├── .gitignore ├── README.md ├── deepnote └── MedML Workshops in Deepnote │ ├── Notebook 1.ipynb │ ├── Init notebook.ipynb │ ├── Cells PCA.ipynb │ └── medml-workshops-deepnote-MedML Workshops in Deepnote-Cells PCA.ipynb ├── jupyter-introduction.ipynb └── cells-pca.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints/ 2 | 3 | *.sw* 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Medical data processing and ML workshops 2 | 3 | 4 | 5 | 6 | Workshop for medical students interested in data processing and machine learning, for ML students interested in medical data and for anyone with programming or data analysis background interested in ML. 7 | 8 | 9 | We'll introduce Python basics, tools for processing and visualizing data, dive into ML and DL basics, play with CNNs and GANs. 10 | 11 | WIP 12 | 13 | 14 | ### Parts 15 | 16 | 1. [Introduction — motivation, data and tools](./introduction.ipynb) [Launch in Deepnote](https://beta.deepnote.com/project/a53ef107-6e31-47d1-87c3-dd74da9267fe#%2Fmedical-data.ipynb) 17 | 2. CNNs, organ segmentation, tumours 18 | 3. Augmenting data with GANs 19 | 20 | 21 | ### WIP Notebooks 22 | 23 | - [Jupyter notebooks and Google Colaboratory intro](./jupyter-introduction.ipynb) 24 | - [Cell data and PCA](./cells-pca.ipynb) 25 | - [Keras and CNNs](./colab-keras-intro.ipynb) 26 | - [Kidney tumours](./kidney-tumours.ipynb) 27 | - [PPG/ECG data playground](./ppg-ecg-playground.ipynb) [Launch in Deepnote](https://beta.deepnote.com/project/a53ef107-6e31-47d1-87c3-dd74da9267fe#%2Fppg-ecg-playground.ipynb) 28 | 29 | 30 | ### My other courses 31 | 32 | - [Git workshop](https://github.com/webdev-js-evenings/git-workshop) 33 | - [Testing workshop](https://github.com/webdev-js-evenings/testing-workshop) 34 | - [BI-SVZ](https://github.com/ImprolabFIT/BI-SVZ-coursework/blob/master/tutorials/index.adoc) 35 | -------------------------------------------------------------------------------- /deepnote/MedML Workshops in Deepnote/Notebook 1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "source": "# Start writing code here...", 6 | "metadata": { 7 | "cell_id": "7f43fcba11414416ac7885d6ddd7235f", 8 | "deepnote_cell_type": "code" 9 | }, 10 | "outputs": [], 11 | "execution_count": null 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "source": "\nCreated in deepnote.com \nCreated in Deepnote", 16 | "metadata": { 17 | "created_in_deepnote_cell": true, 18 | "deepnote_cell_type": "markdown" 19 | } 20 | } 21 | ], 22 | "nbformat": 4, 23 | "nbformat_minor": 0, 24 | "metadata": { 25 | "deepnote": {}, 26 | "orig_nbformat": 2, 27 | "deepnote_notebook_id": "9cd814aab02642d494ca9c05bd4327a3", 28 | "deepnote_execution_queue": [] 29 | } 30 | } -------------------------------------------------------------------------------- /deepnote/MedML Workshops in Deepnote/Init notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": "## Initialization Notebook\nThis is your initialization notebook.\n\n**What's this for?**\n\nYou can put custom code you want us to run every time we setup your environment in here. \n\n**Awesome! Anything I should not put in here?**\n\nPlease don't install `jupyter` or `jedi` packages, they would break your Deepnote environment. Also, no need to put `!pip install`s here, we already save those automatically!\n\n**I want to learn more!**\n\nGreat! Just [head over to our docs](https://deepnote.com/docs/custom-initialization).", 6 | "metadata": { 7 | "cell_id": "2e2531b70da94854b2e75f90c8d04cbd", 8 | "deepnote_cell_type": "markdown" 9 | } 10 | }, 11 | { 12 | "cell_type": "code", 13 | "source": "%%bash\n# If your project has a 'requirements.txt' file, we'll install it here apart from blacklisted packages that interfere with Deepnote (see above).\nif test -f requirements.txt\n then\n sed -i '/jedi/d;/jupyter/d;' ./requirements.txt\n pip install -r ./requirements.txt\n else echo \"There's no requirements.txt, so nothing to install. This is the case with most projects.\"\nfi", 14 | "metadata": { 15 | "cell_id": "8383d6218a2d4a288fca52318919eb82", 16 | "source_hash": "60b37886", 17 | "execution_start": 1684228235183, 18 | "execution_millis": 51, 19 | "deepnote_to_be_reexecuted": false, 20 | "deepnote_cell_type": "code" 21 | }, 22 | "outputs": [ 23 | { 24 | "name": "stdout", 25 | "text": "There's no requirements.txt, so nothing to install. This is the case with most projects.\n", 26 | "output_type": "stream" 27 | } 28 | ], 29 | "execution_count": 1 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "source": "\nCreated in deepnote.com \nCreated in Deepnote", 34 | "metadata": { 35 | "created_in_deepnote_cell": true, 36 | "deepnote_cell_type": "markdown" 37 | } 38 | } 39 | ], 40 | "nbformat": 4, 41 | "nbformat_minor": 0, 42 | "metadata": { 43 | "deepnote": {}, 44 | "orig_nbformat": 2, 45 | "deepnote_notebook_id": "e367197ec2af40d2ac8f2f2851db3243", 46 | "deepnote_execution_queue": [] 47 | } 48 | } -------------------------------------------------------------------------------- /jupyter-introduction.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "\n", 12 | "# Práce s Jupyter notebooky\n" 13 | ] 14 | }, 15 | { 16 | "cell_type": "markdown", 17 | "metadata": {}, 18 | "source": [ 19 | "# Co je Jupyter a IPython?" 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "Jupyter notebook je webová aplikace pro interaktivní programování, zaznamenávání, vizualizaci a prezentace výsledků. Typicky se používá při analýze dat, problémech počítačového vidění, ale také pro matematické úkoly a třeba i trénovaní neurónových sítí.\n", 34 | "\n", 35 | "Jupyter je platforma kolem Jupyter notebooku, která se kromě notebooku stará o různá rozšírení, kolaboraci a nasazení do různých prostrředí. Více detailů na webu [jupyter.org](https://jupyter.org)\n", 36 | "\n", 37 | "Jupyter vznikl jako nadstavba nad IPython notebook, který v \"data\" světě začali používat také programátori v Julii a Rku. Proto Ju(lia) - Py(thon) - e(R) = Jupyter.\n", 38 | "\n", 39 | "V Jupyter notebooku se používají dva typy buněk — textové a kód. Kód je v našem případe Pythoní, no ve standardní instalaci Jupyteru je možné přepnout na jazyk Julia a nebo R. Existují také rozšíření do pro další jazyky.\n", 40 | "\n", 41 | "Kromě kódu a text v markdownu je možné použít HTML a zpouštět také systémové příkazy.\n", 42 | "\n", 43 | "## Zkratky\n", 44 | "\n", 45 | "Jupyter rozlišuje dva módy - **Command** a **Edit**. V Command módu jsou jedno a více-písmenové zkratky na zrychlení práce pro úpravu buněk, evaluaci a pod.\n", 46 | "\n", 47 | "- přístup ke všem příkazům `CTRL`+`SHIFT`+`P`\n", 48 | "\n", 49 | "\n", 50 | "- přepnutí mezi módy Command -> Edit `ENTER`\n", 51 | "\n", 52 | "\n", 53 | "- přepnutí mezi módy Edit -> Command `ESC`\n", 54 | "\n", 55 | "\n", 56 | "- šipky zaručí pohyb mezi buňkami\n", 57 | "\n", 58 | "\n", 59 | "- vložení buňky\n", 60 | " - `A` nad aktuální\n", 61 | " - `B` pod aktuální\n", 62 | " - `SHIFT`+`M` spojení buňek\n", 63 | "\n", 64 | "\n", 65 | "- zmazání buňky `d d`\n", 66 | "\n", 67 | "\n", 68 | "- změna typu buňky\n", 69 | " - `M` markdown\n", 70 | " - `Y` code\n", 71 | " - `1` nadpis 1\n", 72 | " - `2` nadpis 2\n", 73 | "\n", 74 | "\n", 75 | "- evaluace\n", 76 | " - `SHIFT`+`ENTER` evaluace aktuální buňky\n", 77 | " - `CTRL`+`ENTER` evaluace označených buněk" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": {}, 83 | "source": [ 84 | "## IPython triky\n", 85 | "\n", 86 | "- magické příkazy\n", 87 | " - load externého skriptu (třeba s definicemi funkcí)\n", 88 | " - interaktivní matplotlib\n", 89 | " - timing\n", 90 | " - html\n", 91 | " - aliasy, makra, debugger, pip, conda\n", 92 | " - celý seznam [v dokumentaci](https://ipython.readthedocs.io/en/stable/interactive/magics.html)\n", 93 | "\n", 94 | "\n", 95 | "- snapshoty a uložení výsledků\n", 96 | "\n", 97 | "- nápověda příkazem `?`" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 2, 103 | "metadata": { 104 | "code_folding": [] 105 | }, 106 | "outputs": [ 107 | { 108 | "data": { 109 | "text/html": [ 110 | "\n", 111 | "\n", 112 | "
\n", 113 | " HTML v notebooku:\n", 114 | "
\n", 115 | "
\n", 116 | "
" 117 | ], 118 | "text/plain": [ 119 | "" 120 | ] 121 | }, 122 | "metadata": {}, 123 | "output_type": "display_data" 124 | } 125 | ], 126 | "source": [ 127 | "%%html\n", 128 | "\n", 129 | "\n", 130 | "
\n", 131 | " HTML v notebooku:\n", 132 | "
\n", 133 | "
\n", 134 | "
" 135 | ] 136 | }, 137 | { 138 | "cell_type": "markdown", 139 | "metadata": {}, 140 | "source": [ 141 | "## Jupyter extensions\n", 142 | "\n", 143 | "Umožňují rozšíření funkcí notebooku, jako třeba možnosti skrýt bloky textu a kódu, hezky zobrazit určité typy dat, měřit čas evaluaci buňky, a pod.\n", 144 | "\n", 145 | "Instalace:\n", 146 | "\n", 147 | "```\n", 148 | "pip install jupyter_nbextensions_configurator jupyter_contrib_nbextensions\n", 149 | "jupyter contrib nbextension install --user\n", 150 | "jupyter nbextensions_configurator enable --user\n", 151 | "```\n", 152 | "\n", 153 | "Ukázka: Codefolding, Collapsible Headings - [Nbextensions](http://localhost:8888/tree#nbextensions_configurator)" 154 | ] 155 | }, 156 | { 157 | "cell_type": "markdown", 158 | "metadata": {}, 159 | "source": [ 160 | "## Google Colab\n", 161 | "\n", 162 | "- Jupyter prostředí na steroidech v Google Cloudu\n", 163 | "- propojení přes GitHub\n", 164 | "- dostupné zdroje na běh zdarma, pro univerzity a výzkum navyše dostupné extra zdroje\n", 165 | "\n", 166 | "\n", 167 | "- trénovaní NNs na GPU/TPU\n", 168 | " - MNIST + jednoduchá konvoluční neurónová síť\n", 169 | "\n", 170 | "\n", 171 | "- dotazování na BigQuery datasety\n", 172 | " - počet .js filů ve všetch JS repozitářích na GitHubu\n", 173 | " - zkoumání variantů genómů" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 3, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "data": { 183 | "text/html": [ 184 | "Open in Colab badge:\n", 185 | "\"Open" 186 | ], 187 | "text/plain": [ 188 | "" 189 | ] 190 | }, 191 | "metadata": {}, 192 | "output_type": "display_data" 193 | } 194 | ], 195 | "source": [ 196 | "%%html\n", 197 | "Open in Colab badge:\n", 198 | "\"Open" 199 | ] 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "metadata": {}, 204 | "source": [ 205 | "# Příklady\n" 206 | ] 207 | }, 208 | { 209 | "cell_type": "markdown", 210 | "metadata": {}, 211 | "source": [ 212 | "## Vektor a vypsaní grafu z minulého cvičení" 213 | ] 214 | }, 215 | { 216 | "cell_type": "code", 217 | "execution_count": null, 218 | "metadata": {}, 219 | "outputs": [], 220 | "source": [ 221 | "import numpy as np\n", 222 | "\n", 223 | "def generate_vector(start, end, step):\n", 224 | " return np.arange(start, end, step)\n", 225 | "\n", 226 | "start = -4*np.pi\n", 227 | "end = 4*np.pi\n", 228 | "step = 0.01\n", 229 | "\n", 230 | "x = generate_vector(start, end, step)" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": null, 236 | "metadata": {}, 237 | "outputs": [], 238 | "source": [ 239 | "# magický příkaz v akci\n", 240 | "%matplotlib inline\n", 241 | "\n", 242 | "import numpy as np\n", 243 | "import matplotlib.pyplot as plt\n", 244 | "\n", 245 | "y = np.sin(x)\n", 246 | "plt.title('Funkce sin(x)')\n", 247 | "plt.plot(x, y)" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "metadata": {}, 253 | "source": [ 254 | "### Systémové příkazy" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": null, 260 | "metadata": {}, 261 | "outputs": [], 262 | "source": [ 263 | "pwd" 264 | ] 265 | }, 266 | { 267 | "cell_type": "code", 268 | "execution_count": null, 269 | "metadata": {}, 270 | "outputs": [], 271 | "source": [ 272 | "# znovu magický příkaz\n", 273 | "%cd .." 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": null, 279 | "metadata": {}, 280 | "outputs": [], 281 | "source": [ 282 | "pwd" 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": null, 288 | "metadata": {}, 289 | "outputs": [], 290 | "source": [ 291 | "cd \"H:\\\\Documents\\\\bi-svz\\\\tutorials\\\\files\\\\2\\\\\"" 292 | ] 293 | }, 294 | { 295 | "cell_type": "code", 296 | "execution_count": null, 297 | "metadata": {}, 298 | "outputs": [], 299 | "source": [ 300 | "alias home cd \"H:\\\\Documents\\\\bi-svz\\\\tutorials\\\\files\\\\2\\\\\"" 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "execution_count": null, 306 | "metadata": {}, 307 | "outputs": [], 308 | "source": [ 309 | "home" 310 | ] 311 | }, 312 | { 313 | "cell_type": "markdown", 314 | "metadata": {}, 315 | "source": [ 316 | "### Načení obrázku z disku\n", 317 | "\n", 318 | "Vyzkoušíme si načíst obrázek z disku a jednoduché transformace pomocí knihoven matplotlib a OpenCV. V BI-SVZ budeme používat OpenCV hodně, takže je vhodné se s ní seznámit.\n", 319 | "\n", 320 | "Můžete se podívat na široké možnosti knihovny v její [dokumentaci](https://docs.opencv.org/master/). OpenCV je napsaná v C++, proto definice její API je zdokumentovaná taky pro C++. My budeme používat wrapper pro Python (`opencv-python`), který podporuje ty samé funkce." 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": null, 326 | "metadata": {}, 327 | "outputs": [], 328 | "source": [ 329 | "imgPath = 'images/metal.jpg'" 330 | ] 331 | }, 332 | { 333 | "cell_type": "code", 334 | "execution_count": null, 335 | "metadata": {}, 336 | "outputs": [], 337 | "source": [ 338 | "import matplotlib.pyplot as plt\n", 339 | "import matplotlib.image as mpimg\n", 340 | "\n", 341 | "img = mpimg.imread(imgPath)\n", 342 | "imgplot = plt.imshow(img)\n", 343 | "\n", 344 | "plt.colorbar()\n", 345 | "plt.show() " 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": null, 351 | "metadata": {}, 352 | "outputs": [], 353 | "source": [ 354 | "lum_img = img[:,:,0]\n", 355 | "imgplot = plt.imshow(lum_img)\n", 356 | "plt.show()" 357 | ] 358 | }, 359 | { 360 | "cell_type": "code", 361 | "execution_count": null, 362 | "metadata": {}, 363 | "outputs": [], 364 | "source": [ 365 | "plt.imshow(lum_img, cmap=\"hot\")\n", 366 | "plt.colorbar()\n", 367 | "plt.show()" 368 | ] 369 | }, 370 | { 371 | "cell_type": "code", 372 | "execution_count": null, 373 | "metadata": {}, 374 | "outputs": [], 375 | "source": [ 376 | "plt.hist(img.ravel())\n", 377 | "plt.show()" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": null, 383 | "metadata": {}, 384 | "outputs": [], 385 | "source": [ 386 | "# přes OpenCV (z minulého cvičení)\n", 387 | "\n", 388 | "import cv2\n", 389 | "image = cv2.imread(imgPath)\n", 390 | "\n", 391 | "cv2.cvtColor(image, cv2.COLOR_BGR2RGB, image)\n", 392 | "\n", 393 | "plt.imshow(image, interpolation='bilinear')\n", 394 | "plt.show()" 395 | ] 396 | }, 397 | { 398 | "cell_type": "code", 399 | "execution_count": null, 400 | "metadata": {}, 401 | "outputs": [], 402 | "source": [ 403 | "# histogram\n", 404 | "\n", 405 | "color = ('b','g','r')\n", 406 | "for i,col in enumerate(color):\n", 407 | " histr = cv2.calcHist([image],[i],None,[256],[0,256])\n", 408 | " plt.plot(histr,color = col)\n", 409 | " plt.xlim([0,256])\n", 410 | " plt.ylim([0,8000])\n", 411 | "\n", 412 | "plt.show()" 413 | ] 414 | }, 415 | { 416 | "cell_type": "markdown", 417 | "metadata": {}, 418 | "source": [ 419 | "# Zdroje pro samostudium\n", 420 | "\n", 421 | "- Python zpracování obrazu — [opencv](https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_geometric_transformations/py_geometric_transformations.html#geometric-transformations), [tesseract](https://github.com/tesseract-ocr/tesseract)\n", 422 | "- Python data a strojové učení — pandas, matplotlib, numpy, scipy, scikit, pytorch, tensorflow\n", 423 | "- [Markdown cheatsheet](https://www.markdownguide.org/cheat-sheet)" 424 | ] 425 | } 426 | ], 427 | "metadata": { 428 | "kernelspec": { 429 | "display_name": "Python 3", 430 | "language": "python", 431 | "name": "python3" 432 | }, 433 | "language_info": { 434 | "codemirror_mode": { 435 | "name": "ipython", 436 | "version": 3 437 | }, 438 | "file_extension": ".py", 439 | "mimetype": "text/x-python", 440 | "name": "python", 441 | "nbconvert_exporter": "python", 442 | "pygments_lexer": "ipython3", 443 | "version": "3.7.0" 444 | }, 445 | "varInspector": { 446 | "cols": { 447 | "lenName": 16, 448 | "lenType": 16, 449 | "lenVar": 40 450 | }, 451 | "kernels_config": { 452 | "python": { 453 | "delete_cmd_postfix": "", 454 | "delete_cmd_prefix": "del ", 455 | "library": "var_list.py", 456 | "varRefreshCmd": "print(var_dic_list())" 457 | }, 458 | "r": { 459 | "delete_cmd_postfix": ") ", 460 | "delete_cmd_prefix": "rm(", 461 | "library": "var_list.r", 462 | "varRefreshCmd": "cat(var_dic_list()) " 463 | } 464 | }, 465 | "types_to_exclude": [ 466 | "module", 467 | "function", 468 | "builtin_function_or_method", 469 | "instance", 470 | "_Feature" 471 | ], 472 | "window_display": false 473 | } 474 | }, 475 | "nbformat": 4, 476 | "nbformat_minor": 2 477 | } 478 | -------------------------------------------------------------------------------- /cells-pca.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "colab_type": "text", 7 | "id": "Q1kApdvezk6Z" 8 | }, 9 | "source": [ 10 | "## Connect sheet and load data\n", 11 | "\n", 12 | "- sheet data - TODO\n", 13 | "- [python sheets api docs](https://github.com/burnash/gspread#more-examples)\n", 14 | "- [pandas dataframe docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 0, 20 | "metadata": { 21 | "colab": {}, 22 | "colab_type": "code", 23 | "id": "eDs_CZhNzSXl" 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "from google.colab import auth\n", 28 | "auth.authenticate_user()" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 0, 34 | "metadata": { 35 | "colab": {}, 36 | "colab_type": "code", 37 | "id": "sSOKw9s-zoL3" 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "import gspread\n", 42 | "from oauth2client.client import GoogleCredentials\n", 43 | "\n", 44 | "gc = gspread.authorize(GoogleCredentials.get_application_default())" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "colab": {}, 52 | "colab_type": "code", 53 | "id": "s8tkAT_Ozyp-" 54 | }, 55 | "outputs": [], 56 | "source": [ 57 | "bunky_sheet_url = '' # TODO\n", 58 | "sheet = gc.open_by_url(bunky_sheet_url)\n", 59 | "worksheet = sheet.get_worksheet(0)\n", 60 | "worksheet_hypo = sheet.get_worksheet(0)\n", 61 | "worksheet_draslik = sheet.get_worksheet(1)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 0, 67 | "metadata": { 68 | "colab": {}, 69 | "colab_type": "code", 70 | "id": "nv8fMNORAP_V" 71 | }, 72 | "outputs": [], 73 | "source": [ 74 | "import pandas as pd\n", 75 | "from sklearn.preprocessing import StandardScaler\n", 76 | "from sklearn.decomposition import PCA\n", 77 | "\n", 78 | "import matplotlib\n", 79 | "import matplotlib.pyplot as plt\n", 80 | "\n", 81 | "matplotlib.rcParams['figure.dpi'] = 150" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 0, 87 | "metadata": { 88 | "colab": {}, 89 | "colab_type": "code", 90 | "id": "MfVS_YVfz5ud" 91 | }, 92 | "outputs": [], 93 | "source": [ 94 | "# raw data\n", 95 | "\n", 96 | "data_hypo = worksheet_hypo.get_all_values()\n", 97 | "features_hypo = data_hypo[0]\n", 98 | "clean_data_hypo = data_hypo[1:]\n", 99 | "\n", 100 | "data_draslik = worksheet_draslik.get_all_values()\n", 101 | "features_draslik = data_draslik[0]\n", 102 | "clean_data_draslik = data_draslik[1:]" 103 | ] 104 | }, 105 | { 106 | "cell_type": "code", 107 | "execution_count": 0, 108 | "metadata": { 109 | "colab": {}, 110 | "colab_type": "code", 111 | "id": "5AEKQY6Sz_3f" 112 | }, 113 | "outputs": [], 114 | "source": [ 115 | "# pandas dataframe and clean\n", 116 | "\n", 117 | "def fixCell(cell):\n", 118 | " try:\n", 119 | " return float(cell.replace(',','.'))\n", 120 | " except ValueError:\n", 121 | " return 0 if cell == '' else cell\n", 122 | "\n", 123 | "\n", 124 | "# pandata_full = pd.DataFrame.from_records(clean_data)# , columns=features)\n", 125 | "\n", 126 | "pandata_hypo = pd.DataFrame.from_records(clean_data_hypo)\n", 127 | "pandata_hypo = pandata_hypo.drop(0, axis=1)\n", 128 | "\n", 129 | "pandata_draslik = pd.DataFrame.from_records(clean_data_draslik)\n", 130 | "pandata_draslik = pandata_draslik.drop(0, axis=1)\n" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 0, 136 | "metadata": { 137 | "colab": {}, 138 | "colab_type": "code", 139 | "id": "VhW09zyB6j0g" 140 | }, 141 | "outputs": [], 142 | "source": [ 143 | "pandata_hypo = pandata_hypo.applymap(fixCell)\n", 144 | "pandata_draslik = pandata_draslik.applymap(fixCell)" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 0, 150 | "metadata": { 151 | "colab": {}, 152 | "colab_type": "code", 153 | "id": "GgCpqhsW3LZ8" 154 | }, 155 | "outputs": [], 156 | "source": [ 157 | "pandata_draslik" 158 | ] 159 | }, 160 | { 161 | "cell_type": "code", 162 | "execution_count": 0, 163 | "metadata": { 164 | "colab": {}, 165 | "colab_type": "code", 166 | "id": "cqJwRBNl0W1A" 167 | }, 168 | "outputs": [], 169 | "source": [ 170 | "# normalize\n", 171 | "\n", 172 | "pandata_hypo_norm = pandata_hypo.loc[:, [1, 2, 3, 4]].values\n", 173 | "pandata_hypo_norm = StandardScaler().fit_transform(pandata_hypo_norm)\n", 174 | "\n", 175 | "pandata_draslik_norm = pandata_draslik.loc[:, [1, 2, 3, 4]].values\n", 176 | "pandata_draslik_norm = StandardScaler().fit_transform(pandata_draslik_norm)" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": { 182 | "colab_type": "text", 183 | "id": "Lz-SwUU9B4yb" 184 | }, 185 | "source": [ 186 | "## Try PCA\n", 187 | "[inspiration](http://bit.ly/2HkM0xP)" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 0, 193 | "metadata": { 194 | "colab": {}, 195 | "colab_type": "code", 196 | "id": "sTmZwhIo1H2X" 197 | }, 198 | "outputs": [], 199 | "source": [ 200 | "pca = PCA(n_components=2)\n", 201 | "pc_hypo_raw = pca.fit_transform(pandata_hypo_norm)\n", 202 | "pc_hypo = pd.DataFrame(data = pc_hypo_raw, columns = ['pc1', 'pc2'])\n", 203 | "\n", 204 | "pc_draslik_raw = pca.fit_transform(pandata_draslik_norm)\n", 205 | "pc_draslik = pd.DataFrame(data = pc_draslik_raw, columns = ['pc1', 'pc2'])\n", 206 | "\n", 207 | "\n", 208 | "#finalDf = pd.concat([principalDf, pandata_full[[0]]], axis = 1)" 209 | ] 210 | }, 211 | { 212 | "cell_type": "markdown", 213 | "metadata": { 214 | "colab_type": "text", 215 | "id": "ERRyhh2_E5vI" 216 | }, 217 | "source": [ 218 | "## Plot" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 0, 224 | "metadata": { 225 | "colab": { 226 | "base_uri": "https://localhost:8080/", 227 | "height": 635 228 | }, 229 | "colab_type": "code", 230 | "id": "eexDBCO39Xyv", 231 | "outputId": "05e30c67-5f01-4bc7-e1a2-79cd14c5a8a0" 232 | }, 233 | "outputs": [ 234 | { 235 | "data": { 236 | "image/png": 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mN4/QTZfPiLoZKCGOa0VYEwEACAspRgDgIm5rRVgTAQAIEwECALiI21oR1kQA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\nctGm9fuUhKVLKhdvWu9WpwAwC7c5BRa1MzesycaTHpDNW6a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237 | "text/plain": [ 238 | "
" 239 | ] 240 | }, 241 | "metadata": { 242 | "tags": [] 243 | }, 244 | "output_type": "display_data" 245 | } 246 | ], 247 | "source": [ 248 | "pc_hypo.plot(y='pc1', x='pc2', style='.', title=worksheet_hypo.title)\n", 249 | "plt.show()" 250 | ] 251 | }, 252 | { 253 | "cell_type": "code", 254 | "execution_count": 0, 255 | "metadata": { 256 | "colab": { 257 | "base_uri": "https://localhost:8080/", 258 | "height": 614 259 | }, 260 | "colab_type": "code", 261 | "id": "QeHqPX3z-Bw2", 262 | "outputId": "8edc5527-02e0-40aa-df35-c9ebc7580022" 263 | }, 264 | "outputs": [ 265 | { 266 | "data": { 267 | "image/png": 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AQArNmDQukvMSIAAAAAAptKSrXa0tFvp5CRAAAACAFJo1tU0rFs0LPUggQAAAAABSanFn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\nIEiSJEnqMSBIkiRJ6vk/nVkat6JpPHsAAAAASUVORK5CYII=\n", 268 | "text/plain": [ 269 | "
" 270 | ] 271 | }, 272 | "metadata": { 273 | "tags": [] 274 | }, 275 | "output_type": "display_data" 276 | } 277 | ], 278 | "source": [ 279 | "pc_draslik.plot(y='pc1', x='pc2', style='.', title=worksheet_draslik.title)\n", 280 | "plt.show()" 281 | ] 282 | } 283 | ], 284 | "metadata": { 285 | "colab": { 286 | "collapsed_sections": [ 287 | "Q1kApdvezk6Z", 288 | "Lz-SwUU9B4yb" 289 | ], 290 | "name": "bunky.ipynb", 291 | "provenance": [], 292 | "version": "0.3.2" 293 | }, 294 | "kernelspec": { 295 | "display_name": "Python 3", 296 | "language": "python", 297 | "name": "python3" 298 | }, 299 | "language_info": { 300 | "codemirror_mode": { 301 | "name": "ipython", 302 | "version": 3 303 | }, 304 | "file_extension": ".py", 305 | "mimetype": "text/x-python", 306 | "name": "python", 307 | "nbconvert_exporter": "python", 308 | "pygments_lexer": "ipython3", 309 | "version": "3.7.0" 310 | } 311 | }, 312 | "nbformat": 4, 313 | "nbformat_minor": 1 314 | } 315 | -------------------------------------------------------------------------------- /deepnote/MedML Workshops in Deepnote/Cells PCA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": "## Connect sheet and load data\n\n- sheet data - TODO\n- [python sheets api docs](https://github.com/burnash/gspread#more-examples)\n- [pandas dataframe docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)", 6 | "metadata": { 7 | "id": "Q1kApdvezk6Z", 8 | "cell_id": "05d65120eb764555aa91a6a11bfc3ce2", 9 | "colab_type": "text", 10 | "deepnote_cell_type": "markdown" 11 | } 12 | }, 13 | { 14 | "cell_type": "code", 15 | "source": "from google.colab import auth\nauth.authenticate_user()", 16 | "metadata": { 17 | "id": "eDs_CZhNzSXl", 18 | "colab": {}, 19 | "cell_id": "ddc497290ece4f12a188f8fb1cf4ed56", 20 | "colab_type": "code", 21 | "deepnote_cell_type": "code" 22 | }, 23 | "outputs": [], 24 | "execution_count": 0 25 | }, 26 | { 27 | "cell_type": "code", 28 | "source": "import gspread\nfrom oauth2client.client import GoogleCredentials\n\ngc = gspread.authorize(GoogleCredentials.get_application_default())", 29 | "metadata": { 30 | "id": "sSOKw9s-zoL3", 31 | "colab": {}, 32 | "cell_id": "136f2300c2af4abd91094ad117beccf8", 33 | "colab_type": "code", 34 | "deepnote_cell_type": "code" 35 | }, 36 | "outputs": [], 37 | "execution_count": 0 38 | }, 39 | { 40 | "cell_type": "code", 41 | "source": "bunky_sheet_url = '' # TODO\nsheet = gc.open_by_url(bunky_sheet_url)\nworksheet = sheet.get_worksheet(0)\nworksheet_hypo = sheet.get_worksheet(0)\nworksheet_draslik = sheet.get_worksheet(1)", 42 | "metadata": { 43 | "id": "s8tkAT_Ozyp-", 44 | "colab": {}, 45 | "cell_id": "b62baf117c0c4ba28c54201e797fac19", 46 | "colab_type": "code", 47 | "deepnote_cell_type": "code" 48 | }, 49 | "outputs": [], 50 | "execution_count": null 51 | }, 52 | { 53 | "cell_type": "code", 54 | "source": "import pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nmatplotlib.rcParams['figure.dpi'] = 150", 55 | "metadata": { 56 | "id": "nv8fMNORAP_V", 57 | "colab": {}, 58 | "cell_id": "38a978de8cad40adaf53c35edfccb8f4", 59 | "colab_type": "code", 60 | "deepnote_cell_type": "code" 61 | }, 62 | "outputs": [], 63 | "execution_count": 0 64 | }, 65 | { 66 | "cell_type": "code", 67 | "source": "# raw data\n\ndata_hypo = worksheet_hypo.get_all_values()\nfeatures_hypo = data_hypo[0]\nclean_data_hypo = data_hypo[1:]\n\ndata_draslik = worksheet_draslik.get_all_values()\nfeatures_draslik = data_draslik[0]\nclean_data_draslik = data_draslik[1:]", 68 | "metadata": { 69 | "id": "MfVS_YVfz5ud", 70 | "colab": {}, 71 | "cell_id": "3a746dbd20da4893921b77bd9f3c3382", 72 | "colab_type": "code", 73 | "deepnote_cell_type": "code" 74 | }, 75 | "outputs": [], 76 | "execution_count": 0 77 | }, 78 | { 79 | "cell_type": "code", 80 | "source": "# pandas dataframe and clean\n\ndef fixCell(cell):\n try:\n return float(cell.replace(',','.'))\n except ValueError:\n return 0 if cell == '' else cell\n\n\n# pandata_full = pd.DataFrame.from_records(clean_data)# , columns=features)\n\npandata_hypo = pd.DataFrame.from_records(clean_data_hypo)\npandata_hypo = pandata_hypo.drop(0, axis=1)\n\npandata_draslik = pd.DataFrame.from_records(clean_data_draslik)\npandata_draslik = pandata_draslik.drop(0, axis=1)\n", 81 | "metadata": { 82 | "id": "5AEKQY6Sz_3f", 83 | "colab": {}, 84 | "cell_id": "b26bcc7a8bba40baa80440f628579980", 85 | "colab_type": "code", 86 | "deepnote_cell_type": "code" 87 | }, 88 | "outputs": [], 89 | "execution_count": 0 90 | }, 91 | { 92 | "cell_type": "code", 93 | "source": "pandata_hypo = pandata_hypo.applymap(fixCell)\npandata_draslik = pandata_draslik.applymap(fixCell)", 94 | "metadata": { 95 | "id": "VhW09zyB6j0g", 96 | "colab": {}, 97 | "cell_id": "6261a178a43b4ffcb313231aee9eefda", 98 | "colab_type": "code", 99 | "deepnote_cell_type": "code" 100 | }, 101 | "outputs": [], 102 | "execution_count": 0 103 | }, 104 | { 105 | "cell_type": "code", 106 | "source": "pandata_draslik", 107 | "metadata": { 108 | "id": "GgCpqhsW3LZ8", 109 | "colab": {}, 110 | "cell_id": "402088f1f96b447ab77fbb9a217cdefc", 111 | "colab_type": "code", 112 | "deepnote_cell_type": "code" 113 | }, 114 | "outputs": [], 115 | "execution_count": 0 116 | }, 117 | { 118 | "cell_type": "code", 119 | "source": "# normalize\n\npandata_hypo_norm = pandata_hypo.loc[:, [1, 2, 3, 4]].values\npandata_hypo_norm = StandardScaler().fit_transform(pandata_hypo_norm)\n\npandata_draslik_norm = pandata_draslik.loc[:, [1, 2, 3, 4]].values\npandata_draslik_norm = StandardScaler().fit_transform(pandata_draslik_norm)", 120 | "metadata": { 121 | "id": "cqJwRBNl0W1A", 122 | "colab": {}, 123 | "cell_id": "ec21806b5e5b4e6eafe0b1006b469566", 124 | "colab_type": "code", 125 | "deepnote_cell_type": "code" 126 | }, 127 | "outputs": [], 128 | "execution_count": 0 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "source": "## Try PCA\n[inspiration](http://bit.ly/2HkM0xP)", 133 | "metadata": { 134 | "colab_type": "text", 135 | "id": "Lz-SwUU9B4yb", 136 | "cell_id": "04d077e473894d9eb6412215b74979c6", 137 | "deepnote_cell_type": "markdown" 138 | } 139 | }, 140 | { 141 | "cell_type": "code", 142 | "source": "pca = PCA(n_components=2)\npc_hypo_raw = pca.fit_transform(pandata_hypo_norm)\npc_hypo = pd.DataFrame(data = pc_hypo_raw, columns = ['pc1', 'pc2'])\n\npc_draslik_raw = pca.fit_transform(pandata_draslik_norm)\npc_draslik = pd.DataFrame(data = pc_draslik_raw, columns = ['pc1', 'pc2'])\n\n\n#finalDf = pd.concat([principalDf, pandata_full[[0]]], axis = 1)", 143 | "metadata": { 144 | "id": "sTmZwhIo1H2X", 145 | "colab": {}, 146 | "cell_id": "ad22de4f6a174dcaa6c1e89e2021e0a8", 147 | "colab_type": "code", 148 | "deepnote_cell_type": "code" 149 | }, 150 | "outputs": [], 151 | "execution_count": 0 152 | }, 153 | { 154 | "cell_type": "markdown", 155 | "source": "## Plot", 156 | "metadata": { 157 | "colab_type": "text", 158 | "id": "ERRyhh2_E5vI", 159 | "cell_id": "e3f47e9d61e54fb3a76ef37f6378ec11", 160 | "deepnote_cell_type": "markdown" 161 | } 162 | }, 163 | { 164 | "cell_type": "code", 165 | "source": "pc_hypo.plot(y='pc1', x='pc2', style='.', title=worksheet_hypo.title)\nplt.show()", 166 | "metadata": { 167 | "id": "eexDBCO39Xyv", 168 | "colab": { 169 | "height": 635, 170 | "base_uri": "https://localhost:8080/" 171 | }, 172 | "cell_id": "0399435a21634eeda662a07ee60bfd0e", 173 | "outputId": "05e30c67-5f01-4bc7-e1a2-79cd14c5a8a0", 174 | "colab_type": "code", 175 | "deepnote_cell_type": "code" 176 | }, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "image/png": 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AoG5Za1VPm+QmkTFGxphI20CAAAAAkFLWWh08eFAHDhzQ4cOHdeLEiaibBA9OPfVUjRo1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\nctGm9fuUhKVLKhdvWu9WpwAwC7c5BRa1MzesycaTHpDNW6a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181 | "text/plain": "
" 182 | }, 183 | "metadata": { 184 | "tags": [] 185 | }, 186 | "output_type": "display_data" 187 | } 188 | ], 189 | "execution_count": 0 190 | }, 191 | { 192 | "cell_type": "code", 193 | "source": "pc_draslik.plot(y='pc1', x='pc2', style='.', title=worksheet_draslik.title)\nplt.show()", 194 | "metadata": { 195 | "id": "QeHqPX3z-Bw2", 196 | "colab": { 197 | "height": 614, 198 | "base_uri": "https://localhost:8080/" 199 | }, 200 | "cell_id": "fc20e7d2d14b4a70963fa0c52cf8cd7e", 201 | "outputId": "8edc5527-02e0-40aa-df35-c9ebc7580022", 202 | "colab_type": "code", 203 | "deepnote_cell_type": "code" 204 | }, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "image/png": 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AQArNmDQukvMSIAAAAAAptKSrXa0tFvp5CRAAAACAFJo1tU0rFs0LPUggQAAAAABSanFn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\nIEiSJEnqMSBIkiRJ6vk/nVkat6JpPHsAAAAASUVORK5CYII=\n", 209 | "text/plain": "
" 210 | }, 211 | "metadata": { 212 | "tags": [] 213 | }, 214 | "output_type": "display_data" 215 | } 216 | ], 217 | "execution_count": 0 218 | }, 219 | { 220 | "cell_type": "markdown", 221 | "source": "\nCreated in deepnote.com \nCreated in Deepnote", 222 | "metadata": { 223 | "created_in_deepnote_cell": true, 224 | "deepnote_cell_type": "markdown" 225 | } 226 | } 227 | ], 228 | "nbformat": 4, 229 | "nbformat_minor": 0, 230 | "metadata": { 231 | "colab": { 232 | "collapsed_sections": [ 233 | "Q1kApdvezk6Z", 234 | "Lz-SwUU9B4yb" 235 | ], 236 | "name": "bunky.ipynb", 237 | "provenance": [], 238 | "version": "0.3.2" 239 | }, 240 | "kernelspec": { 241 | "display_name": "Python 3", 242 | "language": "python", 243 | "name": "python3" 244 | }, 245 | "language_info": { 246 | "codemirror_mode": { 247 | "name": "ipython", 248 | "version": 3 249 | }, 250 | "file_extension": ".py", 251 | "mimetype": "text/x-python", 252 | "name": "python", 253 | "nbconvert_exporter": "python", 254 | "pygments_lexer": "ipython3", 255 | "version": "3.7.0" 256 | }, 257 | "deepnote_notebook_id": "f5f6bc7dbb824febbee273e2591bb6a6", 258 | "deepnote": {}, 259 | "deepnote_execution_queue": [] 260 | } 261 | } -------------------------------------------------------------------------------- /deepnote/MedML Workshops in Deepnote/medml-workshops-deepnote-MedML Workshops in Deepnote-Cells PCA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "source": "## Connect sheet and load data - nested\n\n- sheet data - TODO\n- [python sheets api docs](https://github.com/burnash/gspread#more-examples)\n- [pandas dataframe docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html)", 6 | "metadata": { 7 | "id": "Q1kApdvezk6Z", 8 | "cell_id": "334957c3f78447ccbefe32defb17b0a7", 9 | "colab_type": "text", 10 | "deepnote_cell_type": "markdown" 11 | } 12 | }, 13 | { 14 | "cell_type": "code", 15 | "source": "from google.colab import auth\nauth.authenticate_user()", 16 | "metadata": { 17 | "id": "eDs_CZhNzSXl", 18 | "colab": {}, 19 | "cell_id": "e47c2bbd2bb5442da936d276c47c062e", 20 | "colab_type": "code", 21 | "deepnote_cell_type": "code" 22 | }, 23 | "outputs": [], 24 | "execution_count": 0 25 | }, 26 | { 27 | "cell_type": "code", 28 | "source": "import gspread\nfrom oauth2client.client import GoogleCredentials\n\ngc = gspread.authorize(GoogleCredentials.get_application_default())", 29 | "metadata": { 30 | "id": "sSOKw9s-zoL3", 31 | "colab": {}, 32 | "cell_id": "fc67b3400d73481e927477200c7245dd", 33 | "colab_type": "code", 34 | "deepnote_cell_type": "code" 35 | }, 36 | "outputs": [], 37 | "execution_count": 0 38 | }, 39 | { 40 | "cell_type": "code", 41 | "source": "bunky_sheet_url = '' # TODO\nsheet = gc.open_by_url(bunky_sheet_url)\nworksheet = sheet.get_worksheet(0)\nworksheet_hypo = sheet.get_worksheet(0)\nworksheet_draslik = sheet.get_worksheet(1)", 42 | "metadata": { 43 | "id": "s8tkAT_Ozyp-", 44 | "colab": {}, 45 | "cell_id": "7d8eba92877a47faa5bd6f4bfcc5682c", 46 | "colab_type": "code", 47 | "deepnote_cell_type": "code" 48 | }, 49 | "outputs": [], 50 | "execution_count": null 51 | }, 52 | { 53 | "cell_type": "code", 54 | "source": "import pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\n\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nmatplotlib.rcParams['figure.dpi'] = 150", 55 | "metadata": { 56 | "id": "nv8fMNORAP_V", 57 | "colab": {}, 58 | "cell_id": "0e60403d8f3645849163028d3af224bd", 59 | "colab_type": "code", 60 | "deepnote_cell_type": "code" 61 | }, 62 | "outputs": [], 63 | "execution_count": 0 64 | }, 65 | { 66 | "cell_type": "code", 67 | "source": "# raw data\n\ndata_hypo = worksheet_hypo.get_all_values()\nfeatures_hypo = data_hypo[0]\nclean_data_hypo = data_hypo[1:]\n\ndata_draslik = worksheet_draslik.get_all_values()\nfeatures_draslik = data_draslik[0]\nclean_data_draslik = data_draslik[1:]", 68 | "metadata": { 69 | "id": "MfVS_YVfz5ud", 70 | "colab": {}, 71 | "cell_id": "9af6bfd140f6438589f11d1dcc3bb035", 72 | "colab_type": "code", 73 | "deepnote_cell_type": "code" 74 | }, 75 | "outputs": [], 76 | "execution_count": 0 77 | }, 78 | { 79 | "cell_type": "code", 80 | "source": "# pandas dataframe and clean\n\ndef fixCell(cell):\n try:\n return float(cell.replace(',','.'))\n except ValueError:\n return 0 if cell == '' else cell\n\n\n# pandata_full = pd.DataFrame.from_records(clean_data)# , columns=features)\n\npandata_hypo = pd.DataFrame.from_records(clean_data_hypo)\npandata_hypo = pandata_hypo.drop(0, axis=1)\n\npandata_draslik = pd.DataFrame.from_records(clean_data_draslik)\npandata_draslik = pandata_draslik.drop(0, axis=1)\n", 81 | "metadata": { 82 | "id": "5AEKQY6Sz_3f", 83 | "colab": {}, 84 | "cell_id": "774866c4058c4e259c186fbcd96722b2", 85 | "colab_type": "code", 86 | "deepnote_cell_type": "code" 87 | }, 88 | "outputs": [], 89 | "execution_count": 0 90 | }, 91 | { 92 | "cell_type": "code", 93 | "source": "pandata_hypo = pandata_hypo.applymap(fixCell)\npandata_draslik = pandata_draslik.applymap(fixCell)", 94 | "metadata": { 95 | "id": "VhW09zyB6j0g", 96 | "colab": {}, 97 | "cell_id": "5846e519b4884c038621e67008ab7b88", 98 | "colab_type": "code", 99 | "deepnote_cell_type": "code" 100 | }, 101 | "outputs": [], 102 | "execution_count": 0 103 | }, 104 | { 105 | "cell_type": "code", 106 | "source": "pandata_draslik", 107 | "metadata": { 108 | "id": "GgCpqhsW3LZ8", 109 | "colab": {}, 110 | "cell_id": "16f972f231df4a81a948f5d91b8436bd", 111 | "colab_type": "code", 112 | "deepnote_cell_type": "code" 113 | }, 114 | "outputs": [], 115 | "execution_count": 0 116 | }, 117 | { 118 | "cell_type": "code", 119 | "source": "# normalize\n\npandata_hypo_norm = pandata_hypo.loc[:, [1, 2, 3, 4]].values\npandata_hypo_norm = StandardScaler().fit_transform(pandata_hypo_norm)\n\npandata_draslik_norm = pandata_draslik.loc[:, [1, 2, 3, 4]].values\npandata_draslik_norm = StandardScaler().fit_transform(pandata_draslik_norm)", 120 | "metadata": { 121 | "id": "cqJwRBNl0W1A", 122 | "colab": {}, 123 | "cell_id": "1c1149dfafef44c78b2eea4314499b47", 124 | "colab_type": "code", 125 | "deepnote_cell_type": "code" 126 | }, 127 | "outputs": [], 128 | "execution_count": 0 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "source": "## Try PCA\n[inspiration](http://bit.ly/2HkM0xP)", 133 | "metadata": { 134 | "colab_type": "text", 135 | "id": "Lz-SwUU9B4yb", 136 | "cell_id": "e831f0712c7f4c58ac184513e6fd3c3d", 137 | "deepnote_cell_type": "markdown" 138 | } 139 | }, 140 | { 141 | "cell_type": "code", 142 | "source": "pca = PCA(n_components=2)\npc_hypo_raw = pca.fit_transform(pandata_hypo_norm)\npc_hypo = pd.DataFrame(data = pc_hypo_raw, columns = ['pc1', 'pc2'])\n\npc_draslik_raw = pca.fit_transform(pandata_draslik_norm)\npc_draslik = pd.DataFrame(data = pc_draslik_raw, columns = ['pc1', 'pc2'])\n\n\n#finalDf = pd.concat([principalDf, pandata_full[[0]]], axis = 1)", 143 | "metadata": { 144 | "id": "sTmZwhIo1H2X", 145 | "colab": {}, 146 | "cell_id": "2dd69ac217b54412be16a625948c8ea9", 147 | "colab_type": "code", 148 | "deepnote_cell_type": "code" 149 | }, 150 | "outputs": [], 151 | "execution_count": 0 152 | }, 153 | { 154 | "cell_type": "markdown", 155 | "source": "## Plot", 156 | "metadata": { 157 | "colab_type": "text", 158 | "id": "ERRyhh2_E5vI", 159 | "cell_id": "442ae7493b5b411bb5197cea4ba68bcb", 160 | "deepnote_cell_type": "markdown" 161 | } 162 | }, 163 | { 164 | "cell_type": "code", 165 | "source": "pc_hypo.plot(y='pc1', x='pc2', style='.', title=worksheet_hypo.title)\nplt.show()", 166 | "metadata": { 167 | "id": "eexDBCO39Xyv", 168 | "colab": { 169 | "height": 635, 170 | "base_uri": "https://localhost:8080/" 171 | }, 172 | "cell_id": "492cf5d8221548189f6fc7e8e1f65aa8", 173 | "outputId": "05e30c67-5f01-4bc7-e1a2-79cd14c5a8a0", 174 | "colab_type": "code", 175 | "deepnote_cell_type": "code" 176 | }, 177 | "outputs": [ 178 | { 179 | "data": { 180 | "image/png": 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AoG5Za1VPm+QmkTFGxphI20CAAAAAkFLWWh08eFAHDhzQ4cOHdeLEiaibBA9OPfVUjRo1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\nctGm9fuUhKVLKhdvWu9WpwAwC7c5BRa1MzesycaTHpDNW6a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181 | "text/plain": "
" 182 | }, 183 | "metadata": { 184 | "tags": [] 185 | }, 186 | "output_type": "display_data" 187 | } 188 | ], 189 | "execution_count": 0 190 | }, 191 | { 192 | "cell_type": "code", 193 | "source": "pc_draslik.plot(y='pc1', x='pc2', style='.', title=worksheet_draslik.title)\nplt.show()", 194 | "metadata": { 195 | "id": "QeHqPX3z-Bw2", 196 | "colab": { 197 | "height": 614, 198 | "base_uri": "https://localhost:8080/" 199 | }, 200 | "cell_id": "c43c581dbe6d4430bbb9d984163e894d", 201 | "outputId": "8edc5527-02e0-40aa-df35-c9ebc7580022", 202 | "colab_type": "code", 203 | "deepnote_cell_type": "code" 204 | }, 205 | "outputs": [ 206 | { 207 | "data": { 208 | "image/png": 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AQArNmDQukvMSIAAAAAAptKSrXa0tFvp5CRAAAACAFJo1tU0rFs0LPUggQAAAAABSanFn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\n052by15xd5IefmKvWkwq9RAfc/TrbR4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209 | "text/plain": "
" 210 | }, 211 | "metadata": { 212 | "tags": [] 213 | }, 214 | "output_type": "display_data" 215 | } 216 | ], 217 | "execution_count": 0 218 | }, 219 | { 220 | "cell_type": "markdown", 221 | "source": "\nCreated in deepnote.com \nCreated in Deepnote", 222 | "metadata": { 223 | "created_in_deepnote_cell": true, 224 | "deepnote_cell_type": "markdown" 225 | } 226 | } 227 | ], 228 | "nbformat": 4, 229 | "nbformat_minor": 0, 230 | "metadata": { 231 | "colab": { 232 | "collapsed_sections": [ 233 | "Q1kApdvezk6Z", 234 | "Lz-SwUU9B4yb" 235 | ], 236 | "name": "bunky.ipynb", 237 | "provenance": [], 238 | "version": "0.3.2" 239 | }, 240 | "kernelspec": { 241 | "display_name": "Python 3", 242 | "language": "python", 243 | "name": "python3" 244 | }, 245 | "language_info": { 246 | "codemirror_mode": { 247 | "name": "ipython", 248 | "version": 3 249 | }, 250 | "file_extension": ".py", 251 | "mimetype": "text/x-python", 252 | "name": "python", 253 | "nbconvert_exporter": "python", 254 | "pygments_lexer": "ipython3", 255 | "version": "3.7.0" 256 | }, 257 | "deepnote_notebook_id": "a93f220521a141c88caf8f6012ddceaf", 258 | "deepnote": {}, 259 | "deepnote_execution_queue": [] 260 | } 261 | } --------------------------------------------------------------------------------