Dataset.from_numpy#
- classmethod Dataset.from_numpy(*args: ndarray, columns: Optional[Sequence[Hashable]] = None, label_name: Optional[Hashable] = None, **kwargs) TDataset[source]#
Create Dataset instance from numpy arrays.
- Parameters
- *args: np.ndarray
Numpy array of data columns, and second optional numpy array of labels.
- columnst.Sequence[Hashable] , default: None
names for the columns. If none provided, the names that will be automatically assigned to the columns will be: 1 - n (where n - number of columns)
- label_namet.Hashable , default: None
labels column name. If none is provided, the name ‘target’ will be used.
- **kwargsDict
additional arguments that will be passed to the main Dataset constructor.
- Returns
- ——-
- Dataset
instance of the Dataset
- Raises
- ——
- DeepchecksValueError
if receives zero or more than two numpy arrays. if columns (args[0]) is not two dimensional numpy array. if labels (args[1]) is not one dimensional numpy array. if features array or labels array is empty.
Examples
>>> import numpy >>> from deepchecks.tabular import Dataset
>>> features = numpy.array([[0.25, 0.3, 0.3], ... [0.14, 0.75, 0.3], ... [0.23, 0.39, 0.1]]) >>> labels = numpy.array([0.1, 0.1, 0.7]) >>> dataset = Dataset.from_numpy(features, labels)
Creating dataset only from features array.
>>> dataset = Dataset.from_numpy(features)
Passing additional arguments to the main Dataset constructor
>>> dataset = Dataset.from_numpy(features, labels, max_categorical_ratio=0.5)
Specifying features and label columns names.
>>> dataset = Dataset.from_numpy( ... features, labels, ... columns=['sensor-1', 'sensor-2', 'sensor-3'], ... label_name='labels' ... )