{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# source retention_automl/h20env/bin/activate" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [], "source": [ "import h2o" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n" ] }, { "data": { "text/html": [ "\n", " \n", "
\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
H2O_cluster_uptime:57 mins 15 secs
H2O_cluster_timezone:Europe/Paris
H2O_data_parsing_timezone:UTC
H2O_cluster_version:3.42.0.2
H2O_cluster_version_age:1 month and 2 days
H2O_cluster_name:H2O_from_python_georgy_17emvs
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:1.628 Gb
H2O_cluster_total_cores:8
H2O_cluster_allowed_cores:8
H2O_cluster_status:locked, healthy
H2O_connection_url:http://localhost:54321
H2O_connection_proxy:{\"http\": null, \"https\": null}
H2O_internal_security:False
Python_version:3.10.12 final
\n", "
\n" ], "text/plain": [ "-------------------------- -----------------------------\n", "H2O_cluster_uptime: 57 mins 15 secs\n", "H2O_cluster_timezone: Europe/Paris\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.42.0.2\n", "H2O_cluster_version_age: 1 month and 2 days\n", "H2O_cluster_name: H2O_from_python_georgy_17emvs\n", "H2O_cluster_total_nodes: 1\n", "H2O_cluster_free_memory: 1.628 Gb\n", "H2O_cluster_total_cores: 8\n", "H2O_cluster_allowed_cores: 8\n", "H2O_cluster_status: locked, healthy\n", "H2O_connection_url: http://localhost:54321\n", "H2O_connection_proxy: {\"http\": null, \"https\": null}\n", "H2O_internal_security: False\n", "Python_version: 3.10.12 final\n", "-------------------------- -----------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "\n", "h2o.init()\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from tqdm import tqdm\n", "import numpy as np\n", "\n", "import medmnist\n", "from medmnist import INFO, Evaluator" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MedMNIST v2.2.3 @ https://github.com/MedMNIST/MedMNIST/\n" ] } ], "source": [ "print(f\"MedMNIST v{medmnist.__version__} @ {medmnist.HOMEPAGE}\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from medmnist import OCTMNIST" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "npz_file = np.load('octmnist.npz')" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [], "source": [ " x_train = npz_file['train_images']\n", " y_train = npz_file['train_labels']\n", " x_val = npz_file['val_images']\n", " y_val = npz_file['val_labels']\n", " x_test = npz_file['test_images']\n", " y_test = npz_file['test_labels']\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "\n", " size = x_train[0].size\n", " X_train = x_train.reshape(x_train.shape[0], size, )\n", " X_val = x_val.reshape(x_val.shape[0], size, )\n", " X_test = x_test.reshape(x_test.shape[0], size, )" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 23, 24, 25, ..., 12, 14, 14],\n", " [ 34, 33, 31, ..., 14, 14, 13],\n", " [172, 168, 161, ..., 11, 11, 11],\n", " ...,\n", " [ 35, 41, 52, ..., 14, 14, 14],\n", " [ 28, 27, 28, ..., 10, 10, 9],\n", " [ 27, 27, 27, ..., 255, 255, 255]], dtype=uint8)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(97477, 784)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "X_train_flat = X_train.flatten()\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(97477, 784)" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.shape" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3],\n", " [2],\n", " [3],\n", " [3],\n", " [0],\n", " [2],\n", " [1],\n", " [0],\n", " [0],\n", " [3],\n", " [0],\n", " [1],\n", " [3],\n", " [3],\n", " [3],\n", " [2],\n", " [2],\n", " [3],\n", " [0],\n", " [2],\n", " [1],\n", " [0],\n", " [0],\n", " [3],\n", " [0],\n", " [0],\n", " 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"execution_count": 70, "metadata": {}, "outputs": [], "source": [ "data_val = np.concatenate((X_val, y_val), axis=1)\n" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "data_test = np.concatenate((X_test, y_test), axis=1)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n" ] } ], "source": [ "train = h2o.H2OFrame(data_train)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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C218 C219 C220 C221 C222 C223 C224 C225 C226 C227 C228 C229 C230 C231 C232 C233 C234 C235 C236 C237 C238 C239 C240 C241 C242 C243 C244 C245 C246 C247 C248 C249 C250 C251 C252 C253 C254 C255 C256 C257 C258 C259 C260 C261 C262 C263 C264 C265 C266 C267 C268 C269 C270 C271 C272 C273 C274 C275 C276 C277 C278 C279 C280 C281 C282 C283 C284 C285 C286 C287 C288 C289 C290 C291 C292 C293 C294 C295 C296 C297 C298 C299 C300 C301 C302 C303 C304 C305 C306 C307 C308 C309 C310 C311 C312 C313 C314 C315 C316 C317 C318 C319 C320 C321 C322 C323 C324 C325 C326 C327 C328 C329 C330 C331 C332 C333 C334 C335 C336 C337 C338 C339 C340 C341 C342 C343 C344 C345 C346 C347 C348 C349 C350 C351 C352 C353 C354 C355 C356 C357 C358 C359 C360 C361 C362 C363 C364 C365 C366 C367 C368 C369 C370 C371 C372 C373 C374 C375 C376 C377 C378 C379 C380 C381 C382 C383 C384 C385 C386 C387 C388 C389 C390 C391 C392 C393 C394 C395 C396 C397 C398 C399 C400 C401 C402 C403 C404 C405 C406 C407 C408 C409 C410 C411 C412 C413 C414 C415 C416 C417 C418 C419 C420 C421 C422 C423 C424 C425 C426 C427 C428 C429 C430 C431 C432 C433 C434 C435 C436 C437 C438 C439 C440 C441 C442 C443 C444 C445 C446 C447 C448 C449 C450 C451 C452 C453 C454 C455 C456 C457 C458 C459 C460 C461 C462 C463 C464 C465 C466 C467 C468 C469 C470 C471 C472 C473 C474 C475 C476 C477 C478 C479 C480 C481 C482 C483 C484 C485 C486 C487 C488 C489 C490 C491 C492 C493 C494 C495 C496 C497 C498 C499 C500 C501 C502 C503 C504 C505 C506 C507 C508 C509 C510 C511 C512 C513 C514 C515 C516 C517 C518 C519 C520 C521 C522 C523 C524 C525 C526 C527 C528 C529 C530 C531 C532 C533 C534 C535 C536 C537 C538 C539 C540 C541 C542 C543 C544 C545 C546 C547 C548 C549 C550 C551 C552 C553 C554 C555 C556 C557 C558 C559 C560 C561 C562 C563 C564 C565 C566 C567 C568 C569 C570 C571 C572 C573 C574 C575 C576 C577 C578 C579 C580 C581 C582 C583 C584 C585 C586 C587 C588 C589 C590 C591 C592 C593 C594 C595 C596 C597 C598 C599 C600 C601 C602 C603 C604 C605 C606 C607 C608 C609 C610 C611 C612 C613 C614 C615 C616 C617 C618 C619 C620 C621 C622 C623 C624 C625 C626 C627 C628 C629 C630 C631 C632 C633 C634 C635 C636 C637 C638 C639 C640 C641 C642 C643 C644 C645 C646 C647 C648 C649 C650 C651 C652 C653 C654 C655 C656 C657 C658 C659 C660 C661 C662 C663 C664 C665 C666 C667 C668 C669 C670 C671 C672 C673 C674 C675 C676 C677 C678 C679 C680 C681 C682 C683 C684 C685 C686 C687 C688 C689 C690 C691 C692 C693 C694 C695 C696 C697 C698 C699 C700 C701 C702 C703 C704 C705 C706 C707 C708 C709 C710 C711 C712 C713 C714 C715 C716 C717 C718 C719 C720 C721 C722 C723 C724 C725 C726 C727 C728 C729 C730 C731 C732 C733 C734 C735 C736 C737 C738 C739 C740 C741 C742 C743 C744 C745 C746 C747 C748 C749 C750 C751 C752 C753 C754 C755 C756 C757 C758 C759 C760 C761 C762 C763 C764 C765 C766 C767 C768 C769 C770 C771 C772 C773 C774 C775 C776 C777 C778 C779 C780 C781 C782 C783 C784 C785\n", "---- ---- ---- ---- ---- ---- ---- ---- ---- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 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'C755',\n", " 'C756',\n", " 'C757',\n", " 'C758',\n", " 'C759',\n", " 'C760',\n", " 'C761',\n", " 'C762',\n", " 'C763',\n", " 'C764',\n", " 'C765',\n", " 'C766',\n", " 'C767',\n", " 'C768',\n", " 'C769',\n", " 'C770',\n", " 'C771',\n", " 'C772',\n", " 'C773',\n", " 'C774',\n", " 'C775',\n", " 'C776',\n", " 'C777',\n", " 'C778',\n", " 'C779',\n", " 'C780',\n", " 'C781',\n", " 'C782',\n", " 'C783',\n", " 'C784']" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_columns" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "y = columns[-1]" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [], "source": [ "train[y] = train[y].asfactor()" ] }, { "cell_type": "code", "execution_count": 117, "metadata": {}, "outputs": [], "source": [ "test[y] = test[y].asfactor()" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": [ "val[y] = val[y].asfactor()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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C218 C219 C220 C221 C222 C223 C224 C225 C226 C227 C228 C229 C230 C231 C232 C233 C234 C235 C236 C237 C238 C239 C240 C241 C242 C243 C244 C245 C246 C247 C248 C249 C250 C251 C252 C253 C254 C255 C256 C257 C258 C259 C260 C261 C262 C263 C264 C265 C266 C267 C268 C269 C270 C271 C272 C273 C274 C275 C276 C277 C278 C279 C280 C281 C282 C283 C284 C285 C286 C287 C288 C289 C290 C291 C292 C293 C294 C295 C296 C297 C298 C299 C300 C301 C302 C303 C304 C305 C306 C307 C308 C309 C310 C311 C312 C313 C314 C315 C316 C317 C318 C319 C320 C321 C322 C323 C324 C325 C326 C327 C328 C329 C330 C331 C332 C333 C334 C335 C336 C337 C338 C339 C340 C341 C342 C343 C344 C345 C346 C347 C348 C349 C350 C351 C352 C353 C354 C355 C356 C357 C358 C359 C360 C361 C362 C363 C364 C365 C366 C367 C368 C369 C370 C371 C372 C373 C374 C375 C376 C377 C378 C379 C380 C381 C382 C383 C384 C385 C386 C387 C388 C389 C390 C391 C392 C393 C394 C395 C396 C397 C398 C399 C400 C401 C402 C403 C404 C405 C406 C407 C408 C409 C410 C411 C412 C413 C414 C415 C416 C417 C418 C419 C420 C421 C422 C423 C424 C425 C426 C427 C428 C429 C430 C431 C432 C433 C434 C435 C436 C437 C438 C439 C440 C441 C442 C443 C444 C445 C446 C447 C448 C449 C450 C451 C452 C453 C454 C455 C456 C457 C458 C459 C460 C461 C462 C463 C464 C465 C466 C467 C468 C469 C470 C471 C472 C473 C474 C475 C476 C477 C478 C479 C480 C481 C482 C483 C484 C485 C486 C487 C488 C489 C490 C491 C492 C493 C494 C495 C496 C497 C498 C499 C500 C501 C502 C503 C504 C505 C506 C507 C508 C509 C510 C511 C512 C513 C514 C515 C516 C517 C518 C519 C520 C521 C522 C523 C524 C525 C526 C527 C528 C529 C530 C531 C532 C533 C534 C535 C536 C537 C538 C539 C540 C541 C542 C543 C544 C545 C546 C547 C548 C549 C550 C551 C552 C553 C554 C555 C556 C557 C558 C559 C560 C561 C562 C563 C564 C565 C566 C567 C568 C569 C570 C571 C572 C573 C574 C575 C576 C577 C578 C579 C580 C581 C582 C583 C584 C585 C586 C587 C588 C589 C590 C591 C592 C593 C594 C595 C596 C597 C598 C599 C600 C601 C602 C603 C604 C605 C606 C607 C608 C609 C610 C611 C612 C613 C614 C615 C616 C617 C618 C619 C620 C621 C622 C623 C624 C625 C626 C627 C628 C629 C630 C631 C632 C633 C634 C635 C636 C637 C638 C639 C640 C641 C642 C643 C644 C645 C646 C647 C648 C649 C650 C651 C652 C653 C654 C655 C656 C657 C658 C659 C660 C661 C662 C663 C664 C665 C666 C667 C668 C669 C670 C671 C672 C673 C674 C675 C676 C677 C678 C679 C680 C681 C682 C683 C684 C685 C686 C687 C688 C689 C690 C691 C692 C693 C694 C695 C696 C697 C698 C699 C700 C701 C702 C703 C704 C705 C706 C707 C708 C709 C710 C711 C712 C713 C714 C715 C716 C717 C718 C719 C720 C721 C722 C723 C724 C725 C726 C727 C728 C729 C730 C731 C732 C733 C734 C735 C736 C737 C738 C739 C740 C741 C742 C743 C744 C745 C746 C747 C748 C749 C750 C751 C752 C753 C754 C755 C756 C757 C758 C759 C760 C761 C762 C763 C764 C765 C766 C767 C768 C769 C770 C771 C772 C773 C774 C775 C776 C777 C778 C779 C780 C781 C782 C783 C784 C785\n", "---- ---- ---- ---- ---- ---- ---- ---- ---- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 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24 21 15 15 23 31 21 18 17 18 16 14 19 27 21 20 18 17 16 17 18 18 20 20 20 20 20 16 19 36 55 61 50 35 34 21 13 18 20 15 14 18 21 20 20 19 20 21 23 24 19 19 20 20 40 63 91 107 110 107 109 112 76 54 28 18 20 24 20 14 14 15 18 19 20 18 15 14 19 19 19 20 98 103 109 111 108 103 100 98 104 92 63 30 16 21 24 19 23 23 23 23 21 19 17 16 19 19 19 18 102 86 77 92 118 125 106 82 80 91 88 61 32 18 14 12 17 16 14 14 15 18 21 23 19 19 18 18 67 89 116 129 129 130 140 151 77 74 75 73 57 33 25 30 23 24 25 25 23 20 16 13 18 18 18 18 108 122 127 107 75 59 68 85 123 75 45 56 65 57 60 79 83 86 89 86 74 54 34 21 17 17 18 19 95 84 64 54 49 38 38 56 87 87 60 47 61 53 52 84 79 82 87 91 92 88 81 76 48 38 26 20 70 61 46 39 38 27 24 38 67 91 79 60 65 60 52 67 72 70 69 69 71 75 78 79 87 84 81 78 53 46 34 31 35 26 19 28 25 72 81 61 62 63 53 51 51 58 67 75 78 74 68 63 73 76 80 81 48 42 30 29 36 29 21 26 14 58 78 70 75 82 77 73 56 68 85 96 96 87 74 65 53 56 59 61 42 37 24 20 27 24 18 22 20 40 54 61 70 75 77 81 81 83 84 84 84 85 89 92 66 63 60 61 34 32 20 14 20 19 15 20 25 22 27 39 44 44 49 57 66 64 60 56 56 63 76 85 78 71 65 65 26 29 21 14 19 18 15 21 30 23 28 38 37 36 41 44 34 37 39 38 37 40 47 53 69 66 63 64 17 24 20 14 18 17 14 20 14 13 24 31 26 28 33 30 32 34 34 31 27 29 36 43 39 42 45 46 14 14 14 14 14 14 14 14 15 16 17 19 20 22 23 24 27 27 27 26 25 27 31 34 35 37 39 40 13 13 13 13 13 13 13 13 13 14 15 16 17 18 19 19 20 21 21 20 19 21 24 27 27 29 31 32 12 12 12 12 12 12 12 12 11 11 11 12 12 13 13 13 12 13 13 13 12 13 16 19 17 19 21 21 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 8 9 10 9 8 8 11 13 10 12 14 14 0\n", " 34 33 31 29 29 29 30 30 33 31 29 30 33 34 33 30 36 37 40 45 53 63 72 77 97 102 111 120 32 31 30 29 29 31 32 33 35 33 32 33 36 37 36 34 36 34 31 29 29 32 35 37 27 28 30 31 28 27 26 25 25 27 28 29 30 29 28 29 31 32 31 30 41 38 35 31 30 31 33 34 37 36 34 32 31 29 28 27 26 27 28 29 29 28 28 27 27 26 25 25 29 28 26 26 27 30 33 35 28 27 26 26 36 35 34 34 35 37 39 40 38 38 36 34 31 29 28 28 30 29 27 26 27 28 30 32 35 35 34 33 31 31 32 33 36 39 42 44 40 40 39 36 32 29 29 29 34 34 34 34 35 37 39 41 31 32 33 33 28 27 27 28 29 32 34 36 32 34 34 32 28 25 26 27 25 25 25 26 28 31 33 34 28 29 31 32 33 32 30 28 27 27 27 28 28 30 31 30 26 25 26 28 35 34 32 30 28 27 27 27 29 28 27 26 41 39 33 26 25 29 30 27 35 25 24 33 34 27 26 32 39 53 70 83 87 87 88 89 74 72 64 53 59 61 62 63 72 83 90 90 95 88 77 59 34 20 35 59 96 105 115 121 122 122 124 126 139 140 136 130 82 83 82 81 88 98 103 103 111 117 122 112 90 77 93 119 105 106 106 102 96 93 94 96 95 99 100 98 58 59 58 59 68 82 92 94 84 87 93 96 88 78 77 82 93 92 88 83 77 74 75 77 74 74 69 62 100 94 82 69 65 68 70 68 83 76 71 69 69 69 72 76 65 70 78 88 99 110 120 126 140 133 117 99 112 113 112 114 124 139 150 153 140 142 143 142 141 146 157 166 180 178 172 164 156 147 140 136 136 129 113 94 70 72 73 75 82 92 96 95 100 106 111 111 108 106 107 108 94 93 90 88 85 81 77 74 72 70 63 54 50 54 56 57 60 64 61 56 52 49 47 52 62 70 69 64 64 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29 31 33 34 34 31 31 30 30 30 29 29 29 32 29 27 27 29 31 30 29 33 33 33 33 25 25 25 25 25 25 25 25 28 29 29 29 29 29 29 29 30 27 23 23 25 26 25 24 26 26 25 25 32 31 29 28 27 28 30 31 33 29 25 24 27 28 27 25 24 25 27 29 31 30 28 26 30 30 29 28 31 31 30 28 27 26 25 24 20 21 24 27 29 29 25 22 35 30 25 23 25 27 26 25 23 23 23 23 27 27 29 30 30 30 30 30 37 34 28 22 19 20 25 30 26 23 20 21 25 27 26 24 26 26 25 25 59 64 73 84 98 111 120 126 118 109 92 69 47 32 26 25 19 28 40 50 53 51 48 47 37 36 34 32 155 151 143 135 128 123 120 119 110 113 113 104 84 61 41 31 57 73 92 101 99 94 92 94 84 83 81 79 106 104 99 95 91 89 88 88 79 86 95 100 97 88 78 72 72 83 93 93 86 81 85 91 92 92 91 91 83 83 83 83 81 79 77 75 81 80 77 75 73 69 65 62 72 74 74 71 68 67 72 76 75 75 75 75 91 96 105 114 121 123 124 123 122 123 127 132 135 132 124 118 122 119 118 119 123 127 128 128 134 134 133 132 123 121 119 118 118 120 122 123 123 123 123 124 125 126 127 128 134 139 144 144 140 137 139 141 135 132 128 124 62 61 61 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255 255 255 255 254 255 255 253 252 251 252 252 253 254 255 255 255 255 255 255 255 255 254 252 249 247 245 242 238 233 228 224 222 216 214 210 206 222 219 211 202 192 183 176 172 152 147 139 130 122 113 105 100 83 81 78 73 67 61 57 54 42 40 36 32 47 45 41 36 31 26 23 21 32 30 28 28 28 27 25 23 24 24 24 23 22 20 18 17 26 25 23 22 23 24 25 26 27 28 29 29 21 20 21 24 27 30 30 30 21 22 24 25 27 27 27 27 21 21 21 21 34 33 31 29 26 23 21 20 31 29 26 24 23 21 18 15 20 20 21 21 20 19 18 18 23 23 23 23 21 22 22 23 23 24 24 24 21 20 21 23 27 29 29 29 28 28 28 27 26 24 22 21 21 21 21 21 26 26 26 25 24 24 23 23 28 26 24 23 24 23 21 19 19 19 20 21 21 20 20 19 18 19 20 20 29 28 26 23 21 22 26 30 16 18 21 24 25 23 21 20 21 21 22 23 24 24 24 25 21 21 22 22 16 18 21 24 25 23 20 17 29 26 20 16 16 18 23 26 22 22 22 21 21 21 21 21 20 20 20 20 38 32 24 20 21 22 22 22 20 21 23 24 24 24 23 22 25 24 22 21 20 20 20 20 27 26 26 25 86 74 58 46 41 38 34 30 17 19 22 24 25 24 21 20 23 23 22 21 20 20 21 21 21 20 19 18 117 113 108 104 100 90 75 64 53 45 32 21 15 15 19 23 17 17 18 19 19 19 19 19 22 21 21 20 99 97 96 98 101 101 98 95 87 80 67 52 38 28 22 20 18 19 22 25 26 25 23 22 22 21 20 19 103 95 83 73 68 70 75 79 81 82 82 77 67 53 38 29 36 39 43 46 47 44 40 37 25 23 20 17 177 167 148 123 98 78 66 60 60 61 63 64 63 60 56 54 58 61 66 69 69 64 58 54 58 55 51 48 109 122 141 156 159 150 134 123 90 77 62 55 54 52 44 37 49 51 54 56 57 56 54 52 50 54 60 63 79 82 86 94 104 115 125 131 142 136 128 123 116 104 88 76 60 58 54 49 46 44 43 43 47 49 51 51 47 48 51 56 64 72 80 85 84 89 98 108 116 117 113 108 106 101 92 80 68 58 50 46 45 45 45 42 36 40 45 49 49 45 40 36 46 50 56 63 69 74 78 81 83 86 91 93 92 87 81 77 67 68 69 69 33 33 34 34 35 36 37 37 44 45 44 41 38 38 41 44 39 44 52 59 63 62 60 57 63 67 72 75 20 21 24 27 31 33 35 36 16 19 22 25 27 28 30 32 39 38 35 33 30 29 28 27 33 38 45 49 12 16 22 27 29 26 22 19 20 21 22 23 23 23 21 20 27 26 26 26 28 31 34 36 29 32 36 38 18 18 17 17 18 19 20 21 25 21 16 14 15 17 18 18 17 19 21 22 22 19 16 14 28 29 29 28 14 14 14 15 15 16 16 17 17 17 17 17 17 17 17 17 15 15 16 16 16 17 17 17 17 17 17 17 13 13 14 14 15 15 15 16 15 15 15 15 15 15 15 15 13 13 13 14 14 15 15 15 15 15 15 15 12 12 13 13 13 14 14 14 13 13 13 13 13 13 13 13 10 11 11 11 12 12 12 13 13 13 13 13 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 10 10 10 10 11 11 11 12 12 12 12 12 3\n", "[97477 rows x 785 columns]\n" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C785'" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "from h2o.automl import H2OAutoML" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [], "source": [ "aml = H2OAutoML(max_models=30, seed=1, nfolds=0, include_algos = [\"GLM\", \"DeepLearning\", \"DRF\", 'XGBoost','GBM', 'StackedEnsemble' ], max_runtime_secs=3600)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AutoML progress: |" ] }, { "name": "stdout", "output_type": "stream", "text": [ "███████████████████████████████████████████████████████████████| (done) 100%\n" ] }, { "data": { "text/html": [ "
Model Details\n",
       "=============\n",
       "H2OXGBoostEstimator : XGBoost\n",
       "Model Key: XGBoost_1_AutoML_10_20230827_185738\n",
       "
\n", "
\n", " \n", "
\n", " \n", " \n", " \n", "\n", " \n", "\n", "
Model Summary:
number_of_trees
165.0
\n", "
\n", "
\n", "
ModelMetricsMultinomial: xgboost\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.002309651920454201\n",
       "RMSE: 0.04805883810969842\n",
       "LogLoss: 0.02370788812229064\n",
       "Mean Per-Class Error: 0.004465681119258331\n",
       "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
       "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).
\n", "
\n", " \n", "
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Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
0123ErrorRate
33405.012.067.00.00.002359379 / 33,484
6.010204.00.03.00.00088129 / 10,213
83.00.07645.026.00.0140573109 / 7,754
0.05.021.046000.00.000564926 / 46,026
33494.010221.07733.046029.00.0022877223 / 97,477
\n", "
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\n", " \n", " \n", " \n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Top-4 Hit Ratios:
khit_ratio
10.9977122
21.0
31.0
41.0
\n", "
\n", "
\n", "
ModelMetricsMultinomial: xgboost\n",
       "** Reported on validation data. **\n",
       "\n",
       "MSE: 0.1043210937592856\n",
       "RMSE: 0.3229877610054065\n",
       "LogLoss: 0.3740512932232667\n",
       "Mean Per-Class Error: 0.26671870057692704\n",
       "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
       "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).
\n", "
\n", " \n", "
\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
0123ErrorRate
3470.038.040.0173.00.0674550251 / 3,721
120.0826.012.0177.00.2722467309 / 1,135
157.09.0264.0432.00.6937355598 / 862
124.014.033.04943.00.0334376171 / 5,114
3871.0887.0349.05725.00.12269201,329 / 10,832
\n", "
\n", "
\n", "
\n", " \n", "
\n", " \n", " \n", " \n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Top-4 Hit Ratios:
khit_ratio
10.8773079
20.9599335
30.9906757
41.0
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Scoring History:
timestampdurationnumber_of_treestraining_rmsetraining_loglosstraining_classification_errortraining_auctraining_pr_aucvalidation_rmsevalidation_loglossvalidation_classification_errorvalidation_aucvalidation_pr_auc
2023-08-27 18:57:39 0.506 sec0.00.751.38629440.5278271nannan0.751.38629440.5278804nannan
2023-08-27 18:59:28 1 min 49.704 sec5.00.47322930.64808510.1505586nannan0.52241180.76950560.2232275nannan
2023-08-27 19:00:14 2 min 35.359 sec10.00.36495930.42611130.1075331nannan0.44785090.60745510.1948855nannan
2023-08-27 19:00:54 3 min 15.987 sec15.00.30732150.31865050.0782338nannan0.41484460.53528580.1801145nannan
2023-08-27 19:01:33 3 min 54.979 sec20.00.26638640.25211960.0547309nannan0.39640010.49596140.1650665nannan
2023-08-27 19:02:16 4 min 38.228 sec25.00.23988170.21349700.0414765nannan0.38404650.47046660.1585118nannan
2023-08-27 19:02:56 5 min 17.731 sec30.00.21682640.18321310.0309201nannan0.37567740.45383470.1551883nannan
2023-08-27 19:03:35 5 min 57.107 sec35.00.19528930.15761930.0219129nannan0.36958200.44113550.1509417nannan
2023-08-27 19:04:14 6 min 36.098 sec40.00.17705760.13705920.0159833nannan0.36222900.42692080.1452179nannan
2023-08-27 19:04:54 7 min 15.395 sec45.00.16134390.12056880.0116027nannan0.35759650.41906520.1420790nannan
------------------------------------------
2023-08-27 19:14:3816 min 59.627 sec120.00.06138160.03502570.0022877nannan0.32892730.37832520.1258309nannan
2023-08-27 19:15:1617 min 37.983 sec125.00.05942670.03342450.0022877nannan0.32818120.37744940.1255539nannan
2023-08-27 19:15:5718 min 18.419 sec130.00.05737820.03177050.0022877nannan0.32750150.37696150.1244461nannan
2023-08-27 19:16:3418 min 56.031 sec135.00.05559390.03029870.0022877nannan0.32701430.37681430.1251846nannan
2023-08-27 19:17:1219 min 33.918 sec140.00.05401660.02898580.0022877nannan0.32643610.37646030.1250923nannan
2023-08-27 19:17:5020 min 11.872 sec145.00.05243340.02760340.0022877nannan0.32544230.37551530.1246307nannan
2023-08-27 19:18:2720 min 49.076 sec150.00.05114930.02644940.0022877nannan0.32444950.37463880.1236152nannan
2023-08-27 19:19:0521 min 26.625 sec155.00.04992570.02541240.0022877nannan0.32406770.37454460.1239845nannan
2023-08-27 19:19:4222 min 4.126 sec160.00.04898820.02457310.0022877nannan0.32345510.37448660.1233383nannan
2023-08-27 19:20:2022 min 41.543 sec165.00.04805880.02370790.0022877nannan0.32298780.37405130.1226920nannan
\n", "
\n", "
[34 rows x 14 columns]
\n", "
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Variable Importances:
variablerelative_importancescaled_importancepercentage
C5713952.10571291.00.0124100
C2663759.36914060.95123190.0118048
C3223261.49243160.82525440.0102414
C2942904.35327150.73488750.0091200
C3512887.26123050.73056280.0090663
C3502824.39721680.71465630.0088689
C3232539.68579100.64261590.0079749
C2382450.67675780.62009390.0076954
C2392309.45971680.58436180.0072519
C2672205.80444340.55813400.0069264
------------
C74845.68552400.01155980.0001435
C77545.53287510.01152120.0001430
C77745.52322770.01151870.0001429
C73444.59109500.01128290.0001400
C72143.39782710.01098090.0001363
C76043.01130680.01088310.0001351
C73542.60637660.01078070.0001338
C74237.09507370.00938620.0001165
C73336.37290950.00920340.0001142
C69433.65393070.00851540.0001057
\n", "
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[784 rows x 4 columns]
\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.
" ], "text/plain": [ "Model Details\n", "=============\n", "H2OXGBoostEstimator : XGBoost\n", "Model Key: XGBoost_1_AutoML_10_20230827_185738\n", "\n", "\n", "Model Summary: \n", " number_of_trees\n", "-- -----------------\n", " 165\n", "\n", "ModelMetricsMultinomial: xgboost\n", "** Reported on train data. **\n", "\n", "MSE: 0.002309651920454201\n", "RMSE: 0.04805883810969842\n", "LogLoss: 0.02370788812229064\n", "Mean Per-Class Error: 0.004465681119258331\n", "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "\n", "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n", "0 1 2 3 Error Rate\n", "----- ----- ---- ----- ----------- ------------\n", "33405 12 67 0 0.00235934 79 / 33,484\n", "6 10204 0 3 0.00088123 9 / 10,213\n", "83 0 7645 26 0.0140573 109 / 7,754\n", "0 5 21 46000 0.000564898 26 / 46,026\n", "33494 10221 7733 46029 0.00228772 223 / 97,477\n", "\n", "Top-4 Hit Ratios: \n", "k hit_ratio\n", "--- -----------\n", "1 0.997712\n", "2 1\n", "3 1\n", "4 1\n", "\n", "ModelMetricsMultinomial: xgboost\n", "** Reported on validation data. **\n", "\n", "MSE: 0.1043210937592856\n", "RMSE: 0.3229877610054065\n", "LogLoss: 0.3740512932232667\n", "Mean Per-Class Error: 0.26671870057692704\n", "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "\n", "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n", "0 1 2 3 Error Rate\n", "---- --- --- ---- --------- --------------\n", "3470 38 40 173 0.067455 251 / 3,721\n", "120 826 12 177 0.272247 309 / 1,135\n", "157 9 264 432 0.693735 598 / 862\n", "124 14 33 4943 0.0334376 171 / 5,114\n", "3871 887 349 5725 0.122692 1,329 / 10,832\n", "\n", "Top-4 Hit Ratios: \n", "k hit_ratio\n", "--- -----------\n", "1 0.877308\n", "2 0.959933\n", "3 0.990676\n", "4 1\n", "\n", "Scoring History: \n", " timestamp duration number_of_trees training_rmse training_logloss training_classification_error training_auc training_pr_auc validation_rmse validation_logloss validation_classification_error validation_auc validation_pr_auc\n", "--- ------------------- ----------------- ----------------- -------------------- -------------------- ------------------------------- -------------- ----------------- ------------------- -------------------- --------------------------------- ---------------- -------------------\n", " 2023-08-27 18:57:39 0.506 sec 0.0 0.75 1.3862943611198288 0.5278270771566625 nan nan 0.75 1.3862943611198304 0.5278803545051699 nan nan\n", " 2023-08-27 18:59:28 1 min 49.704 sec 5.0 0.47322925366008617 0.6480851028746106 0.15055859330919089 nan nan 0.5224117951654701 0.7695056210332487 0.22322747415066468 nan nan\n", " 2023-08-27 19:00:14 2 min 35.359 sec 10.0 0.3649593072151875 0.4261112977149786 0.10753305908060364 nan nan 0.44785090195884786 0.607455092378414 0.19488552437223042 nan nan\n", " 2023-08-27 19:00:54 3 min 15.987 sec 15.0 0.3073215075866799 0.3186504905125533 0.07823383977758856 nan nan 0.41484459752267455 0.5352857507872109 0.18011447562776958 nan nan\n", " 2023-08-27 19:01:33 3 min 54.979 sec 20.0 0.2663863594823045 0.252119638931567 0.054730859587389845 nan nan 0.39640008079731265 0.4959614479693781 0.16506646971935007 nan nan\n", " 2023-08-27 19:02:16 4 min 38.228 sec 25.0 0.23988165228001998 0.21349696653940023 0.0414764508550735 nan nan 0.3840465022249038 0.47046661594852196 0.15851181683899557 nan nan\n", " 2023-08-27 19:02:56 5 min 17.731 sec 30.0 0.21682639551758834 0.18321311944402094 0.030920114488546015 nan nan 0.37567739113149556 0.4538346946445562 0.15518833087149186 nan nan\n", " 2023-08-27 19:03:35 5 min 57.107 sec 35.0 0.19528925108964956 0.1576192820814794 0.02191286149553228 nan nan 0.36958195585867976 0.4411355215598676 0.15094165435745938 nan nan\n", " 2023-08-27 19:04:14 6 min 36.098 sec 40.0 0.1770575566571507 0.13705920489801587 0.015983257588969707 nan nan 0.36222895668553867 0.4269208087062777 0.1452178729689808 nan nan\n", " 2023-08-27 19:04:54 7 min 15.395 sec 45.0 0.16134385982154778 0.12056879457284446 0.011602737055920884 nan nan 0.35759646066735024 0.41906520766729594 0.14207902511078285 nan nan\n", "--- --- --- --- --- --- --- --- --- --- --- --- --- ---\n", " 2023-08-27 19:14:38 16 min 59.627 sec 120.0 0.061381597997187926 0.03502569008528235 0.0022877191542620312 nan nan 0.32892725521561367 0.37832518513191504 0.1258308714918759 nan nan\n", " 2023-08-27 19:15:16 17 min 37.983 sec 125.0 0.05942671145017929 0.03342454367903281 0.0022877191542620312 nan nan 0.3281811949502604 0.3774494168651968 0.1255539143279173 nan nan\n", " 2023-08-27 19:15:57 18 min 18.419 sec 130.0 0.0573781919236857 0.03177047147772751 0.0022877191542620312 nan nan 0.3275014811799285 0.3769615432958693 0.12444608567208272 nan nan\n", " 2023-08-27 19:16:34 18 min 56.031 sec 135.0 0.05559393915651089 0.030298682421210147 0.0022877191542620312 nan nan 0.32701429263592474 0.3768142846795543 0.12518463810930577 nan nan\n", " 2023-08-27 19:17:12 19 min 33.918 sec 140.0 0.054016610470453814 0.02898584635428729 0.0022877191542620312 nan nan 0.3264361464312762 0.3764603020149872 0.12509231905465287 nan nan\n", " 2023-08-27 19:17:50 20 min 11.872 sec 145.0 0.05243341627816896 0.027603422758369512 0.0022877191542620312 nan nan 0.3254422836518343 0.37551527908857674 0.12463072378138848 nan nan\n", " 2023-08-27 19:18:27 20 min 49.076 sec 150.0 0.05114931809426985 0.026449436992709713 0.0022877191542620312 nan nan 0.32444949247595717 0.374638773277248 0.1236152141802068 nan nan\n", " 2023-08-27 19:19:05 21 min 26.625 sec 155.0 0.04992566170919226 0.025412422909315904 0.0022877191542620312 nan nan 0.32406765214822714 0.37454459615284685 0.12398449039881831 nan nan\n", " 2023-08-27 19:19:42 22 min 4.126 sec 160.0 0.048988153528094 0.024573144353729356 0.0022877191542620312 nan nan 0.32345510657043625 0.37448664186982716 0.12333825701624815 nan nan\n", " 2023-08-27 19:20:20 22 min 41.543 sec 165.0 0.04805883810969842 0.02370788812229064 0.0022877191542620312 nan nan 0.3229877610054065 0.3740512932232667 0.122692023633678 nan nan\n", "[34 rows x 14 columns]\n", "\n", "\n", "Variable Importances: \n", "variable relative_importance scaled_importance percentage\n", "---------- --------------------- -------------------- ----------------------\n", "C571 3952.105712890625 1.0 0.012410006584018965\n", "C266 3759.369140625 0.9512319289342454 0.011804794501003045\n", "C322 3261.492431640625 0.8252543501057121 0.01024141191830218\n", "C294 2904.353271484375 0.7348875466592949 0.009119959292555394\n", "C351 2887.26123046875 0.730562753180245 0.009066288577005864\n", "C350 2824.397216796875 0.7146562926149745 0.008868889296662418\n", "C323 2539.685791015625 0.6426158548168145 0.007974866989271643\n", "C238 2450.6767578125 0.620093928616105 0.007695369736836049\n", "C239 2309.459716796875 0.5843618274845447 0.007251934126532554\n", "C267 2205.804443359375 0.5581339679666663 0.006926446217230959\n", "--- --- --- ---\n", "C748 45.685523986816406 0.011559793008016827 0.00014345710733938523\n", "C775 45.532875061035156 0.011521168300868091 0.0001429777744693636\n", "C777 45.52322769165039 0.011518727230186885 0.00014294748076613777\n", "C734 44.591094970703125 0.011282869996432503 0.0001400204909423574\n", "C721 43.3978271484375 0.010980937834452744 0.0001362735108242615\n", "C760 43.01130676269531 0.01088313670922437 0.00013505979821625292\n", "C735 42.60637664794922 0.010780677376361555 0.0001337882772208312\n", "C742 37.09507369995117 0.009386154216208786 0.00011648223562176841\n", "C733 36.37290954589844 0.009203425259415641 0.00011421456806487456\n", "C694 33.6539306640625 0.008515442933192076 0.00010567670286675142\n", "[784 rows x 4 columns]\n", "\n", "\n", "[tips]\n", "Use `model.explain()` to inspect the model.\n", "--\n", "Use `h2o.display.toggle_user_tips()` to switch on/off this section." ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aml.train(x=x_columns, y=y, training_frame=train, validation_frame=val)\n" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
model_id rmse mse mae rmsle mean_residual_deviance
GBM_1_AutoML_8_20230827_175902 0.6672190.4451810.48641 nan 0.445181
XGBoost_1_AutoML_8_20230827_1759020.7670590.5883790.540892 nan 0.588379
GLM_1_AutoML_8_20230827_175902 1.12538 1.26648 0.965374 nan 1.26648 
[3 rows x 6 columns]
" ], "text/plain": [ "model_id rmse mse mae rmsle mean_residual_deviance\n", "---------------------------------- -------- -------- -------- ------- ------------------------\n", "GBM_1_AutoML_8_20230827_175902 0.667219 0.445181 0.48641 nan 0.445181\n", "XGBoost_1_AutoML_8_20230827_175902 0.767059 0.588379 0.540892 nan 0.588379\n", "GLM_1_AutoML_8_20230827_175902 1.12538 1.26648 0.965374 nan 1.26648\n", "[3 rows x 6 columns]\n" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lb = aml.leaderboard\n", "lb.head(rows=lb.nrows) # Print all rows instead of default (10 rows)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": {}, "outputs": [], "source": [ "gbm = aml.get_best_model(algorithm=\"H2OXGBoostEstimator\")" ] }, { "cell_type": "code", "execution_count": 135, "metadata": {}, "outputs": [], "source": [ "gbm" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [], "source": [ "xgb = aml.get_best_model(algorithm=\"XGBoost\")" ] }, { "cell_type": "code", "execution_count": 139, "metadata": {}, "outputs": [], "source": [ "model_path = h2o.save_model(model=xgb, force=True)\n" ] }, { "cell_type": "code", "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/mnt/c/Users/MI/Documents/Machine learning/RetinalOCT/mnt/c/Users/MI/Documents/Machine learning/RetinalOCT/XGBoost_1_AutoML_10_20230827_185738'" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h2o.save_model(model=xgb, path='mnt/c/Users/MI/Documents/Machine learning/RetinalOCT/', force=True)" ] }, { "cell_type": "code", "execution_count": 144, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AutoML progress: |███████████████████████████████████████████████████████████████| (done) 100%\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
model_id auc mean_per_class_error logloss rmse mse
DeepLearning_grid_1_AutoML_11_20230827_202719_model_3 nan 0.446577 0.7353 0.4511130.203503
DeepLearning_grid_1_AutoML_11_20230827_202719_model_10 nan 0.396667 0.7653710.4388460.192586
DeepLearning_grid_1_AutoML_11_20230827_202719_model_4 nan 0.441386 1.02973 0.4792070.22964
DeepLearning_grid_1_AutoML_11_20230827_202719_model_7 nan 0.45181 0.8407430.4579750.209741
DeepLearning_grid_1_AutoML_11_20230827_202719_model_5 nan 0.410723 0.5709210.4154280.17258
DeepLearning_1_AutoML_11_20230827_202719 nan 0.447378 0.6575110.4523340.204606
DeepLearning_grid_1_AutoML_11_20230827_202719_model_1 nan 0.416768 0.6632250.4249730.180602
DeepLearning_grid_1_AutoML_11_20230827_202719_model_2 nan 0.427947 0.6803610.4344640.188759
DeepLearning_grid_1_AutoML_11_20230827_202719_model_6 nan 0.453433 0.6784760.44613 0.199032
DeepLearning_grid_1_AutoML_11_20230827_202719_model_9 nan 0.433762 0.6620910.4387390.192492
DeepLearning_grid_1_AutoML_11_20230827_202719_model_8 nan 0.45202 0.9838950.4681420.219157
[11 rows x 6 columns]
" ], "text/plain": [ "model_id auc mean_per_class_error logloss rmse mse\n", "------------------------------------------------------ ----- ---------------------- --------- -------- --------\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_3 nan 0.446577 0.7353 0.451113 0.203503\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_10 nan 0.396667 0.765371 0.438846 0.192586\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_4 nan 0.441386 1.02973 0.479207 0.22964\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_7 nan 0.45181 0.840743 0.457975 0.209741\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_5 nan 0.410723 0.570921 0.415428 0.17258\n", "DeepLearning_1_AutoML_11_20230827_202719 nan 0.447378 0.657511 0.452334 0.204606\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_1 nan 0.416768 0.663225 0.424973 0.180602\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_2 nan 0.427947 0.680361 0.434464 0.188759\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_6 nan 0.453433 0.678476 0.44613 0.199032\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_9 nan 0.433762 0.662091 0.438739 0.192492\n", "DeepLearning_grid_1_AutoML_11_20230827_202719_model_8 nan 0.45202 0.983895 0.468142 0.219157\n", "[11 rows x 6 columns]\n" ] }, "execution_count": 144, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aml = H2OAutoML(max_models=30, seed=1, nfolds=0, include_algos = [\"DeepLearning\",], max_runtime_secs=1800, sort_metric = 'AUC')\n", "aml.train(x=x_columns, y=y, training_frame=train, validation_frame=val)\n", "lb = aml.leaderboard\n", "lb.head(rows=lb.nrows) # Print all rows instead of default (10 rows)\n" ] }, { "cell_type": "code", "execution_count": 145, "metadata": {}, "outputs": [], "source": [ "best_model = aml.get_best_model()" ] }, { "cell_type": "code", "execution_count": 146, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Model Details\n",
       "=============\n",
       "H2ODeepLearningEstimator : Deep Learning\n",
       "Model Key: DeepLearning_grid_1_AutoML_11_20230827_202719_model_3\n",
       "
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Status of Neuron Layers: predicting C785, 4-class classification, multinomial distribution, CrossEntropy loss, 15,784 weights/biases, 336.8 KB, 1,446,343 training samples, mini-batch size 1
layerunitstypedropoutl1l2mean_raterate_rmsmomentummean_weightweight_rmsmean_biasbias_rms
1784Input15.0
220RectifierDropout10.00.00.00.04179380.04741470.0-0.00952460.4904313-13.71305404.8445454
34Softmax0.00.00.00223090.00878220.0-13.22139526.7183456-10.10116160.7718136
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ModelMetricsMultinomial: deeplearning\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.19893824522529294\n",
       "RMSE: 0.4460249378961819\n",
       "LogLoss: 0.7049146795973394\n",
       "Mean Per-Class Error: 0.4471327045455622\n",
       "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
       "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).
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Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
0123ErrorRate
2888.0115.00.0541.00.1851016656 / 3,544
221.0479.00.0310.00.5257426531 / 1,010
170.08.00.0612.01.0790 / 790
324.036.00.04274.00.0776867360 / 4,634
3603.0638.00.05737.00.23421532,337 / 9,978
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Top-4 Hit Ratios:
khit_ratio
10.7657847
20.9117057
30.9742433
40.9999999
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ModelMetricsMultinomial: deeplearning\n",
       "** Reported on validation data. **\n",
       "\n",
       "MSE: 0.2035027579792075\n",
       "RMSE: 0.4511127996180196\n",
       "LogLoss: 0.7353003902285303\n",
       "Mean Per-Class Error: 0.44657733162610136\n",
       "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n",
       "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).
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Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
0123ErrorRate
3027.0109.00.0585.00.1865090694 / 3,721
231.0545.00.0359.00.5198238590 / 1,135
160.012.00.0690.01.0862 / 862
361.048.00.04705.00.0799765409 / 5,114
3779.0714.00.06339.00.23587522,555 / 10,832
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Top-4 Hit Ratios:
khit_ratio
10.7641248
20.9069424
30.9706425
40.9999999
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Scoring History:
timestampdurationtraining_speedepochsiterationssamplestraining_rmsetraining_loglosstraining_r2training_classification_errortraining_auctraining_pr_aucvalidation_rmsevalidation_loglossvalidation_r2validation_classification_errorvalidation_aucvalidation_pr_auc
2023-08-27 20:35:21 0.000 secNone0.000.0nannannannannannannannannannannannan
2023-08-27 20:35:24 7 min 20.420 sec20227 obs/sec0.7078080168995.00.52774440.99598770.85073050.3216075nannan0.52797980.99592740.84921720.3225628nannan
2023-08-27 20:35:31 7 min 27.145 sec21000 obs/sec2.12035663206686.00.48342560.82683000.87474840.2705953nannan0.48667560.83269470.87188610.2756647nannan
2023-08-27 20:35:38 7 min 33.706 sec21371 obs/sec3.53409525344493.00.46258120.77227690.88531680.2467428nannan0.46578280.77920310.88264980.2475074nannan
2023-08-27 20:35:44 7 min 39.937 sec21923 obs/sec4.94485887482010.00.46203460.74573190.88558770.2488475nannan0.46401300.75611660.88353980.2516617nannan
2023-08-27 20:35:50 7 min 45.593 sec22672 obs/sec6.36072109620024.00.45930790.79963030.88693410.2452395nannan0.45906410.80752980.88601080.2434453nannan
2023-08-27 20:35:55 7 min 51.113 sec23265 obs/sec7.774151911757801.00.45321350.75087260.88991470.2346162nannan0.45806300.77539110.88650740.2388294nannan
2023-08-27 20:36:01 7 min 56.804 sec23605 obs/sec9.188844513895701.00.44826750.72209960.89230430.2330126nannan0.45044540.73868220.89025080.2331979nannan
2023-08-27 20:36:06 8 min 2.221 sec23997 obs/sec10.6016804151033420.00.44602490.70491470.89337910.2342153nannan0.45111280.73530040.88992530.2358752nannan
2023-08-27 20:36:11 8 min 7.508 sec24372 obs/sec12.0141777171171106.00.44855690.73834070.89216520.2323111nannan0.45545740.77306320.88779490.2427068nannan
2023-08-27 20:36:17 8 min 12.904 sec24629 obs/sec13.4256491191308692.00.45257540.80115250.89022440.2361195nannan0.45685360.84376930.88710590.2392910nannan
2023-08-27 20:36:22 8 min 18.182 sec24901 obs/sec14.8377874211446343.00.44783160.75249250.89251360.2321106nannan0.45424090.78539880.88839350.2328287nannan
2023-08-27 20:36:22 8 min 18.505 sec24892 obs/sec14.8377874211446343.00.44602490.70491470.89337910.2342153nannan0.45111280.73530040.88992530.2358752nannan
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Variable Importances:
variablerelative_importancescaled_importancepercentage
C6171.01.00.0051016
C6440.93394540.93394540.0047646
C6450.87487970.87487970.0044633
C3790.87486410.87486410.0044632
C3780.85847110.85847110.0043796
C5890.79783940.79783940.0040703
C3230.77215530.77215530.0039392
C3500.77111270.77111270.0039339
C3220.72711220.72711220.0037095
C4350.71974790.71974790.0036719
------------
C7530.07993530.07993530.0004078
C7670.07961980.07961980.0004062
C7520.07799930.07799930.0003979
C7750.07466860.07466860.0003809
C7090.07431170.07431170.0003791
C7760.07261590.07261590.0003705
C7510.07228520.07228520.0003688
C7730.06812170.06812170.0003475
C370.06790010.06790010.0003464
C7740.06566170.06566170.0003350
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[784 rows x 4 columns]
\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.
" ], "text/plain": [ "Model Details\n", "=============\n", "H2ODeepLearningEstimator : Deep Learning\n", "Model Key: DeepLearning_grid_1_AutoML_11_20230827_202719_model_3\n", "\n", "\n", "Status of Neuron Layers: predicting C785, 4-class classification, multinomial distribution, CrossEntropy loss, 15,784 weights/biases, 336.8 KB, 1,446,343 training samples, mini-batch size 1\n", " layer units type dropout l1 l2 mean_rate rate_rms momentum mean_weight weight_rms mean_bias bias_rms\n", "-- ------- ------- ---------------- --------- ---- ---- -------------------- -------------------- ---------- -------------------- ------------------ ------------------- ------------------\n", " 1 784 Input 15.0\n", " 2 20 RectifierDropout 10.0 0.0 0.0 0.04179383390948381 0.04741466045379639 0.0 -0.00952461083580731 0.4904313087463379 -13.713053991207634 4.844545364379883\n", " 3 4 Softmax 0.0 0.0 0.002230887040605012 0.008782170712947845 0.0 -13.221395155787468 6.718345642089844 -10.101161555604683 0.7718136310577393\n", "\n", "ModelMetricsMultinomial: deeplearning\n", "** Reported on train data. **\n", "\n", "MSE: 0.19893824522529294\n", "RMSE: 0.4460249378961819\n", "LogLoss: 0.7049146795973394\n", "Mean Per-Class Error: 0.4471327045455622\n", "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "\n", "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n", "0 1 2 3 Error Rate\n", "---- --- --- ---- --------- -------------\n", "2888 115 0 541 0.185102 656 / 3,544\n", "221 479 0 310 0.525743 531 / 1,010\n", "170 8 0 612 1 790 / 790\n", "324 36 0 4274 0.0776867 360 / 4,634\n", "3603 638 0 5737 0.234215 2,337 / 9,978\n", "\n", "Top-4 Hit Ratios: \n", "k hit_ratio\n", "--- -----------\n", "1 0.765785\n", "2 0.911706\n", "3 0.974243\n", "4 1\n", "\n", "ModelMetricsMultinomial: deeplearning\n", "** Reported on validation data. **\n", "\n", "MSE: 0.2035027579792075\n", "RMSE: 0.4511127996180196\n", "LogLoss: 0.7353003902285303\n", "Mean Per-Class Error: 0.44657733162610136\n", "AUC table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "AUCPR table was not computed: it is either disabled (model parameter 'auc_type' was set to AUTO or NONE) or the domain size exceeds the limit (maximum is 50 domains).\n", "\n", "Confusion Matrix: Row labels: Actual class; Column labels: Predicted class\n", "0 1 2 3 Error Rate\n", "---- --- --- ---- --------- --------------\n", "3027 109 0 585 0.186509 694 / 3,721\n", "231 545 0 359 0.519824 590 / 1,135\n", "160 12 0 690 1 862 / 862\n", "361 48 0 4705 0.0799765 409 / 5,114\n", "3779 714 0 6339 0.235875 2,555 / 10,832\n", "\n", "Top-4 Hit Ratios: \n", "k hit_ratio\n", "--- -----------\n", "1 0.764125\n", "2 0.906942\n", "3 0.970642\n", "4 1\n", "\n", "Scoring History: \n", " timestamp duration training_speed epochs iterations samples training_rmse training_logloss training_r2 training_classification_error training_auc training_pr_auc validation_rmse validation_logloss validation_r2 validation_classification_error validation_auc validation_pr_auc\n", "-- ------------------- ---------------- ---------------- -------- ------------ ----------- --------------- ------------------ ------------- ------------------------------- -------------- ----------------- ----------------- -------------------- --------------- --------------------------------- ---------------- -------------------\n", " 2023-08-27 20:35:21 0.000 sec 0 0 0 nan nan nan nan nan nan nan nan nan nan nan nan\n", " 2023-08-27 20:35:24 7 min 20.420 sec 20227 obs/sec 0.707808 1 68995 0.527744 0.995988 0.85073 0.321608 nan nan 0.52798 0.995927 0.849217 0.322563 nan nan\n", " 2023-08-27 20:35:31 7 min 27.145 sec 21000 obs/sec 2.12036 3 206686 0.483426 0.82683 0.874748 0.270595 nan nan 0.486676 0.832695 0.871886 0.275665 nan nan\n", " 2023-08-27 20:35:38 7 min 33.706 sec 21371 obs/sec 3.5341 5 344493 0.462581 0.772277 0.885317 0.246743 nan nan 0.465783 0.779203 0.88265 0.247507 nan nan\n", " 2023-08-27 20:35:44 7 min 39.937 sec 21923 obs/sec 4.94486 7 482010 0.462035 0.745732 0.885588 0.248847 nan nan 0.464013 0.756117 0.88354 0.251662 nan nan\n", " 2023-08-27 20:35:50 7 min 45.593 sec 22672 obs/sec 6.36072 9 620024 0.459308 0.79963 0.886934 0.24524 nan nan 0.459064 0.80753 0.886011 0.243445 nan nan\n", " 2023-08-27 20:35:55 7 min 51.113 sec 23265 obs/sec 7.77415 11 757801 0.453213 0.750873 0.889915 0.234616 nan nan 0.458063 0.775391 0.886507 0.238829 nan nan\n", " 2023-08-27 20:36:01 7 min 56.804 sec 23605 obs/sec 9.18884 13 895701 0.448267 0.7221 0.892304 0.233013 nan nan 0.450445 0.738682 0.890251 0.233198 nan nan\n", " 2023-08-27 20:36:06 8 min 2.221 sec 23997 obs/sec 10.6017 15 1.03342e+06 0.446025 0.704915 0.893379 0.234215 nan nan 0.451113 0.7353 0.889925 0.235875 nan nan\n", " 2023-08-27 20:36:11 8 min 7.508 sec 24372 obs/sec 12.0142 17 1.17111e+06 0.448557 0.738341 0.892165 0.232311 nan nan 0.455457 0.773063 0.887795 0.242707 nan nan\n", " 2023-08-27 20:36:17 8 min 12.904 sec 24629 obs/sec 13.4256 19 1.30869e+06 0.452575 0.801152 0.890224 0.236119 nan nan 0.456854 0.843769 0.887106 0.239291 nan nan\n", " 2023-08-27 20:36:22 8 min 18.182 sec 24901 obs/sec 14.8378 21 1.44634e+06 0.447832 0.752492 0.892514 0.232111 nan nan 0.454241 0.785399 0.888393 0.232829 nan nan\n", " 2023-08-27 20:36:22 8 min 18.505 sec 24892 obs/sec 14.8378 21 1.44634e+06 0.446025 0.704915 0.893379 0.234215 nan nan 0.451113 0.7353 0.889925 0.235875 nan nan\n", "\n", "Variable Importances: \n", "variable relative_importance scaled_importance percentage\n", "---------- --------------------- ------------------- ----------------------\n", "C617 1.0 1.0 0.00510162559603795\n", "C644 0.93394535779953 0.93394535779953 0.004764639542650904\n", "C645 0.8748797178268433 0.8748797178268433 0.004463308761919883\n", "C379 0.8748641014099121 0.8748641014099121 0.004463229092807548\n", "C378 0.8584710955619812 0.8584710955619812 0.004379598114577744\n", "C589 0.7978394031524658 0.7978394031524658 0.004070277920650261\n", "C323 0.7721552848815918 0.7721552848815918 0.003939247165467904\n", "C350 0.7711127400398254 0.7711127400398254 0.003933928492018131\n", "C322 0.7271121740341187 0.7271121740341187 0.00370945407824326\n", "C435 0.7197479009628296 0.7197479009628296 0.0036718843142465587\n", "--- --- --- ---\n", "C753 0.07993525266647339 0.07993525266647339 0.0004077997310290414\n", "C767 0.07961980253458023 0.07961980253458023 0.00040619042256190177\n", "C752 0.0779992938041687 0.0779992938041687 0.00039792319374423133\n", "C775 0.07466858625411987 0.07466858625411987 0.00038093117085398535\n", "C709 0.07431168854236603 0.07431168854236603 0.0003791104123525346\n", "C776 0.07261592894792557 0.07261592894792557 0.0003704592818008102\n", "C751 0.07228519767522812 0.07228519767522812 0.0003687720146746067\n", "C773 0.06812173128128052 0.06812173128128052 0.0003475315679509998\n", "C37 0.06790006160736084 0.06790006160736084 0.00034640069226866575\n", "C774 0.0656617134809494 0.0656617134809494 0.0003349814781741216\n", "[784 rows x 4 columns]\n", "\n", "\n", "[tips]\n", "Use `model.explain()` to inspect the model.\n", "--\n", "Use `h2o.display.toggle_user_tips()` to switch on/off this section." ] }, "execution_count": 146, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_model" ] }, { "cell_type": "code", "execution_count": 147, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/mnt/c/Users/MI/Documents/Machine learning/RetinalOCT/mnt/c/Users/MI/Documents/Machine learning/RetinalOCT/DeepLearning_grid_1_AutoML_11_20230827_202719_model_3'" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "h2o.save_model(model=best_model, path='mnt/c/Users/MI/Documents/Machine learning/RetinalOCT/', force=True)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |" ] }, { "name": "stdout", "output_type": "stream", "text": [ "████████████████████████████████████████████████████████████████| (done) 100%\n" ] } ], "source": [ "sample = x_test[5]\n", "y_sample = y_test[5]\n", "y_sample\n", "sample_flatten = sample.flatten()\n", "df_sample = pd.DataFrame([sample_flatten], columns=x_columns)\n", "\n", "df_sample\n", "type(sample_flatten)\n", "sample_flatten.shape\n", "sampleh2o = h2o.H2OFrame(df_sample)" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2], dtype=uint8)" ] }, "execution_count": 187, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_sample" ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n" ] } ], "source": [ "preds = best_model.predict(sampleh2o)" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deeplearning prediction progress: |██████████████████████████████████████████████| (done) 100%\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
predict p0 p1 p2 p3
30.04435280.03557470.05060110.869471
[1 row x 5 columns]
" ], "text/plain": [ " predict p0 p1 p2 p3\n", "--------- --------- --------- --------- --------\n", " 3 0.0443528 0.0355747 0.0506011 0.869471\n", "[1 row x 5 columns]\n" ] }, "execution_count": 189, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_model.predict(sampleh2o)" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
predict p0 p1 p2 p3
00.5306510.09742220.07959090.292336
[1 row x 5 columns]
" ], "text/plain": [ " predict p0 p1 p2 p3\n", "--------- -------- --------- --------- --------\n", " 0 0.530651 0.0974222 0.0795909 0.292336\n", "[1 row x 5 columns]\n" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "preds" ] } ], "metadata": { "kernelspec": { "display_name": "h20env", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }