lib5c.util.lowess module

Module for performing lowess fitting. Consists mostly of a convenience wrapper around statsmodels.nonparametric.smoothers_lowess.lowess().

lib5c.util.lowess.constant_fit(x, y, logx=False, logy=False, agg='median')[source]

Same signature as lowess_fit() and group_fit(), but instead of fitting y against x, simply applies an aggregating function to y.

Parameters

  • x (Any) – Ignored, present only for signature parity with other fitters.

  • y (np.ndarray) – The y values to fit.

  • logx (Any) – Ignored, present only for signature parity with other fitters.

  • logy (bool) – Pass True to perform the fit on the scale of log(y).

  • agg ({'median', 'mean', 'lowess'}) – The function to use to aggregate y-values.

Returns

This function takes in x values, ignores them completely, and simply returns the constant estimated y value on the original y scale (regardless of what is passed for logy).

Return type

function

lib5c.util.lowess.group_fit(x, y, logx=False, logy=False, agg='median', left_boundary=None, right_boundary=None, n_windows=100, window_width=0.2)[source]

Simpler alternative to lowess fitting using a sliding window mean.

Parameters

  • y (x,) – The x and y values to fit, respectively.

  • logy (logx,) – Pass True to perform the fit on the scale of log(x) and/or log(y), respectively.

  • agg ({'median', 'mean', 'lowess'}) – The function to use to aggregate within groups.

  • right_boundary (left_boundary,) – Allows specifying boundaries for the fit, in the original x space. If a float is passed, the returned fit will return the farthest left or farthest right lowess-estimated y_hat (from the original fitting set) for all points which are left or right of the specified left or right boundary point, respectively. Pass None to use linear extrapolation for these points instead.

  • n_windows (int) – The number of windows to use (spaced uniformly across the range of x).

  • window_width (float) – The width of each window, defined as a fraction of its x-value.

Returns

This function takes in x values on the original x scale and returns estimated y values on the original y scale (regardless of what is passed for logx and logy). This function will still return sane estimates for y even at points not in the original fitting set by performing linear interpolation in the space the fit was performed in.

Return type

function

lib5c.util.lowess.lowess_agg(y, it=3)[source]

Performs an aggregation operation equivalent to lowess. Should behave like an outlier-resistant mean.

Parameters

  • y (np.ndarray) – The values to aggregate.

  • it (int) – The number of residual-based reweightings to perform.

Returns

The lowess-implemented outlier-resistant mean.

Return type

float

lib5c.util.lowess.lowess_fit(x, y, logx=False, logy=False, left_boundary=None, right_boundary=None, frac=0.3, delta=0.01)[source]

Opinionated convenience wrapper for lowess smoothing.

Parameters

  • y (x,) – The x and y values to fit, respectively.

  • logy (logx,) – Pass True to perform the fit on the scale of log(x) and/or log(y), respectively.

  • right_boundary (left_boundary,) – Allows specifying boundaries for the fit, in the original x space. If a float is passed, the returned fit will return the farthest left or farthest right lowess-estimated y_hat (from the original fitting set) for all points which are left or right of the specified left or right boundary point, respectively. Pass None to use linear extrapolation for these points instead.

  • frac (float) – The lowess smoothing fraction to use.

  • delta (float) – Distance (on the scale of x or log(x)) within which to use linear interpolation when constructing the initial fit, expressed as a fraction of the range of x or log(x).

Returns

This function takes in x values on the original x scale and returns estimated y values on the original y scale (regardless of what is passed for logx and logy). This function will still return sane estimates for y even at points not in the original fitting set by performing linear interpolation in the space the fit was performed in.

Return type

function

Notes

No filtering of input values is performed; clients are expected to handle this if desired. NaN values should not break the function, but x points with zero values passed when logx is True are expected to break the function.

The default value of the delta parameter is set to be non-zero, matching the behavior of lowess smoothing in R and improving performance.

Linear interpolation between x-values in the original fitting set is used to provide a familiar functional interface to the fitted function.

Boundary conditions on the fitted function are exposed via left_boundary and right_boundary, mostly as a convenience for points where x == 0 when fitting was performed on the scale of log(x).

When left_boundary or right_boundary are None (this is the default) the fitted function will be linearly extrapolated for points beyond the lowest and highest x-values in x.