return_X_y=True)
>>> print(X.shape)
    (765, 11)