View Source Scholar.Preprocessing.RobustScaler (Scholar v0.4.0)
Scale features using statistics that are robust to outliers.
This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).
Summary
Functions
Compute the median and quantiles to be used for scaling.
Computes the scaling parameters and applies them to transform the tensor.
Performs centering and scaling of the tensor using a fitted scaler.
Functions
Compute the median and quantiles to be used for scaling.
Options
:quantile_range- Quantile range as a tuple {q_min, q_max} defining the range of quantiles to include. Must satisfy 0.0 < q_min < q_max < 100.0. The default value is{25.0, 75.0}.
Return values
Returns a struct with the following parameters:
:iqr- the calculated interquartile range.:medians- the calculated medians of each feature across samples.
Examples
iex> Scholar.Preprocessing.RobustScaler.fit(Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]]))
%Scholar.Preprocessing.RobustScaler{
medians: Nx.tensor([1, 0, 0]),
iqr: Nx.tensor([1.0, 1.0, 1.5])
}Computes the scaling parameters and applies them to transform the tensor.
Examples
iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
iex> Scholar.Preprocessing.RobustScaler.fit_transform(t)
#Nx.Tensor<
f32[3][3]
[
[0.0, -1.0, 1.3333333730697632],
[1.0, 0.0, 0.0],
[-1.0, 1.0, -0.6666666865348816]
]
>Performs centering and scaling of the tensor using a fitted scaler.
Examples
iex> t = Nx.tensor([[1, -1, 2], [2, 0, 0], [0, 1, -1]])
iex> scaler = Scholar.Preprocessing.RobustScaler.fit(t)
%Scholar.Preprocessing.RobustScaler{
medians: Nx.tensor([1, 0, 0]),
iqr: Nx.tensor([1.0, 1.0, 1.5])
}
iex> Scholar.Preprocessing.RobustScaler.transform(scaler, t)
#Nx.Tensor<
f32[3][3]
[
[0.0, -1.0, 1.3333333730697632],
[1.0, 0.0, 0.0],
[-1.0, 1.0, -0.6666666865348816]
]
>