Code Documentation: Plots

mastml.plots Module

This module contains classes used for generating different types of analysis plots

Scatter:

This class contains a variety of scatter plot types, e.g. parity (predicted vs. true) plots

Error:

This class contains plotting methods used to better quantify the model errors and uncertainty quantification.

Histogram:

This class contains methods for constructing histograms of data distributions and visualization of model residuals.

Line:

This class contains methods for making line plots, e.g. for constructing learning curves of model performance vs. amount of data or number of features.

Functions

ceil	Return the ceiling of x as an Integral.
check_dimensions(y)	Method to check the dimensions of supplied data.
figaspect(arg)	Calculate the width and height for a figure with a specified aspect ratio.
get_divisor(high, low)	Method to obtain a sensible divisor based on range of two values
log(x, [base=math.e])	Return the logarithm of x to the given base.
make_axes_locatable(axes)
make_axis_same(ax, max1, min1)	Method to make the x and y ticks for each axis the same.
make_fig_ax([aspect_ratio, x_align, left])	Method to make matplotlib figure and axes objects.
make_fig_ax_square([aspect, aspect_ratio])	Method to make square shaped matplotlib figure and axes objects.
make_plots(plots, y_true, y_pred, groups, …)	Helper function to make collections of different types of plots after a single or multiple data splits are evaluated.
mark_inset(parent_axes, inset_axes, loc1, …)	Draw a box to mark the location of an area represented by an inset axes.
nice_mean(ls)	Method to return mean of a list or equivalent array with NaN values
nice_names()
nice_range(lower, upper)	Method to create a range of values, including the specified start and end points, with nicely spaced intervals
nice_std(ls)	Method to return standard deviation of a list or equivalent array with NaN values
plot_stats(fig, stats[, x_align, y_align, …])	Method that prints stats onto the plot.
r2_score(y_true, y_pred, *[, sample_weight, …])	R^2 (coefficient of determination) regression score function.
recursive_max(arr)	Method to recursively find the max value of an array of iterables.
recursive_max_and_min(arr)	Method to recursively return max and min of values or iterables in array
recursive_min(arr)	Method to recursively find the min value of an array of iterables.
reset_index(y)
round_down(num, divisor)	Method to return a rounded down number
round_up(num, divisor)	Method to return a rounded up number
rounder(delta)	Method to obtain number of decimal places to report on plots
stat_to_string(name, value, nice_names)	Method that converts a metric object into a string for displaying on a plot
trim_array(arr_list)	Method used to trim a set of arrays to make all arrays the same shape
zoomed_inset_axes(parent_axes, zoom[, loc, …])	Create an anchored inset axes by scaling a parent axes.

Classes

Error	Class to make plots related to model error assessment and uncertainty quantification
ErrorUtils	Collection of functions to conduct error analysis on certain types of models (uncertainty quantification), and prepare residual and model error data for plotting, as well as recalibrate model errors with various methods
Figure([figsize, dpi, facecolor, edgecolor, …])	The top level container for all the plot elements.
FigureCanvas	alias of matplotlib.backends.backend_agg.FigureCanvasAgg
FontProperties([family, style, variant, …])	A class for storing and manipulating font properties.
Histogram	Class to generate histogram plots, such as histograms of residual values
Iterable
Line	Class containing methods for constructing line plots
LinearRegression(*[, fit_intercept, …])	Ordinary least squares Linear Regression.
Metrics(metrics_list[, metrics_type])	Class containing access to a wide range of metrics from scikit-learn and a number of MAST-ML custom-written metrics
Scatter	Class to generate scatter plots, such as parity plots showing true vs.
gaussian_kde(dataset[, bw_method, weights])	Representation of a kernel-density estimate using Gaussian kernels.

Class Inheritance Diagram