Skip to content

outliers

LocalZScoreGenerator

Bases: OutliersGenerator

Randomly generates locally extreme values. Given a time interval (a window) of size l, an outlier is generated by setting the value a time t

Source code in badgers/generators/time_series/outliers.py 
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
class LocalZScoreGenerator(OutliersGenerator):
    """
    Randomly generates locally extreme values.
    Given a time interval (a window) of size l, an outlier is generated by setting the value a time t
    """

    def __init__(self, random_generator=default_rng(seed=0), n_outliers: int = 10,
                 local_window_size: int = 10):
        """

        :param random_generator: a random number generator
        :param n_outliers: the number of outliers to generate
        :param  local_window_size: the width (number of data points) of the local window to compute local Z-Score
        """
        super().__init__(random_generator=random_generator, n_outliers=n_outliers)
        self.local_window_size = local_window_size

    def generate(self, X, y, **params):
        """
        Computes indices of extreme values using a uniform distribution.
        Computes the values at random outside the range [mean(X) - 3 sigma(X), mean(X) + 3 sigma(X)].
        The sign of the extreme value is the same as the value being replaced.

        :param X:
        :return: the transformed array
        """
        # TODO input validation!
        if X.ndim < 2:
            raise ValueError(
                "Expected 2D array. "
                "Reshape your data either using array.reshape(-1, 1) if "
                "your data has a single feature or array.reshape(1, -1) "
                "if it contains a single sample."
            )
        # generate extreme values indices and values
        self.outliers_indices_ = self.random_generator.choice(X.shape[0], size=self.n_outliers, replace=False, p=None)

        for idx in self.outliers_indices_:
            local_window = X[idx - int(self.local_window_size / 2):idx + int(self.local_window_size / 2), :]
            local_mean = local_window.mean(axis=0)
            local_std = local_window.std(axis=0)
            value = local_mean + random_sign(self.random_generator, size=X.shape[1]) * (
                3. * local_std + self.random_generator.exponential(size=X.shape[1]))
            # updating with new outliers
            X[idx, :] = value

        return X, y

__init__(random_generator=default_rng(seed=0), n_outliers=10, local_window_size=10)

:param random_generator: a random number generator :param n_outliers: the number of outliers to generate :param local_window_size: the width (number of data points) of the local window to compute local Z-Score

Source code in badgers/generators/time_series/outliers.py 
73
74
75
76
77
78
79
80
81
82
def __init__(self, random_generator=default_rng(seed=0), n_outliers: int = 10,
             local_window_size: int = 10):
    """

    :param random_generator: a random number generator
    :param n_outliers: the number of outliers to generate
    :param  local_window_size: the width (number of data points) of the local window to compute local Z-Score
    """
    super().__init__(random_generator=random_generator, n_outliers=n_outliers)
    self.local_window_size = local_window_size

generate(X, y, **params)

Computes indices of extreme values using a uniform distribution. Computes the values at random outside the range [mean(X) - 3 sigma(X), mean(X) + 3 sigma(X)]. The sign of the extreme value is the same as the value being replaced.

:param X: :return: the transformed array

Source code in badgers/generators/time_series/outliers.py 
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
def generate(self, X, y, **params):
    """
    Computes indices of extreme values using a uniform distribution.
    Computes the values at random outside the range [mean(X) - 3 sigma(X), mean(X) + 3 sigma(X)].
    The sign of the extreme value is the same as the value being replaced.

    :param X:
    :return: the transformed array
    """
    # TODO input validation!
    if X.ndim < 2:
        raise ValueError(
            "Expected 2D array. "
            "Reshape your data either using array.reshape(-1, 1) if "
            "your data has a single feature or array.reshape(1, -1) "
            "if it contains a single sample."
        )
    # generate extreme values indices and values
    self.outliers_indices_ = self.random_generator.choice(X.shape[0], size=self.n_outliers, replace=False, p=None)

    for idx in self.outliers_indices_:
        local_window = X[idx - int(self.local_window_size / 2):idx + int(self.local_window_size / 2), :]
        local_mean = local_window.mean(axis=0)
        local_std = local_window.std(axis=0)
        value = local_mean + random_sign(self.random_generator, size=X.shape[1]) * (
            3. * local_std + self.random_generator.exponential(size=X.shape[1]))
        # updating with new outliers
        X[idx, :] = value

    return X, y

OutliersGenerator

Bases: GeneratorMixin

Base class for transformers that generate point outliers in time-series data

Source code in badgers/generators/time_series/outliers.py 
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
class OutliersGenerator(GeneratorMixin):
    """
    Base class for transformers that generate point outliers in time-series data
    """

    def __init__(self, random_generator=default_rng(seed=0), n_outliers: int = 10):
        """
        :param random_generator: a random number generator
        :param n_outliers: the number of outliers to generate
        """
        self.random_generator = random_generator
        self.n_outliers = n_outliers
        self.outliers_indices_ = []

    @abc.abstractmethod
    def generate(self, X, y, **params) -> Tuple:
        pass

__init__(random_generator=default_rng(seed=0), n_outliers=10)

:param random_generator: a random number generator :param n_outliers: the number of outliers to generate

Source code in badgers/generators/time_series/outliers.py 
15
16
17
18
19
20
21
22
def __init__(self, random_generator=default_rng(seed=0), n_outliers: int = 10):
    """
    :param random_generator: a random number generator
    :param n_outliers: the number of outliers to generate
    """
    self.random_generator = random_generator
    self.n_outliers = n_outliers
    self.outliers_indices_ = []

RandomZerosGenerator

Bases: OutliersGenerator

Randomly set data points to 0

Source code in badgers/generators/time_series/outliers.py 
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
class RandomZerosGenerator(OutliersGenerator):
    """
    Randomly set data points to 0
    """

    def __init__(self, random_generator=default_rng(seed=0), n_outliers: int = 10):
        """

        :param random_generator: a random number generator
        :param n_outliers: the number of outliers to generate
        """
        super().__init__(random_generator=random_generator, n_outliers=n_outliers)

    def generate(self, X, y, **params) -> Tuple:
        """
        Randomly set values to zero
        :param X:
        :param y:
        :param params:
        :return:
        """
        # TODO input validation!
        if X.ndim < 2:
            raise ValueError(
                "Expected 2D array. "
                "Reshape your data either using array.reshape(-1, 1) if "
                "your data has a single feature or array.reshape(1, -1) "
                "if it contains a single sample."
            )
        # generate extreme values indices and values
        self.outliers_indices_ = self.random_generator.choice(X.shape[0], size=self.n_outliers, replace=False, p=None)

        for idx in self.outliers_indices_:
            X[idx, :] = 0

        return X, y

__init__(random_generator=default_rng(seed=0), n_outliers=10)

:param random_generator: a random number generator :param n_outliers: the number of outliers to generate

Source code in badgers/generators/time_series/outliers.py 
34
35
36
37
38
39
40
def __init__(self, random_generator=default_rng(seed=0), n_outliers: int = 10):
    """

    :param random_generator: a random number generator
    :param n_outliers: the number of outliers to generate
    """
    super().__init__(random_generator=random_generator, n_outliers=n_outliers)

generate(X, y, **params)

Randomly set values to zero :param X: :param y: :param params: :return:

Source code in badgers/generators/time_series/outliers.py 
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
def generate(self, X, y, **params) -> Tuple:
    """
    Randomly set values to zero
    :param X:
    :param y:
    :param params:
    :return:
    """
    # TODO input validation!
    if X.ndim < 2:
        raise ValueError(
            "Expected 2D array. "
            "Reshape your data either using array.reshape(-1, 1) if "
            "your data has a single feature or array.reshape(1, -1) "
            "if it contains a single sample."
        )
    # generate extreme values indices and values
    self.outliers_indices_ = self.random_generator.choice(X.shape[0], size=self.n_outliers, replace=False, p=None)

    for idx in self.outliers_indices_:
        X[idx, :] = 0

    return X, y