load_occupancy(roles: bool = True, as_pandas: bool = False, as_dict: bool = False) → Union[Tuple[Union[getml.data.data_frame.DataFrame, pandas.core.frame.DataFrame], …], Dict[str, Union[getml.data.data_frame.DataFrame, pandas.core.frame.DataFrame]]]¶
Binary classification dataset on occupancy detection
The occupancy detection data set is a very simple multivariate time series from the UCI Machine Learning Repository. It is a binary classification problem. The task is to predict room occupancy from Temperature, Humidity, Light and CO2.
The original publication is: Candanedo, L. M., & Feldheim, V. (2016). Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy and Buildings, 112, 28-39.
- roles (bool):
Return data with roles set
- as_pandas (bool):
Return data as pandas.DataFrame s
- as_dict (bool):
Return data as dict with df.name s as keys and df s as values.
Tuple containing (sorted alphabetically by df.name`s) the data as :class:`~getml.DataFrame s or
pandas.DataFrames (if as_pandas is True) or
The following DataFrames are returned:
>>> population_train, population_test, _ = getml.datasets.load_occupancy() >>> type(occupancy_train) ... getml.data.data_frame.DataFrame
For an full analysis of the occupancy dataset including all necessary preprocessing steps please refer to getml-examples.
Roles can be set ad-hoc by supplying the respective flag. If roles is False, all columns in the returned
DataFramess have roles
unused_float. This dataset contains no units. Before using them in an analysis, a data model needs to be constructed using