# alibi\_detect.utils.perturbation ## Functions ### `apply_mask` ```python apply_mask(X: numpy.ndarray, mask_size: tuple = (4, 4), n_masks: int = 1, coord: Optional[tuple] = None, channels: list = [0, 1, 2], mask_type: str = 'uniform', noise_distr: tuple = (0, 1), noise_rng: tuple = (0, 1), clip_rng: tuple = (0, 1)) -> Tuple[numpy.ndarray, numpy.ndarray] ``` Mask images. Can zero out image patches or add normal or uniformly distributed noise. | Name | Type | Default | Description | | ------------- | ----------------- | ----------- | ------------------------------------------------------------------------------------------------ | | `X` | `numpy.ndarray` | | Batch of instances to be masked. | | `mask_size` | `tuple` | `(4, 4)` | Tuple with the size of the mask. | | `n_masks` | `int` | `1` | Number of masks applied for each instance in the batch X. | | `coord` | `Optional[tuple]` | `None` | Upper left (x,y)-coordinates for the mask. | | `channels` | `list` | `[0, 1, 2]` | Channels of the image to apply the mask to. | | `mask_type` | `str` | `'uniform'` | Type of mask. One of 'uniform', 'random' (both additive noise) or 'zero' (zero values for mask). | | `noise_distr` | `tuple` | `(0, 1)` | Mean and standard deviation for noise of 'random' mask type. | | `noise_rng` | `tuple` | `(0, 1)` | Min and max value for noise of 'uniform' type. | | `clip_rng` | `tuple` | `(0, 1)` | Min and max values for the masked instances. | **Returns** * Type: `Tuple[numpy.ndarray, numpy.ndarray]` ### `brightness` ```python brightness(x: numpy.ndarray, strength: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Change brightness of image. | Name | Type | Default | Description | | ---------- | ----------------- | ------- | ---------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `strength` | `float` | | Strength of brightness change. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `clipped_zoom` ```python clipped_zoom(x: numpy.ndarray, zoom_factor: float) -> numpy.ndarray ``` Helper function for zoom blur. | Name | Type | Default | Description | | ------------- | --------------- | ------- | ------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `zoom_factor` | `float` | | Zoom strength. | **Returns** * Type: `numpy.ndarray` ### `contrast` ```python contrast(x: numpy.ndarray, strength: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Change contrast of image. | Name | Type | Default | Description | | ---------- | ----------------- | ------- | ------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `strength` | `float` | | Strength of contrast change. Lower is actually more contrast. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `defocus_blur` ```python defocus_blur(x: numpy.ndarray, radius: int, alias_blur: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Apply defocus blur. | Name | Type | Default | Description | | ------------ | ----------------- | ------- | ---------------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `radius` | `int` | | Radius for the Gaussian kernel. | | `alias_blur` | `float` | | Standard deviation for the Gaussian kernel in both X and Y directions. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `disk` ```python disk(radius: float, alias_blur: float = 0.1, dtype = ) -> numpy.ndarray ``` Helper function for defocus blur. | Name | Type | Default | Description | | ------------ | ------- | ------------------------- | ---------------------------------------------------------------------- | | `radius` | `float` | | Radius for the Gaussian kernel. | | `alias_blur` | `float` | `0.1` | Standard deviation for the Gaussian kernel in both X and Y directions. | | `dtype` | | `` | Data type. | **Returns** * Type: `numpy.ndarray` ### `elastic_transform` ```python elastic_transform(x: numpy.ndarray, mult_dxdy: float, sigma: float, rnd_rng: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Apply elastic transformation to instance. | Name | Type | Default | Description | | ----------- | ----------------- | ------- | ------------------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `mult_dxdy` | `float` | | Multiplier for the Gaussian noise in x and y directions. | | `sigma` | `float` | | Standard deviation determining the strength of the Gaussian perturbation. | | `rnd_rng` | `float` | | Range for random uniform noise. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `fog` ```python fog(x: numpy.ndarray, fractal_mult: float, wibbledecay: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Apply fog to instance. | Name | Type | Default | Description | | -------------- | ----------------- | ------- | ----------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `fractal_mult` | `float` | | Strength applied to `plasma_fractal` output. | | `wibbledecay` | `float` | | Decay factor for size of noise that is applied. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `gaussian_blur` ```python gaussian_blur(x: numpy.ndarray, sigma: float, channel_axis: int = -1, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Apply Gaussian blur. | Name | Type | Default | Description | | -------------- | ----------------- | ------- | --------------------------------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `sigma` | `float` | | Standard deviation determining the strength of the blur. | | `channel_axis` | `int` | `-1` | Denotes the axis of the colour channel. If `None` the image is assumed to be grayscale. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `gaussian_noise` ```python gaussian_noise(x: numpy.ndarray, stdev: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Inject Gaussian noise. | Name | Type | Default | Description | | -------- | ----------------- | ------- | ---------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `stdev` | `float` | | Standard deviation of noise. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `glass_blur` ```python glass_blur(x: numpy.ndarray, sigma: float, max_delta: int, iterations: int, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Apply glass blur. | Name | Type | Default | Description | | ------------ | ----------------- | ------- | ------------------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `sigma` | `float` | | Standard deviation determining the strength of the Gaussian perturbation. | | `max_delta` | `int` | | Maximum pixel range for the blurring. | | `iterations` | `int` | | Number of blurring iterations. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `impulse_noise` ```python impulse_noise(x: numpy.ndarray, amount: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Inject salt & pepper noise. | Name | Type | Default | Description | | -------- | ----------------- | ------- | ------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `amount` | `float` | | Proportion of pixels to replace with noise. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `inject_outlier_categorical` ```python inject_outlier_categorical(X: numpy.ndarray, cols: List[int], perc_outlier: int, y: Optional[numpy.ndarray] = None, cat_perturb: Optional[dict] = None, X_fit: Optional[numpy.ndarray] = None, disc_perc: list = [25, 50, 75], smooth: float = 1.0) -> alibi_detect.utils.data.Bunch ``` Inject outliers in categorical variables of tabular data. | Name | Type | Default | Description | | -------------- | ------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | `X` | `numpy.ndarray` | | Tabular data with categorical variables to perturb (inject outliers). | | `cols` | `List[int]` | | Columns of X that are categorical and can be perturbed. | | `perc_outlier` | `int` | | Percentage of observations which are perturbed to outliers. For multiple numerical features, the percentage is evenly split across the features. | | `y` | `Optional[numpy.ndarray]` | `None` | Outlier labels. | | `cat_perturb` | `Optional[dict]` | `None` | Dictionary mapping each category in the categorical variables to their furthest neighbour. | | `X_fit` | `Optional[numpy.ndarray]` | `None` | Optional data used to infer pairwise distances from. | | `disc_perc` | `list` | `[25, 50, 75]` | List with percentiles used in binning of numerical features used for the 'abdm' pairwise distance measure. | | `smooth` | `float` | `1.0` | Smoothing exponent between 0 and 1 for the distances. Lower values will smooth the difference in distance metric between different features. | **Returns** * Type: `alibi_detect.utils.data.Bunch` ### `inject_outlier_tabular` ```python inject_outlier_tabular(X: numpy.ndarray, cols: List[int], perc_outlier: int, y: Optional[numpy.ndarray] = None, n_std: float = 2.0, min_std: float = 1.0) -> alibi_detect.utils.data.Bunch ``` Inject outliers in numerical tabular data. | Name | Type | Default | Description | | -------------- | ------------------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `X` | `numpy.ndarray` | | Tabular data to perturb (inject outliers). | | `cols` | `List[int]` | | Columns of X that are numerical and can be perturbed. | | `perc_outlier` | `int` | | Percentage of observations which are perturbed to outliers. For multiple numerical features, the percentage is evenly split across the features. | | `y` | `Optional[numpy.ndarray]` | `None` | Outlier labels. | | `n_std` | `float` | `2.0` | Number of feature-wise standard deviations used to perturb the original data. | | `min_std` | `float` | `1.0` | Minimum number of standard deviations away from the current observation. This is included because of the stochastic nature of the perturbation which could lead to minimal perturbations without a floor. | **Returns** * Type: `alibi_detect.utils.data.Bunch` ### `inject_outlier_ts` ```python inject_outlier_ts(X: numpy.ndarray, perc_outlier: int, perc_window: int = 10, n_std: float = 2.0, min_std: float = 1.0) -> alibi_detect.utils.data.Bunch ``` Inject outliers in both univariate and multivariate time series data. | Name | Type | Default | Description | | -------------- | --------------- | ------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `X` | `numpy.ndarray` | | Time series data to perturb (inject outliers). | | `perc_outlier` | `int` | | Percentage of observations which are perturbed to outliers. For multivariate data, the percentage is evenly split across the individual time series. | | `perc_window` | `int` | `10` | Percentage of the observations used to compute the standard deviation used in the perturbation. | | `n_std` | `float` | `2.0` | Number of standard deviations in the window used to perturb the original data. | | `min_std` | `float` | `1.0` | Minimum number of standard deviations away from the current observation. This is included because of the stochastic nature of the perturbation which could lead to minimal perturbations without a floor. | **Returns** * Type: `alibi_detect.utils.data.Bunch` ### `jpeg_compression` ```python jpeg_compression(x: numpy.ndarray, strength: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Simulate changes due to JPEG compression for an image. | Name | Type | Default | Description | | ---------- | ----------------- | ------- | ---------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `strength` | `float` | | Strength of compression (>1). Lower is actually more compressed. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `pixelate` ```python pixelate(x: numpy.ndarray, strength: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Change coarseness of pixels for an image. | Name | Type | Default | Description | | ---------- | ----------------- | ------- | -------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `strength` | `float` | | Strength of pixelation (<1). Lower is actually more pixelated. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `plasma_fractal` ```python plasma_fractal(mapsize: int = 256, wibbledecay: float = 3.0) -> numpy.ndarray ``` Helper function to apply fog to instance. Generates a heightmap using diamond-square algorithm. Returns a square 2d array, side length 'mapsize', of floats in range 0-255. 'mapsize' must be a power of two. | Name | Type | Default | Description | | ------------- | ------- | ------- | ----------- | | `mapsize` | `int` | `256` | | | `wibbledecay` | `float` | `3.0` | | **Returns** * Type: `numpy.ndarray` ### `saturate` ```python saturate(x: numpy.ndarray, strength: tuple, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Change colour saturation of image. | Name | Type | Default | Description | | ---------- | ----------------- | ------- | ----------------------------------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `strength` | `tuple` | | Strength of saturation change. Tuple consists of (multiplier, shift) of the perturbation. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `scale_minmax` ```python scale_minmax(x: numpy.ndarray, xrange: Optional[tuple] = None) -> Tuple[numpy.ndarray, bool] ``` Minmax scaling to \[0,1]. | Name | Type | Default | Description | | -------- | ----------------- | ------- | ---------------------------------- | | `x` | `numpy.ndarray` | | Numpy array to be scaled. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `Tuple[numpy.ndarray, bool]` ### `shot_noise` ```python shot_noise(x: numpy.ndarray, lam: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Inject Poisson noise. | Name | Type | Default | Description | | -------- | ----------------- | ------- | ---------------------------------------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `lam` | `float` | | Scalar for the lambda parameter determining the expectation of the interval. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `speckle_noise` ```python speckle_noise(x: numpy.ndarray, stdev: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Inject speckle noise. | Name | Type | Default | Description | | -------- | ----------------- | ------- | ---------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `stdev` | `float` | | Standard deviation of noise. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` ### `zoom_blur` ```python zoom_blur(x: numpy.ndarray, max_zoom: float, step_zoom: float, xrange: Optional[tuple] = None) -> numpy.ndarray ``` Apply zoom blur. | Name | Type | Default | Description | | ----------- | ----------------- | ------- | ---------------------------------------------- | | `x` | `numpy.ndarray` | | Instance to be perturbed. | | `max_zoom` | `float` | | Max zoom strength. | | `step_zoom` | `float` | | Step size to go from 1 to `max_zoom` strength. | | `xrange` | `Optional[tuple]` | `None` | Tuple with min and max data range. | **Returns** * Type: `numpy.ndarray` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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