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{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"COkMuAOy2J5o","executionInfo":{"status":"ok","timestamp":1697366282630,"user_tz":-420,"elapsed":636,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"f-_1KQbkorXJ","executionInfo":{"status":"ok","timestamp":1697366285620,"user_tz":-420,"elapsed":2997,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}},"outputId":"a7619ee9-097f-43a5-9ee5-8d5f300c3e66"},"execution_count":2,"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"]}]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":118654,"status":"ok","timestamp":1697366404268,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"},"user_tz":-420},"id":"lqNWRhS42u4P","outputId":"c7c16739-fc77-40f0-b68d-1beb34612358"},"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 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alembic>=1.5.0->optuna) (4.5.0)\n","Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna) (3.0.0)\n","Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->optuna-dashboard) (1.11.3)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->optuna-dashboard) (1.3.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->optuna-dashboard) (3.2.0)\n","Requirement already satisfied: MarkupSafe>=0.9.2 in /usr/local/lib/python3.10/dist-packages (from Mako->alembic>=1.5.0->optuna) (2.1.3)\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-7fbs41ic\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-7fbs41ic\n"," Resolved https://github.com/R-N/ml-utility-loss to commit e9512918d69ed70bcd15f1c49c3e237aef190374\n"," Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: alpharelu in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (1.1)\n","Requirement already satisfied: entmax in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (1.1)\n","Requirement already satisfied: accelerate>=0.20.3 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (0.23.0)\n","Requirement already satisfied: catboost>=1.0.3 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (1.2.2)\n","Requirement already satisfied: category-encoders>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (2.6.2)\n","Requirement already satisfied: datasets>=2.6.1 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (2.14.5)\n","Requirement already satisfied: delu==0.0.18 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filename=ml_utility_loss-0.1.1-py3-none-any.whl size=378135 sha256=dfedbaf2fefc49165faf5f8076b7e8d2e93f51b5e239e9bb752e13950fb30a6c\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-0j32_l8x/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":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2269,"status":"ok","timestamp":1697366406517,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"},"user_tz":-420},"id":"nrYplK5Rkgcn","outputId":"1b0aeb1b-5830-4eac-cf9d-20164ef1cd7b"},"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 35.6M 0 --:--:-- --:--:-- --:--:-- 208M\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":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":44610,"status":"ok","timestamp":1697366451114,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"},"user_tz":-420},"id":"MdkECEu12xpL","outputId":"888cd75e-cc7d-4291-8f9a-36d8eb29e6d0"},"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 | 11.73 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":6,"metadata":{"id":"ns5hFcVL2yvs","executionInfo":{"status":"ok","timestamp":1697366451114,"user_tz":-420,"elapsed":8,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"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":49,"metadata":{"id":"UdvXYv3c3LXy","executionInfo":{"status":"ok","timestamp":1697371015863,"user_tz":-420,"elapsed":395,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"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":50,"metadata":{"id":"Vrl2QkoV3o_8","executionInfo":{"status":"ok","timestamp":1697371016527,"user_tz":-420,"elapsed":10,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"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":58,"metadata":{"executionInfo":{"elapsed":437,"status":"ok","timestamp":1697375566727,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"},"user_tz":-420},"id":"a-SjylvlYl7i"},"outputs":[],"source":["from ml_utility_loss.util import mkdir\n","\n","model_name = \"realtabformer\"\n","df_name = 4\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":59,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1697375568298,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"},"user_tz":-420},"id":"-YDZUU9QUCwx","outputId":"d58f6423-b8ec-40a2-8dd4-1bc8405c123a"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/realtabformer/treatment/4\n"]}],"source":["print(model_dir_2)"]},{"cell_type":"code","execution_count":60,"metadata":{"executionInfo":{"elapsed":2,"status":"ok","timestamp":1697375568298,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"},"user_tz":-420},"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":61,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["5d9f976475944e2a81cd0c9275506397","3e35fe806b89478095eceacc6fbc34fd","d82f2afd44994874a294fec19002288d","b068b16016304e3c850c25cf179c5f10","081ccef9fc0447729d20f6d8a6d8a97e","a0fb1c6dcd174361922e24e20a3b164e","7ed5d9895e434c558dfa0dcc2dfcea46","747a98a778c548beadb95750ee1bbc8f","95e8f8876fed44639890995e87367599","3167aaa3c0cf4a41b5e39786e3a7d29f","9ed27597a7e941cfa25c9b456cb0e1bf","fc2eba8850dd41e1a861b9d1b7ce20e5","a57286c6f88b44c6ae276882f0e1ba70","12441e7ca0a145f0ad678be101348abf","a5189896982b4959be7cafa35f3f96bd","396bf3cb09664fa98a2f51666a2742bb","1d5802cad1b9492694d0a7b0da0d3a1f","979af1a438e44b6585586a6df8fcec5a","b667c2e346ff4c1f958f5e4d3c96c005","2d4eda89856b445e99ceb5aa5f1e5e40","b2cce945e98549e187e2b1345c8c9f40","a0ef9c05008d434e9a4531c50fbe881e","dfd14eba042f46239f09988e334ed1a8","bca636083b9849578f5d98320a99620a","4c2adf8d2e554a588a403111ce134faf","e8db760a5181453d89691bd87f57bb4f","31426986ce2b416c94a7dd5086060847","3ce2a73342a841bcb582c9fe7a1586f9","b1ac9925b7e04479acb32712ca93ba19","68ab34e7b3dd4e7f80d89f0e27e56a54","389daf12a7df4abd8c1cef780cb0184e","a0d66c101476476dae1d0952e618ca66","6fdbe37535c94378a550352230d61e30","ccf832a19ce046eb8d1766dc2113f211","6a48bd2c26fb412389e3aa06ca00c589","1d6e05a3def04932aeed7a7686456e71","8245a457e3f14fc78b7d557ad788bf21","ac08e916df804fd5bf3c3ca9deb088ed","07e3fd93ff74488bb6130ea29506ced6","3b03034d3d4642d9ba3285b96cb87255","e223defd7a674ef0bcb4d281bed89da1","d897d73f3bd64063b013317f46286c32","caf9fd7fa2f74fa591dfaca99318c78a","6dd63543efbc4da8948cea58eaf0abe8","a34e9598d9f2450c82face91d0a6d88d","935b5e0ccb4142ebb89ecaecbbcbee7c","27920703cf6a44b0b144948fc01ab593","e21bdec638a44f7a828d0ab6f67d53fd","87751e4f8a8e48a7b3cd32cfb0418734","9459e08eff85461bb2e29b9ea912b97e","a3a0f6f21ab1444e874420d2f0d1a69e","26cfb7c03c644149b94944532b0edccf","ba08c7b3dd714247954a54aab6f3ef72","4c24f016a16049838c0f470e1b99a95e","3043fb6ca70c4bb2a32be5a98df3a243","6694a17a532d474a8ae9fbb0a64d5d32","d0b69659d33c46f484bc3e3651517765","b612f49af6fe4595b2f97dafe7745448","000f08d8c7a04635b96ec67b640f7f50","fffefb1b0d4a4fbbbbfea59e38aa9476","0711a165b2434f78b37db0abd0d649cb","0a7841c06e7841338d58a624e66ea8db","1b341c9946b54e4c8c6cc7180c5e303d","e53f4c6ebac6423d88f8fe5127c29a41","a06aef5c57164c188517940d98c0f53c","aa2eebff68e441288291e86f3b70bce5","e9c586690ac3455785e6df8014a643c2","30e6453c66aa41d8b478559a70bf9617","31fd37a49a9f408c8f2f8ab9f7205066","4f3f5d76806746ce8c083b5bfd33fcb4","1763ecb3f99b4ee38b4609b411f0a50e","5c0b94a28a0947fab3d1340e67fe555c","60e8b123849d48dba78ca67ab2555db0","30b19b90c41341748c0629abfc285ae6","1b87b800cdf14b0eac1ae86bbe82b562","a5467caa124a40d0b5ba5a1ea604be56","df7eaae923744268aea5159ed4579317","17bbd2aae26a404c8a733059e2a31e10","cce4152a3a2a407ba776fd5bb285557a","d24b1ce014064e4d8e46cbe59ea9859d","65ba610c6173448c98e7409c467cf381","d9c97d30fd364a1ea8ce178e6e7f54cd","415f01c28770460e8107f9cac63d16cd","052fb06c3a6349e6981b01a6bb013010","a5ed33f8fed34d9485fc88033ae31e16","7f804defc4174da6a1ac98fd19d549c6","5d44f2aabd83401799776e5dd4cc5054","7e8df54f1dd246b283371f49036c3d45","c4b129b97a8745c286d26b28fd3ed302","5dce8f90d4f2468ea33adc5cb280b1ac","a6fe0675b867416494781806b8ce31d6","a7d8225ed5dc4c83807e6b2b67cbb386","607416b35d3643808106ae17c3d424be","a46473384718452e8c4068428d33ad3f","025ea703271141a3a31b1037a4f46f93","944ab07282f64933a6197cd1b73a555f","6ff768d218874da7bbb87eceb56f0c12","30ecb892f7ff467ea6164fdd22c82fc2","9610f57f2f5646e9a2c71e845e2a8940","9500fed4ebac4fcd9a768181b55b9854","e708a15705404c3b823c6af87996ca7d","4a899f0916f047daad2591e6cb4bc6ae","97995f6851bf473ea2990b56133cf0ca","ed791cf16d6f481d8a0e9163aa421381","d92d3ddbbe154b178505cf5b9a9ff8b9","ae689fddc76347668287f6abf74a91fb","d8fd71c9aa6348f1a04d856b4bcd039c","8bc584f537bd4be59cc9e4bf3d9c3408","722306dba95946138dee7c442774ff2b","922fc2046c4c4b0381db4e472c98c8b6","eeec3b5f018941b8a06efcd935d8a412","5ec4cafa4c46443082d4096fb99a51a3","161d54ab44374f47addc3138dc095456","b7ca4951ca9347caa517b9771383a3b6","a1388786d192439c81c493062e652ecb","01101a18e51f49888bafa16b64c6fdd6","e6696bc93ad84b168fd114ef846a2ac7","dfa4a7097f7a47fb8f4bbb31e85f58b3","e97a3cbaba044df392bd3ac34c168fa1","c06f203ca61541e7b27f5fe4add2f368","6f993588dbea4af395c5e6735c98541a"]},"id":"wGsDQTlk8Zl1","outputId":"1c64092e-bc19-483b-a7e0-1bc67b0ff856","executionInfo":{"status":"ok","timestamp":1697377710290,"user_tz":-420,"elapsed":2141994,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"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":"5d9f976475944e2a81cd0c9275506397"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Sensitivity threshold summary:\n","count 100.000000\n","mean -0.000114\n","std 0.005279\n","min -0.013925\n","25% -0.003249\n","50% -0.000208\n","75% 0.002496\n","max 0.015127\n","dtype: float64\n","Sensitivity threshold: 0.009475753604193973 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 03:06, 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>0.937100</td>\n"," </tr>\n"," <tr>\n"," <td>200</td>\n"," <td>0.697100</td>\n"," </tr>\n"," <tr>\n"," <td>300</td>\n"," <td>0.675300</td>\n"," </tr>\n"," <tr>\n"," <td>400</td>\n"," <td>0.660700</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":"fc2eba8850dd41e1a861b9d1b7ce20e5"}},"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: 0.009475753604193973, val_sensitivity: -0.023942769768457838, val_sensitivities: [-0.024650502402795982, -0.024825251201397985, -0.024999999999999994, -0.024999999999999994, -0.024694189602446476, -0.024475753604193972, -0.02386413280908694, -0.023339886413280907, -0.024038881607688946, -0.024999999999999994, -0.02299038881607689, -0.022990388816076894, -0.022553516819571868, -0.022597204019222366, -0.0231214504150284]\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 03:38, 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>0.630900</td>\n"," </tr>\n"," <tr>\n"," <td>700</td>\n"," <td>0.621500</td>\n"," </tr>\n"," <tr>\n"," <td>800</td>\n"," <td>0.611300</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":"dfd14eba042f46239f09988e334ed1a8"}},"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: 0.009475753604193973, val_sensitivity: -0.019445900684432794, val_sensitivities: [-0.018468763652249892, -0.019145915246832682, -0.018665356050677154, -0.020674967234600262, -0.01717999126256007, -0.019342507645259938, -0.01969200524246396, -0.017332896461336827, -0.019975972040192226, -0.019189602446483184, -0.021199213630406288, -0.020150720838794232, -0.020806028833551767, -0.018577981651376148, -0.021286588029707294]\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 03:20, 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>0.601400</td>\n"," </tr>\n"," <tr>\n"," <td>1000</td>\n"," <td>0.595100</td>\n"," </tr>\n"," <tr>\n"," <td>1100</td>\n"," <td>0.586300</td>\n"," </tr>\n"," <tr>\n"," <td>1200</td>\n"," <td>0.579800</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":"ccf832a19ce046eb8d1766dc2113f211"}},"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: 0.009475753604193973, val_sensitivity: -0.012288481141692151, val_sensitivities: [-0.006214504150283967, -0.008005679335954564, -0.007241153342070774, -0.012177806902577542, -0.010954565312363477, -0.01792267365661861, -0.0141218872870249, -0.012702053298383573, -0.015214067278287466, -0.014012669287898646, -0.011784622105723024, -0.014515072083879421, -0.013510266491917867, -0.014340323285277415, -0.011609873307121012]\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 03:30, 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>0.573900</td>\n"," </tr>\n"," <tr>\n"," <td>1400</td>\n"," <td>0.568600</td>\n"," </tr>\n"," <tr>\n"," <td>1500</td>\n"," <td>0.561800</td>\n"," </tr>\n"," <tr>\n"," <td>1600</td>\n"," <td>0.555200</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":"a34e9598d9f2450c82face91d0a6d88d"}},"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: 0.009475753604193973, val_sensitivity: -0.013045725935634193, val_sensitivities: [-0.013772389689820879, -0.013182612494539099, -0.015257754477937964, -0.01737658366098733, -0.016044124071647008, -0.016371778069025775, -0.017070773263433817, -0.011937527304499781, -0.016917868064657053, -0.013947138488422894, -0.008748361730013107, -0.010757972913936218, -0.007809086937527304, -0.006738750546089994, -0.009753167321974662]\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 03:34, 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>0.547000</td>\n"," </tr>\n"," <tr>\n"," <td>1800</td>\n"," <td>0.544900</td>\n"," </tr>\n"," <tr>\n"," <td>1900</td>\n"," <td>0.535500</td>\n"," </tr>\n"," <tr>\n"," <td>2000</td>\n"," <td>0.527000</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":"6694a17a532d474a8ae9fbb0a64d5d32"}},"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: 0.009475753604193973, val_sensitivity: -0.010331294597349642, val_sensitivities: [-0.00973132372214941, -0.00975316732197466, -0.010670598514635211, -0.011566186107470509, -0.005537352555701179, -0.00992791612057667, -0.00973132372214941, -0.005559196155526429, -0.009949759720401923, -0.008944954128440366, -0.01370685889034513, -0.012461773700305812, -0.011697247706422017, -0.013663171690694626, -0.01206858890345129]\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 03:16, 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>0.514600</td>\n"," </tr>\n"," <tr>\n"," <td>2200</td>\n"," <td>0.512800</td>\n"," </tr>\n"," <tr>\n"," <td>2300</td>\n"," <td>0.501600</td>\n"," </tr>\n"," <tr>\n"," <td>2400</td>\n"," <td>0.485700</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":"e9c586690ac3455785e6df8014a643c2"}},"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: 0.009475753604193973, val_sensitivity: -0.006409640308722877, val_sensitivities: [-0.0020205329838357353, -0.0021297509829619914, -0.003287461773700305, -0.008158584534731322, -0.0023918741808650057, -0.008879423328964612, -0.007983835736129315, -0.0040956749672345985, -0.010168195718654436, -0.0031345565749235464, -0.007546963739624289, -0.008901266928789862, -0.008333333333333333, -0.007284840541721276, -0.011828309305373525]\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 03:19, 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>0.460100</td>\n"," </tr>\n"," <tr>\n"," <td>2600</td>\n"," <td>0.457300</td>\n"," </tr>\n"," <tr>\n"," <td>2700</td>\n"," <td>0.440300</td>\n"," </tr>\n"," <tr>\n"," <td>2800</td>\n"," <td>0.420900</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":"17bbd2aae26a404c8a733059e2a31e10"}},"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: 35, sensitivity_threshold: 0.009475753604193973, val_sensitivity: 0.006074705111402359, val_sensitivities: [0.0013215377894276988, 0.0048602009611183935, 0.002566622979467017, 0.0007317605941459163, 0.004641764962865881, 0.010998252512013982, 0.007590650939274793, 0.017747924858016604, 0.009600262123197903, 0.011828309305373525, 0.004576234163390128, 0.0027850589777195294, 0.002326343381389254, 0.007394058540847533, 0.0021515945827872444]\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 03:11, 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>0.380400</td>\n"," </tr>\n"," <tr>\n"," <td>3000</td>\n"," <td>0.383100</td>\n"," </tr>\n"," <tr>\n"," <td>3100</td>\n"," <td>0.363600</td>\n"," </tr>\n"," <tr>\n"," <td>3200</td>\n"," <td>0.343700</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":"c4b129b97a8745c286d26b28fd3ed302"}},"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: 40, sensitivity_threshold: 0.009475753604193973, val_sensitivity: 0.019830348041357217, val_sensitivities: [0.022072957623416344, 0.022007426823940586, 0.02530581039755352, 0.015978593272171257, 0.01934250764525994, 0.016197029270423772, 0.016502839667977286, 0.029696373962429017, 0.014471384884228923, 0.021417649628658803, 0.018490607252075145, 0.016502839667977286, 0.017966360856269115, 0.02248798602009611, 0.019014853647881174]\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='3690' max='3690' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [3690/3690 03:35, Epoch 44/45]\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>3300</td>\n"," <td>0.298300</td>\n"," </tr>\n"," <tr>\n"," <td>3400</td>\n"," <td>0.303300</td>\n"," </tr>\n"," <tr>\n"," <td>3500</td>\n"," <td>0.286500</td>\n"," </tr>\n"," <tr>\n"," <td>3600</td>\n"," <td>0.270200</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":"9500fed4ebac4fcd9a768181b55b9854"}},"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: 45, sensitivity_threshold: 0.009475753604193973, val_sensitivity: 0.04867409349060726, val_sensitivities: [0.05046963739624291, 0.057044560943643526, 0.04125163826998691, 0.04304281345565749, 0.05381170816950634, 0.04756443861948449, 0.04468108344255134, 0.054750982961992135, 0.04690913062472695, 0.042999126256006985, 0.04299912625600699, 0.048918741808650065, 0.05018567059851464, 0.05267584097859328, 0.05280690257754478]\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":"eeec3b5f018941b8a06efcd935d8a412"}},"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":62,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qUHHYJNRJdDy","executionInfo":{"status":"ok","timestamp":1697377728068,"user_tz":-420,"elapsed":17783,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}},"outputId":"e7c948be-9fd8-452d-c02d-22a9b302e8f4"},"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":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H_IKFmZT-Afy","executionInfo":{"status":"ok","timestamp":1697377728912,"user_tz":-420,"elapsed":848,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}},"outputId":"98fbc993-3215-457a-8024-628252682981"},"execution_count":63,"outputs":[{"output_type":"stream","name":"stdout","text":["best-disc-model mean-best-disc-model rtf_config.json\n"]}]},{"cell_type":"code","execution_count":64,"metadata":{"id":"7SQgzf5-P249","executionInfo":{"status":"ok","timestamp":1697377728912,"user_tz":-420,"elapsed":4,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"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":64,"metadata":{"id":"nB724X33v4Qd","executionInfo":{"status":"ok","timestamp":1697377728912,"user_tz":-420,"elapsed":3,"user":{"displayName":"Xstinky Music","userId":"16602649867630946699"}}},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[]},"kernelspec":{"display_name":"Python 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