├── README.md ├── .gitignore ├── ASD-DiagNet.ipynb └── LICENSE /README.md: -------------------------------------------------------------------------------- 1 | # ASD-DiagNet 2 | This repository contains the implementation of ASD-DiagNet algorithm. 3 | 4 | ## Research article 5 | Please cite the following paper if you use our work: 6 | 7 | 8 | Taban Eslami, Fahad Saeed, Vahid Mirjalili, Alvis Fong and Angela Laird (2019) **ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data**, Frontiers in Neuroinformatics, 13 (2019): 70. [Paper link](https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2019.00070/full) 9 | 10 | ## Enviroment Setup 11 | ### Hardware requirements 12 | - A server containing CUDA enabled GPU with compute capability 3.5 or above. 13 | 14 | ### Software requirements 15 | - Python version 3.5 or above 16 | - Pytorch version 0.4.1 17 | - CUDA version 8 or above 18 | - Jupyter notebook 19 | 20 | ## Parameter setting 21 | Please provide the parameters in the first cell of Jupyter notebook as follows: 22 | 23 | - Atlas name: ("cc200", "aal", or "dosenbach160") 24 | 25 | e.g. `p_ROI = "cc200"` 26 | 27 | 28 | - Number of k for k-fold cross-validation: 29 | 30 | e.g. `p_fold = 10` 31 | 32 | 33 | - Classification mode: ("whole" or "percenter") 34 | 35 | e.g. `p_mode = "percenter"` 36 | 37 | 38 | - Name of the center: (in case of performing per-center classification) 39 | 40 | e.g. `p_center = "Stanford"` 41 | 42 | 43 | - Classification method: ("ASD-DiagNet", "rf" or "SVM)) 44 | 45 | e.g. `p_Method = "ASD-DiagNet"` 46 | 47 | 48 | - Utilizing augmentation technique: (in case of using ASD-DiagNet, True or False) 49 | 50 | e.g. `p_augmentation = False` 51 | 52 | 53 | In case of any questions please contact: fsaeed@fiu.edu 54 | 55 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /ASD-DiagNet.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "tags": [ 8 | "parameters" 9 | ] 10 | }, 11 | "outputs": [], 12 | "source": [ 13 | "#options: cc200, dosenbach160, aal\n", 14 | "p_ROI = \"cc200\"\n", 15 | "p_fold = 10\n", 16 | "p_center = \"Stanford\"\n", 17 | "p_mode = \"whole\"\n", 18 | "p_augmentation = True\n", 19 | "p_Method = \"ASD-DiagNet\"" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": null, 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "parameter_list = [p_ROI,p_fold,p_center,p_mode,p_augmentation,p_Method]\n", 29 | "print(\"*****List of patameters****\")\n", 30 | "print(\"ROI atlas: \",p_ROI)\n", 31 | "print(\"per Center or whole: \",p_mode)\n", 32 | "if p_mode == 'percenter':\n", 33 | " print(\"Center's name: \",p_center)\n", 34 | "print(\"Method's name: \",p_Method)\n", 35 | "if p_Method == \"ASD-DiagNet\":\n", 36 | " print(\"Augmentation: \",p_augmentation)\n" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": null, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "import pandas as pd\n", 46 | "import numpy as np\n", 47 | "import matplotlib.pyplot as plt\n", 48 | "import os\n", 49 | "from functools import reduce\n", 50 | "from sklearn.impute import SimpleImputer\n", 51 | "import time\n", 52 | "from torch.utils.data import Dataset\n", 53 | "from torch.utils.data import DataLoader\n", 54 | "import torch\n", 55 | "import pyprind\n", 56 | "import sys\n", 57 | "import pickle\n", 58 | "import torch.nn as nn\n", 59 | "import torch.nn.functional as F\n", 60 | "from sklearn.model_selection import KFold, StratifiedKFold\n", 61 | "import torch.optim as optim\n", 62 | "from sklearn.metrics import confusion_matrix\n", 63 | "from scipy import stats\n", 64 | "from sklearn import tree\n", 65 | "import functools\n", 66 | "import numpy.ma as ma # for masked arrays\n", 67 | "import pyprind\n", 68 | "import random\n", 69 | "from sklearn.svm import SVC\n", 70 | "from sklearn.ensemble import RandomForestClassifier" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "## Importing the data " 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [ 86 | "def get_key(filename):\n", 87 | " f_split = filename.split('_')\n", 88 | " if f_split[3] == 'rois':\n", 89 | " key = '_'.join(f_split[0:3]) \n", 90 | " else:\n", 91 | " key = '_'.join(f_split[0:2])\n", 92 | " return key" 93 | ] 94 | }, 95 | { 96 | "cell_type": "code", 97 | "execution_count": null, 98 | "metadata": {}, 99 | "outputs": [], 100 | "source": [ 101 | "data_main_path = '/home/taban/autism/paper_autism/acerta-abide/acerta-abide/data/functionals/cpac/filt_global/rois_'+p_ROI#cc200'#path to time series data\n", 102 | "flist = os.listdir(data_main_path)\n", 103 | "print(len(flist))\n", 104 | "\n", 105 | "for f in range(len(flist)):\n", 106 | " flist[f] = get_key(flist[f])\n", 107 | " \n", 108 | "\n", 109 | "df_labels = pd.read_csv('/home/taban/autism/paper_autism/acerta-abide/acerta-abide/data/phenotypes/Phenotypic_V1_0b_preprocessed1.csv')#path \n", 110 | "\n", 111 | "df_labels.DX_GROUP = df_labels.DX_GROUP.map({1: 1, 2:0})\n", 112 | "print(len(df_labels))\n", 113 | "\n", 114 | "labels = {}\n", 115 | "for row in df_labels.iterrows():\n", 116 | " file_id = row[1]['FILE_ID']\n", 117 | " y_label = row[1]['DX_GROUP']\n", 118 | " if file_id == 'no_filename':\n", 119 | " continue\n", 120 | " assert(file_id not in labels)\n", 121 | " labels[file_id] = y_label" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "### Helper functions for computing correlations" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": null, 134 | "metadata": {}, 135 | "outputs": [], 136 | "source": [ 137 | "def get_label(filename):\n", 138 | " assert (filename in labels)\n", 139 | " return labels[filename]\n", 140 | "\n", 141 | "\n", 142 | "def get_corr_data(filename):\n", 143 | " #print(filename)\n", 144 | " for file in os.listdir(data_main_path):\n", 145 | " if file.startswith(filename):\n", 146 | " df = pd.read_csv(os.path.join(data_main_path, file), sep='\\t')\n", 147 | " \n", 148 | " with np.errstate(invalid=\"ignore\"):\n", 149 | " corr = np.nan_to_num(np.corrcoef(df.T))\n", 150 | " mask = np.invert(np.tri(corr.shape[0], k=-1, dtype=bool))\n", 151 | " m = ma.masked_where(mask == 1, mask)\n", 152 | " return ma.masked_where(m, corr).compressed()\n", 153 | "\n", 154 | "def get_corr_matrix(filename):\n", 155 | " for file in os.listdir(data_main_path):\n", 156 | " if file.startswith(filename):\n", 157 | " df = pd.read_csv(os.path.join(data_main_path, file), sep='\\t')\n", 158 | " with np.errstate(invalid=\"ignore\"):\n", 159 | " corr = np.nan_to_num(np.corrcoef(df.T))\n", 160 | " return corr\n", 161 | "\n", 162 | "def confusion(g_turth,predictions):\n", 163 | " tn, fp, fn, tp = confusion_matrix(g_turth,predictions).ravel()\n", 164 | " accuracy = (tp+tn)/(tp+fp+tn+fn)\n", 165 | " sensitivity = (tp)/(tp+fn)\n", 166 | " specificty = (tn)/(tn+fp)\n", 167 | " return accuracy,sensitivity,specificty\n", 168 | "\n", 169 | "def get_regs(samplesnames,regnum):\n", 170 | " datas = []\n", 171 | " for sn in samplesnames:\n", 172 | " datas.append(all_corr[sn][0])\n", 173 | " datas = np.array(datas)\n", 174 | " avg=[]\n", 175 | " for ie in range(datas.shape[1]):\n", 176 | " avg.append(np.mean(datas[:,ie]))\n", 177 | " avg=np.array(avg)\n", 178 | " highs=avg.argsort()[-regnum:][::-1]\n", 179 | " lows=avg.argsort()[:regnum][::-1]\n", 180 | " regions=np.concatenate((highs,lows),axis=0)\n", 181 | " return regions\n" 182 | ] 183 | }, 184 | { 185 | "cell_type": "markdown", 186 | "metadata": {}, 187 | "source": [ 188 | "## Helper fnuctions for computing correlations" 189 | ] 190 | }, 191 | { 192 | "cell_type": "code", 193 | "execution_count": null, 194 | "metadata": {}, 195 | "outputs": [], 196 | "source": [ 197 | "if not os.path.exists('./correlations_file'+p_ROI+'.pkl'):\n", 198 | " pbar=pyprind.ProgBar(len(flist))\n", 199 | " all_corr = {}\n", 200 | " for f in flist:\n", 201 | " \n", 202 | " lab = get_label(f)\n", 203 | " all_corr[f] = (get_corr_data(f), lab)\n", 204 | " pbar.update()\n", 205 | "\n", 206 | " print('Corr-computations finished')\n", 207 | "\n", 208 | " pickle.dump(all_corr, open('./correlations_file'+p_ROI+'.pkl', 'wb'))\n", 209 | " print('Saving to file finished')\n", 210 | "\n", 211 | "else:\n", 212 | " all_corr = pickle.load(open('./correlations_file'+p_ROI+'.pkl', 'rb'))" 213 | ] 214 | }, 215 | { 216 | "cell_type": "markdown", 217 | "metadata": {}, 218 | "source": [ 219 | "## Computing eigenvalues and eigenvector" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": null, 225 | "metadata": {}, 226 | "outputs": [], 227 | "source": [ 228 | "if p_Method==\"ASD-DiagNet\":\n", 229 | " eig_data = {}\n", 230 | " pbar = pyprind.ProgBar(len(flist))\n", 231 | " for f in flist: \n", 232 | " d = get_corr_matrix(f)\n", 233 | " eig_vals, eig_vecs = np.linalg.eig(d)\n", 234 | "\n", 235 | " for ev in eig_vecs.T:\n", 236 | " np.testing.assert_array_almost_equal(1.0, np.linalg.norm(ev))\n", 237 | "\n", 238 | " sum_eigvals = np.sum(np.abs(eig_vals))\n", 239 | " # Make a list of (eigenvalue, eigenvector, norm_eigval) tuples\n", 240 | " eig_pairs = [(np.abs(eig_vals[i]), eig_vecs[:,i], np.abs(eig_vals[i])/sum_eigvals)\n", 241 | " for i in range(len(eig_vals))]\n", 242 | "\n", 243 | " # Sort the (eigenvalue, eigenvector) tuples from high to low\n", 244 | " eig_pairs.sort(key=lambda x: x[0], reverse=True)\n", 245 | "\n", 246 | " eig_data[f] = {'eigvals':np.array([ep[0] for ep in eig_pairs]),\n", 247 | " 'norm-eigvals':np.array([ep[2] for ep in eig_pairs]),\n", 248 | " 'eigvecs':[ep[1] for ep in eig_pairs]}\n", 249 | " pbar.update()" 250 | ] 251 | }, 252 | { 253 | "cell_type": "markdown", 254 | "metadata": {}, 255 | "source": [ 256 | "## Calculating Eros similarity" 257 | ] 258 | }, 259 | { 260 | "cell_type": "code", 261 | "execution_count": null, 262 | "metadata": {}, 263 | "outputs": [], 264 | "source": [ 265 | "def norm_weights(sub_flist):\n", 266 | " num_dim = len(eig_data[flist[0]]['eigvals'])\n", 267 | " norm_weights = np.zeros(shape=num_dim)\n", 268 | " for f in sub_flist:\n", 269 | " norm_weights += eig_data[f]['norm-eigvals'] \n", 270 | " return norm_weights\n", 271 | "\n", 272 | "def cal_similarity(d1, d2, weights, lim=None):\n", 273 | " res = 0.0\n", 274 | " if lim is None:\n", 275 | " weights_arr = weights.copy()\n", 276 | " else:\n", 277 | " weights_arr = weights[:lim].copy()\n", 278 | " weights_arr /= np.sum(weights_arr)\n", 279 | " for i,w in enumerate(weights_arr):\n", 280 | " res += w*np.inner(d1[i], d2[i])\n", 281 | " return res" 282 | ] 283 | }, 284 | { 285 | "cell_type": "markdown", 286 | "metadata": {}, 287 | "source": [ 288 | "## Defining dataset class" 289 | ] 290 | }, 291 | { 292 | "cell_type": "code", 293 | "execution_count": null, 294 | "metadata": {}, 295 | "outputs": [], 296 | "source": [ 297 | "class CC200Dataset(Dataset):\n", 298 | " def __init__(self, pkl_filename=None, data=None, samples_list=None, \n", 299 | " augmentation=False, aug_factor=1, num_neighbs=5,\n", 300 | " eig_data=None, similarity_fn=None, verbose=False,regs=None):\n", 301 | " self.regs=regs\n", 302 | " if pkl_filename is not None:\n", 303 | " if verbose:\n", 304 | " print ('Loading ..!', end=' ')\n", 305 | " self.data = pickle.load(open(pkl_filename, 'rb'))\n", 306 | " elif data is not None:\n", 307 | " self.data = data.copy()\n", 308 | " \n", 309 | " else:\n", 310 | " sys.stderr.write('Eigther PKL file or data is needed!')\n", 311 | " return \n", 312 | "\n", 313 | " #if verbose:\n", 314 | " # print ('Preprocess..!', end=' ')\n", 315 | " if samples_list is None:\n", 316 | " self.flist = [f for f in self.data]\n", 317 | " else:\n", 318 | " self.flist = [f for f in samples_list]\n", 319 | " self.labels = np.array([self.data[f][1] for f in self.flist])\n", 320 | " \n", 321 | " current_flist = np.array(self.flist.copy())\n", 322 | " current_lab0_flist = current_flist[self.labels == 0]\n", 323 | " current_lab1_flist = current_flist[self.labels == 1]\n", 324 | " #if verbose:\n", 325 | " # print(' Num Positive : ', len(current_lab1_flist), end=' ')\n", 326 | " # print(' Num Negative : ', len(current_lab0_flist), end=' ')\n", 327 | " \n", 328 | " \n", 329 | " if augmentation:\n", 330 | " self.num_data = aug_factor * len(self.flist)\n", 331 | " self.neighbors = {}\n", 332 | " pbar = pyprind.ProgBar(len(self.flist))\n", 333 | " weights = norm_weights(samples_list)#??\n", 334 | " for f in self.flist:\n", 335 | " label = self.data[f][1]\n", 336 | " candidates = (set(current_lab0_flist) if label == 0 else set(current_lab1_flist))\n", 337 | " candidates.remove(f)\n", 338 | " eig_f = eig_data[f]['eigvecs']\n", 339 | " sim_list = []\n", 340 | " for cand in candidates:\n", 341 | " eig_cand = eig_data[cand]['eigvecs']\n", 342 | " sim = similarity_fn(eig_f, eig_cand,weights)\n", 343 | " sim_list.append((sim, cand))\n", 344 | " sim_list.sort(key=lambda x: x[0], reverse=True)\n", 345 | " self.neighbors[f] = [item[1] for item in sim_list[:num_neighbs]]#list(candidates)#[item[1] for item in sim_list[:num_neighbs]]\n", 346 | " \n", 347 | " else:\n", 348 | " self.num_data = len(self.flist)\n", 349 | "\n", 350 | " \n", 351 | " def __getitem__(self, index):\n", 352 | " if index < len(self.flist):\n", 353 | " fname = self.flist[index]\n", 354 | " data = self.data[fname][0].copy() #get_corr_data(fname, mode=cal_mode) \n", 355 | " data = data[self.regs].copy()\n", 356 | " label = (self.labels[index],)\n", 357 | " return torch.FloatTensor(data), torch.FloatTensor(label)\n", 358 | " else:\n", 359 | " f1 = self.flist[index % len(self.flist)]\n", 360 | " d1, y1 = self.data[f1][0], self.data[f1][1]\n", 361 | " d1=d1[self.regs]\n", 362 | " f2 = np.random.choice(self.neighbors[f1])\n", 363 | " d2, y2 = self.data[f2][0], self.data[f2][1]\n", 364 | " d2=d2[self.regs]\n", 365 | " assert y1 == y2\n", 366 | " r = np.random.uniform(low=0, high=1)\n", 367 | " label = (y1,)\n", 368 | " data = r*d1 + (1-r)*d2\n", 369 | " return torch.FloatTensor(data), torch.FloatTensor(label)\n", 370 | "\n", 371 | " def __len__(self):\n", 372 | " return self.num_data" 373 | ] 374 | }, 375 | { 376 | "cell_type": "markdown", 377 | "metadata": {}, 378 | "source": [ 379 | "## Definig data loader function" 380 | ] 381 | }, 382 | { 383 | "cell_type": "code", 384 | "execution_count": null, 385 | "metadata": {}, 386 | "outputs": [], 387 | "source": [ 388 | "def get_loader(pkl_filename=None, data=None, samples_list=None,\n", 389 | " batch_size=64, \n", 390 | " num_workers=1, mode='train',\n", 391 | " *, augmentation=False, aug_factor=1, num_neighbs=5,\n", 392 | " eig_data=None, similarity_fn=None, verbose=False,regions=None):\n", 393 | " \"\"\"Build and return data loader.\"\"\"\n", 394 | " if mode == 'train':\n", 395 | " shuffle = True\n", 396 | " else:\n", 397 | " shuffle = False\n", 398 | " augmentation=False\n", 399 | "\n", 400 | " dataset = CC200Dataset(pkl_filename=pkl_filename, data=data, samples_list=samples_list,\n", 401 | " augmentation=augmentation, aug_factor=aug_factor, \n", 402 | " eig_data=eig_data, similarity_fn=similarity_fn, verbose=verbose,regs=regions)\n", 403 | "\n", 404 | " data_loader = DataLoader(dataset,\n", 405 | " batch_size=batch_size,\n", 406 | " shuffle=shuffle,\n", 407 | " num_workers=num_workers)\n", 408 | " \n", 409 | " return data_loader" 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "metadata": {}, 415 | "source": [ 416 | "## Defining Autoencoder class" 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": null, 422 | "metadata": {}, 423 | "outputs": [], 424 | "source": [ 425 | "class MTAutoEncoder(nn.Module):\n", 426 | " def __init__(self, num_inputs=990, \n", 427 | " num_latent=200, tied=True,\n", 428 | " num_classes=2, use_dropout=False):\n", 429 | " super(MTAutoEncoder, self).__init__()\n", 430 | " self.tied = tied\n", 431 | " self.num_latent = num_latent\n", 432 | " \n", 433 | " self.fc_encoder = nn.Linear(num_inputs, num_latent)\n", 434 | " \n", 435 | " if not tied:\n", 436 | " self.fc_decoder = nn.Linear(num_latent, num_inputs)\n", 437 | " \n", 438 | " self.fc_encoder = nn.Linear(num_inputs, num_latent)\n", 439 | " \n", 440 | " if use_dropout:\n", 441 | " self.classifier = nn.Sequential (\n", 442 | " nn.Dropout(p=0.5),\n", 443 | " nn.Linear(self.num_latent, 1),\n", 444 | " \n", 445 | " )\n", 446 | " else:\n", 447 | " self.classifier = nn.Sequential (\n", 448 | " nn.Linear(self.num_latent, 1),\n", 449 | " )\n", 450 | " \n", 451 | " \n", 452 | " def forward(self, x, eval_classifier=False):\n", 453 | " x = self.fc_encoder(x)\n", 454 | " x = torch.tanh(x)\n", 455 | " if eval_classifier:\n", 456 | " x_logit = self.classifier(x)\n", 457 | " else:\n", 458 | " x_logit = None\n", 459 | " \n", 460 | " if self.tied:\n", 461 | " x = F.linear(x, self.fc_encoder.weight.t())\n", 462 | " else:\n", 463 | " x = self.fc_decoder(x)\n", 464 | " \n", 465 | " return x, x_logit\n", 466 | "\n", 467 | "mtae = MTAutoEncoder()\n", 468 | "\n", 469 | "mtae" 470 | ] 471 | }, 472 | { 473 | "cell_type": "markdown", 474 | "metadata": {}, 475 | "source": [ 476 | "## Defining training and testing functions" 477 | ] 478 | }, 479 | { 480 | "cell_type": "code", 481 | "execution_count": null, 482 | "metadata": {}, 483 | "outputs": [], 484 | "source": [ 485 | "def train(model, epoch, train_loader, p_bernoulli=None, mode='both', lam_factor=1.0):\n", 486 | " model.train()\n", 487 | " train_losses = []\n", 488 | " for i,(batch_x,batch_y) in enumerate(train_loader):\n", 489 | " if len(batch_x) != batch_size:\n", 490 | " continue\n", 491 | " if p_bernoulli is not None:\n", 492 | " if i == 0:\n", 493 | " p_tensor = torch.ones_like(batch_x).to(device)*p_bernoulli\n", 494 | " rand_bernoulli = torch.bernoulli(p_tensor).to(device)\n", 495 | "\n", 496 | " data, target = batch_x.to(device), batch_y.to(device)\n", 497 | " optimizer.zero_grad()\n", 498 | "\n", 499 | " if mode in ['both', 'ae']:\n", 500 | " if p_bernoulli is not None:\n", 501 | " rec_noisy, _ = model(data*rand_bernoulli, False)\n", 502 | " loss_ae = criterion_ae(rec_noisy, data) / len(batch_x)\n", 503 | " else:\n", 504 | " rec, _ = model(data, False)\n", 505 | " loss_ae = criterion_ae(rec, data) / len(batch_x)\n", 506 | "\n", 507 | " if mode in ['both', 'clf']:\n", 508 | " rec_clean, logits = model(data, True)\n", 509 | " loss_clf = criterion_clf(logits, target)\n", 510 | "\n", 511 | " if mode == 'both':\n", 512 | " loss_total = loss_ae + lam_factor*loss_clf\n", 513 | " train_losses.append([loss_ae.detach().cpu().numpy(), \n", 514 | " loss_clf.detach().cpu().numpy()])\n", 515 | " elif mode == 'ae':\n", 516 | " loss_total = loss_ae\n", 517 | " train_losses.append([loss_ae.detach().cpu().numpy(), \n", 518 | " 0.0])\n", 519 | " elif mode == 'clf':\n", 520 | " loss_total = loss_clf\n", 521 | " train_losses.append([0.0, \n", 522 | " loss_clf.detach().cpu().numpy()])\n", 523 | "\n", 524 | " loss_total.backward()\n", 525 | " optimizer.step()\n", 526 | "\n", 527 | " return train_losses \n", 528 | "\n", 529 | "def test(model, criterion, test_loader, \n", 530 | " eval_classifier=False, num_batch=None):\n", 531 | " test_loss, n_test, correct = 0.0, 0, 0\n", 532 | " all_predss=[]\n", 533 | " if eval_classifier:\n", 534 | " y_true, y_pred = [], []\n", 535 | " with torch.no_grad():\n", 536 | " model.eval()\n", 537 | " for i,(batch_x,batch_y) in enumerate(test_loader, 1):\n", 538 | " if num_batch is not None:\n", 539 | " if i >= num_batch:\n", 540 | " continue\n", 541 | " data = batch_x.to(device)\n", 542 | " rec, logits = model(data, eval_classifier)\n", 543 | "\n", 544 | " test_loss += criterion(rec, data).detach().cpu().numpy() \n", 545 | " n_test += len(batch_x)\n", 546 | " if eval_classifier:\n", 547 | " proba = torch.sigmoid(logits).detach().cpu().numpy()\n", 548 | " preds = np.ones_like(proba, dtype=np.int32)\n", 549 | " preds[proba < 0.5] = 0\n", 550 | " all_predss.extend(preds)###????\n", 551 | " y_arr = np.array(batch_y, dtype=np.int32)\n", 552 | "\n", 553 | " correct += np.sum(preds == y_arr)\n", 554 | " y_true.extend(y_arr.tolist())\n", 555 | " y_pred.extend(proba.tolist())\n", 556 | " mlp_acc,mlp_sens,mlp_spef = confusion(y_true,all_predss)\n", 557 | "\n", 558 | " return mlp_acc,mlp_sens,mlp_spef#,correct/n_test\n", 559 | "\n", 560 | "\n" 561 | ] 562 | }, 563 | { 564 | "cell_type": "code", 565 | "execution_count": null, 566 | "metadata": {}, 567 | "outputs": [], 568 | "source": [ 569 | "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", 570 | "print(device)" 571 | ] 572 | }, 573 | { 574 | "cell_type": "code", 575 | "execution_count": null, 576 | "metadata": { 577 | "scrolled": true 578 | }, 579 | "outputs": [], 580 | "source": [ 581 | "\n", 582 | "if p_Method == \"ASD-DiagNet\" and p_mode == \"whole\":\n", 583 | " \n", 584 | " num_corr = len(all_corr[flist[0]][0])\n", 585 | " print(\"num_corr: \",num_corr)\n", 586 | " \n", 587 | " start =time.time()\n", 588 | " batch_size = 8\n", 589 | " learning_rate_ae, learning_rate_clf = 0.0001, 0.0001\n", 590 | " num_epochs = 25\n", 591 | "\n", 592 | " p_bernoulli = None\n", 593 | " augmentation = p_augmentation\n", 594 | " use_dropout = False\n", 595 | "\n", 596 | " aug_factor = 2\n", 597 | " num_neighbs = 5\n", 598 | " lim4sim = 2\n", 599 | " n_lat = int(num_corr/4)\n", 600 | " print(n_lat)\n", 601 | " start= time.time()\n", 602 | "\n", 603 | " print('p_bernoulli: ', p_bernoulli)\n", 604 | " print('augmentaiton: ', augmentation, 'aug_factor: ', aug_factor, \n", 605 | " 'num_neighbs: ', num_neighbs, 'lim4sim: ', lim4sim)\n", 606 | " print('use_dropout: ', use_dropout, '\\n')\n", 607 | "\n", 608 | "\n", 609 | " sim_function = functools.partial(cal_similarity, lim=lim4sim)\n", 610 | " crossval_res_kol=[]\n", 611 | " y_arr = np.array([get_label(f) for f in flist])\n", 612 | " flist = np.array(flist)\n", 613 | " kk=0 \n", 614 | " for rp in range(10):\n", 615 | " kf = StratifiedKFold(n_splits=p_fold, random_state=1, shuffle=True)\n", 616 | " np.random.shuffle(flist)\n", 617 | " y_arr = np.array([get_label(f) for f in flist])\n", 618 | " for kk,(train_index, test_index) in enumerate(kf.split(flist, y_arr)):\n", 619 | " train_samples, test_samples = flist[train_index], flist[test_index]\n", 620 | "\n", 621 | "\n", 622 | " verbose = (True if (kk == 0) else False)\n", 623 | "\n", 624 | " regions_inds = get_regs(train_samples,int(num_corr/4))\n", 625 | "\n", 626 | " num_inpp = len(regions_inds)\n", 627 | " n_lat = int(num_inpp/2)\n", 628 | " train_loader=get_loader(data=all_corr, samples_list=train_samples, \n", 629 | " batch_size=batch_size, mode='train',\n", 630 | " augmentation=augmentation, aug_factor=aug_factor, \n", 631 | " num_neighbs=num_neighbs, eig_data=eig_data, similarity_fn=sim_function, \n", 632 | " verbose=verbose,regions=regions_inds)\n", 633 | "\n", 634 | " test_loader=get_loader(data=all_corr, samples_list=test_samples, \n", 635 | " batch_size=batch_size, mode='test', augmentation=False, \n", 636 | " verbose=verbose,regions=regions_inds)\n", 637 | "\n", 638 | " model = MTAutoEncoder(tied=True, num_inputs=num_inpp, num_latent=n_lat, use_dropout=use_dropout)\n", 639 | " model.to(device)\n", 640 | " criterion_ae = nn.MSELoss(reduction='sum')\n", 641 | " criterion_clf = nn.BCEWithLogitsLoss()\n", 642 | " optimizer = optim.SGD([{'params': model.fc_encoder.parameters(), 'lr': learning_rate_ae},\n", 643 | " {'params': model.classifier.parameters(), 'lr': learning_rate_clf}],\n", 644 | " momentum=0.9)\n", 645 | "\n", 646 | " for epoch in range(1, num_epochs+1):\n", 647 | " if epoch <= 20:\n", 648 | " train_losses = train(model, epoch, train_loader, p_bernoulli, mode='both')\n", 649 | " else:\n", 650 | " train_losses = train(model, epoch, train_loader, p_bernoulli, mode='clf')\n", 651 | "\n", 652 | "\n", 653 | " res_mlp = test(model, criterion_ae, test_loader, eval_classifier=True)\n", 654 | " print(test(model, criterion_ae, test_loader, eval_classifier=True))\n", 655 | " crossval_res_kol.append(res_mlp)\n", 656 | " print(\"averages:\")\n", 657 | " print(np.mean(np.array(crossval_res_kol),axis = 0))\n", 658 | " finish= time.time()\n", 659 | "\n", 660 | " print(finish-start)\n", 661 | "\n" 662 | ] 663 | }, 664 | { 665 | "cell_type": "code", 666 | "execution_count": null, 667 | "metadata": {}, 668 | "outputs": [], 669 | "source": [ 670 | "\n", 671 | "if p_Method == \"ASD-DiagNet\" and p_mode == \"percenter\":\n", 672 | " num_corr = len(all_corr[flist[0]][0])\n", 673 | "\n", 674 | " flist = os.listdir(data_main_path)\n", 675 | "\n", 676 | " for f in range(len(flist)):\n", 677 | " flist[f] = get_key(flist[f])\n", 678 | " \n", 679 | " centers_dict = {}\n", 680 | " for f in flist:\n", 681 | " key = f.split('_')[0]\n", 682 | "\n", 683 | " if key not in centers_dict:\n", 684 | " centers_dict[key] = []\n", 685 | " centers_dict[key].append(f)\n", 686 | "\n", 687 | " \n", 688 | "\n", 689 | " flist = np.array(centers_dict[p_center])\n", 690 | " \n", 691 | " start =time.time()\n", 692 | " #flist = np.array(sorted(os.listdir(data_main_path)))\n", 693 | " batch_size = 8\n", 694 | " learning_rate_ae, learning_rate_clf = 0.0001, 0.0001\n", 695 | " num_epochs = 25\n", 696 | "\n", 697 | " p_bernoulli = None\n", 698 | " augmentation = p_augmentation\n", 699 | " use_dropout = False\n", 700 | "\n", 701 | " aug_factor = 2\n", 702 | " num_neighbs = 5\n", 703 | " lim4sim = 2\n", 704 | " n_lat = int(num_corr/4)\n", 705 | "\n", 706 | "\n", 707 | " print('p_bernoulli: ', p_bernoulli)\n", 708 | " print('augmentaiton: ', augmentation, 'aug_factor: ', aug_factor, \n", 709 | " 'num_neighbs: ', num_neighbs, 'lim4sim: ', lim4sim)\n", 710 | " print('use_dropout: ', use_dropout, '\\n')\n", 711 | "\n", 712 | "\n", 713 | " sim_function = functools.partial(cal_similarity, lim=lim4sim)\n", 714 | " all_rp_res=[]\n", 715 | " y_arr = np.array([get_label(f) for f in flist])\n", 716 | "\n", 717 | " kk=0 \n", 718 | " crossval_res_kol_kol=[]\n", 719 | " for rp in range(10):\n", 720 | " print(\"========================\")\n", 721 | " crossval_res_kol = []\n", 722 | " start= time.time()\n", 723 | " kf = StratifiedKFold(n_splits=p_fold)\n", 724 | " #np.random.shuffle(flist)\n", 725 | " y_arr = np.array([get_label(f) for f in flist])\n", 726 | " for kk,(train_index, test_index) in enumerate(kf.split(flist, y_arr)):\n", 727 | " \n", 728 | " train_samples, test_samples = flist[train_index], flist[test_index]\n", 729 | "\n", 730 | " verbose = (True if (kk == 0) else False)\n", 731 | "\n", 732 | " regions_inds = get_regs(train_samples,int(num_corr/4))\n", 733 | " num_inpp = len(regions_inds)\n", 734 | " n_lat = int(num_inpp/2)\n", 735 | " num_inpp = len(regions_inds)\n", 736 | " train_loader=get_loader(data=all_corr, samples_list=train_samples, \n", 737 | " batch_size=batch_size, mode='train',\n", 738 | " augmentation=augmentation, aug_factor=aug_factor, \n", 739 | " num_neighbs=num_neighbs, eig_data=eig_data, similarity_fn=sim_function, \n", 740 | " verbose=verbose,regions=regions_inds)\n", 741 | "\n", 742 | " test_loader=get_loader(data=all_corr, samples_list=test_samples, \n", 743 | " batch_size=batch_size, mode='test', augmentation=False, \n", 744 | " verbose=verbose,regions=regions_inds)\n", 745 | "\n", 746 | " model = MTAutoEncoder(tied=True, num_inputs=num_inpp, num_latent=n_lat, use_dropout=use_dropout)\n", 747 | " model.to(device)\n", 748 | " criterion_ae = nn.MSELoss(reduction='sum')\n", 749 | " criterion_clf = nn.BCEWithLogitsLoss()\n", 750 | " optimizer = optim.SGD([{'params': model.fc_encoder.parameters(), 'lr': learning_rate_ae},\n", 751 | " {'params': model.classifier.parameters(), 'lr': learning_rate_clf}],\n", 752 | " momentum=0.9)\n", 753 | "\n", 754 | " for epoch in range(1, num_epochs+1):\n", 755 | " if epoch <= 20:\n", 756 | " train_losses = train(model, epoch, train_loader, p_bernoulli, mode='both')\n", 757 | " else:\n", 758 | " train_losses = train(model, epoch, train_loader, p_bernoulli, mode='clf')\n", 759 | "\n", 760 | "\n", 761 | " res_mlp = test(model, criterion_ae, test_loader, eval_classifier=True)\n", 762 | " #print(\"fold\",kk+1,\":\",test(model, criterion_ae, test_loader, eval_classifier=True))\n", 763 | " crossval_res_kol.append(res_mlp)\n", 764 | " print(\"Result of repeat \",rp,\":\")\n", 765 | " print(np.mean(np.array(crossval_res_kol),axis = 0))\n", 766 | " all_rp_res.append(np.mean(np.array(crossval_res_kol),axis = 0))\n", 767 | " finish= time.time()\n", 768 | "\n", 769 | " print(\"Running time:\",finish-start)\n", 770 | " print(\"Avergae result of 10 repeats: \",np.mean(np.array(all_rp_res),axis = 0))" 771 | ] 772 | }, 773 | { 774 | "cell_type": "code", 775 | "execution_count": null, 776 | "metadata": {}, 777 | "outputs": [], 778 | "source": [ 779 | "if p_Method != \"ASD-DiagNet\" and p_mode == \"whole\":\n", 780 | " \n", 781 | " clf = SVC(gamma = 'auto') if p_Method == 'SVM' else RandomForestClassifier(n_estimators=100)\n", 782 | " overall_result = []\n", 783 | " for rp in range(10):\n", 784 | " kf = StratifiedKFold(n_splits=p_fold, random_state=1, shuffle=True)\n", 785 | " np.random.shuffle(flist)\n", 786 | " y_arr = np.array([get_label(f) for f in flist])\n", 787 | " res = []\n", 788 | " for kk,(train_index, test_index) in enumerate(kf.split(flist, y_arr)):\n", 789 | " train_samples, test_samples = np.array(flist)[train_index], np.array(flist)[test_index]\n", 790 | " train_data = []\n", 791 | " train_labels = []\n", 792 | " test_data = []\n", 793 | " test_labels = []\n", 794 | "\n", 795 | " for i in train_samples:\n", 796 | " train_data.append(all_corr[i][0])\n", 797 | " train_labels.append(all_corr[i][1])\n", 798 | "\n", 799 | " for i in test_samples:\n", 800 | " test_data.append(all_corr[i][0])\n", 801 | " test_labels.append(all_corr[i][1])\n", 802 | "\n", 803 | " \n", 804 | " clf.fit(train_data,train_labels)\n", 805 | " pr = clf.predict(test_data)\n", 806 | " res.append(confusion(test_labels,pr))\n", 807 | " \n", 808 | " print(\"repeat: \",rp,np.mean(res, axis=0).tolist())\n", 809 | " overall_result.append(np.mean(res, axis=0).tolist()) \n", 810 | " print(\"---------------Result of repeating 10 times-------------------\")\n", 811 | " print(np.mean(np.array(overall_result), axis=0).tolist())" 812 | ] 813 | }, 814 | { 815 | "cell_type": "code", 816 | "execution_count": null, 817 | "metadata": {}, 818 | "outputs": [], 819 | "source": [ 820 | "random.seed(19)\n", 821 | "np.random.seed(19)\n", 822 | "if p_Method != \"ASD-DiagNet\" and p_mode == \"percenter\":\n", 823 | " \n", 824 | " clf = SVC(gamma = 'auto') if p_Method == 'SVM' else RandomForestClassifier(n_estimators=100)\n", 825 | " overall_result = []\n", 826 | " for rp in range(10):\n", 827 | " kf = StratifiedKFold(n_splits=p_fold, random_state=1, shuffle=True)\n", 828 | " np.random.shuffle(flist)\n", 829 | " y_arr = np.array([get_label(f) for f in flist])\n", 830 | " res = []\n", 831 | " for kk,(train_index, test_index) in enumerate(kf.split(flist, y_arr)):\n", 832 | " train_samples, test_samples = np.array(flist)[train_index], np.array(flist)[test_index]\n", 833 | " train_data = []\n", 834 | " train_labels = []\n", 835 | " test_data = []\n", 836 | " test_labels = []\n", 837 | "\n", 838 | " for i in train_samples:\n", 839 | " train_data.append(all_corr[i][0])\n", 840 | " train_labels.append(all_corr[i][1])\n", 841 | "\n", 842 | " for i in test_samples:\n", 843 | " test_data.append(all_corr[i][0])\n", 844 | " test_labels.append(all_corr[i][1])\n", 845 | "\n", 846 | " clf.fit(train_data,train_labels)\n", 847 | " pr = clf.predict(test_data)\n", 848 | " res.append(confusion(test_labels,pr))\n", 849 | " \n", 850 | " print(\"repeat: \",rp,np.mean(res, axis=0).tolist())\n", 851 | " overall_result.append(np.mean(res, axis=0).tolist()) \n", 852 | " print(\"---------------Result of repeating 10 times for: \",p_center,\"-------------------\")\n", 853 | " print(np.mean(np.array(overall_result), axis=0).tolist())" 854 | ] 855 | }, 856 | { 857 | "cell_type": "code", 858 | "execution_count": null, 859 | "metadata": {}, 860 | "outputs": [], 861 | "source": [] 862 | }, 863 | { 864 | "cell_type": "code", 865 | "execution_count": null, 866 | "metadata": {}, 867 | "outputs": [], 868 | "source": [] 869 | }, 870 | { 871 | "cell_type": "code", 872 | "execution_count": null, 873 | "metadata": {}, 874 | "outputs": [], 875 | "source": [] 876 | } 877 | ], 878 | "metadata": { 879 | "celltoolbar": "Tags", 880 | "kernelspec": { 881 | "display_name": "Python 3", 882 | "language": "python", 883 | "name": "python3" 884 | }, 885 | "language_info": { 886 | "codemirror_mode": { 887 | "name": "ipython", 888 | "version": 3 889 | }, 890 | "file_extension": ".py", 891 | "mimetype": "text/x-python", 892 | "name": "python", 893 | "nbconvert_exporter": "python", 894 | "pygments_lexer": "ipython3", 895 | "version": "3.7.0" 896 | } 897 | }, 898 | "nbformat": 4, 899 | "nbformat_minor": 2 900 | } 901 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------