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{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"COkMuAOy2J5o"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2880,"status":"ok","timestamp":1697371783176,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"},"user_tz":-420},"id":"frk7IEStoDLc","outputId":"342903fd-4dc3-49db-e6a6-f433f93565cc"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":117493,"status":"ok","timestamp":1697371900667,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"},"user_tz":-420},"id":"lqNWRhS42u4P","outputId":"195873ea-dc48-49e5-a571-7a819144341a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: optuna in /usr/local/lib/python3.10/dist-packages (3.3.0)\n","Requirement already satisfied: optuna-dashboard in /usr/local/lib/python3.10/dist-packages (0.12.0)\n","Requirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (1.12.0)\n","Requirement already satisfied: cmaes>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (0.10.0)\n","Requirement already satisfied: colorlog in /usr/local/lib/python3.10/dist-packages (from optuna) (6.7.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from optuna) (1.26.1)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from optuna) (23.2)\n","Requirement already satisfied: sqlalchemy>=1.3.0 in 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in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook->jupyter>=1.0.0->ml-utility-loss==0.1.1) (2.21)\n","Collecting git+https://github.com/R-N/ml-utility-loss\n"," Cloning https://github.com/R-N/ml-utility-loss to /tmp/pip-req-build-x60ffe6m\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-x60ffe6m\n"," Resolved https://github.com/R-N/ml-utility-loss to commit e9512918d69ed70bcd15f1c49c3e237aef190374\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Building wheels for collected packages: ml-utility-loss\n"," Building wheel for ml-utility-loss (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for ml-utility-loss: filename=ml_utility_loss-0.1.1-py3-none-any.whl size=378135 sha256=3a86f84ff278165130abf3a6f7bb9388a0377e9c13393abc27d261f32187fb33\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-6thuu3xt/wheels/54/30/38/31c28079b5f07b6c793211d38f72e7f0be6764bfdfe016df74\n","Successfully built ml-utility-loss\n","Installing collected packages: ml-utility-loss\n"," Attempting uninstall: ml-utility-loss\n"," Found existing installation: ml-utility-loss 0.1.1\n"," Uninstalling ml-utility-loss-0.1.1:\n"," Successfully uninstalled ml-utility-loss-0.1.1\n","Successfully installed ml-utility-loss-0.1.1\n"]}],"source":["!pip install optuna optuna-dashboard\n","!pip install git+https://github.com/R-N/ml-utility-loss\n","!pip install git+https://github.com/R-N/ml-utility-loss --force-reinstall --no-deps"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3484,"status":"ok","timestamp":1697371904143,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"},"user_tz":-420},"id":"nrYplK5Rkgcn","outputId":"dcf4eeae-56b2-4c93-dc33-063aec15149e"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n"," % Total % Received % Xferd Average Speed Time Time Time Current\n"," Dload Upload Total Spent Left Speed\n"," 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n"," 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n","100 34.7M 100 34.7M 0 0 13.1M 0 0:00:02 0:00:02 --:--:-- 17.5M\n"]}],"source":["%cd /content\n","!curl -Ls https://github.com/ekzhang/bore/releases/download/v0.4.0/bore-v0.4.0-x86_64-unknown-linux-musl.tar.gz | tar zx -C /usr/bin\n","!curl -Lo /usr/bin/cloudflared https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 && chmod +x /usr/bin/cloudflared"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":44342,"status":"ok","timestamp":1697371948480,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"},"user_tz":-420},"id":"MdkECEu12xpL","outputId":"73829e79-70c2-4e12-c738-ce15d90e5cfc"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","Cloning into 'ml-utility-loss'...\n","remote: Enumerating objects: 54455, done.\u001b[K\n","remote: Counting objects: 100% (7368/7368), done.\u001b[K\n","remote: Compressing objects: 100% (3271/3271), done.\u001b[K\n","remote: Total 54455 (delta 4163), reused 7285 (delta 4081), pack-reused 47087\u001b[K\n","Receiving objects: 100% (54455/54455), 239.42 MiB | 10.83 MiB/s, done.\n","Resolving deltas: 100% (6211/6211), done.\n","Updating files: 100% (50325/50325), done.\n","/content/ml-utility-loss\n"]}],"source":["%cd /content\n","!rm -rf ml-utility-loss\n","!git clone https://github.com/R-N/ml-utility-loss\n","%cd ml-utility-loss\n","!rm -rf ml_utility_loss"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ns5hFcVL2yvs"},"outputs":[],"source":["datasets = [\n"," \"insurance\",\n"," \"treatment\",\n"," \"contraceptive\"\n","]\n","\n","model_dir = \"/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UdvXYv3c3LXy"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import json\n","import os\n","\n","dataset_dir = \"datasets\"\n","dataset_name = \"treatment\"\n","\n","df = pd.read_csv(os.path.join(dataset_dir, f\"{dataset_name}.csv\"))\n","with open(os.path.join(dataset_dir, f\"{dataset_name}.json\")) as f:\n"," info = json.load(f)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Vrl2QkoV3o_8"},"outputs":[],"source":["from ml_utility_loss.util import split_df_ratio\n","\n","task = info[\"task\"]\n","target = info[\"target\"]\n","cat_features = info[\"cat_features\"]\n","mixed_features = info[\"mixed_features\"]\n","longtail_features = info[\"longtail_features\"]\n","integer_features = info[\"integer_features\"]\n","\n","dfs = {\n"," \"df\": df,\n","}\n","dfs_test = {}\n","for i in range(5):\n"," train, test = split_df_ratio(df, ratio=0.2, i=i, seed=42)\n"," dfs[i] = train\n"," dfs_test[i] = test\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a-SjylvlYl7i"},"outputs":[],"source":["from ml_utility_loss.util import mkdir\n","\n","model_name = \"realtabformer\"\n","df_name = 3\n","#model_name_2 = f\"{model_name}_{dataset_name}_{df_name}\"\n","model_dir_2 = os.path.join(model_dir, model_name, dataset_name, str(df_name))\n","mkdir(model_dir_2)\n","params_path = os.path.join(model_dir_2, f\"params.json\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1697375527010,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"},"user_tz":-420},"id":"-YDZUU9QUCwx","outputId":"e7608573-f8cb-4b44-a8db-0fb088d61147"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/realtabformer/treatment/3\n"]}],"source":["print(model_dir_2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NgahtU1q9uLO"},"outputs":[],"source":["\n","from ml_utility_loss.tuning import create_objective\n","import ml_utility_loss.synthesizers.realtabformer.params as PARAMS\n","from ml_utility_loss.util import filter_dict_2, filter_dict\n","from copy import deepcopy\n","\n","params = getattr(PARAMS, dataset_name).BEST\n","\n","model_params={\n"," **params,\n","}\n","\n","model_params[\"epochs\"] = min(model_params.pop(\"epochs\", 100), 100)\n","\n","model_params_ = deepcopy(model_params)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["c50b731f02a449a9b45103069f4f7f1f","8b711dcc883b4a5ea5580b6033044568","941d784de1f94bf382b875d2f4e902d8","06f1d325461e45d986ca92ed7763c06c","cf183620ec88415181febd8d639656e5","a79d746c0992418d9d2b1c8b93f7c52f","b0ad7760d42c4e8e844f46a4c6fd7d27","5bbafc9b78434f96b3320f860c6a1666","ebfb668fada444e28794b30122f0e123","34cd0bea60554071933074b75a7c6689","12513cd366e54425843cce520414d0f3","ef91306c079a4b9796591e1a6e666874","d25d12efe79e41c99eb5b0bc5d5b278d","d548ef70fcfc477e92ae9af81f0f8bcc","f1758d61d6254a16800764c56a5d1ea7","fa5f3621d3e049b4a890ca7b1b6600e3","361ecc4d770648d7949bdd3f6bfa6499","5daab13d10b443e4a1561c12805145f6","e64aa696fd70473ea134104b15af24c6","8b778a0c8bf64b3b83cdf3cace237466","7e54d622953b4b6ca0704ad3665503d2","57f504d7ffb64e61af63a66ff76903de","bf63dd54300d4fdd8c04d513f4b07047","ae49cf470e204098b762407d81722665","8a8f6cb142924968a6496250ddfce370","d8c9917906eb453cbc5d16332a9ddb7a","ab6e5c7cd6a04727905ca923838c0711","3fdc0e7efc6f4fae9162d007bd8a7754","c072c1aa9b314f01bfc41e08367a3347","41a67df594794567870ad2d766d7b8b3","d226377f400543279429aed766014cb8","1508878514f9426bae5cb899906897d8","80f4c00c043446c395f942b8efc85e8f","eedb89563da64fd69618531c7af28366","6183322a7f144c429c1a9220fccf3102","df241bccc5fe49cd9effd698c177b46f","cbd73a419cfb4457a150f9d09e6dbdce","873691950555404e8dda87a87491acd8","f9d79250133c42e2ba024b8b6ce0733a","384dbbe207524ab6ad39b19fedad9446","115abcf1bd294be482a6fb8fd193e40f","08ac24b6bff24736933c3431de441ce9","8899ac88f69c47a787aa057cdd561828","7a0e0cb2035e46529f5ba0f0562ba6a8","ae196c5000854740baa5faad6531602a","3e819133ed9e4eaea312c707c16f6f60","40d456808e0a49fcb78372a33e3a99db","3c9f6dbbacaa4d40a280cbb43a64c8cd","b3a8803744e24a97ba9ad443b70b61e7","eccfcfbc465f4065bff22d7765f0d794","97f774495fa0442cb7f6083f023c8845","eb0a1f436f1c4c919fadec62e96980e3","c97476f4de954a37b97c6d34068711c1","f9fbc4382ba04d5281f771ca48f33f23","ae56815848f0456f864e35662fb0a9ee","319d77e9f652475db816d4fbb5f42f89","b3f3d7ed2a2f402ba7cf23d2c7709b77","a314d3c360f747089d805bd9ea8d874c","b8032ab440f149cebc4e944f075daa58","94518a027dfb4c8abb8112f6af25f01e","a055d0605f7d44b7ae578396be6b8a35","7e4e211fbb654858a4d7131512a2e156","77241e8a21cc437ab546eef3bfb66111","4c111d0edcbb414b92bc47941e9df128","8ac5c2a99882456383591447a97d6bca","8a8fea33d1984f568874eccfc86ca52f","161a6ea319bc4e4a9af00ef7a6c5d4f7","59255ba6f844480a950d61523f836d75","99a5727d6c024a5e84b816b986c05bff","a3fe086e52d546ae8a81e3e14f2be071","c78bec0a3b0549c487f618dc39e0a296","705ff4150b9d49bd8fa5a19aec2f8173","3ae433a5b343421fb870671b14160a4f","e3ec2d9ee6914eb984ac3636ff71c44b","fb1424cbb5d34c83b529863e52311455","0ba102c6e8714bfbace58a4d79ba1026","8ccbb46c770d411db2bcbb8d09171e09","6ad13c2d84b340d780be712d4c86f5d2","e1204eb294ac4d6c80e3b3f18ee0771a","fb3e30e67e134f4294e6e4d892f061db","4c53038cabf941d79ff863bc8e195c07","81f9a037dd0b43f89cec8b55fad1e73e","58c65a1092d64e90b49426ec1b7c23d1","7cbe63107dcc431d91a0e913ecebc279","ad7acc761883460c8a2ddd1ce84bacab","3f279fbd912945f69f77a37af2425dd1","a44c7162354646ef8338914237a006e5","863ee701258e4c2e91a94d608ff2b7ac","b27fb1cc29354907a9773c7157e6a22a","45106287b1e64d9fbb448019f9bc97f7","c603fb37b6fa42a0bc98597da14045e0","8ab8db55aeea43e39012aedd38653864","7523d6b525bc4f3daca41b9adf43dd0f","ff646bde1bab434a97c67fd9dc9914ab","1f38110bc95544d5aa0b842705ad8bea","aeb7948012344df0a25aa92412117239","5d85e782e67d4ff7ac35bc543c978d53","b92fc83410384efb885fcdaba81ad1d1","ae372347fc534a87bf62ffaa405f7656","ade87809c53a4b5084b1c85c1467a70d","369d5cd7f6a0401782fd4aa8ae87de1f","f20b40a0908d4fe2a8d19c2c919c16ef","3d8a2be8a7e84d1e88f8ee9534d556df","09211e163bb04856a4bc1316ffa95a81","fddcbb928b924d22a418578923be212c","13d33a992b114ba7b4f98c6bd3f4738d","c1ac23add90647d1957fddafb90e410e","f87bd0e19c224e8f968162579d6db022","ec919e48b3814671bfdc7b134850ab74","f8683dc126714884a483d741c979d73f"]},"id":"wGsDQTlk8Zl1","outputId":"98d4ce23-83d8-4665-eb22-aaa03d3075c4","executionInfo":{"status":"ok","timestamp":1697377433501,"user_tz":-420,"elapsed":1906493,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Computing the sensitivity threshold...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ml_utility_loss/synthesizers/realtabformer/wrapper.py:184: UserWarning: The `bos_token_id`, `eos_token_id`, and `vocab_size` attributes will be replaced when the `.fit` method is run.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/ml_utility_loss/synthesizers/realtabformer/wrapper.py:400: UserWarning: Duplicate rate (0.0) in the data is zero. The `qt_interval` will be set to qt_interval_unique=100.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/ml_utility_loss/synthesizers/realtabformer/wrapper.py:427: UserWarning: qt_interval adjusted from 100 to 21...\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Bootstrap round: 0%| | 0/100 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c50b731f02a449a9b45103069f4f7f1f"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Sensitivity threshold summary:\n","count 100.000000\n","mean 0.000021\n","std 0.004963\n","min -0.010845\n","25% -0.003834\n","50% 0.000164\n","75% 0.003828\n","max 0.012331\n","dtype: float64\n","Sensitivity threshold: 0.007889908256880733 qt_max: 0.05\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='410' max='410' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [410/410 03:21, Epoch 4/5]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>100</td>\n"," <td>0.933800</td>\n"," </tr>\n"," <tr>\n"," <td>200</td>\n"," <td>0.695700</td>\n"," </tr>\n"," <tr>\n"," <td>300</td>\n"," <td>0.675500</td>\n"," </tr>\n"," <tr>\n"," <td>400</td>\n"," <td>0.659600</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ef91306c079a4b9796591e1a6e666874"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 5, sensitivity_threshold: 0.007889908256880733, val_sensitivity: -0.022904470656764233, val_sensitivities: [-0.023034076015727396, -0.02347094801223241, -0.023645696810834425, -0.022335080821319356, -0.022378768020969855, -0.023536478811708166, -0.022728265618173878, -0.023536478811708166, -0.02443206640454347, -0.023689384010484926, -0.02294670161642639, -0.02167977282656182, -0.022706422018348625, -0.021330275229357796, -0.022116644823066845]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='820' max='820' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [820/820 03:31, Epoch 9/10]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>500</td>\n"," <td>0.643100</td>\n"," </tr>\n"," <tr>\n"," <td>600</td>\n"," <td>0.629500</td>\n"," </tr>\n"," <tr>\n"," <td>700</td>\n"," <td>0.621000</td>\n"," </tr>\n"," <tr>\n"," <td>800</td>\n"," <td>0.611200</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bf63dd54300d4fdd8c04d513f4b07047"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 10, sensitivity_threshold: 0.007889908256880733, val_sensitivity: -0.019469200524246395, val_sensitivities: [-0.016153342070773263, -0.016546526867627784, -0.01711446046308432, -0.01551987767584098, -0.01549803407601573, -0.02135211882918305, -0.02185452162516383, -0.02058759283529926, -0.022728265618173874, -0.021505024027959806, -0.021417649628658806, -0.019451725644386193, -0.020172564438619485, -0.02008519003931848, -0.022051114023591084]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='1230' max='1230' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [1230/1230 03:25, Epoch 14/15]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>900</td>\n"," <td>0.602000</td>\n"," </tr>\n"," <tr>\n"," <td>1000</td>\n"," <td>0.593300</td>\n"," </tr>\n"," <tr>\n"," <td>1100</td>\n"," <td>0.584600</td>\n"," </tr>\n"," <tr>\n"," <td>1200</td>\n"," <td>0.579600</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"eedb89563da64fd69618531c7af28366"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 15, sensitivity_threshold: 0.007889908256880733, val_sensitivity: -0.011805009465559922, val_sensitivities: [-0.007852774137177805, -0.009862385321100918, -0.007656181738750545, -0.005144167758846657, -0.00791830493665356, -0.012308868501529053, -0.012614678899082566, -0.012003058103975535, -0.01737658366098733, -0.012592835299257319, -0.016000436871996506, -0.012854958497160332, -0.01486456968108344, -0.013117081695063346, -0.014908256880733948]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='1640' max='1640' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [1640/1640 03:15, Epoch 19/20]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>1300</td>\n"," <td>0.572900</td>\n"," </tr>\n"," <tr>\n"," <td>1400</td>\n"," <td>0.568600</td>\n"," </tr>\n"," <tr>\n"," <td>1500</td>\n"," <td>0.561000</td>\n"," </tr>\n"," <tr>\n"," <td>1600</td>\n"," <td>0.555100</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae196c5000854740baa5faad6531602a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 20, sensitivity_threshold: 0.007889908256880733, val_sensitivity: -0.012872433377020534, val_sensitivities: [-0.012418086500655309, -0.01698339886413281, -0.014078200087374402, -0.013990825688073395, -0.015650939274792487, -0.010342944517256444, -0.01117300131061599, -0.006738750546089994, -0.01250546089995631, -0.008136740934906073, -0.018425076452599394, -0.012964176496286587, -0.013357361293141108, -0.014755351681957189, -0.01156618610747051]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='2050' max='2050' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [2050/2050 03:17, Epoch 24/25]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>1700</td>\n"," <td>0.547200</td>\n"," </tr>\n"," <tr>\n"," <td>1800</td>\n"," <td>0.544100</td>\n"," </tr>\n"," <tr>\n"," <td>1900</td>\n"," <td>0.536200</td>\n"," </tr>\n"," <tr>\n"," <td>2000</td>\n"," <td>0.527600</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"319d77e9f652475db816d4fbb5f42f89"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 25, sensitivity_threshold: 0.007889908256880733, val_sensitivity: -0.006920780544633756, val_sensitivities: [-0.01010266491917868, -0.01174093490607252, -0.0075251201397990375, -0.00903232852774137, -0.007656181738750545, -0.0004914809960681511, -0.006301878549584972, 0.0002512013979903897, -0.005821319353429444, 0.0005570117955439066, -0.010998252512013982, -0.009097859327217125, -0.009272608125819135, -0.01029925731760594, -0.00628003494975972]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='2460' max='2460' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [2460/2460 03:29, Epoch 29/30]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>2100</td>\n"," <td>0.514700</td>\n"," </tr>\n"," <tr>\n"," <td>2200</td>\n"," <td>0.513500</td>\n"," </tr>\n"," <tr>\n"," <td>2300</td>\n"," <td>0.503400</td>\n"," </tr>\n"," <tr>\n"," <td>2400</td>\n"," <td>0.487400</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"161a6ea319bc4e4a9af00ef7a6c5d4f7"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 30, sensitivity_threshold: 0.007889908256880733, val_sensitivity: 0.0028098150575214817, val_sensitivities: [0.004685452162516384, 0.007459589340323288, 0.004641764962865881, 0.01152249890782001, 0.01259283529925732, 0.005493665356050679, 0.00298165137614679, 0.010714285714285713, -0.00027304499781563904, 0.002304499781564003, -0.00700087374399301, -0.00156181738750546, -0.0032656181738750538, -0.0022608125819134984, -0.005886850152905198]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='2870' max='2870' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [2870/2870 03:25, Epoch 34/35]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>2500</td>\n"," <td>0.463500</td>\n"," </tr>\n"," <tr>\n"," <td>2600</td>\n"," <td>0.461700</td>\n"," </tr>\n"," <tr>\n"," <td>2700</td>\n"," <td>0.443800</td>\n"," </tr>\n"," <tr>\n"," <td>2800</td>\n"," <td>0.425400</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ad13c2d84b340d780be712d4c86f5d2"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Saving not-best model...\n","Critic round: 35, sensitivity_threshold: 0.007889908256880733, val_sensitivity: 0.0095012378039901, val_sensitivities: [0.007153778942769769, 0.007328527741371779, 0.007219309742245524, 0.01490825688073395, 0.011063783311489736, 0.015607252075141983, 0.010889034512887728, 0.014667977282656184, 0.006323722149410225, 0.016131498470948014, 0.004161205766710354, 0.009927916120576672, 0.003287461773700307, 0.0036588029707295766, 0.010190039318479685]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='3280' max='3280' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [3280/3280 03:24, Epoch 39/40]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>2900</td>\n"," <td>0.385600</td>\n"," </tr>\n"," <tr>\n"," <td>3000</td>\n"," <td>0.386600</td>\n"," </tr>\n"," <tr>\n"," <td>3100</td>\n"," <td>0.369200</td>\n"," </tr>\n"," <tr>\n"," <td>3200</td>\n"," <td>0.350100</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b27fb1cc29354907a9773c7157e6a22a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 1408 samples generated. Sampling efficiency is: 100.0000%\n","Critic round: 40, sensitivity_threshold: 0.007889908256880733, val_sensitivity: 0.019852191641182466, val_sensitivities: [0.024126256006989952, 0.01980122324159022, 0.022990388816076894, 0.028844473569244213, 0.031684141546526864, 0.0222258628221931, 0.018272171253822633, 0.01947356924421145, 0.011762778505897771, 0.021373962429008304, 0.01147881170816951, 0.020915246832678026, 0.014820882481432943, 0.014733508082131934, 0.015279598077763221]\n","Stopping training, no improvement in critic...\n"]},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/2648 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ade87809c53a4b5084b1c85c1467a70d"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","69\n","Generated 0 invalid samples out of total 2688 samples generated. Sampling efficiency is: 100.0000%\n"]}],"source":["from ml_utility_loss.synthesizers.realtabformer.wrapper import REaLTabFormer\n","from ml_utility_loss.synthesizers.realtabformer.params.default import GPT2_PARAMS, REALTABFORMER_PARAMS\n","\n","from transformers.models.gpt2 import GPT2Config\n","\n","train = dfs[df_name]\n","\n","num_bootstrap = model_params.pop(\"num_bootstrap\", 100)\n","\n","gpt2_params = filter_dict(model_params, GPT2_PARAMS)\n","realtabformer_params = filter_dict(model_params, REALTABFORMER_PARAMS)\n","\n","# Non-relational or parent table.\n","rtf_model = REaLTabFormer(\n"," tabular_config=GPT2Config(**gpt2_params),\n"," **realtabformer_params\n",")\n","\n","rtf_model.experiment_id = str(df_name)\n","rtf_model.fit(\n"," train,\n"," num_bootstrap=num_bootstrap\n",")\n","\n","synth_df = rtf_model.sample(n_samples=len(train))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qUHHYJNRJdDy","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1697377447882,"user_tz":-420,"elapsed":14387,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"}},"outputId":"c96b8e20-3f17-44db-afe5-e26625dfa917"},"outputs":[{"output_type":"stream","name":"stdout","text":["Copying artefacts from: best-disc-model\n","Copying artefacts from: mean-best-disc-model\n","Copying artefacts from: not-best-disc-model\n","Copying artefacts from: last-epoch-model\n"]}],"source":["import torch\n","from copy import deepcopy\n","import json\n","import locale\n","locale.getpreferredencoding = lambda: \"UTF-8\"\n","\n","rtf_model.experiment_id = str(df_name)\n","rtf_model.save(model_dir_2)\n","!rm -rf {model_dir_2}/{df_name}/not-best-disc-model\n","!rm -rf {model_dir_2}/{df_name}/last-epoch-model\n","!rm -rf {model_dir_2}/{df_name}/rtf_model.pt\n","with open(params_path, \"w\") as f:\n"," json.dump(model_params_, f, indent=4)"]},{"cell_type":"code","source":["!ls {model_dir_2}/{df_name}"],"metadata":{"id":"tFaNVV55-bbt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1697377448897,"user_tz":-420,"elapsed":1019,"user":{"displayName":"Xrinky Stinky","userId":"04754368263729372317"}},"outputId":"e9e57bd7-4fb4-4d16-9108-f1a78fa85f00"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["best-disc-model mean-best-disc-model rtf_config.json\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7SQgzf5-P249"},"outputs":[],"source":["synth_path = os.path.join(model_dir_2, f\"synth.csv\")\n","train_path = os.path.join(model_dir_2, f\"train.csv\")\n","val_path = os.path.join(model_dir_2, f\"val.csv\")\n","test_path = os.path.join(model_dir_2, f\"test.csv\")\n","\n","synth_df.to_csv(synth_path)\n","dfs[df_name].to_csv(train_path)\n","dfs_test[df_name].to_csv(val_path)\n","dfs_test[df_name].to_csv(test_path)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"nB724X33v4Qd"},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 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