GPy/GPy/plotting/gpy_plot/latent_plots.py

#===============================================================================
# Copyright (c) 2015, Max Zwiessele
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#===============================================================================
import numpy as np
from . import pl
from .plot_util import get_x_y_var, get_free_dims, get_which_data_ycols,\
    get_which_data_rows, update_not_existing_kwargs, helper_predict_with_model,\
    helper_for_plot_data
import itertools
from GPy.plotting.gpy_plot.plot_util import scatter_label_generator, subsample_X

def _wait_for_updates(view, updates):
    if updates:
        clear = raw_input('yes or enter to deactivate updates - otherwise still do updates - use plots[imshow].deactivate() to clear')
        if clear.lower() in 'yes' or clear == '':
            view.deactivate()
    else:
        view.deactivate()

def plot_prediction_fit(self, plot_limits=None,
        which_data_rows='all', which_data_ycols='all', 
        fixed_inputs=None, resolution=None,
        plot_raw=False, apply_link=False, visible_dims=None,
        predict_kw=None, scatter_kwargs=None, **plot_kwargs):
    """
    Plot the fit of the (Bayesian)GPLVM latent space prediction to the outputs.
    This scatters two output dimensions against each other and a line
    from the prediction in two dimensions between them.
    
    Give the Y_metadata in the predict_kw if you need it.
    
    :param which_data_rows: which of the training data to plot (default all)
    :type which_data_rows: 'all' or a slice object to slice self.X, self.Y
    :param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
    :param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
    :type fixed_inputs: a list of tuples
    :param int resolution: The resolution of the prediction [defaults are 1D:200, 2D:50]
    :param bool plot_raw: plot the latent function (usually denoted f) only?
    :param bool apply_link: whether to apply the link function of the GP to the raw prediction.
    :param array-like visible_dims: which columns of the input X (!) to plot (array-like or list of ints)
    :param dict predict_kw: the keyword arguments for the prediction. If you want to plot a specific kernel give dict(kern=<specific kernel>) in here
    :param dict sactter_kwargs: kwargs for the scatter plot, specific for the plotting library you are using
    :param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
    """
    canvas, kwargs = pl.get_new_canvas(plot_kwargs)
    plots = _plot_prediction_fit(self, canvas, plot_limits, which_data_rows, which_data_ycols, 
                                 fixed_inputs, resolution, plot_raw, 
                                 apply_link, visible_dims,
                                 predict_kw, scatter_kwargs, **kwargs)
    return pl.show_canvas(canvas, plots)

def _plot_prediction_fit(self, canvas, plot_limits=None,
        which_data_rows='all', which_data_ycols='all', 
        fixed_inputs=None, resolution=None,
        plot_raw=False, apply_link=False, visible_dims=False,
        predict_kw=None, scatter_kwargs=None, **plot_kwargs):
    
    ycols = get_which_data_ycols(self, which_data_ycols)
    rows = get_which_data_rows(self, which_data_rows)

    if visible_dims is None:
        visible_dims = self.get_most_significant_input_dimensions()[:1]

    X, _, Y, _, free_dims, Xgrid, _, _, _, _, resolution = helper_for_plot_data(self, plot_limits, visible_dims, fixed_inputs, resolution)
    
    plots = {}
    
    if len(free_dims)<2:
        if len(free_dims)==1:
            if scatter_kwargs is None:
                scatter_kwargs = {}
            update_not_existing_kwargs(scatter_kwargs, pl.defaults.data_y_1d)  # @UndefinedVariable
            plots['output'] = pl.scatter(canvas, Y[rows, ycols[0]], Y[rows, ycols[1]],
                                      color=X[rows, free_dims[0]],
                                      **scatter_kwargs)
            if predict_kw is None:
                predict_kw = {}
            mu, _, _ = helper_predict_with_model(self, Xgrid, plot_raw, 
                                              apply_link, None, 
                                              ycols, predict_kw)
            update_not_existing_kwargs(plot_kwargs, pl.defaults.data_y_1d_plot)  # @UndefinedVariable
            plots['output_fit'] = pl.plot(canvas, mu[:, 0], mu[:, 1], **plot_kwargs)
        else:
            pass #Nothing to plot!
    else:
        raise NotImplementedError("Cannot plot in more then one dimension.")
    return plots
    
def plot_magnification(self, labels=None, which_indices=None,
                resolution=60, legend=True,
                plot_limits=None,
                updates=False, 
                mean=True, covariance=True, 
                kern=None, marker='<>^vsd', 
                num_samples=1000,
                imshow_kwargs=None, **kwargs):
    """
    Plot the magnification factor of the GP on the inputs. This is the 
    density of the GP as a gray scale.
    
    :param array-like labels: a label for each data point (row) of the inputs
    :param (int, int) which_indices: which input dimensions to plot against each other
    :param int resolution: the resolution at which we predict the magnification factor
    :param bool legend: whether to plot the legend on the figure
    :param plot_limits: the plot limits for the plot
    :type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
    :param bool updates: if possible, make interactive updates using the specific library you are using
    :param bool mean: use the mean of the Wishart embedding for the magnification factor
    :param bool covariance: use the covariance of the Wishart embedding for the magnification factor
    :param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
    :param str marker: markers to use - cycle if more labels then markers are given
    :param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples. 
    :param imshow_kwargs: the kwargs for the imshow (magnification factor)
    :param kwargs: the kwargs for the scatter plots
    """
    input_1, input_2 = self.get_most_significant_input_dimensions(which_indices)

    from .. import Tango
    Tango.reset()
    
    if labels is None:
        labels = np.ones(self.num_data)
        legend = False # No legend if there is no labels given
    
    canvas, kwargs = pl.get_new_canvas(xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **kwargs)

    X, _, _, _, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, plot_limits, (input_1, input_2), None, resolution)
    X, labels = subsample_X(X, labels)
    
    def plot_function(x):
        Xtest_full = np.zeros((x.shape[0], X.shape[1]))
        Xtest_full[:, [input_1, input_2]] = x
        mf = self.predict_magnification(Xtest_full, kern=kern, mean=mean, covariance=covariance)
        return mf

    imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl.defaults.magnification)
    Y = plot_function(Xgrid[:, [input_1, input_2]]).reshape(resolution, resolution).T[::-1, :]
    view = pl.imshow(canvas, Y, 
                     (xmin[0], xmin[1], xmax[1], xmax[1]), 
                     None, plot_function, resolution,
                     vmin=Y.min(), vmax=Y.max(), 
                     **imshow_kwargs)

    scatters = []    
    for x, y, this_label, _, m in scatter_label_generator(labels, X, input_1, input_2, marker):
        update_not_existing_kwargs(kwargs, pl.defaults.latent_scatter)
        scatters.append(pl.scatter(canvas, x, y, marker=m, color=Tango.nextMedium(), label=this_label, **kwargs))
    
    plots = pl.show_canvas(canvas, dict(scatter=scatters, imshow=view), legend=legend, xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]))
    _wait_for_updates(view, updates)
    return plots


def plot_latent(self, labels=None, which_indices=None,
                resolution=60, legend=True,
                plot_limits=None,
                updates=False, 
                kern=None, marker='<>^vsd', 
                num_samples=1000,
                imshow_kwargs=None, **kwargs):
    """
    Plot the latent space of the GP on the inputs. This is the 
    density of the GP posterior as a grey scale and the 
    scatter plot of the input dimemsions selected by which_indices.
    
    :param array-like labels: a label for each data point (row) of the inputs
    :param (int, int) which_indices: which input dimensions to plot against each other
    :param int resolution: the resolution at which we predict the magnification factor
    :param bool legend: whether to plot the legend on the figure
    :param plot_limits: the plot limits for the plot
    :type plot_limits: (xmin, xmax, ymin, ymax) or ((xmin, xmax), (ymin, ymax))
    :param bool updates: if possible, make interactive updates using the specific library you are using
    :param :py:class:`~GPy.kern.Kern` kern: the kernel to use for prediction
    :param str marker: markers to use - cycle if more labels then markers are given
    :param int num_samples: the number of samples to plot maximally. We do a stratified subsample from the labels, if the number of samples (in X) is higher then num_samples. 
    :param imshow_kwargs: the kwargs for the imshow (magnification factor)
    :param kwargs: the kwargs for the scatter plots
    """
    input_1, input_2 = self.get_most_significant_input_dimensions(which_indices)

    from .. import Tango
    Tango.reset()
    
    if labels is None:
        labels = np.ones(self.num_data)
        legend = False # No legend if there is no labels given
    
    canvas, kwargs = pl.get_new_canvas(xlabel='latent dimension %i' % input_1, ylabel='latent dimension %i' % input_2, **kwargs)

    X, _, _, _, _, Xgrid, _, _, xmin, xmax, resolution = helper_for_plot_data(self, plot_limits, (input_1, input_2), None, resolution)
    X, labels = subsample_X(X, labels)
    
    def plot_function(x):
        Xtest_full = np.zeros((x.shape[0], X.shape[1]))
        Xtest_full[:, [input_1, input_2]] = x
        mf = np.log(self.predict(Xtest_full, kern=kern)[1])
        return mf

    imshow_kwargs = update_not_existing_kwargs(imshow_kwargs, pl.defaults.latent)
    Y = plot_function(Xgrid[:, [input_1, input_2]]).reshape(resolution, resolution).T[::-1, :]
    view = pl.imshow(canvas, Y, 
                     (xmin[0], xmin[1], xmax[1], xmax[1]), 
                     None, plot_function, resolution,
                     vmin=Y.min(), vmax=Y.max(), 
                     **imshow_kwargs)

    scatters = []    
    for x, y, this_label, _, m in scatter_label_generator(labels, X, input_1, input_2, marker):
        update_not_existing_kwargs(kwargs, pl.defaults.latent_scatter)
        scatters.append(pl.scatter(canvas, x, y, marker=m, color=Tango.nextMedium(), label=this_label, **kwargs))
    
    plots = pl.show_canvas(canvas, dict(scatter=scatters, imshow=view), legend=legend, xlim=(xmin[0], xmax[0]), ylim=(xmin[1], xmax[1]))
    _wait_for_updates(view, updates)
    return plots