From a59f1b34c59e0ee1a226f926306156803a3ef66a Mon Sep 17 00:00:00 2001 From: uncircLe <50541954+uncircle@users.noreply.github.com> Date: Wed, 3 Jul 2024 17:53:04 +0300 Subject: [PATCH] add introduction for data, use GN to augment data and test with baseline --- tutorials/EEG_test.ipynb | 849 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 812 insertions(+), 37 deletions(-) diff --git a/tutorials/EEG_test.ipynb b/tutorials/EEG_test.ipynb index c3fb0e2..ca290af 100644 --- a/tutorials/EEG_test.ipynb +++ b/tutorials/EEG_test.ipynb @@ -1,12 +1,54 @@ { "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# 0. Introduction\n", + "\n", + "data: synchronized_brainwave_dataset.csv\n", + "\n", + "row of data:\n", + "\n", + "\\#: the index of eath row\n", + "\n", + "id: participant_id, integer value in the range of 1 to 30, means 30 participants.\n", + "\n", + "\n", + "indra_time: time of all stimulus events\n", + "\n", + "browser_latency\n", + "\n", + "reading_time\n", + "\n", + "attention_esense: The Attention Meter algorithm indicates the intensity of mental “focus” or “attention.” The value ranges from 0 to 100. The attention level increases when a user focuses on a single thought or an external object, and decreases when distracted.(https://neurosky.com/biosensors/eeg-sensor/algorithms/)\n", + "\n", + "meditation_esense: The Meditation Meter algorithm indicates the level of mental “calmness” or “relaxation.” The value ranges from 0 to 100, and increases when users relax the mind and decreases when they are uneasy or stressed.(https://neurosky.com/biosensors/eeg-sensor/algorithms/)\n", + "\n", + "eeg_power: Tuple represents the magnitude of 8 commonly-recognized types of EEG frequency bands -- delta (0.5 - 2.75Hz), theta (3.5 - 6.75Hz), low-alpha (7.5 - 9.25Hz), high-alpha (10 - 11.75Hz), low-beta (13 - 16.75Hz), high-beta (18 - 29.75Hz), low-gamma (31 - 39.75Hz), and mid-gamma (41 - 49.75Hz). These values have no units and are only meaningful for comparison to the values for the other frequency bands within a sample.(https://courses.ischool.berkeley.edu/i290-shda/f14/ColorCountingChallenge/)\n", + "\n", + "raw_values: Tuple containing raw sample values acquired by the sensor\n", + "\n", + "signal_quality: A signal quality of 0 is optimal. Values over 128 indicate that the headset was placed incorrectly.\n", + "\n", + "createdAt: when data were created\n", + "\n", + "updatedAt: when data were updated\n", + "\n", + "label: Indicate what the participant is doing.\n", + "\n", + "The device only has one electrode and it is placed roughly at fp1, according to the dataset creator.(https://www.kaggle.com/datasets/berkeley-biosense/synchronized-brainwave-dataset/discussion/284896)\n" + ], + "id": "8eabc8e8f0eef50e" + }, { "cell_type": "markdown", "id": "f4666ce8cbcbde16", "metadata": {}, "source": [ "# 1. Baseline\n", - "classify for math and relax in synchronized_brainwave_dataset data" + "\n", + "In this baseline, we want to classify labels math and relax using column raw_data. We have 934 rows of data for relax and 936 rows for math. Each row has a raw_data column which is a 512 length list, this 512 length list acts as feature in our model.\n" ] }, { @@ -75,23 +117,23 @@ "id": "a380d2f85b0a7235", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:17.235969Z", - "start_time": "2024-06-29T15:09:16.506146Z" + "end_time": "2024-07-03T13:40:36.472122Z", + "start_time": "2024-07-03T13:40:35.382006Z" } }, "source": [ "df = pd.read_csv(\"../data/synchronized_brainwave_dataset.csv\")" ], "outputs": [], - "execution_count": 3 + "execution_count": 81 }, { "cell_type": "code", "id": "c4a62977f983c13e", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:17.253091Z", - "start_time": "2024-06-29T15:09:17.237400Z" + "end_time": "2024-07-03T13:40:38.819162Z", + "start_time": "2024-07-03T13:40:38.795639Z" } }, "source": [ @@ -123,45 +165,363 @@ ] } ], - "execution_count": 4 + "execution_count": 82 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T13:42:58.496323Z", + "start_time": "2024-07-03T13:42:58.487262Z" + } + }, + "cell_type": "code", + "source": "# relax", + "id": "e1faafa95d9313d", + "outputs": [ + { + "data": { + "text/plain": [ + " Unnamed: 0 id indra_time browser_latency \\\n", + "13274 26880 7 2015-05-09 23:32:53.910 14 \n", + "13275 2427 11 2015-05-09 23:32:53.915 -171 \n", + "13276 24351 5 2015-05-09 23:32:54.072 1022 \n", + "13277 537 1 2015-05-09 23:32:54.153 13404 \n", + "13278 4859 13 2015-05-09 23:32:54.303 -297 \n", + "... ... .. ... ... \n", + "23263 20366 29 2015-05-09 23:44:21.136 954517 \n", + "23264 15358 23 2015-05-09 23:44:21.148 1534 \n", + "23265 15906 24 2015-05-09 23:44:21.171 -1,572 \n", + "23266 22345 30 2015-05-09 23:44:21.273 759 \n", + "23267 17775 26 2015-05-09 23:44:21.306 -1,168 \n", + "\n", + " reading_time attention_esense meditation_esense \\\n", + "13274 2015-05-09 16:32:54.000 80 61 \n", + "13275 2015-05-09 16:32:54.135 56 40 \n", + "13276 2015-05-09 16:32:53.057 13 48 \n", + "13277 2015-05-09 16:32:40.884 51 67 \n", + "13278 2015-05-09 16:32:54.779 60 66 \n", + "... ... ... ... \n", + "23263 2015-05-09 17:00:15.599 38 91 \n", + "23264 2015-05-09 16:44:19.717 56 87 \n", + "23265 2015-05-09 16:44:22.830 100 70 \n", + "23266 2015-05-09 16:44:20.605 48 81 \n", + "23267 2015-05-09 16:44:23.123 64 64 \n", + "\n", + " eeg_power \\\n", + "13274 [5044.0, 10156.0, 3281.0, 10403.0, 12393.0, 10266.0, 1949.0, 2937.0] \n", + "13275 [548188.0, 67192.0, 20298.0, 4142.0, 30576.0, 18237.0, 5603.0, 4783.0] \n", + "13276 [449571.0, 83093.0, 15379.0, 34656.0, 6750.0, 10348.0, 5315.0, 2585.0] \n", + "13277 [85497.0, 20547.0, 2723.0, 3270.0, 2522.0, 2209.0, 449.0, 393.0] \n", + "13278 [72768.0, 44080.0, 25974.0, 16079.0, 12995.0, 27132.0, 33264.0, 9121.0] \n", + "... ... \n", + "23263 [69315.0, 29092.0, 17868.0, 85907.0, 13333.0, 24188.0, 7027.0, 4696.0] \n", + "23264 [22375.0, 17940.0, 27965.0, 2261.0, 6953.0, 7502.0, 8088.0, 10833.0] \n", + "23265 [19774.0, 12690.0, 6436.0, 16572.0, 6092.0, 7105.0, 5188.0, 7603.0] 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\n", + "

934 rows × 13 columns

\n", + "
" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 89 }, { "cell_type": "code", "id": "4f7830db118c92f8", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:17.256317Z", - "start_time": "2024-06-29T15:09:17.253958Z" + "end_time": "2024-07-03T13:42:30.825998Z", + "start_time": "2024-07-03T13:42:30.820223Z" } }, "source": [ "relax_math = pd.concat([relax, math], axis=0)" ], "outputs": [], - "execution_count": 5 + "execution_count": 85 }, { "cell_type": "code", "id": "8ec9a86b566b3d04", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:18.385911Z", - "start_time": "2024-06-29T15:09:17.256958Z" + "end_time": "2024-07-03T13:42:32.545300Z", + "start_time": "2024-07-03T13:42:31.211900Z" } }, "source": [ "relax_math['raw_values'] = relax_math['raw_values'].apply(ast.literal_eval)" ], "outputs": [], - "execution_count": 6 + "execution_count": 86 }, { "cell_type": "code", "id": "ce5226398f21e70e", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:18.414664Z", - "start_time": "2024-06-29T15:09:18.386726Z" + "end_time": "2024-07-03T13:42:40.677489Z", + "start_time": "2024-07-03T13:42:40.659197Z" } }, "source": [ @@ -176,13 +536,42 @@ "max_len = max(relax_math['raw_values'].apply(len))\n", "relax_math['raw_values'] = relax_math['raw_values'].apply(lambda x: np.pad(x, (0, max_len - len(x)), 'constant') if len(x) < max_len else x)\n", "\n", + "# print(relax_math['label'][:10])" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "13274 relax\n", + "13275 relax\n", + "13276 relax\n", + "13277 relax\n", + "13278 relax\n", + "13279 relax\n", + "13280 relax\n", + "13281 relax\n", + "13282 relax\n", + "13283 relax\n", + "Name: label, dtype: object\n" + ] + } + ], + "execution_count": 88 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "# convert label, relax=1, math=0\n", "label_encoder = LabelEncoder()\n", "relax_math['label'] = label_encoder.fit_transform(relax_math['label'])\n", "\n", "features_matrix = np.stack(relax_math['raw_values'].values)" ], - "outputs": [], - "execution_count": 7 + "id": "7b17eea04832d53e" }, { "cell_type": "code", @@ -233,15 +622,35 @@ "id": "894e15a5f35baa30", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:18.433229Z", - "start_time": "2024-06-29T15:09:18.431536Z" + "end_time": "2024-07-03T13:35:28.996114Z", + "start_time": "2024-07-03T13:35:28.992520Z" } }, - "source": [ - "# features_matrix" + "source": "relax_math['label']", + "outputs": [ + { + "data": { + "text/plain": [ + "13274 1\n", + "13275 1\n", + "13276 1\n", + "13277 1\n", + "13278 1\n", + " ..\n", + "23828 0\n", + "23829 0\n", + "23830 0\n", + "23831 0\n", + "23832 0\n", + "Name: label, Length: 1870, dtype: int64" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } ], - "outputs": [], - "execution_count": 10 + "execution_count": 80 }, { "cell_type": "code", @@ -268,7 +677,7 @@ "start_time": "2024-06-29T15:09:18.438708Z" } }, - "source": [], + "source": "print(y_train)", "outputs": [], "execution_count": 11 }, @@ -285,8 +694,8 @@ "id": "cae5dc7dddfb509f", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:18.474784Z", - "start_time": "2024-06-29T15:09:18.440738Z" + "end_time": "2024-07-03T13:32:12.242688Z", + "start_time": "2024-07-03T13:32:12.213569Z" } }, "source": [ @@ -307,15 +716,52 @@ ")\n" ], "outputs": [], - "execution_count": 12 + "execution_count": 76 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T13:33:25.759967Z", + "start_time": "2024-07-03T13:33:25.755310Z" + } + }, + "cell_type": "code", + "source": [ + "# print(X_train_ts.shape)\n", + "# print(y_train.shape)\n", + "# print(y_train[:10])" + ], + "id": "ccf73c4641739fc1", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1496, 8, 64)\n", + "(1496,)\n", + "23502 0\n", + "13518 1\n", + "14015 0\n", + "14116 0\n", + "23055 1\n", + "23549 0\n", + "13870 0\n", + "14210 0\n", + "23132 1\n", + "13837 0\n", + "Name: label, dtype: int64\n" + ] + } + ], + "execution_count": 79 }, { "cell_type": "code", "id": "fdf47499ec99cfdb", "metadata": { "ExecuteTime": { - "end_time": "2024-06-29T15:09:23.682727Z", - "start_time": "2024-06-29T15:09:18.475754Z" + "end_time": "2024-07-02T22:57:19.857002Z", + "start_time": "2024-07-02T22:57:19.061859Z" } }, "source": [ @@ -328,8 +774,22 @@ " verbose=0\n", ")" ], - "outputs": [], - "execution_count": 13 + "outputs": [ + { + "ename": "InvalidArgumentError", + "evalue": "Graph execution error:\n\nDetected at node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert defined at (most recent call last):\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/runpy.py\", line 87, in _run_code\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 701, in start\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 205, in start\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/asyncio/events.py\", line 80, in _run\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 534, in dispatch_queue\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 523, in process_one\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 429, in dispatch_shell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 767, in execute_request\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 429, in do_execute\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n\n File \"/var/folders/xh/h88wz4193gzg53_x42zm8ngm0000gn/T/ipykernel_56104/3722611138.py\", line 2, in \n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1807, in fit\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1401, in train_function\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1384, in step_function\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1373, in run_step\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1151, in train_step\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1209, in compute_loss\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/compile_utils.py\", line 277, in __call__\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/losses.py\", line 143, in __call__\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/losses.py\", line 270, in call\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/losses.py\", line 2454, in sparse_categorical_crossentropy\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/backend.py\", line 5775, in sparse_categorical_crossentropy\n\nassertion failed: [Condition x == y did not hold element-wise:] [x (sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [32 1] [y (sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [32 2]\n\t [[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert}}]] [Op:__inference_train_function_113702]", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mInvalidArgumentError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[58], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;66;03m# Model training\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m history_ts \u001B[38;5;241m=\u001B[39m \u001B[43mmodel_ts\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mfit\u001B[49m\u001B[43m(\u001B[49m\n\u001B[1;32m 3\u001B[0m \u001B[43m \u001B[49m\u001B[43mX_train_ts\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_train\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 4\u001B[0m \u001B[43m \u001B[49m\u001B[43mepochs\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 5\u001B[0m \u001B[43m \u001B[49m\u001B[43mbatch_size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m32\u001B[39;49m\u001B[43m,\u001B[49m\n\u001B[1;32m 6\u001B[0m \u001B[43m \u001B[49m\u001B[43mvalidation_data\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mX_val_ts\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43my_val\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[1;32m 7\u001B[0m \u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\n\u001B[1;32m 8\u001B[0m \u001B[43m)\u001B[49m\n", + "File \u001B[0;32m/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py:70\u001B[0m, in \u001B[0;36mfilter_traceback..error_handler\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 67\u001B[0m filtered_tb \u001B[38;5;241m=\u001B[39m _process_traceback_frames(e\u001B[38;5;241m.\u001B[39m__traceback__)\n\u001B[1;32m 68\u001B[0m \u001B[38;5;66;03m# To get the full stack trace, call:\u001B[39;00m\n\u001B[1;32m 69\u001B[0m \u001B[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001B[39;00m\n\u001B[0;32m---> 70\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m e\u001B[38;5;241m.\u001B[39mwith_traceback(filtered_tb) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[1;32m 71\u001B[0m \u001B[38;5;28;01mfinally\u001B[39;00m:\n\u001B[1;32m 72\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m filtered_tb\n", + "File \u001B[0;32m/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/tensorflow-2.15.1-py3.9-macosx-11.1-arm64.egg/tensorflow/python/eager/execute.py:53\u001B[0m, in \u001B[0;36mquick_execute\u001B[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001B[0m\n\u001B[1;32m 51\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 52\u001B[0m ctx\u001B[38;5;241m.\u001B[39mensure_initialized()\n\u001B[0;32m---> 53\u001B[0m tensors \u001B[38;5;241m=\u001B[39m pywrap_tfe\u001B[38;5;241m.\u001B[39mTFE_Py_Execute(ctx\u001B[38;5;241m.\u001B[39m_handle, device_name, op_name,\n\u001B[1;32m 54\u001B[0m inputs, attrs, num_outputs)\n\u001B[1;32m 55\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m core\u001B[38;5;241m.\u001B[39m_NotOkStatusException \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[1;32m 56\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m name \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n", + "\u001B[0;31mInvalidArgumentError\u001B[0m: Graph execution error:\n\nDetected at node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert defined at (most recent call last):\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/runpy.py\", line 197, in _run_module_as_main\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/runpy.py\", line 87, in _run_code\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel_launcher.py\", line 17, in \n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/traitlets/config/application.py\", line 1075, in launch_instance\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelapp.py\", line 701, in start\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/tornado/platform/asyncio.py\", line 205, in start\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/asyncio/base_events.py\", line 601, in run_forever\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/asyncio/base_events.py\", line 1905, in _run_once\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/asyncio/events.py\", line 80, in _run\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 534, in dispatch_queue\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 523, in process_one\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 429, in dispatch_shell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/kernelbase.py\", line 767, in execute_request\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/ipkernel.py\", line 429, in do_execute\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/ipykernel/zmqshell.py\", line 549, in run_cell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3024, in run_cell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3079, in _run_cell\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3284, in run_cell_async\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3466, in run_ast_nodes\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/IPython/core/interactiveshell.py\", line 3526, in run_code\n\n File \"/var/folders/xh/h88wz4193gzg53_x42zm8ngm0000gn/T/ipykernel_56104/3722611138.py\", line 2, in \n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/utils/traceback_utils.py\", line 65, in error_handler\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1807, in fit\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1401, in train_function\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1384, in step_function\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1373, in run_step\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1151, in train_step\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/training.py\", line 1209, in compute_loss\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/engine/compile_utils.py\", line 277, in __call__\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/losses.py\", line 143, in __call__\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/losses.py\", line 270, in call\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/losses.py\", line 2454, in sparse_categorical_crossentropy\n\n File \"/opt/homebrew/anaconda3/envs/tsgm_env/lib/python3.9/site-packages/keras/src/backend.py\", line 5775, in sparse_categorical_crossentropy\n\nassertion failed: [Condition x == y did not hold element-wise:] [x (sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/Shape_1:0) = ] [32 1] [y (sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/strided_slice:0) = ] [32 2]\n\t [[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/assert_equal_1/Assert/Assert}}]] [Op:__inference_train_function_113702]" + ] + } + ], + "execution_count": 58 }, { "cell_type": "code", @@ -585,7 +1045,9 @@ "source": [ "# 2. Augmentations\n", "\n", - "augment X_train_ts and y_train using GAN" + "augment X_train_ts and y_train using GAN\n", + "\n", + "## 2.1 cgan_base_c4_l1" ] }, { @@ -826,10 +1288,7 @@ } }, "cell_type": "code", - "source": [ - "print(y_train_onehot_32[:5])\n", - "\n" - ], + "source": "print(y_train_onehot_32[:5])", "id": "362b3df76d8cd9a1", "outputs": [ { @@ -960,13 +1419,329 @@ ], "execution_count": 47 }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "seems we have a model collapse\n", + "\n", + "next let's try some simple model\n" + ], + "id": "205b7af6b4364588" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 2.2 GaussianNoise", + "id": "cb0b1018e8212d1b" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T13:25:15.006250Z", + "start_time": "2024-07-03T13:25:15.003673Z" + } + }, + "cell_type": "code", + "source": "X_train_ts_scaled.shape", + "id": "82f5d8ec719db836", + "outputs": [ + { + "data": { + "text/plain": [ + "(1496, 8, 64)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 62 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T13:49:19.707311Z", + "start_time": "2024-07-03T13:49:19.685519Z" + } + }, + "cell_type": "code", + "source": [ + "gn_aug = tsgm.models.augmentations.GaussianNoise()\n", + "n_gen = 1024\n", + "X_y_gen = gn_aug.generate(X=X_train_ts_scaled_32, y=y_train_onehot_32, n_samples=n_gen)" + ], + "id": "fdb28803b0ee2def", + "outputs": [], + "execution_count": 91 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T13:59:18.838815Z", + "start_time": "2024-07-03T13:59:18.833005Z" + } + }, + "cell_type": "code", + "source": [ + "print(X_y_gen[0].shape)\n", + "print(X_train_ts.shape)\n", + "print(y_train.shape)\n", + "print(X_y_gen[1].shape)\n", + "y_train" + ], + "id": "4e9a99edf2ce4833", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1024, 8, 64)\n", + "(1496, 8, 64)\n", + "(1496,)\n", + "(1024, 2)\n" + ] + }, + { + "data": { + "text/plain": [ + "23502 0\n", + "13518 1\n", + "14015 0\n", + "14116 0\n", + "23055 1\n", + " ..\n", + "13999 0\n", + "14163 0\n", + "23194 1\n", + "23422 0\n", + "13995 0\n", + "Name: label, Length: 1496, dtype: int64" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 98 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "add generated data to X_train_ts, y_train, test generated data in 1.2 time series model, the original data size is around 1500, generated data size is around 1000.\n", + "id": "39d42287027a3fb9" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T13:50:30.065332Z", + "start_time": "2024-07-03T13:50:30.062487Z" + } + }, + "cell_type": "code", + "source": "", + "id": "ef54dffbd2d3cf49", + "outputs": [ + { + "data": { + "text/plain": [ + "(1496, 8, 64)" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 93 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T14:11:53.768026Z", + "start_time": "2024-07-03T14:11:53.756065Z" + } + }, + "cell_type": "code", + "source": [ + "# concate and shuffle\n", + "X_combined = np.concatenate([X_train_ts, X_y_gen[0]], axis=0)\n", + "y_train_one_hot = to_categorical(y_train, num_classes=2)\n", + "y_combined = np.concatenate([y_train_one_hot, X_y_gen[1]], axis=0)\n", + "shuffled_indices = np.random.permutation(X_combined.shape[0])\n", + "\n", + "X_gn = X_combined[shuffled_indices]\n", + "y_gn_onehot = y_combined[shuffled_indices]\n", + "y_gn = np.argmax(y_gn_onehot, axis=1)" + ], + "id": "2aa51afb08ef53da", + "outputs": [], + "execution_count": 102 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T14:11:53.948070Z", + "start_time": "2024-07-03T14:11:53.945431Z" + } + }, + "cell_type": "code", + "source": [ + "print(\"Shape of combined and shuffled features:\", X_gn.shape)\n", + "print(\"Shape of combined and shuffled labels:\", y_gn.shape)" + ], + "id": "43dabc2f741e7c0b", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of combined and shuffled features: (2520, 8, 64)\n", + "Shape of combined and shuffled labels: (2520,)\n" + ] + } + ], + "execution_count": 103 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T14:15:02.872958Z", + "start_time": "2024-07-03T14:15:02.842272Z" + } + }, + "cell_type": "code", + "source": [ + "model_ts_gn_architecture = zoo['clf_cn'](seq_len, feat_dim, output_dim)\n", + "model_ts_gn = model_ts_gn_architecture.model\n", + "\n", + "model_ts_gn.compile(\n", + " optimizer='adam',\n", + " loss='sparse_categorical_crossentropy',\n", + " metrics=['accuracy']\n", + ")" + ], + "id": "3bb9e0824889ea2d", + "outputs": [], + "execution_count": 105 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T14:15:11.710463Z", + "start_time": "2024-07-03T14:15:03.491173Z" + } + }, + "cell_type": "code", + "source": [ + "# Model training\n", + "history_ts_gn = model_ts_gn.fit(\n", + " X_gn, y_gn,\n", + " epochs=100,\n", + " batch_size=32,\n", + " validation_data=(X_val_ts, y_val),\n", + " verbose=0\n", + ")" + ], + "id": "9f613c1839a7e0d3", + "outputs": [], + "execution_count": 106 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T14:15:13.469991Z", + "start_time": "2024-07-03T14:15:13.431637Z" + } + }, + "cell_type": "code", + "source": [ + "val_loss_ts_gn, val_acc_ts_gn = model_ts_gn.evaluate(X_val_ts, y_val)\n", + "print('val loss in ts_gn model:', val_loss_ts_gn)\n", + "print(\"val accuracy in ts_gn model:\", val_acc_ts_gn)" + ], + "id": "98e8ddcedb1051a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 [==============================] - 0s 710us/step - loss: 0.9224 - accuracy: 0.5588\n", + "val loss in ts model: 0.922390878200531\n", + "val accuracy in ts model: 0.5588235259056091\n" + ] + } + ], + "execution_count": 107 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2024-07-03T14:15:45.873443Z", + "start_time": "2024-07-03T14:15:45.778041Z" + } + }, + "cell_type": "code", + "source": [ + "# Plot training & validation loss values\n", + "plt.figure(figsize=(10, 5))\n", + "plt.plot(history_ts_gn.history['loss'], label='Train Loss')\n", + "plt.plot(history_ts_gn.history['val_loss'], label='Validation Loss')\n", + "plt.title('gn Model Loss')\n", + "plt.ylabel('Loss')\n", + "plt.xlabel('Epoch')\n", + "plt.legend(loc='upper right')\n", + "plt.grid(True)\n", + "plt.show()" + ], + "id": "e8e874c3c88122d0", + "outputs": [ + { + "data": { + "text/plain": [ + "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 108 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "val loss in ts_gn model: 0.922390878200531\n", + "\n", + "val accuracy in ts_gn model: 0.5588235259056091\n", + "\n", + "compared with original\n", + "\n", + "val loss in ts model: 2.050161123275757\n", + "\n", + "val accuracy in ts model: 0.6016042828559875\n", + "\n", + "the loss decresed significently and accuracy decreased slightly\n" + ], + "id": "eecc6ae7eed147f9" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## 2.3 cBetaVAE", + "id": "e53fe0c7e9887061" + }, { "metadata": {}, "cell_type": "code", "outputs": [], "execution_count": null, "source": "", - "id": "3f94f12a716ac9eb" + "id": "192524c3617c9587" } ], "metadata": {