cpdbench.dataset.CPDNdarrayDataset 

 1from numpy import ndarray
 2
 3from cpdbench.dataset.CPDDataset import CPDDataset
 4
 5
 6class CPDNdarrayDataset(CPDDataset):
 7    """Implementation of CPDDataset where the data source is a standard 2D numpy array
 8    """
 9
10    def get_validation_preview(self) -> tuple[ndarray, list[int]]:
11        return self._validation_array, self._validation_ground_truths
12
13    def __init__(self, numpy_array: ndarray, ground_truths: list[int], validation_amount: int = -1):
14        """Constructor
15        :param numpy_array: the main dataset as 2D numpy array
16        :param ground_truths: the ground truth changepoints of the dataset as int list
17        :param validation_amount: the number of datapoints (in the 2nd dimension) to use for validation purposes.
18        """
19        self._ndarray = numpy_array
20        self._ground_truths = ground_truths
21        if validation_amount == -1:
22            self._validation_array = self._ndarray[:, :]
23        else:
24            self._validation_array = self._ndarray[:, 0:validation_amount]
25        validation_array_length = self._validation_array.shape[1]
26        self._validation_ground_truths = [el for el in self._ground_truths if el < validation_array_length]
27
28    def init(self) -> None:
29        pass
30
31    def get_signal(self) -> tuple[ndarray, list[int]]:
32        return self._ndarray, self._ground_truths
class CPDNdarrayDataset(cpdbench.dataset.CPDDataset.CPDDataset): 
 7class CPDNdarrayDataset(CPDDataset):
 8    """Implementation of CPDDataset where the data source is a standard 2D numpy array
 9    """
10
11    def get_validation_preview(self) -> tuple[ndarray, list[int]]:
12        return self._validation_array, self._validation_ground_truths
13
14    def __init__(self, numpy_array: ndarray, ground_truths: list[int], validation_amount: int = -1):
15        """Constructor
16        :param numpy_array: the main dataset as 2D numpy array
17        :param ground_truths: the ground truth changepoints of the dataset as int list
18        :param validation_amount: the number of datapoints (in the 2nd dimension) to use for validation purposes.
19        """
20        self._ndarray = numpy_array
21        self._ground_truths = ground_truths
22        if validation_amount == -1:
23            self._validation_array = self._ndarray[:, :]
24        else:
25            self._validation_array = self._ndarray[:, 0:validation_amount]
26        validation_array_length = self._validation_array.shape[1]
27        self._validation_ground_truths = [el for el in self._ground_truths if el < validation_array_length]
28
29    def init(self) -> None:
30        pass
31
32    def get_signal(self) -> tuple[ndarray, list[int]]:
33        return self._ndarray, self._ground_truths

Implementation of CPDDataset where the data source is a standard 2D numpy array

CPDNdarrayDataset( numpy_array: numpy.ndarray, ground_truths: list, validation_amount: int = -1) 
14    def __init__(self, numpy_array: ndarray, ground_truths: list[int], validation_amount: int = -1):
15        """Constructor
16        :param numpy_array: the main dataset as 2D numpy array
17        :param ground_truths: the ground truth changepoints of the dataset as int list
18        :param validation_amount: the number of datapoints (in the 2nd dimension) to use for validation purposes.
19        """
20        self._ndarray = numpy_array
21        self._ground_truths = ground_truths
22        if validation_amount == -1:
23            self._validation_array = self._ndarray[:, :]
24        else:
25            self._validation_array = self._ndarray[:, 0:validation_amount]
26        validation_array_length = self._validation_array.shape[1]
27        self._validation_ground_truths = [el for el in self._ground_truths if el < validation_array_length]

Constructor

Parameters
  • numpy_array: the main dataset as 2D numpy array
  • ground_truths: the ground truth changepoints of the dataset as int list
  • validation_amount: the number of datapoints (in the 2nd dimension) to use for validation purposes.
def get_validation_preview(self) -> tuple[numpy.ndarray, list[int]]: 
11    def get_validation_preview(self) -> tuple[ndarray, list[int]]:
12        return self._validation_array, self._validation_ground_truths

Return a smaller part of the complete signal for fast runtime validation.

Returns

A 2D ndarray containing the timeseries (time x feature)

def init(self) -> None: 
29    def init(self) -> None:
30        pass

Initialization method to prepare the dataset. Examples: Open a file, open a db connection etc.

def get_signal(self) -> tuple[numpy.ndarray, list[int]]: 
32    def get_signal(self) -> tuple[ndarray, list[int]]:
33        return self._ndarray, self._ground_truths

Returns the timeseries as numpy array.

Returns

A 2D ndarray containing the timeseries (time x feature)