{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fit a Sparse Tensor Decomposition Model\n", "\n", "This notebook demonstrates how to fit a sparse tensor decomposition model to data, using the `SparseCP` class of the `Barnacle` library. First we'll generate a simulated data tensor with added noise, then fit a sparse tensor decomposition model to this simulation. We'll evaluate the result using tools from Barnacle and the [tlviz](http://tensorly.org/viz/stable/index.html) library to compare factor matrices between the model and the simulation ground truth. Finally we'll explore how multiple random initializations is good practice when fitting CP tensor decomposition models, and can result in improved model solutions." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import numpy as np\n", "import scipy\n", "import tensorly as tl\n", "import tlviz\n", "from barnacle import (\n", " SparseCP, \n", " visualize_3d_tensor, \n", " simulated_sparse_tensor, \n", " plot_factors_heatmap\n", ")\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "axis0=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# generate simulated data tensor\n", "\n", "true_rank = 5\n", "true_shape = [20, 15, 10]\n", "true_densities = [.2, .4, .6]\n", "\n", "# re-seed simulated data until all factor matrices are full rank\n", "full_rank = False\n", "while not full_rank:\n", " # generate simulated tensor\n", " sim_tensor = simulated_sparse_tensor(\n", " shape=true_shape, \n", " rank=true_rank, \n", " densities=true_densities, \n", " factor_dist_list=[\n", " scipy.stats.uniform(loc=-1, scale=2), \n", " scipy.stats.uniform(), \n", " scipy.stats.uniform()\n", " ], \n", " random_state=9481\n", " )\n", " # check that all factors are full rank\n", " full_rank = np.all([np.linalg.matrix_rank(factor) == true_rank for factor in sim_tensor.factors])\n", "\n", "# add 20% noise to simulated dataset\n", "noisy_sim_tensor = sim_tensor.to_tensor(noise_level=0.2, sparse_noise=True, random_state=4791)\n", "\n", "# visualize noisy simulated tensor data\n", "fig = visualize_3d_tensor(\n", " noisy_sim_tensor,\n", " shell=False, \n", " midpoint=0, \n", " show_colorbar=True,\n", " label_axes=True, \n", " range_color=[-1, 1], \n", " opacity=1, \n", ")\n", "fig.show()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Beginning initialization 1 of 1\n", "loss: 18.26476536487174\n", "loss: 7.390206063801999\n", "loss: 7.023123827077382\n", "loss: 7.011798742456526\n", "loss: 7.011523418863808\n", "loss: 7.011422224375263\n", "loss: 7.011348799819072\n", "loss: 7.011275583735396\n", "loss: 7.0111964079706794\n", "loss: 7.011110165842045\n", "loss: 7.011016398799992\n", "loss: 7.010914197193543\n", "loss: 7.010803097188486\n", "loss: 7.010682594299325\n", "loss: 7.010552363496999\n", "loss: 7.01041193663957\n", "loss: 7.010260864275709\n", "loss: 7.010098858630826\n", "loss: 7.009925489797718\n", "loss: 7.009741483281008\n", "loss: 7.009547323999183\n", "loss: 7.009343444840475\n", "loss: 7.009130401361401\n", "loss: 7.008909225955648\n", "loss: 7.008666501146237\n", "loss: 7.008391447615166\n", "loss: 7.008081409998965\n", "loss: 7.007735817511848\n", "loss: 7.007354122148607\n", "loss: 7.00693784146193\n", "loss: 7.006490258340945\n", "loss: 7.006015720793433\n", "loss: 7.005518412423505\n", "loss: 7.005004394257134\n", "loss: 7.004483809984444\n", "loss: 7.0039648940689725\n", "loss: 7.003452579179688\n", "loss: 7.002967257596921\n", "loss: 7.00255241799881\n", "loss: 7.002300180799768\n", "loss: 7.002096457474012\n", "loss: 7.001936496562364\n", "loss: 7.0017964377290465\n", "loss: 7.001662987886994\n", "loss: 7.001539333625742\n", "loss: 7.001426988364198\n", "loss: 7.001325628882855\n", "loss: 7.001233712868817\n", "loss: 7.001185273216217\n", "loss: 7.00118397780891\n", "Algorithm converged after 50 iterations\n" ] } ], "source": [ "# fit sparse tensor decomposition model to simulated data\n", "\n", "# instantiate sparse cp model\n", "model = SparseCP(\n", " rank=5, \n", " lambdas=[0.4, 0, 0],\n", " nonneg_modes=[1, 2], \n", " random_state=72258\n", ")\n", "\n", "# fit model to data\n", "cp = model.fit_transform(noisy_sim_tensor, verbose=3)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great, the algorithm converged and we have a fit model! \n", "\n", "Now we can compare how well the factor matrices of our fit model match the ground truth factor matrices used to generate the simulated data. We'll do this in two ways:\n", "1. We'll calculate a factor match score that quantifies the similarity of the two sets of factors. This is accomplished using the `factor_match_score` function from the [tlviz](http://tensorly.org/viz/stable/index.html) Python library (part of the Tensorly ecosystem). A score of 1 indicates identical factor matrices, whereas a score of 0 indicates complete dissimilarity. \n", "2. We'll visually compare the two sets of factor matrices using the `plot_factors_heatmap` function of the Barnacle library.\n", "\n", "Note that in CP tensor decompositions, the order of factors is arbitrary. So for both comparisons, we first have to permute the components (factor matrix columns) so that we are comparing the most similar components between models. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The FMS between model and simulation factor matrices is: 0.57\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compare factor matrices of the model to those used to generate the simulation\n", "\n", "# calculate factor match score (FMS) between two sets of factor matrices\n", "fms, permutation = tlviz.factor_tools.factor_match_score(\n", " sim_tensor, \n", " cp, \n", " return_permutation=True\n", ")\n", "print(f'The FMS between model and simulation factor matrices is: {fms:.2f}')\n", "\n", "# permute model components to align with simulation components\n", "permuted_cp = tlviz.factor_tools.permute_cp_tensor(cp, permutation)\n", "\n", "# plot factor heatmaps\n", "heatmap_params = {'vmin':-1, 'vmax':1, 'cmap':'seismic', 'center':0}\n", "fig, ax = plot_factors_heatmap(\n", " tl.cp_normalize(permuted_cp).factors, \n", " reference_factors=tl.cp_normalize(sim_tensor).factors,\n", " mask_thold=[0, 0], \n", " ratios=True, \n", " heatmap_kwargs=heatmap_params\n", ")\n", "ax[0][0].set_title('Ground Truth');\n", "ax[0][1].set_title('Model');\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok so the FMS between the model and simulation is 0.57 -- not terrible, but not great. And visually comparing the two sets of factor matrices we can see some similarities, but also some places where the model weights diverge significantly from the simulation ground truth. To improve the model, some more information about CP models could be helpful.\n", "\n", "CP models are fit by solving a non-convex optimization problem. This means that even if the algorithm converges, it is possible it converged on a local minimum that doesn't represent the overall best solution to the problem. Therefore in practice, it is common to fit several models with different random initializations, and select the solution that results in the lowest error as the overall best fit model. We can also compare the solutions between initializations to increase confidence in the selected solution." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Beginning initialization 1 of 10\n", "Algorithm converged after 50 iterations\n", "\n", "Beginning initialization 2 of 10\n", "Algorithm converged after 19 iterations\n", "\n", "Beginning initialization 3 of 10\n", "Algorithm converged after 75 iterations\n", "\n", "Beginning initialization 4 of 10\n", "Algorithm converged after 31 iterations\n", "\n", "Beginning initialization 5 of 10\n", "Algorithm converged after 11 iterations\n", "\n", "Beginning initialization 6 of 10\n", "Algorithm converged after 10 iterations\n", "\n", "Beginning initialization 7 of 10\n", "Algorithm converged after 33 iterations\n", "\n", "Beginning initialization 8 of 10\n", "Algorithm converged after 7 iterations\n", "\n", "Beginning initialization 9 of 10\n", "Algorithm converged after 43 iterations\n", "\n", "Beginning initialization 10 of 10\n", "Algorithm converged after 31 iterations\n" ] } ], "source": [ "# fit sparse tensor decomposition model to simulated data, with 10 random initializations\n", "\n", "# instantiate sparse cp model\n", "model2 = SparseCP(\n", " rank=5, \n", " lambdas=[0.4, 0, 0],\n", " nonneg_modes=[1, 2], \n", " random_state=72258, \n", " n_initializations=10\n", ")\n", "\n", "# fit model to data\n", "cp2 = model2.fit_transform(noisy_sim_tensor, verbose=2)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compare solutions between initializations\n", "\n", "fig, ax = tlviz.visualisation.optimisation_diagnostic_plots(\n", " model2.candidate_losses_, \n", " n_iter_max=100\n", ")\n", "ax[0].set_ylabel('Loss');\n", "ax[1].set_ylabel('Loss (Log scale)');\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like all 10 initializations converged, and there were a range of different solutions. Two independent random initializations both converged with a losses around 7.75, suggesting this may represent a local minimum. We see other potential minima where multiple initializations all converged around a similar loss value, such as ~7.45 (three initializations), and ~7.0 (two initializations). The best model is one of three that settled around a loss of 6.7, with the lowest loss solution converging in just 7 iterations.\n", "\n", "Next we'll again compare the factor matrices from this lowest loss model to the ground truth simulation. We'll see that multiple initializations resulted in a model that more closely matches ground truth, both in terms of FMS score and visual comparison of the factor matrices." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The FMS between model and simulation factor matrices is: 0.74\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# compare factor matrices of the model to those used to generate the simulation\n", "\n", "# calculate factor match score (FMS) between two sets of factor matrices\n", "fms2, permutation2 = tlviz.factor_tools.factor_match_score(\n", " sim_tensor, \n", " cp2, \n", " return_permutation=True\n", ")\n", "print(f'The FMS between model and simulation factor matrices is: {fms2:.2f}')\n", "\n", "# permute model components to align with simulation components\n", "permuted_cp2 = tlviz.factor_tools.permute_cp_tensor(cp2, permutation2)\n", "\n", "# plot factor heatmaps\n", "heatmap_params = {'vmin':-1, 'vmax':1, 'cmap':'seismic', 'center':0}\n", "fig, ax = plot_factors_heatmap(\n", " tl.cp_normalize(permuted_cp2).factors, \n", " reference_factors=tl.cp_normalize(sim_tensor).factors,\n", " mask_thold=[0, 0], \n", " ratios=True, \n", " heatmap_kwargs=heatmap_params\n", ")\n", "ax[0][0].set_title('Ground Truth');\n", "ax[0][1].set_title('Model');\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.8" } }, "nbformat": 4, "nbformat_minor": 4 }