plot cp factors

[caglayantuna]

I will submit a PR soon to add two plot cp factor functions. I wanted to add a notebook here to show their outputs with different options.

[MarieRoald]

Cool! @yngvem and I have also been working on visualisation for TensorLy! We were unsure if it was the right scope for the main TensorLy repository, so we made it as a standalone package. It is still somewhat lacking in the documentation, but I think it covers most of what we are interested in implementation-wise. We hope to have it ready for release by the end of April :)

We just got it up on ReadTheDocs, and you can see the plotting functions here and some example scripts here if you're interested.

Edit: Updated URLs

[caglayantuna]

It seems very nice, I didn't know about your package. As we talked with Jean before, he is eager to have some visualization functions in tensorly. I was thinking to add only following function to cp_tensor ;

def plot_cp_factors(cp_tensors, title=None, mode_info=None, permute_factors=False, x_lim=None, y_lim=None, print=False):
    """
    Plots each factors columnwise as line plots.
    Several cp_tensors may be provided as input, in which case the factors of each cp_tensor are plot in subplots.
    Parameters
    ----------
    cp_tensor : cp_tensors or list of cp_tensors
        The input factors as cp_tensor to be visualized.
    title: list of string
        Information about the each cp_tensor in order to name the figures properly.
        Default=None
    mode_info: list of string
        Information about the each mode
        Default:None
    permute_factors : bool
        Permutes factors by using first cp tensor as a reference for permutation.
        Default:False
    x_lim: list
        Range for x axis
        Default:None
    y_lim: list
        Range for y axis
        Default:None
    print: bool
        Set to True if, on top of returning the figure and the axes, this function should show the figures while running.
    Returns
    -------
    fig : matplotlib figure
    axs : ndarray
    """
    if permute_factors:
        n_cp_tensors = len(cp_tensors)
        if n_cp_tensors == 1:
            raise ValueError('Only one cp tensor is not enough for permutation')
        permuted_tensors, _ = cp_permute_factors(cp_tensors[0], cp_tensors[1:])
        if n_cp_tensors == 2:
            cp_tensors[1] = permuted_tensors.cp_copy()
        else:
            for i in range(n_cp_tensors - 1):
                cp_tensors[i + 1] = permuted_tensors[i].cp_copy()

    if isinstance(cp_tensors, CPTensor):
        cp_tensors = [cp_tensors]
    colors = ['b', 'g', 'r', "y", "c", "m"]
    if print is False:
        plt.ioff()
    if title:
        if len(cp_tensors) != len(title):
            raise ValueError('{0} title is given but there are {1} cp tensors as an input'.format(len(title), len(cp_tensors)))
    if mode_info:
        if len(mode_info) != len(cp_tensors[0].factors):
            raise ValueError('{0} mode_info is given but there are {1} modes in the given cp tensors'.format(len(mode_info), len(cp_tensors[0].factors)))
    for i in range(len(cp_tensors)):
        factor = cp_tensors[i].factors
        rank = T.shape(factor[0])[1]
        fig, axs = plt.subplots(rank, len(factor))
        plt.subplots_adjust(hspace=1.5)
        fig.set_size_inches(15, fig.get_figheight(), forward=True)
        for j in range(len(factor)):
            if title:
                fig.suptitle(str(title[i]))
            else:
                fig.suptitle(str(i + 1) + ". tensor")
            for r in range(rank):
                axs[r, j].plot(factor[j][:, r], color=colors[j])
                if x_lim:
                    axs[r, j].set_xlim(x_lim)
                if y_lim:
                    axs[r, j].set_ylim(y_lim)
                if mode_info:
                    axs[r, j].set_title(str(j + 1) + ". factor" + ' ' + str(r + 1) + ". rank:" + ' ' + str(mode_info[j]))
                else:
                    axs[r, j].set_title(str(j + 1) + ". factor" + ' ' + str(r + 1) + ". rank")
    return fig, axs

Your package has wider perspective (core heatmap etc), we can have separate visualization module with your functions in Tensorly. What is your opinion about it @JeanKossaifi ?

[JeanKossaifi]

As we discussed, visualization is something we really need in TensorLy and your project does that really well @MarieRoald so would love to have it in TensorLy!

[cohenjer]

Hey @JeanKossaifi @MarieRoald @caglayantuna,
Great to see how developed Marie's package is, it is a great piece of software really needed in tensorly, not just for visualisation but also many kind of post-processing/validation.

I was wondering if we could still push forward with the small visualisation functions we have been working on with @caglayantuna; I suspect merging @MarieRoald's code will take some time considering the number of functionalities it has, while our contribution is more modest and could serve as a quick visualisation patch while we work out the full thing.