From becc788f1c35259e977ecc9c8a96e86e1986e0c2 Mon Sep 17 00:00:00 2001 From: paulfogeladvestis Date: Wed, 10 Apr 2024 15:58:07 +0200 Subject: [PATCH] added federated nmf notebook --- examples/federated_nmf.ipynb | 263 +++++++++++++++++++++++++++++++++++ 1 file changed, 263 insertions(+) create mode 100644 examples/federated_nmf.ipynb diff --git a/examples/federated_nmf.ipynb b/examples/federated_nmf.ipynb new file mode 100644 index 0000000..19fe6d5 --- /dev/null +++ b/examples/federated_nmf.ipynb @@ -0,0 +1,263 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "# !pip install mvlearn==0.5.0 wordcloud==1.9.3 matplotlib==3.3.4 distinctipy==1.3.4 networkx==3.2.1 umap==0.1.1 hoggorm==0.13.3 adilsm==0.0.7 scipy==1.9.1\n", + "\n", + "# scipy==1.12.0 not used (due to changes in SVDS) to reproduce presented results in ref paper" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install -e .." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "adilsm version=0.0.9\n" + ] + } + ], + "source": [ + "import numpy as np # type: ignore\n", + "import pandas as pd # type: ignore\n", + "import matplotlib.pyplot as plt # type: ignore\n", + "import adilsm.adilsm as ilsm\n", + "from adnmtf import NMF # type: ignore\n", + "from sklearn.linear_model import LinearRegression\n", + "\n", + "RESULTS_PATH = './'\n", + "\n", + "df = pd.read_csv(RESULTS_PATH + r'ALL-AML Brunet.csv')\n", + "m0 = df.values[:,2:].astype(np.float_)\n", + "\n", + "(n,p) = np.shape(m0)\n", + "\n", + "# for Brunet only:\n", + "m0 = np.log2(df.values[:,2:].astype(np.float_))\n", + "m0-=np.repeat(np.min(m0, axis=0)[:,np.newaxis].T, n, axis=0)\n", + "\n", + "m0 = np.random.permutation(m0.T).T\n", + "\n", + "(n,p) = np.shape(m0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "n_slices = 10\n", + "n_comp = 4\n", + "p_slice = int(p/n_slices)\n", + "Xs=[m0[:,i*p_slice:(i+1)*p_slice] for i in range(n_slices)]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "my_nmfmodel = NMF(n_components=n_comp, leverage=None, max_iter=200, tol=1.e-6, verbose=-1)\n", + "result = my_nmfmodel.fit_transform(m0)\n", + "\n", + "w_nmf = result.w\n", + "h_nmf = result.h\n", + "\n", + "Ws = []\n", + "Hs = []\n", + "for i in range(n_slices):\n", + " result = my_nmfmodel.fit_transform(Xs[i])\n", + " Ws.append(result.w)\n", + " Hs.append(result.h)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error ism before straightening: 0.25\n", + "error ism after straightening: 0.25\n", + "error(federated nmf): 0.42\n", + "error(standard nmf): 0.4\n" + ] + } + ], + "source": [ + "# Perform ISM on NMF slices\n", + "n_embedding, n_themes = [n_comp,n_comp]\n", + "\n", + "ilsm_result = ilsm.ism(Ws, n_embedding, n_themes, norm_columns=False, update_h_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "hv = ilsm_result['HV']\n", + "hv_sparse = ilsm_result['HV_SPARSE']\n", + "hhii = ilsm_result['HHII']\n", + "w_ism = ilsm_result['W']\n", + "h_ism = ilsm_result['H']\n", + "q_ism = ilsm_result['Q']\n", + "Xs_emb = ilsm_result['EMBEDDING']\n", + "Xs_norm = ilsm_result['NORMED_VIEWS']\n", + "\n", + "# Chain-multiplication to retrieve view-mapping to original matrix\n", + "h_fnmf = np.empty((0, 4))\n", + "for i in range(n_slices):\n", + " h_fnmf = np.vstack((h_fnmf, Hs[i] @ hv[i]))\n", + "\n", + "# normalize w_ism and h_fnmf by max column in w_ism as in w_nmf\n", + "max_values = np.max(w_ism, axis=0)\n", + "w_fnmf = np.divide(w_ism, np.where(max_values == 0, 1, max_values))\n", + "h_fnmf = np.multiply(h_fnmf, max_values)\n", + "\n", + "# Calculate error\n", + "error = np.linalg.norm(m0 - w_fnmf @ h_fnmf.T) / np.linalg.norm(m0)\n", + "print('error(federated nmf): ',round(error, 2))\n", + "\n", + "error = np.linalg.norm(m0 - w_nmf @ h_nmf.T) / np.linalg.norm(m0)\n", + "print('error(standard nmf): ',round(error, 2))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "error ism before straightening: 0.04\n", + "error ism after straightening: 0.04\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Perform ISM on reduced matrices w_fnmf and w_nmf\n", + "# ISM is expected to recognize that w_fnmf and w_nmf convey the same information up to some noise,\n", + "# albeit with the columns of either matrix swapped around.\n", + "# Heatmaps of the loadings of w_fnmf and w_nmf columns on ISM components show the effective permutation. \n", + "\n", + "Xs = [w_fnmf, w_nmf]\n", + "n_embedding, n_themes = [n_comp,n_comp]\n", + "\n", + "ilsm_result = ilsm.ism(Xs, n_embedding, n_themes, norm_columns=False, update_h_ism=True,\n", + " max_iter_mult=200, fast_mult_rules=True, sparsity_coeff=.8)\n", + "hv = ilsm_result['HV']\n", + "hv_sparse = ilsm_result['HV_SPARSE']\n", + "hhii = ilsm_result['HHII']\n", + "w_ism = ilsm_result['W']\n", + "h_ism = ilsm_result['H']\n", + "q_ism = ilsm_result['Q']\n", + "Xs_emb = ilsm_result['EMBEDDING']\n", + "Xs_norm = ilsm_result['NORMED_VIEWS']\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 5), constrained_layout=True)\n", + "\n", + "ax[0].imshow(hv[0], cmap='viridis', aspect='auto')\n", + "# Add labels and title\n", + "ax[0].set_xlabel('Component')\n", + "ax[0].set_ylabel('Column')\n", + "ax[0].set_title('Loadings of w_fnmf columns on ISM components')\n", + "\n", + "ax[1].imshow(hv[1], cmap='viridis', aspect='auto')\n", + "# Add labels and title\n", + "ax[1].set_xlabel('Component')\n", + "ax[1].set_ylabel('Column')\n", + "ax[1].set_title('Loadings of w_nmf columns on ISM components')\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "\n", + "# Apply back-permutation to obtain comparable components and plot scatterplot\n", + "w_fnmf_perm = w_fnmf[:,np.argmax(hv[0], axis=0)]\n", + "w_nmf_perm = w_nmf[:,np.argmax(hv[1], axis=0)]\n", + "\n", + "regressor = LinearRegression()\n", + "\n", + "fig, ax = plt.subplots(1, n_comp, figsize=(20, 5), constrained_layout=True)\n", + "for i in range(4):\n", + " regressor.fit(w_fnmf_perm[:,i].reshape(-1, 1), w_nmf_perm[:,i])\n", + " ax[i].plot(w_fnmf_perm[:,i], regressor.predict(w_fnmf_perm[:,i].reshape(-1, 1)), color='r', label='Linear Trend')\n", + " ax[i].scatter(w_fnmf_perm[:,i], w_nmf_perm[:,i], color='b')\n", + " ax[i].set_xlabel('w_fnmf('+str(i)+')')\n", + " ax[i].set_ylabel('w_nmf('+str(i)+')')\n", + " ax[i].set_title('nmf versus fnmf W component '+str(i))\n", + " ax[i].grid(True)\n", + " ax[i].legend()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.9.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}