Plotting Module

mlai.plot

Plotting utilities and visualization functions for the MLAI library.

This module provides a wide range of plotting functions for illustrating machine learning concepts, model fits, matrix visualizations, and more. It is designed to support both teaching and research by offering publication-quality figures and interactive visualizations.

Key features: - Matrix and covariance visualizations - Regression and classification plots - Model fit diagnostics (RMSE, holdout, cross-validation) - Neural network diagrams - Utility functions for figure generation

Dependencies: - numpy - matplotlib - (optional) daft, IPython, mpl_toolkits.mplot3d

Some functions expect models following the MLAI interface (e.g., LM, GP).

mlai.plot.pred_range(x, portion=0.2, points=200, randomize=False)[source]: Generate a range of prediction points based on the input array x. :type x: :param x: Input array (1D or 2D, numeric). :type portion: :param portion: Fraction of the span to extend beyond min/max (default: 0.2). :type points: :param points: Number of points in the generated range (default: 200). :type randomize: :param randomize: If True, randomly shuffle the generated points (default: False). :returns: Numpy array of prediction points.

mlai.plot.matrix(A, ax=None, bracket_width=3, bracket_style='square', type='values', colormap=None, highlight=False, highlight_row=None, highlight_col=None, highlight_width=3, highlight_color=[0, 0, 0], prec='.3', zoom=False, zoom_row=None, zoom_col=None, bracket_color=[0, 0, 0], fontsize=16)[source]

Plot a matrix with optional highlighting and custom brackets.

Parameters:

A – Matrix to plot (2D numpy array or list of lists).
ax – Matplotlib axis to draw the plot on (optional).
bracket_width – Width of the bracket lines (default: 3).
bracket_style – Style of brackets (‘square’ or ‘round’, default: ‘square’).
type – Display type (‘values’, ‘entries’, etc., default: ‘values’).
colormap – Colormap for matrix values (optional).
highlight – Whether to highlight a row/column (default: False).
highlight_row – Row to highlight (optional).
highlight_col – Column to highlight (optional).
highlight_width – Width of highlight lines (default: 3).
highlight_color – Color for highlights (default: black).
prec – String precision for values (default: ‘.3’).
zoom – Whether to zoom into a submatrix (default: False).
zoom_row – Row index for zoom (optional).
zoom_col – Column index for zoom (optional).
bracket_color – Color for brackets (default: black).
fontsize – Font size for text (default: 16).

Returns:

Matplotlib axis with the matrix plot.

mlai.plot.base_plot(K, ind=[0, 1], ax=None, contour_color=[0.0, 0.0, 1], contour_style='-', contour_size=4, contour_markersize=4, contour_marker='x', fontsize=20)[source]

Plot a base contour for a covariance matrix.

Parameters:

K – Covariance matrix (2D numpy array).
ind – Indices of variables to plot (default: [0, 1]).
ax – Matplotlib axis to draw the plot on (optional).
contour_color – Color for the contour (default: blue).
contour_style – Line style for the contour (default: ‘-‘).
contour_size – Line width for the contour (default: 4).
contour_markersize – Marker size for the contour (default: 4).
contour_marker – Marker style (default: ‘x’).
fontsize – Font size for labels (default: 20).

Returns:

Matplotlib axis with the contour plot.

mlai.plot.covariance_capacity(rotate_angle=0.7853981633974483, lambda1=0.5, lambda2=0.3, diagrams='../diagrams/gp', fill_color=[1.0, 1.0, 0.0], black_color=[0.0, 0.0, 0.0], blue_color=[0.0, 0.0, 1.0], magenta_color=[1.0, 0.0, 1.0])[source]

Visualize the capacity of a covariance matrix by plotting its eigenvalues and eigenvectors.

Parameters:

rotate_angle – Angle to rotate the covariance ellipse (default: pi/4).
lambda1 – First eigenvalue (default: 0.5).
lambda2 – Second eigenvalue (default: 0.3).
diagrams – Directory to save the plot (default: ‘../diagrams/gp’).
fill_color – Fill color for the ellipse (default: yellow).
black_color – Color for axes and lines (default: black).
blue_color – Color for one eigenvector (default: blue).
magenta_color – Color for the other eigenvector (default: magenta).

mlai.plot.prob_diagram(fontsize=20, diagrams='../diagrams')[source]

Plot a diagram demonstrating marginal and joint probabilities.

Parameters:

fontsize – Font size to use in the plot (default: 20).
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.bernoulli_urn(ax, diagrams='../diagrams')[source]

Plot the urn of Jacob Bernoulli’s analogy for the Bernoulli distribution.

Parameters:

ax – Matplotlib axis to draw the plot on.
diagrams – Directory to save the diagram (default: ‘../diagrams’).

mlai.plot.bayes_billiard(ax, diagrams='../diagrams')[source]

Plot a series of figures representing Thomas Bayes’ billiard table for the Bernoulli distribution representation.

Parameters:

ax – Matplotlib axis to draw the plot on.
diagrams – Directory to save the diagrams (default: ‘../diagrams’).

mlai.plot.hyperplane_coordinates(w, b, plot_limits)[source]

Helper function for plotting the decision boundary of the perceptron.

Parameters:

w – The weight vector for the perceptron.
b – The bias parameter for the perceptron.
plot_limits – Dictionary containing ‘x’ and ‘y’ plot limits.

Returns:

Tuple of (x0, x1) coordinates for the hyperplane line.

mlai.plot.init_perceptron(f, ax, x_plus, x_minus, w, b, fontsize=18)[source]

Initialize a plot for showing the perceptron decision boundary.

Parameters:

f – Matplotlib figure object.
ax – Array of matplotlib axes (should have 2 axes).
x_plus – Positive class data points (numpy array).
x_minus – Negative class data points (numpy array).
w – Weight vector for the perceptron.
b – Bias parameter for the perceptron.
fontsize – Font size for labels and titles (default: 18).

Returns:

Dictionary containing plot handles for updating.

mlai.plot.update_perceptron(h, f, ax, x_plus, x_minus, i, w, b)[source]

Update plots after decision boundary has changed.

Parameters:

h – Dictionary containing plot handles from init_perceptron.
f – Matplotlib figure object.
ax – Array of matplotlib axes.
x_plus – Positive class data points.
x_minus – Negative class data points.
i – Current iteration number.
w – Updated weight vector.
b – Updated bias parameter.

mlai.plot.contour_error(x, y, m_center, c_center, samps=100, width=6.0)[source]

Generate error contour data for regression visualization.

Parameters:

x – Input data points.
y – Target values.
m_center – Center value for slope parameter.
c_center – Center value for intercept parameter.
samps – Number of samples for contour generation (default: 100).
width – Width of the parameter range (default: 6.0).

Returns:

Tuple of (m_vals, c_vals, E_grid) for contour plotting.

mlai.plot.regression_contour(f, ax, m_vals, c_vals, E_grid, fontsize=30)[source]

Plot regression error contours.

Parameters:

f – Matplotlib figure object.
ax – Matplotlib axis object.
m_vals – Slope parameter values.
c_vals – Intercept parameter values.
E_grid – Error values grid.
fontsize – Font size for labels (default: 30).

mlai.plot.init_regression(f, ax, x, y, m_vals, c_vals, E_grid, m_star, c_star, fontsize=20)[source]

Initialize regression visualization plots.

Parameters:

f – Matplotlib figure object.
ax – Array of matplotlib axes.
x – Input data points.
y – Target values.
m_vals – Slope parameter values.
c_vals – Intercept parameter values.
E_grid – Error values grid.
m_star – Optimal slope value.
c_star – Optimal intercept value.
fontsize – Font size for labels (default: 20).

Returns:

Dictionary containing plot handles for updating.

mlai.plot.update_regression(h, f, ax, m_star, c_star, iteration)[source]

Update regression plots during optimization.

Parameters:

h – Dictionary containing plot handles from init_regression.
f – Matplotlib figure object.
ax – Array of matplotlib axes.
m_star – Current optimal slope value.
c_star – Current optimal intercept value.
iteration – Current iteration number.

mlai.plot.regression_contour_fit(x, y, learn_rate=0.01, m_center=1.4, c_center=-3.1, m_star=0.0, c_star=-5.0, max_iters=1000, diagrams='../diagrams')[source]

Plot an evolving contour plot of regression optimisation.

Parameters:

x – Input data points.
y – Target values.
learn_rate – Learning rate for optimization (default: 0.01).
m_center – Center value for slope parameter (default: 1.4).
c_center – Center value for intercept parameter (default: -3.1).
m_star – Initial slope value (default: 0.0).
c_star – Initial intercept value (default: -5.0).
max_iters – Maximum number of iterations (default: 1000).
diagrams – Directory to save the plots (default: ‘../diagrams’).

Returns:

Number of frames generated.

mlai.plot.regression_contour_sgd(x, y, learn_rate=0.01, m_center=1.4, c_center=-3.1, m_star=0.0, c_star=-5.0, max_iters=4000, diagrams='../diagrams')[source]

Plot evolution of the solution of linear regression via SGD.

Parameters:

x – Input data points.
y – Target values.
learn_rate – Learning rate for SGD (default: 0.01).
m_center – Center value for slope parameter (default: 1.4).
c_center – Center value for intercept parameter (default: -3.1).
m_star – Initial slope value (default: 0.0).
c_star – Initial intercept value (default: -5.0).
max_iters – Maximum number of iterations (default: 4000).
diagrams – Directory to save the plots (default: ‘../diagrams’).

Returns:

Number of frames generated.

mlai.plot.over_determined_system(diagrams='../diagrams')[source]

Visualize what happens in an over determined system with linear regression.

Parameters:: diagrams – Directory to save the plots (default: ‘../diagrams’).

mlai.plot.gaussian_of_height(diagrams='../diagrams')[source]

Plot a Gaussian density representing heights.

Parameters:: diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.marathon_fit(model, value, param_name, param_range, xlim, fig, ax, x_val=None, y_val=None, objective=None, diagrams='../diagrams', fontsize=20, objective_ylim=None, prefix='olympic', title=None, png_plot=False, samps=130)[source]

Plot fit of the olympic marathon data alongside error.

Parameters:

model – Model object with a predict method and data attributes.
value – Value to fit.
param_name – Name of the parameter being varied.
param_range – Range of parameter values.
xlim – Limits for the x-axis.
fig – Matplotlib figure object.
ax – Array of matplotlib axes.
x_val – Optional x value for highlighting (default: None).
y_val – Optional y value for highlighting (default: None).
objective – Objective function (optional).
diagrams – Directory to save the plot (default: ‘../diagrams’).
fontsize – Font size for labels (default: 20).
objective_ylim – Y-axis limits for the objective plot (optional).
prefix – Prefix for saved plot filenames (default: ‘olympic’).
title – Title for the plot (optional).
png_plot – Whether to save as PNG (default: False).
samps – Number of samples for prediction (default: 130).

mlai.plot.rmse_fit(x, y, param_name, param_range, model=<class 'mlai.mlai.LM'>, objective_ylim=None, xlim=None, plot_fit=<function marathon_fit>, diagrams='../diagrams', **kwargs)[source]

Fit a model and show RMSE error.

Parameters:

x – The input x data.
y – The input y data.
param_name – The parameter name to vary.
param_range – The range over which to vary the parameter.
model – The model to fit (default is LM).
objective_ylim – The y limits for the plot of the objective.
xlim – The x limits for the plot.
plot_fit – Function to use for plotting the fit.
diagrams – Directory to save the plots (default: ‘../diagrams’).
**kwargs –
Additional keyword arguments passed to plot_fit.

mlai.plot.holdout_fit(x, y, param_name, param_range, model=<class 'mlai.mlai.LM'>, val_start=20, objective_ylim=None, xlim=None, plot_fit=<function marathon_fit>, permute=True, prefix='olympic_val', diagrams='../diagrams', **kwargs)[source]

Fit a model and show holdout error.

Parameters:

x – The input x data.
y – The input y data.
param_name – The parameter name to vary.
param_range – The range over which to vary the parameter.
model – The model to fit (default is LM).
val_start – Starting index for validation set (default: 20).
objective_ylim – The y limits for the plot of the objective.
xlim – The x limits for the plot.
plot_fit – Function to use for plotting the fit.
permute – Whether to permute the data (default: True).
prefix – Prefix for saved plot filenames (default: ‘olympic_val’).
diagrams – Directory to save the plots (default: ‘../diagrams’).
**kwargs –
Additional keyword arguments passed to plot_fit.

mlai.plot.loo_fit(x, y, param_name, param_range, model=<class 'mlai.mlai.LM'>, objective_ylim=None, xlim=None, plot_fit=<function marathon_fit>, prefix='olympic_loo', diagrams='../diagrams', **kwargs)[source]

Fit a model and show leave one out error.

Parameters:

x – The input x data.
y – The input y data.
param_name – The parameter name to vary.
param_range – The range over which to vary the parameter.
model – The model to fit (default is LM).
objective_ylim – The y limits for the plot of the objective.
xlim – The x limits for the plot.
plot_fit – Function to use for plotting the fit.
prefix – Prefix for saved plot filenames (default: ‘olympic_loo’).
diagrams – Directory to save the plots (default: ‘../diagrams’).
**kwargs –
Additional keyword arguments passed to plot_fit.

mlai.plot.cv_fit(x, y, param_name, param_range, model=<class 'mlai.mlai.LM'>, objective_ylim=None, xlim=None, plot_fit=<function marathon_fit>, num_parts=5, diagrams='../diagrams', **kwargs)[source]

Fit a model and show cross validation error.

Parameters:

x – The input x data.
y – The input y data.
param_name – The parameter name to vary.
param_range – The range over which to vary the parameter.
model – The model to fit (default is LM).
objective_ylim – The y limits for the plot of the objective.
xlim – The x limits for the plot.
plot_fit – Function to use for plotting the fit.
num_parts – Number of parts for cross-validation (default: 5).
diagrams – Directory to save the plots (default: ‘../diagrams’).
**kwargs –
Additional keyword arguments passed to plot_fit.

mlai.plot.under_determined_system(diagrams='../diagrams')[source]

Visualize what happens in an under determined system with linear regression.

Parameters:: diagrams – Directory to save the plots (default: ‘../diagrams’).

mlai.plot.bayes_update(diagrams='../diagrams')[source]

Visualize Bayesian updating with a simple example.

Parameters:: diagrams – Directory to save the plots (default: ‘../diagrams’).

mlai.plot.height_weight(h=None, w=None, muh=1.7, varh=0.0225, muw=75, varw=36, diagrams='../diagrams')[source]

Plot height and weight data with Gaussian distributions.

Parameters:

h – Height data (optional).
w – Weight data (optional).
muh – Mean height (default: 1.7).
varh – Variance of height (default: 0.0225).
muw – Mean weight (default: 75).
varw – Variance of weight (default: 36).
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.independent_height_weight(h=None, w=None, muh=1.7, varh=0.0225, muw=75, varw=36, num_samps=20, diagrams='../diagrams')[source]

Plot independent height and weight samples.

Parameters:

h – Height data (optional).
w – Weight data (optional).
muh – Mean height (default: 1.7).
varh – Variance of height (default: 0.0225).
muw – Mean weight (default: 75).
varw – Variance of weight (default: 36).
num_samps – Number of samples to generate (default: 20).
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.correlated_height_weight(h=None, w=None, muh=1.7, varh=0.0225, muw=75, varw=36, num_samps=20, diagrams='../diagrams')[source]

Plot correlated height and weight samples.

Parameters:

h – Height data (optional).
w – Weight data (optional).
muh – Mean height (default: 1.7).
varh – Variance of height (default: 0.0225).
muw – Mean weight (default: 75).
varw – Variance of weight (default: 36).
num_samps – Number of samples to generate (default: 20).
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.two_point_pred(K, f, x, ax=None, ind=[0, 1], conditional_linestyle='-', conditional_linecolor=[1.0, 0.0, 0.0], conditional_size=4, fixed_linestyle='-', fixed_linecolor=[0.0, 1.0, 0.0], fixed_size=4, stub=None, start=0, diagrams='../diagrams')[source]

Plot two-point prediction for Gaussian processes.

Parameters:

K – Covariance matrix.
f – Function values.
x – Input points.
ax – Matplotlib axis (optional).
ind – Indices to plot (default: [0, 1]).
conditional_linestyle – Line style for conditional (default: ‘-‘).
conditional_linecolor – Color for conditional (default: red).
conditional_size – Line width for conditional (default: 4).
fixed_linestyle – Line style for fixed (default: ‘-‘).
fixed_linecolor – Color for fixed (default: green).
fixed_size – Line width for fixed (default: 4).
stub – Stub parameter (optional).
start – Starting index (default: 0).
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.output_augment_x(x, num_outputs)[source]

Augment input x with output dimensions.

Parameters:

x – Input data.
num_outputs – Number of outputs.

Returns:

Augmented input data.

mlai.plot.basis(function, x_min, x_max, fig, ax, loc, text, diagrams='./diagrams', fontsize=20, num_basis=3, num_plots=3)[source]

Plot basis functions.

Parameters:

function – Basis function to plot.
x_min – Minimum x value.
x_max – Maximum x value.
fig – Matplotlib figure.
ax – Matplotlib axis.
loc – Location for text.
text – Text to display.
diagrams – Directory to save the plot (default: ‘./diagrams’).
fontsize – Font size (default: 20).
num_basis – Number of basis functions (default: 3).
num_plots – Number of plots (default: 3).

mlai.plot.computing_covariance(kernel, x, formula, stub, prec='1.2', diagrams='../slides/diagrams/kern')[source]

Visualize covariance computation.

Parameters:

kernel – Kernel function.
x – Input data.
formula – Formula to display.
stub – Stub parameter.
prec – Precision for values (default: ‘1.2’).
diagrams – Directory to save the plots (default: ‘../slides/diagrams/kern’).

mlai.plot.kern_circular_sample(K, mu=None, x=None, filename=None, fig=None, num_samps=5, num_theta=48, multiple=True, diagrams='../diagrams', **kwargs)[source]

Sample from a circular kernel and create animation.

Parameters:

K – Kernel function.
mu – Mean (optional).
x – Input data (optional).
filename – Output filename (optional).
fig – Matplotlib figure (optional).
num_samps – Number of samples (default: 5).
num_theta – Number of theta values (default: 48).
multiple – Whether to show multiple samples (default: True).
diagrams – Directory to save the plots (default: ‘../diagrams’).
**kwargs –
Additional keyword arguments.

Returns:

Animation object.

mlai.plot.animate_covariance_function(kernel_function, x=None, num_samps=5, multiple=False)[source]

Create animation of covariance function samples.

Parameters:

kernel_function – Kernel function to sample from.
x – Input data (optional).
num_samps – Number of samples (default: 5).
multiple – Whether to show multiple samples (default: False).

Returns:

Animation object.

mlai.plot.covariance_func(kernel, x=None, shortname=None, longname=None, comment=None, num_samps=5, diagrams='../diagrams', multiple=False)[source]

Plot covariance function samples.

Parameters:

kernel – Kernel function to sample from.
x – Input data (optional).
shortname – Short name for the kernel (optional).
longname – Long name for the kernel (optional).
comment – Comment to display (optional).
num_samps – Number of samples (default: 5).
diagrams – Directory to save the plot (default: ‘../diagrams’).
multiple – Whether to show multiple samples (default: False).

mlai.plot.rejection_samples(kernel, x=None, num_few=20, num_many=1000, diagrams='../diagrams', **kwargs)[source]

Generate rejection samples from a kernel.

Parameters:

kernel – Kernel function to sample from.
x – Input data (optional).
num_few – Number of few samples (default: 20).
num_many – Number of many samples (default: 1000).
diagrams – Directory to save the plot (default: ‘../diagrams’).
**kwargs –
Additional keyword arguments.

mlai.plot.two_point_sample(kernel_function, diagrams='../diagrams')[source]

Sample from a two-point kernel function.

Parameters:

kernel_function – Kernel function to sample from.
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.poisson(diagrams='../diagrams')[source]

Plot Poisson distribution examples.

Parameters:: diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.logistic(diagrams='../diagrams')[source]

Plot logistic function examples.

Parameters:: diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.height(ax, h, ph)[source]: Plot height as a distribution.

mlai.plot.weight(ax, w, pw)[source]

Plot weight distribution.

Parameters:

ax – Matplotlib axis.
w – Weight values.
pw – Weight probabilities.

mlai.plot.low_rank_approximation(fontsize=25, diagrams='../diagrams')[source]

Visualize low-rank matrix approximation.

Parameters:

fontsize – Font size for labels (default: 25).
diagrams – Directory to save the plot (default: ‘../diagrams’).

mlai.plot.blank_canvas(ax)[source]

Create a blank canvas for plotting.

Parameters:: ax – Matplotlib axis to clear.

mlai.plot.kronecker_illustrate(fontsize=25, figsize=(10, 5), diagrams='../diagrams')[source]: Illustrate a Kronecker product

mlai.plot.kronecker_IK(fontsize=25, figsize=(10, 5), reverse=False, diagrams='../diagrams')[source]: Illustrate a Kronecker product

mlai.plot.kronecker_IK_highlight(fontsize=25, figsize=(10, 5), reverse=False, diagrams='../diagrams')[source]: Illustrate a Kronecker product

mlai.plot.kronecker_WX(fontsize=25, figsize=(10, 5), diagrams='../diagrams')[source]: Illustrate a Kronecker product

mlai.plot.perceptron(x_plus, x_minus, learn_rate=0.1, max_iters=10000, max_updates=30, seed=100001, diagrams='../diagrams')[source]: Fit a perceptron algorithm and record iterations of fit

mlai.plot.dist2(X, Y)[source]: Computer squared distances between two design matrices

mlai.plot.clear_axes(ax)[source]: Clear the axes lines and ticks

mlai.plot.non_linear_difficulty_plot_3(alpha=1.0, rbf_width=2, num_basis_func=3, num_samples=10, number_across=30, fontsize=30, diagrams='../diagrams')[source]: Push a Gaussian density through an RBF network and plot results

mlai.plot.non_linear_difficulty_plot_2(alpha=1.0, rbf_width=2, num_basis_func=3, num_samples=10, number_across=101, fontsize=30, diagrams='../diagrams')[source]: Plot a one dimensional line mapped through a two dimensional mapping.

mlai.plot.non_linear_difficulty_plot_1(alpha=1.0, data_std=0.2, rbf_width=0.1, num_basis_func=100, number_across=200, num_samples=1000, patch_color=[0.3, 0.3, 0.3], fontsize=30, diagrams='../diagrams')[source]: Plot a one dimensional Gaussian pushed through an RBF network.

class mlai.plot.network(layers=None)[source]

Bases: object

Class for drawing a neural network.

__init__(layers=None)[source]

add_layer(layer)[source]

property width: Return the widest layer number

property depth: Return the depth of the network

draw(grid_unit=2.5, node_unit=0.9, observed_style='shaded', line_width=1, origin=[0, 0])[source]: Draw the network using daft

class mlai.plot.layer(width=5, label='', observed=False, fixed=False, text='')[source]

Bases: object

Class for a neural network layer

__init__(width=5, label='', observed=False, fixed=False, text='')[source]

mlai.plot.deep_nn(diagrams='../diagrams')[source]: Draw a deep neural network.

mlai.plot.deep_nn_bottleneck(diagrams='../diagrams')[source]: Draw a deep neural network with bottleneck layers.

mlai.plot.box(lim_val=0.5, side_length=25)[source]: Plot a box for use in deep GP samples.

mlai.plot.stack_gp_sample(kernel=None, latent_dims=[2, 2, 2, 2, 2], side_length=25, lim_val=0.5, num_samps=5, figsize=(1.4, 7), diagrams='../diagrams')[source]: Draw a sample from a deep Gaussian process.

mlai.plot.vertical_chain(depth=5, grid_unit=1.5, node_unit=1, line_width=1.5, shape=None, target='y')[source]: Make a verticle chain representation of a deep GP

mlai.plot.horizontal_chain(depth=5, shape=None, origin=[0, 0], grid_unit=4, node_unit=1.9, line_width=3, target='y')[source]: Plot a horizontal Markov chain.

mlai.plot.shared_gplvm()[source]: Plot graphical model of a Shared GP-LVM

mlai.plot.three_pillars_innovation(diagrams='./diagrams')[source]: Plot graphical model of three pillars of successful innovation

mlai.plot.model_output(model, output_dim=0, scale=1.0, offset=0.0, ax=None, xlabel='$x$', ylabel='$y$', xlim=None, ylim=None, fontsize=20, portion=0.2)[source]: Plot the output of a GP. :type model: :param model: the model for the output plotting. :type output_dim: :param output_dim: the output dimension to plot. :type scale: :param scale: how to scale the output. :type offset: :param offset: how to offset the output. :type ax: :param ax: axis to plot on. :type xlabel: :param xlabel: label for the x axis (default: ‘$x$’). :type ylabel: :param ylabel: label for the y axis (default: ‘$y$’). :type xlim: :param xlim: limits of the x axis :type ylim: :param ylim: limits of the y axis :type fontsize: :param fontsize: fontsize (default 20) :type portion: :param portion: What proportion of the input range to put outside the data.

mlai.plot.model_sample(model, output_dim=0, scale=1.0, offset=0.0, samps=10, ax=None, xlabel='$x$', ylabel='$y$', fontsize=20, portion=0.2, xlim=None, ylim=None)[source]: Plot model output with samples.

mlai.plot.multiple_optima(ax=None, gene_number=937, resolution=80, model_restarts=10, seed=10000, max_iters=300, optimize=True, fontsize=20, diagrams='./diagrams')[source]: Show an example of a multimodal error surface for Gaussian process regression. Gene 937 has bimodal behaviour where the noisy mode is higher.

mlai.plot.google_trends(terms, initials, diagrams='./diagrams')[source]: Plot google trends data for a number of different terms.