load_occupancy¶
-
getml.datasets.
load_occupancy
(roles=False, as_pandas=False)[source]¶ 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.
- Args:
as_pandas (bool):
Return data as pandas.DataFrame s
roles (bool):
Return data with roles set
- Returns:
dict:
Dictionary containing the data as
DataFrame
s orpandas.DataFrame
s (if as_pandas is True). The keys correspond to the name of the DataFrame on theengine
. The following DataFrames are contained in the dictionarytrain
validate
test
Examples:
>>> df_getml = getml.datasets.load_occupancy() >>> type(df_getml["train"]) ... getml.data.data_frame.DataFrame
For an full analysis of the occupancy dataset including all necessary preprocessing steps please refer to getml-examples.
Note:
Roles can be set ad-hoc by supplying the respective flag. If roles is False, all columns in the returned
DataFrames
s have rolesunused_string
orunused_float
. This dataset contains no units. Before using them in an analysis, a data model needs to be constructed usingPlaceholder
s.