Code Documentation: Plot Helper

mastml.plot_helper Module

This module contains a collection of functions which make plots (saved as png files) using matplotlib, generated from some model fits and cross-validation evaluation within a MAST-ML run.

This module also contains a method to create python notebooks containing plotted data and the relevant source code from this module, to enable the user to make their own modifications to the created plots in a straightforward way (useful for tweaking plots for a presentation or publication).

Functions

auc(x, y)	Compute Area Under the Curve (AUC) using the trapezoidal rule.
ceil	Return the ceiling of x as an Integral.
confusion_matrix(y_true, y_pred, *[, …])	Compute confusion matrix to evaluate the accuracy of a classification.
figaspect(arg)	Calculate the width and height for a figure with a specified aspect ratio.
floor	Return the floor of x as an Integral.
get_divisor(high, low)	Method to obtain a sensible divisor based on range of two values
get_histogram_bins(y_df)	Method to obtain the number of bins to use when plotting a histogram
ipynb_maker(plot_func)	This method creates Jupyter Notebooks so user can modify and regenerate the plots produced by MAST-ML.
join(a, *p)	Join two or more pathname components, inserting ‘/’ as needed.
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_error_plots(run, path, …[, groups])
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_train_test_plots(run, path, …[, groups])	General plotting method used to execute sequence of specific plots of train-test data analysis
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
parse_error_data(dataset_stdev, …)
plot_1d_heatmap(xs, heats, savepath[, …])	Method to plot a heatmap for values of a single variable; used for plotting GridSearch results in hyperparameter optimization.
plot_2d_heatmap(xs, ys, heats, savepath[, …])	Method to plot a heatmap for values of two variables; used for plotting GridSearch results in hyperparameter optimization.
plot_3d_heatmap(xs, ys, zs, heats, savepath)	Method to plot a heatmap for values of three variables; used for plotting GridSearch results in hyperparameter optimization.
plot_average_cumulative_normalized_error(…)	Method to plot the cumulative normalized residual errors of a model prediction
plot_average_normalized_error(y_true, …[, …])	Method to plot the normalized residual errors of a model prediction
plot_best_worst_per_point(y_true, …[, …])	Method to create a parity plot (predicted vs.
plot_best_worst_split(y_true, best_run, …)	Method to create a parity plot (predicted vs.
plot_confusion_matrix(y_true, y_pred, …[, …])	Method used to generate a confusion matrix for a classification run.
plot_cumulative_normalized_error(y_true, …)	Method to plot the cumulative normalized residual errors of a model prediction
plot_keras_history(model_history, savepath, …)
plot_learning_curve(train_sizes, train_mean, …)	Method used to plot both data and feature learning curves
plot_learning_curve_convergence(train_sizes, …)	Method used to plot both the convergence of data and feature learning curves as a function of amount of data or features
plot_metric_vs_group(metric, groups, stats, …)	Method to plot the value of a particular calculated metric (e.g.
plot_metric_vs_group_size(metric, groups, …)	Method to plot the value of a particular calculated metric (e.g.
plot_normalized_error(y_true, y_pred, …[, …])	Method to plot the normalized residual errors of a model prediction
plot_precision_recall_curve(y_true, y_pred, …)	Method to calculate and plot the precision-recall curve for classification model results
plot_predicted_vs_true(train_quad, …)	Method to create a parity plot (predicted vs.
plot_predicted_vs_true_bars(y_true, …[, …])	Method to calculate parity plot (predicted vs.
plot_real_vs_predicted_error(y_true, …)
plot_residuals_histogram(y_true, y_pred, …)	Method to calculate and plot the histogram of residuals from regression model
plot_roc_curve(y_true, y_pred, savepath)	Method to calculate and plot the receiver-operator characteristic curve for classification model results
plot_scatter(x, y, savepath[, groups, …])	Method to create a general scatter plot
plot_stats(fig, stats[, x_align, y_align, …])	Method that prints stats onto the plot.
plot_target_histogram(y_df, savepath[, …])	Method to plot the histogram of true y values
precision_recall_curve(y_true, probas_pred, *)	Compute precision-recall pairs for different probability thresholds.
prediction_intervals(model, X, …)	Method to calculate prediction intervals when using Random Forest and Gaussian Process regression models.
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.
roc_curve(y_true, y_score, *[, pos_label, …])	Compute Receiver operating characteristic (ROC).
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
wraps(wrapped[, assigned, updated])	Decorator factory to apply update_wrapper() to a wrapper function
zoomed_inset_axes(parent_axes, zoom[, loc, …])	Create an anchored inset axes by scaling a parent axes.