diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..80aba343fb36d7812c8e2d2a6d5a347065dd748a 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +Model/**/*.json filter=lfs diff=lfs merge=lfs -text +*.json filter=lfs diff=lfs merge=lfs -text diff --git a/Model/Copy of RealTabFormer.ipynb b/Model/Copy of RealTabFormer.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1448a39a5840604515bcabaf6b04261ca794366d --- /dev/null +++ b/Model/Copy of RealTabFormer.ipynb @@ -0,0 +1 @@ +{"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 /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-m00hss_7\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-m00hss_7\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=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"],"text/html":["\n","
\n"," \n"," \n"," [410/410 03:06, Epoch 4/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
1000.937100
2000.697100
3000.675300
4000.660700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [820/820 03:38, Epoch 9/10]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
5000.643100
6000.630900
7000.621500
8000.611300

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [1230/1230 03:20, Epoch 14/15]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
9000.601400
10000.595100
11000.586300
12000.579800

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [1640/1640 03:30, Epoch 19/20]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
13000.573900
14000.568600
15000.561800
16000.555200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [2050/2050 03:34, Epoch 24/25]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
17000.547000
18000.544900
19000.535500
20000.527000

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [2460/2460 03:16, Epoch 29/30]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
21000.514600
22000.512800
23000.501600
24000.485700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [2870/2870 03:19, Epoch 34/35]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
25000.460100
26000.457300
27000.440300
28000.420900

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [3280/3280 03:11, Epoch 39/40]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
29000.380400
30000.383100
31000.363600
32000.343700

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [3690/3690 03:35, Epoch 44/45]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
33000.298300
34000.303300
35000.286500
36000.270200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00=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) (1.26.0)\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 /usr/local/lib/python3.10/dist-packages (from optuna) (2.0.21)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from optuna) (4.66.1)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from optuna) (6.0.1)\n","Requirement already satisfied: bottle in 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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-0lvntdr8\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-0lvntdr8\n"," Resolved https://github.com/R-N/ml-utility-loss to commit 999fa9d4bdbb358553011a9c2171aded09814fc4\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) 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pandas==1.5.3->ml-utility-loss==0.1.1) (2023.3.post1)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2->ml-utility-loss==0.1.1) (1.3.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2->ml-utility-loss==0.1.1) (3.2.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (3.12.4)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (0.18.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (23.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (6.0.1)\n","Requirement 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catboost>=1.0.3->ml-utility-loss==0.1.1) (1.16.0)\n","Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category-encoders>=2.3.0->ml-utility-loss==0.1.1) (0.14.0)\n","Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category-encoders>=2.3.0->ml-utility-loss==0.1.1) (0.5.3)\n","Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (0.3.7)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (3.4.1)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (0.70.15)\n","Requirement already satisfied: fsspec[http]<2023.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (2023.6.0)\n","Requirement already 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aiohttp->datasets>=2.6.1->ml-utility-loss==0.1.1) (1.4.0)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.6.1->ml-utility-loss==0.1.1) (1.3.1)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.28.0->ml-utility-loss==0.1.1) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.28.0->ml-utility-loss==0.1.1) (2.0.6)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.28.0->ml-utility-loss==0.1.1) (2023.7.22)\n","Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy>=1.3.0->optuna>=2.10.1->ml-utility-loss==0.1.1) (3.0.0)\n","Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.10/dist-packages (from 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nbconvert->jupyter>=1.0.0->ml-utility-loss==0.1.1) (0.4)\n","Requirement already satisfied: jinja2>=3.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert->jupyter>=1.0.0->ml-utility-loss==0.1.1) (3.1.2)\n","Requirement already satisfied: jupyter-core>=4.7 in /usr/local/lib/python3.10/dist-packages (from nbconvert->jupyter>=1.0.0->ml-utility-loss==0.1.1) (5.4.0)\n","Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert->jupyter>=1.0.0->ml-utility-loss==0.1.1) (0.2.2)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert->jupyter>=1.0.0->ml-utility-loss==0.1.1) (2.1.3)\n","Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert->jupyter>=1.0.0->ml-utility-loss==0.1.1) (0.8.4)\n","Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from 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https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-yf3xds_g\n"," Resolved https://github.com/R-N/ml-utility-loss to commit 999fa9d4bdbb358553011a9c2171aded09814fc4\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=377794 sha256=9b7126c41fb084aa428702d22bb7d3a3cf9182b8ca66b7d8af8a0f2e9ffa11d6\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-n3qnkp_i/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"]}]},{"cell_type":"code","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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nrYplK5Rkgcn","executionInfo":{"status":"ok","timestamp":1697292409156,"user_tz":-420,"elapsed":1874,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"f5c0d8c8-5db0-41de-d519-1f2de72dde35"},"execution_count":3,"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 28.6M 0 0:00:01 0:00:01 --:--:-- 49.8M\n"]}]},{"cell_type":"code","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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MdkECEu12xpL","executionInfo":{"status":"ok","timestamp":1697292448980,"user_tz":-420,"elapsed":39831,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"20d65f9c-c668-472f-d53d-5d82ae59037e"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","Cloning into 'ml-utility-loss'...\n","remote: Enumerating objects: 54414, done.\u001b[K\n","remote: Counting objects: 100% (7327/7327), done.\u001b[K\n","remote: Compressing objects: 100% (3252/3252), done.\u001b[K\n","remote: Total 54414 (delta 4134), reused 7251 (delta 4059), pack-reused 47087\u001b[K\n","Receiving objects: 100% (54414/54414), 239.42 MiB | 11.05 MiB/s, done.\n","Resolving deltas: 100% (6182/6182), done.\n","Updating files: 100% (50325/50325), done.\n","/content/ml-utility-loss\n"]}]},{"cell_type":"code","source":["datasets = [\n"," \"insurance\",\n"," \"treatment\",\n"," \"contraceptive\"\n","]\n","\n","model_dir = \"/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/\""],"metadata":{"id":"ns5hFcVL2yvs","executionInfo":{"status":"ok","timestamp":1697292448980,"user_tz":-420,"elapsed":9,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import json\n","import os\n","\n","dataset_dir = \"datasets\"\n","dataset_name = \"insurance\"\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)"],"metadata":{"id":"UdvXYv3c3LXy","executionInfo":{"status":"ok","timestamp":1697293687692,"user_tz":-420,"elapsed":345,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":61,"outputs":[]},{"cell_type":"code","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","for i in range(5):\n"," train, test = split_df_ratio(df, ratio=0.2, i=i, seed=42)\n"," dfs[i] = train\n"],"metadata":{"id":"Vrl2QkoV3o_8","executionInfo":{"status":"ok","timestamp":1697293688208,"user_tz":-420,"elapsed":6,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":62,"outputs":[]},{"cell_type":"code","source":["from ml_utility_loss.util import mkdir\n","\n","model_name = \"lct_ae\"\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","model_path = os.path.join(model_dir_2, f\"model.pt\")\n","state_path = os.path.join(model_dir_2, f\"state.json\")\n","params_path = os.path.join(model_dir_2, f\"params.json\")"],"metadata":{"id":"a-SjylvlYl7i","executionInfo":{"status":"ok","timestamp":1697293835449,"user_tz":-420,"elapsed":310,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":83,"outputs":[]},{"cell_type":"code","source":["print(model_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-YDZUU9QUCwx","executionInfo":{"status":"ok","timestamp":1697293835938,"user_tz":-420,"elapsed":3,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"01031387-3520-4bc6-9a19-e68e98ace51d"},"execution_count":84,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/lct_ae/insurance/4/model.pt\n"]}]},{"cell_type":"code","source":["\n","from ml_utility_loss.tuning import create_objective\n","import ml_utility_loss.synthesizers.lct_gan.params as PARAMS\n","from ml_utility_loss.util import filter_dict_2\n","\n","params = getattr(PARAMS, dataset_name).BEST\n","ae_params = filter_dict_2(params, PARAMS.default.AE_PARAMS)\n","\n","dataset_kwargs={\n"," \"categorical_columns\": cat_features,\n"," \"mixed_columns\": mixed_features,\n"," \"log_columns\": longtail_features,\n"," \"integer_columns\": integer_features,\n","}\n","\n","model_params={\n"," **ae_params,\n"," **dataset_kwargs,\n","}"],"metadata":{"id":"NgahtU1q9uLO","executionInfo":{"status":"ok","timestamp":1697293835938,"user_tz":-420,"elapsed":2,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":85,"outputs":[]},{"cell_type":"code","source":["from ml_utility_loss.synthesizers.lct_gan.pipeline import create_ae\n","\n","ae, recon = create_ae(\n"," dfs[df_name],\n"," **model_params\n",")"],"metadata":{"id":"wGsDQTlk8Zl1","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1697293862237,"user_tz":-420,"elapsed":26301,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"7b2b392f-511c-4682-9e19-befd0985961e"},"execution_count":86,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/mixture/_base.py:274: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/mixture/_base.py:274: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/mixture/_base.py:119: ConvergenceWarning: Number of distinct clusters (6) found smaller than n_clusters (10). Possibly due to duplicate points in X.\n"," .fit(X)\n","/usr/local/lib/python3.10/dist-packages/sklearn/mixture/_base.py:274: ConvergenceWarning: Initialization 1 did not converge. Try different init parameters, or increase max_iter, tol or check for degenerate data.\n"," warnings.warn(\n","100%|██████████| 267/267 [00:23<00:00, 11.29it/s]\n"]}]},{"cell_type":"code","source":["import torch\n","from copy import deepcopy\n","import json\n","\n","!rm -rf params_path\n","!rm -rf model_path\n","!rm -rf state_path\n","torch.save(ae, model_path)\n","torch.save(deepcopy(ae.state_dict()), state_path)\n","with open(params_path, \"w\") as f:\n"," json.dump(model_params, f, indent=4)"],"metadata":{"id":"qUHHYJNRJdDy","executionInfo":{"status":"ok","timestamp":1697293862794,"user_tz":-420,"elapsed":560,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":87,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"7SQgzf5-P249","executionInfo":{"status":"ok","timestamp":1697293862794,"user_tz":-420,"elapsed":3,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":87,"outputs":[]}]} \ No newline at end of file diff --git a/Model/LCT-GAN.ipynb b/Model/LCT-GAN.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..9a3d2388a2b0c3b078175d3f43e2ca12af58d18d --- /dev/null +++ b/Model/LCT-GAN.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1697338983293,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"COkMuAOy2J5o"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":134250,"status":"ok","timestamp":1697339117539,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"lqNWRhS42u4P","outputId":"e84ccf2b-2878-42e0-93a3-ff5484659054"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: optuna in /usr/local/lib/python3.10/dist-packages 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in /usr/local/lib/python3.10/dist-packages (from pandas==1.5.3->ml-utility-loss==0.1.1) (2023.3.post1)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2->ml-utility-loss==0.1.1) (1.3.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.2.2->ml-utility-loss==0.1.1) (3.2.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (3.12.4)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (0.18.0)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (23.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (6.0.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (2023.6.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (2.31.0)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0->ml-utility-loss==0.1.1) (0.13.3)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.20.3->ml-utility-loss==0.1.1) (5.9.5)\n","Requirement already satisfied: graphviz in /usr/local/lib/python3.10/dist-packages (from catboost>=1.0.3->ml-utility-loss==0.1.1) (0.20.1)\n","Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost>=1.0.3->ml-utility-loss==0.1.1) (5.15.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from catboost>=1.0.3->ml-utility-loss==0.1.1) (1.16.0)\n","Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from category-encoders>=2.3.0->ml-utility-loss==0.1.1) (0.14.0)\n","Requirement already satisfied: patsy>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from category-encoders>=2.3.0->ml-utility-loss==0.1.1) (0.5.3)\n","Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (0.3.7)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (3.4.1)\n","Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (0.70.15)\n","Requirement already satisfied: fsspec[http]<2023.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (2023.6.0)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets>=2.6.1->ml-utility-loss==0.1.1) (3.8.6)\n","Requirement already satisfied: scikit-plot>=0.3.7 in /usr/local/lib/python3.10/dist-packages (from dython~=0.6.4.post1->ml-utility-loss==0.1.1) (0.3.7)\n","Requirement already satisfied: colorama>=0.3.9 in /usr/local/lib/python3.10/dist-packages (from icecream>=2.1.2->ml-utility-loss==0.1.1) (0.4.6)\n","Requirement already satisfied: pygments>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from icecream>=2.1.2->ml-utility-loss==0.1.1) (2.16.1)\n","Requirement already satisfied: executing>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from icecream>=2.1.2->ml-utility-loss==0.1.1) (2.0.0)\n","Requirement already satisfied: asttokens>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from icecream>=2.1.2->ml-utility-loss==0.1.1) (2.4.0)\n","Requirement already satisfied: notebook in 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contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5.2->ml-utility-loss==0.1.1) (1.1.1)\n","Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5.2->ml-utility-loss==0.1.1) (0.12.1)\n","Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5.2->ml-utility-loss==0.1.1) (4.43.1)\n","Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5.2->ml-utility-loss==0.1.1) (1.4.5)\n","Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5.2->ml-utility-loss==0.1.1) (9.4.0)\n","Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.5.2->ml-utility-loss==0.1.1) (3.1.1)\n","Requirement already satisfied: alembic>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from optuna>=2.10.1->ml-utility-loss==0.1.1) (1.12.0)\n","Requirement already satisfied: cmaes>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from optuna>=2.10.1->ml-utility-loss==0.1.1) (0.10.0)\n","Requirement already satisfied: colorlog in /usr/local/lib/python3.10/dist-packages (from optuna>=2.10.1->ml-utility-loss==0.1.1) (6.7.0)\n","Requirement already satisfied: sqlalchemy>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from optuna>=2.10.1->ml-utility-loss==0.1.1) (2.0.21)\n","Requirement already satisfied: Faker>=10 in /usr/local/lib/python3.10/dist-packages (from rdt>=1.3.0->ml-utility-loss==0.1.1) (19.10.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->ml-utility-loss==0.1.1) (4.5.0)\n","Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->ml-utility-loss==0.1.1) (11.7.99)\n","Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->ml-utility-loss==0.1.1) (8.5.0.96)\n","Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->ml-utility-loss==0.1.1) (11.10.3.66)\n","Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->ml-utility-loss==0.1.1) (11.7.99)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.13.0->ml-utility-loss==0.1.1) (67.7.2)\n","Requirement already satisfied: wheel in /usr/local/lib/python3.10/dist-packages (from nvidia-cublas-cu11==11.10.3.66->torch>=1.13.0->ml-utility-loss==0.1.1) (0.41.2)\n","Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from skorch->ml-utility-loss==0.1.1) (0.9.0)\n","Requirement already satisfied: Mako in 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git+https://github.com/R-N/ml-utility-loss\n"," Cloning https://github.com/R-N/ml-utility-loss to /tmp/pip-req-build-yspnsuf0\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-yspnsuf0\n"," Resolved https://github.com/R-N/ml-utility-loss to commit 999fa9d4bdbb358553011a9c2171aded09814fc4\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=377794 sha256=7a664c2bcb3f0a7a226cb81c9c0b40b83e9f6c8c57a8062fb09e86f9acf2c5ac\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-v_yffpto/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":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3102,"status":"ok","timestamp":1697339120628,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"nrYplK5Rkgcn","outputId":"99ef840a-1e12-4d44-dc63-4582cadbf145"},"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 16.1M 0 0:00:02 0:00:02 --:--:-- 30.5M\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":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":45206,"status":"ok","timestamp":1697339165812,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"MdkECEu12xpL","outputId":"3d9f7090-2f07-4557-ea71-1d4c37e26568"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","Cloning into 'ml-utility-loss'...\n","remote: Enumerating objects: 54414, done.\u001b[K\n","remote: Counting objects: 100% (7327/7327), done.\u001b[K\n","remote: Compressing objects: 100% (3252/3252), done.\u001b[K\n","remote: Total 54414 (delta 4134), reused 7251 (delta 4059), pack-reused 47087\u001b[K\n","Receiving objects: 100% (54414/54414), 239.42 MiB | 10.53 MiB/s, done.\n","Resolving deltas: 100% (6182/6182), 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":5,"metadata":{"executionInfo":{"elapsed":621,"status":"ok","timestamp":1697339166428,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"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":24,"metadata":{"executionInfo":{"elapsed":643,"status":"ok","timestamp":1697339656419,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"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 = \"insurance\"\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":25,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1697339657116,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"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":54,"metadata":{"executionInfo":{"elapsed":8,"status":"ok","timestamp":1697340402293,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"a-SjylvlYl7i"},"outputs":[],"source":["from ml_utility_loss.util import mkdir\n","\n","model_name = \"lct_gan\"\n","df_name = \"df\"\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","model_path = os.path.join(model_dir_2, f\"model.pt\")\n","g_state_path = os.path.join(model_dir_2, f\"g_state.json\")\n","d_state_path = os.path.join(model_dir_2, f\"d_state.json\")\n","params_path = os.path.join(model_dir_2, f\"params.json\")"]},{"cell_type":"code","execution_count":55,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1697340402293,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"-YDZUU9QUCwx","outputId":"861e920a-0dbc-40e6-e66a-8e4337024155"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/lct_gan/insurance/df/model.pt\n"]}],"source":["print(model_path)"]},{"cell_type":"code","execution_count":56,"metadata":{"id":"foDLahGAYvGi","executionInfo":{"status":"ok","timestamp":1697340402293,"user_tz":-420,"elapsed":5,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["import torch\n","\n","ae_model_dir = \"models\"\n","ae_model_name = \"lct_ae\"\n","ae_df_name = df_name\n","ae_model_dir_2 = os.path.join(model_dir, ae_model_name, dataset_name, str(ae_df_name))\n","ae_model_path = os.path.join(ae_model_dir_2, f\"model.pt\")\n","ae_state_path = os.path.join(ae_model_dir_2, f\"state.json\")\n","ae_params_path = os.path.join(ae_model_dir_2, f\"params.json\")\n","\n","lct_ae = torch.load(ae_model_path)"]},{"cell_type":"code","execution_count":57,"metadata":{"id":"NgahtU1q9uLO","executionInfo":{"status":"ok","timestamp":1697340402293,"user_tz":-420,"elapsed":5,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["\n","from ml_utility_loss.tuning import create_objective\n","import ml_utility_loss.synthesizers.lct_gan.params as PARAMS\n","from ml_utility_loss.util import filter_dict_2\n","\n","params = getattr(PARAMS, dataset_name).BEST\n","gan_params = filter_dict_2(params, PARAMS.default.GAN_PARAMS)\n","\n","model_params={\n"," **gan_params,\n","}"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wGsDQTlk8Zl1","executionInfo":{"status":"ok","timestamp":1697340400942,"user_tz":-420,"elapsed":146357,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"b81e3144-3d83-4921-a155-6e342d90d26d"},"outputs":[{"output_type":"stream","name":"stderr","text":[" 0%| | 2/988 [00:00<01:49, 9.00it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 1/988] [D loss: -0.001967] [G loss: -0.487792]\n","[Epoch 2/988] [D loss: -0.002800] [G loss: -0.487687]\n"]},{"output_type":"stream","name":"stderr","text":[" 0%| | 4/988 [00:00<02:27, 6.68it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 3/988] [D loss: -0.004177] [G loss: -0.487615]\n","[Epoch 4/988] [D loss: -0.006083] [G loss: -0.487512]\n"]},{"output_type":"stream","name":"stderr","text":[" 1%| | 6/988 [00:00<02:37, 6.25it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 5/988] [D loss: -0.009377] [G loss: -0.487437]\n","[Epoch 6/988] [D loss: -0.010733] [G loss: -0.487326]\n"]},{"output_type":"stream","name":"stderr","text":[" 1%| | 8/988 [00:01<02:39, 6.14it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 7/988] [D loss: -0.012113] [G loss: -0.487197]\n","[Epoch 8/988] [D loss: -0.013204] [G loss: -0.487061]\n"]},{"output_type":"stream","name":"stderr","text":[" 1%| | 10/988 [00:01<02:43, 5.97it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 9/988] [D loss: -0.015497] [G loss: -0.486880]\n","[Epoch 10/988] [D loss: -0.017150] [G loss: -0.486731]\n"]},{"output_type":"stream","name":"stderr","text":[" 1%| | 12/988 [00:01<02:41, 6.05it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 11/988] [D loss: -0.019195] [G loss: -0.486568]\n","[Epoch 12/988] [D loss: -0.021499] [G loss: -0.486394]\n"]},{"output_type":"stream","name":"stderr","text":[" 1%|▏ | 14/988 [00:02<02:39, 6.10it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 13/988] [D loss: -0.023568] [G loss: -0.486216]\n","[Epoch 14/988] [D loss: -0.024711] [G loss: -0.485999]\n"]},{"output_type":"stream","name":"stderr","text":[" 2%|▏ | 16/988 [00:02<02:39, 6.11it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 15/988] [D loss: -0.027280] [G loss: -0.485797]\n","[Epoch 16/988] [D loss: -0.029786] [G loss: -0.485570]\n"]},{"output_type":"stream","name":"stderr","text":[" 2%|▏ | 18/988 [00:02<02:42, 5.98it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 17/988] [D loss: -0.031553] [G loss: -0.485356]\n","[Epoch 18/988] [D loss: -0.033453] [G loss: -0.485090]\n"]},{"output_type":"stream","name":"stderr","text":[" 2%|▏ | 20/988 [00:03<02:41, 6.00it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 19/988] [D loss: -0.036565] [G loss: -0.484834]\n","[Epoch 20/988] [D loss: -0.039103] [G loss: -0.484598]\n"]},{"output_type":"stream","name":"stderr","text":[" 2%|▏ | 22/988 [00:03<02:39, 6.06it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 21/988] [D loss: -0.039932] [G loss: -0.484329]\n","[Epoch 22/988] [D loss: -0.043630] [G loss: -0.484064]\n"]},{"output_type":"stream","name":"stderr","text":[" 2%|▏ | 24/988 [00:03<02:45, 5.82it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 23/988] [D loss: -0.046178] [G loss: -0.483810]\n","[Epoch 24/988] [D loss: -0.047190] [G loss: -0.483559]\n"]},{"output_type":"stream","name":"stderr","text":["\r 3%|▎ | 25/988 [00:04<03:08, 5.11it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 25/988] [D loss: -0.051640] [G loss: -0.483313]\n"]},{"output_type":"stream","name":"stderr","text":["\r 3%|▎ | 26/988 [00:04<04:36, 3.48it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 26/988] [D loss: -0.051384] [G loss: -0.483079]\n"]},{"output_type":"stream","name":"stderr","text":["\r 3%|▎ | 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[G loss: -0.093763]\n","[Epoch 422/988] [D loss: -0.862335] [G loss: -0.094338]\n"]},{"output_type":"stream","name":"stderr","text":[" 43%|████▎ | 424/988 [01:06<01:02, 9.07it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 423/988] [D loss: -0.866794] [G loss: -0.089582]\n","[Epoch 424/988] [D loss: -0.866295] [G loss: -0.093962]\n"]},{"output_type":"stream","name":"stderr","text":[" 43%|████▎ | 426/988 [01:06<01:01, 9.09it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 425/988] [D loss: -0.860660] [G loss: -0.092949]\n","[Epoch 426/988] [D loss: -0.861894] [G loss: -0.089839]\n"]},{"output_type":"stream","name":"stderr","text":[" 43%|████▎ | 428/988 [01:06<01:01, 9.10it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 427/988] [D loss: -0.859169] [G loss: -0.094655]\n","[Epoch 428/988] [D loss: -0.857354] [G loss: -0.093971]\n"]},{"output_type":"stream","name":"stderr","text":[" 44%|████▎ | 430/988 [01:06<01:02, 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[02:10<00:12, 8.87it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 878/988] [D loss: -0.954358] [G loss: -0.019809]\n","[Epoch 879/988] [D loss: -0.947928] [G loss: -0.017368]\n"]},{"output_type":"stream","name":"stderr","text":[" 89%|████████▉ | 881/988 [02:10<00:11, 9.18it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 880/988] [D loss: -0.954608] [G loss: -0.020888]\n","[Epoch 881/988] [D loss: -0.941385] [G loss: -0.019513]\n"]},{"output_type":"stream","name":"stderr","text":[" 89%|████████▉ | 883/988 [02:10<00:12, 8.68it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 882/988] [D loss: -0.955754] [G loss: -0.017597]\n","[Epoch 883/988] [D loss: -0.954466] [G loss: -0.020029]\n"]},{"output_type":"stream","name":"stderr","text":[" 90%|████████▉ | 885/988 [02:11<00:11, 8.78it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 884/988] [D loss: -0.950333] [G loss: -0.017038]\n","[Epoch 885/988] [D loss: -0.953901] [G loss: -0.018435]\n"]},{"output_type":"stream","name":"stderr","text":[" 90%|████████▉ | 887/988 [02:11<00:12, 8.42it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 886/988] [D loss: -0.947800] [G loss: -0.018010]\n","[Epoch 887/988] [D loss: -0.959346] [G loss: -0.018293]\n"]},{"output_type":"stream","name":"stderr","text":[" 90%|█████████ | 890/988 [02:11<00:10, 8.97it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 888/988] [D loss: -0.958343] [G loss: -0.018873]\n","[Epoch 889/988] [D loss: -0.950453] [G loss: -0.017559]\n","[Epoch 890/988] [D loss: -0.955428] [G loss: -0.017424]\n"]},{"output_type":"stream","name":"stderr","text":[" 90%|█████████ | 892/988 [02:11<00:10, 8.86it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 891/988] [D loss: -0.955345] [G loss: -0.016171]\n","[Epoch 892/988] [D loss: -0.948948] [G loss: -0.018566]\n"]},{"output_type":"stream","name":"stderr","text":[" 90%|█████████ | 894/988 [02:12<00:10, 8.84it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 893/988] [D loss: -0.959127] [G loss: -0.017956]\n","[Epoch 894/988] [D loss: -0.956739] [G loss: -0.017093]\n"]},{"output_type":"stream","name":"stderr","text":[" 91%|█████████ | 896/988 [02:12<00:10, 8.85it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 895/988] [D loss: -0.957254] [G loss: -0.017385]\n","[Epoch 896/988] [D loss: -0.962027] [G loss: -0.015809]\n"]},{"output_type":"stream","name":"stderr","text":[" 91%|█████████ | 898/988 [02:12<00:10, 8.70it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 897/988] [D loss: -0.960387] [G loss: -0.017003]\n","[Epoch 898/988] [D loss: -0.956852] [G loss: -0.017131]\n"]},{"output_type":"stream","name":"stderr","text":[" 91%|█████████ | 900/988 [02:12<00:10, 8.76it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 899/988] [D loss: -0.953860] [G loss: -0.015369]\n","[Epoch 900/988] [D loss: -0.961268] [G loss: -0.016845]\n"]},{"output_type":"stream","name":"stderr","text":["\r 91%|█████████ | 901/988 [02:12<00:09, 8.77it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 901/988] [D loss: -0.954357] [G loss: -0.017134]\n","[Epoch 902/988] [D loss: -0.953674] [G loss: -0.016914]\n"]},{"output_type":"stream","name":"stderr","text":[" 91%|█████████▏| 904/988 [02:13<00:09, 8.98it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 903/988] [D loss: -0.957272] [G loss: -0.016457]\n","[Epoch 904/988] [D loss: -0.958851] [G loss: -0.017330]\n"]},{"output_type":"stream","name":"stderr","text":[" 92%|█████████▏| 906/988 [02:13<00:09, 9.10it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 905/988] [D loss: -0.956581] [G loss: -0.017470]\n","[Epoch 906/988] [D loss: -0.958154] [G loss: -0.016399]\n"]},{"output_type":"stream","name":"stderr","text":[" 92%|█████████▏| 908/988 [02:13<00:08, 9.12it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 907/988] [D loss: -0.950372] [G loss: -0.018585]\n","[Epoch 908/988] [D loss: -0.956895] [G loss: -0.017070]\n"]},{"output_type":"stream","name":"stderr","text":[" 92%|█████████▏| 910/988 [02:13<00:08, 9.14it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 909/988] [D loss: -0.954444] [G loss: -0.017095]\n","[Epoch 910/988] [D loss: -0.954852] [G loss: -0.017294]\n"]},{"output_type":"stream","name":"stderr","text":[" 92%|█████████▏| 912/988 [02:14<00:08, 9.03it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 911/988] [D loss: -0.957813] [G loss: -0.016278]\n","[Epoch 912/988] [D loss: -0.956583] [G loss: -0.017512]\n"]},{"output_type":"stream","name":"stderr","text":[" 93%|█████████▎| 914/988 [02:14<00:08, 8.51it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 913/988] [D loss: -0.959670] [G loss: -0.016297]\n","[Epoch 914/988] [D loss: -0.958910] [G loss: -0.016470]\n"]},{"output_type":"stream","name":"stderr","text":[" 93%|█████████▎| 916/988 [02:14<00:08, 8.75it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 915/988] [D loss: -0.955568] [G loss: -0.015381]\n","[Epoch 916/988] [D loss: -0.952899] [G loss: -0.017699]\n"]},{"output_type":"stream","name":"stderr","text":[" 93%|█████████▎| 918/988 [02:14<00:08, 8.51it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 917/988] [D loss: -0.962094] [G loss: -0.017677]\n","[Epoch 918/988] [D loss: -0.957729] [G loss: -0.016148]\n"]},{"output_type":"stream","name":"stderr","text":[" 93%|█████████▎| 920/988 [02:15<00:07, 8.79it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 919/988] [D loss: -0.961015] [G loss: -0.016532]\n","[Epoch 920/988] [D loss: -0.961126] [G loss: -0.015601]\n"]},{"output_type":"stream","name":"stderr","text":[" 93%|█████████▎| 922/988 [02:15<00:07, 8.93it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 921/988] [D loss: -0.956491] [G loss: -0.016506]\n","[Epoch 922/988] [D loss: -0.960503] [G loss: -0.016300]\n"]},{"output_type":"stream","name":"stderr","text":[" 94%|█████████▎| 924/988 [02:15<00:07, 8.76it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 923/988] [D loss: -0.954540] [G loss: -0.017184]\n","[Epoch 924/988] [D loss: -0.962594] [G loss: -0.017034]\n"]},{"output_type":"stream","name":"stderr","text":[" 94%|█████████▎| 926/988 [02:15<00:07, 8.31it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 925/988] [D loss: -0.960882] [G loss: -0.015420]\n","[Epoch 926/988] [D loss: -0.957333] [G loss: -0.015744]\n"]},{"output_type":"stream","name":"stderr","text":[" 94%|█████████▍| 928/988 [02:16<00:07, 8.57it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 927/988] [D loss: -0.956002] [G loss: -0.016953]\n","[Epoch 928/988] [D loss: -0.960081] [G loss: -0.015395]\n"]},{"output_type":"stream","name":"stderr","text":[" 94%|█████████▍| 930/988 [02:16<00:06, 8.89it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 929/988] [D loss: -0.961662] [G loss: -0.015706]\n","[Epoch 930/988] [D loss: -0.956147] [G loss: -0.016441]\n"]},{"output_type":"stream","name":"stderr","text":[" 94%|█████████▍| 932/988 [02:16<00:06, 8.33it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 931/988] [D loss: -0.963935] [G loss: -0.016333]\n","[Epoch 932/988] [D loss: -0.956286] [G loss: -0.016144]\n"]},{"output_type":"stream","name":"stderr","text":[" 95%|█████████▍| 934/988 [02:16<00:06, 8.34it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 933/988] [D loss: -0.960645] [G loss: -0.015749]\n","[Epoch 934/988] [D loss: -0.957854] [G loss: -0.016009]\n"]},{"output_type":"stream","name":"stderr","text":[" 95%|█████████▍| 936/988 [02:17<00:06, 8.51it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 935/988] [D loss: -0.958364] [G loss: -0.016852]\n","[Epoch 936/988] [D loss: -0.964815] [G loss: -0.015181]\n"]},{"output_type":"stream","name":"stderr","text":[" 95%|█████████▍| 938/988 [02:17<00:05, 8.68it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 937/988] [D loss: -0.960404] [G loss: -0.016350]\n","[Epoch 938/988] [D loss: -0.955896] [G loss: -0.015959]\n"]},{"output_type":"stream","name":"stderr","text":[" 95%|█████████▌| 940/988 [02:17<00:06, 7.00it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 939/988] [D loss: -0.963358] [G loss: -0.016548]\n","[Epoch 940/988] [D loss: -0.960240] [G loss: -0.014904]\n"]},{"output_type":"stream","name":"stderr","text":[" 95%|█████████▌| 942/988 [02:17<00:07, 6.54it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 941/988] [D loss: -0.961946] [G loss: -0.016519]\n","[Epoch 942/988] [D loss: -0.958441] [G loss: -0.015538]\n"]},{"output_type":"stream","name":"stderr","text":[" 96%|█████████▌| 944/988 [02:18<00:07, 6.22it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 943/988] [D loss: -0.964411] [G loss: -0.015125]\n","[Epoch 944/988] [D loss: -0.963003] [G loss: -0.015965]\n"]},{"output_type":"stream","name":"stderr","text":[" 96%|█████████▌| 946/988 [02:18<00:07, 5.79it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 945/988] [D loss: -0.955001] [G loss: -0.015362]\n","[Epoch 946/988] [D loss: -0.961568] [G loss: -0.015250]\n"]},{"output_type":"stream","name":"stderr","text":[" 96%|█████████▌| 948/988 [02:18<00:06, 5.81it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 947/988] [D loss: -0.961294] [G loss: -0.014979]\n","[Epoch 948/988] [D loss: -0.967490] [G loss: -0.016580]\n"]},{"output_type":"stream","name":"stderr","text":[" 96%|█████████▌| 950/988 [02:19<00:06, 5.93it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 949/988] [D loss: -0.962174] [G loss: -0.014716]\n","[Epoch 950/988] [D loss: -0.957479] [G loss: -0.014537]\n"]},{"output_type":"stream","name":"stderr","text":[" 96%|█████████▋| 952/988 [02:19<00:06, 5.79it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 951/988] [D loss: -0.964582] [G loss: -0.015061]\n","[Epoch 952/988] [D loss: -0.961298] [G loss: -0.015874]\n"]},{"output_type":"stream","name":"stderr","text":[" 97%|█████████▋| 954/988 [02:19<00:05, 5.73it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 953/988] [D loss: -0.961541] [G loss: -0.015263]\n","[Epoch 954/988] [D loss: -0.961214] [G loss: -0.014789]\n"]},{"output_type":"stream","name":"stderr","text":[" 97%|█████████▋| 956/988 [02:20<00:05, 5.74it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 955/988] [D loss: -0.965787] [G loss: -0.014932]\n","[Epoch 956/988] [D loss: -0.963930] [G loss: -0.014788]\n"]},{"output_type":"stream","name":"stderr","text":[" 97%|█████████▋| 958/988 [02:20<00:05, 5.66it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 957/988] [D loss: -0.963843] [G loss: -0.016051]\n","[Epoch 958/988] [D loss: -0.970053] [G loss: -0.016312]\n"]},{"output_type":"stream","name":"stderr","text":[" 97%|█████████▋| 960/988 [02:21<00:04, 5.60it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 959/988] [D loss: -0.965926] [G loss: -0.015489]\n","[Epoch 960/988] [D loss: -0.960634] [G loss: -0.014853]\n"]},{"output_type":"stream","name":"stderr","text":[" 97%|█████████▋| 962/988 [02:21<00:04, 5.67it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 961/988] [D loss: -0.960950] [G loss: -0.014874]\n","[Epoch 962/988] [D loss: -0.965992] [G loss: -0.016851]\n"]},{"output_type":"stream","name":"stderr","text":[" 98%|█████████▊| 964/988 [02:21<00:04, 5.57it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 963/988] [D loss: -0.965460] [G loss: -0.015494]\n","[Epoch 964/988] [D loss: -0.964142] [G loss: -0.015332]\n"]},{"output_type":"stream","name":"stderr","text":[" 98%|█████████▊| 966/988 [02:22<00:03, 5.59it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 965/988] [D loss: -0.965986] [G loss: -0.014648]\n","[Epoch 966/988] [D loss: -0.965683] [G loss: -0.014597]\n"]},{"output_type":"stream","name":"stderr","text":[" 98%|█████████▊| 968/988 [02:22<00:03, 5.65it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 967/988] [D loss: -0.962241] [G loss: -0.016366]\n","[Epoch 968/988] [D loss: -0.959926] [G loss: -0.014609]\n"]},{"output_type":"stream","name":"stderr","text":[" 98%|█████████▊| 970/988 [02:22<00:03, 5.63it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 969/988] [D loss: -0.966375] [G loss: -0.016024]\n","[Epoch 970/988] [D loss: -0.964031] [G loss: -0.014858]\n"]},{"output_type":"stream","name":"stderr","text":[" 98%|█████████▊| 972/988 [02:23<00:02, 5.61it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 971/988] [D loss: -0.964569] [G loss: -0.015557]\n","[Epoch 972/988] [D loss: -0.964144] [G loss: -0.014704]\n"]},{"output_type":"stream","name":"stderr","text":[" 99%|█████████▊| 974/988 [02:23<00:02, 5.76it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 973/988] [D loss: -0.959249] [G loss: -0.014681]\n","[Epoch 974/988] [D loss: -0.964101] [G loss: -0.015244]\n"]},{"output_type":"stream","name":"stderr","text":[" 99%|█████████▉| 976/988 [02:23<00:02, 5.78it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 975/988] [D loss: -0.964945] [G loss: -0.014171]\n","[Epoch 976/988] [D loss: -0.964288] [G loss: -0.013362]\n"]},{"output_type":"stream","name":"stderr","text":[" 99%|█████████▉| 978/988 [02:24<00:01, 5.80it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 977/988] [D loss: -0.966671] [G loss: -0.014889]\n","[Epoch 978/988] [D loss: -0.966879] [G loss: -0.015204]\n"]},{"output_type":"stream","name":"stderr","text":[" 99%|█████████▉| 980/988 [02:24<00:01, 5.80it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 979/988] [D loss: -0.964580] [G loss: -0.013388]\n","[Epoch 980/988] [D loss: -0.959383] [G loss: -0.012969]\n"]},{"output_type":"stream","name":"stderr","text":[" 99%|█████████▉| 982/988 [02:24<00:01, 5.70it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 981/988] [D loss: -0.965114] [G loss: -0.013826]\n","[Epoch 982/988] [D loss: -0.967760] [G loss: -0.014902]\n"]},{"output_type":"stream","name":"stderr","text":["100%|█████████▉| 984/988 [02:25<00:00, 5.76it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 983/988] [D loss: -0.967309] [G loss: -0.015151]\n","[Epoch 984/988] [D loss: -0.959872] [G loss: -0.014729]\n"]},{"output_type":"stream","name":"stderr","text":["100%|█████████▉| 986/988 [02:25<00:00, 5.80it/s]"]},{"output_type":"stream","name":"stdout","text":["[Epoch 985/988] [D loss: -0.967377] [G loss: -0.014141]\n","[Epoch 986/988] [D loss: -0.964256] [G loss: -0.013743]\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 988/988 [02:25<00:00, 6.77it/s]\n"]},{"output_type":"stream","name":"stdout","text":["[Epoch 987/988] [D loss: -0.962006] [G loss: -0.013196]\n","[Epoch 988/988] [D loss: -0.962739] [G loss: -0.016487]\n","Number of steps: 2\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 2/2 [00:00<00:00, 17.01it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Sampled data length 2048\n"]}],"source":["from ml_utility_loss.synthesizers.lct_gan.pipeline import create_gan\n","\n","gan, synth_df = create_gan(\n"," lct_ae,\n"," dfs[df_name],\n"," **model_params\n",")"]},{"cell_type":"code","execution_count":52,"metadata":{"id":"qUHHYJNRJdDy","executionInfo":{"status":"ok","timestamp":1697340402292,"user_tz":-420,"elapsed":1366,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["import torch\n","from copy import deepcopy\n","import json\n","\n","!rm -rf params_path\n","!rm -rf model_path\n","!rm -rf g_state_path\n","!rm -rf d_state_path\n","torch.save(gan, model_path)\n","torch.save(deepcopy(gan.generator.state_dict()), g_state_path)\n","torch.save(deepcopy(gan.discriminator.state_dict()), d_state_path)\n","with open(params_path, \"w\") as f:\n"," json.dump(model_params, f, indent=4)"]},{"cell_type":"code","execution_count":53,"metadata":{"id":"7SQgzf5-P249","executionInfo":{"status":"ok","timestamp":1697340402292,"user_tz":-420,"elapsed":8,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"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":53,"metadata":{"id":"nB724X33v4Qd","executionInfo":{"status":"ok","timestamp":1697340402293,"user_tz":-420,"elapsed":8,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"1OXbNqjplMbCRXWmSGUzf6XWVRRn-aF-D","authorship_tag":"ABX9TyM1UxcwnfJ0zp1gGD94prGb"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Model/RealTabFormer extract embeddings.ipynb b/Model/RealTabFormer extract embeddings.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..77ee8b7888e06bff943f7d7f42bb82347d186aa9 --- /dev/null +++ b/Model/RealTabFormer extract embeddings.ipynb @@ -0,0 +1 @@ +{"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":2788,"status":"ok","timestamp":1699424405111,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"HfqAIvK1iGpA","outputId":"c07c4737-5255-407b-eee4-2af7aefdbcd3"},"outputs":[{"name":"stdout","output_type":"stream","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":385631,"status":"ok","timestamp":1699423886508,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"lqNWRhS42u4P","outputId":"9d54b617-f5cd-4b9f-9c52-59963705de9b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting optuna\n"," Downloading optuna-3.4.0-py3-none-any.whl (409 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m409.6/409.6 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting optuna-dashboard\n"," Downloading optuna_dashboard-0.13.0-py3-none-any.whl (5.8 MB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.8/5.8 MB\u001b[0m \u001b[31m60.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting alembic>=1.5.0 (from optuna)\n"," Downloading alembic-1.12.1-py3-none-any.whl (226 kB)\n","\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m226.8/226.8 kB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta 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(from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook->jupyter>=1.0.0->ml-utility-loss==0.1.1) (2.21)\n","Building wheels for collected packages: ml-utility-loss, alpharelu, torchtext\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=435424 sha256=29c698559cc3e0ae7eea0c27b4ed0e357213590e94d01d243fd5afd41e3cfafb\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-4gs049zg/wheels/54/30/38/31c28079b5f07b6c793211d38f72e7f0be6764bfdfe016df74\n"," Building wheel for alpharelu (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for alpharelu: filename=alpharelu-1.1-py3-none-any.whl size=3843 sha256=714546f19a87d883352607e45de8591855f9ac4a36b013eca8eced4fccdae472\n"," Stored in directory: /root/.cache/pip/wheels/19/f4/8b/4d08773d0281d53fe80057c78ca51371bcbe8859a469fba852\n"," Building wheel for torchtext (setup.py) ... \u001b[?25l\u001b[?25hdone\n"," Created wheel for torchtext: filename=torchtext-0.6.1-py3-none-any.whl size=64352 sha256=8eb8ec6526604f0211823a39793269b13a93a89e636a168eaa3045480b821a92\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-4gs049zg/wheels/61/f8/1f/28b8e145b4451f1ae42f92d412bc90463e90f560f41e4d00c8\n","Successfully built ml-utility-loss alpharelu torchtext\n","Installing collected packages: tokenizers, sentencepiece, torchinfo, tomli-w, qtpy, pynvml, nvidia-cuda-runtime-cu11, nvidia-cuda-nvrtc-cu11, nvidia-cublas-cu11, numpy, jedi, executing, dill, colorama, asttokens, nvidia-cudnn-cu11, multiprocess, icecream, huggingface-hub, Faker, transformers, torch, torchvision, torchtext, skorch, scikit-plot, rtdl, rdt, qtconsole, pytorch-ranger, libzero, entmax, delu, datasets, category-encoders, catboost, alpharelu, accelerate, torch_optimizer, dython, jupyter, ml-utility-loss\n"," Attempting uninstall: numpy\n"," Found existing installation: numpy 1.23.5\n"," Uninstalling numpy-1.23.5:\n"," Successfully uninstalled numpy-1.23.5\n"," Attempting uninstall: torch\n"," Found existing installation: torch 2.1.0+cu118\n"," Uninstalling torch-2.1.0+cu118:\n"," Successfully uninstalled torch-2.1.0+cu118\n"," Attempting uninstall: torchvision\n"," Found existing installation: torchvision 0.16.0+cu118\n"," Uninstalling torchvision-0.16.0+cu118:\n"," Successfully uninstalled torchvision-0.16.0+cu118\n"," Attempting uninstall: torchtext\n"," Found existing installation: torchtext 0.16.0\n"," Uninstalling torchtext-0.16.0:\n"," Successfully uninstalled torchtext-0.16.0\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","lida 0.0.10 requires fastapi, which is not installed.\n","lida 0.0.10 requires kaleido, which is not installed.\n","lida 0.0.10 requires python-multipart, which is not installed.\n","lida 0.0.10 requires uvicorn, which is not installed.\n","cupy-cuda11x 11.0.0 requires numpy<1.26,>=1.20, but you have numpy 1.26.1 which is incompatible.\n","torchaudio 2.1.0+cu118 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\n","torchdata 0.7.0 requires torch==2.1.0, but you have torch 1.13.1 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed Faker-19.13.0 accelerate-0.24.1 alpharelu-1.1 asttokens-2.4.1 catboost-1.2.2 category-encoders-2.6.3 colorama-0.4.6 datasets-2.14.6 delu-0.0.18 dill-0.3.7 dython-0.6.8 entmax-1.1 executing-2.0.1 huggingface-hub-0.18.0 icecream-2.1.3 jedi-0.19.1 jupyter-1.0.0 libzero-0.0.8 ml-utility-loss-0.1.1 multiprocess-0.70.15 numpy-1.26.1 nvidia-cublas-cu11-11.10.3.66 nvidia-cuda-nvrtc-cu11-11.7.99 nvidia-cuda-runtime-cu11-11.7.99 nvidia-cudnn-cu11-8.5.0.96 pynvml-11.5.0 pytorch-ranger-0.1.1 qtconsole-5.5.0 qtpy-2.4.1 rdt-1.8.0 rtdl-0.0.13 scikit-plot-0.3.7 sentencepiece-0.1.99 skorch-0.15.0 tokenizers-0.13.3 tomli-w-1.0.0 torch-1.13.1 torch_optimizer-0.3.0 torchinfo-1.8.0 torchtext-0.6.1 torchvision-0.14.1 transformers-4.28.0\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-67ndixnd\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-67ndixnd\n"," Resolved https://github.com/R-N/ml-utility-loss to commit 88cad1fe670a454c22556ffab0b88ff417a72048\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=435424 sha256=210f7ff00596fab3624b9cbbde601e202f0467572831bffb9d4c78d6d9075fd9\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-7f3thfcw/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":1724,"status":"ok","timestamp":1699424406827,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"nrYplK5Rkgcn","outputId":"1c2ed5da-2d6c-40a9-ad4f-b969109dc8b5"},"outputs":[{"name":"stdout","output_type":"stream","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 50.0M 0 --:--:-- --:--:-- --:--:-- 104M\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":{"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"]},{"cell_type":"code","execution_count":409,"metadata":{"executionInfo":{"elapsed":363,"status":"ok","timestamp":1699431279191,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"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_subdir = \"mean-best-disc-model\"\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":410,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1539,"status":"ok","timestamp":1699431282047,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"-YDZUU9QUCwx","outputId":"a3290331-ba34-4283-b12c-fe26143693f9"},"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":411,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1699431282047,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"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":412,"metadata":{"executionInfo":{"elapsed":7,"status":"ok","timestamp":1699431282048,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"wGsDQTlk8Zl1"},"outputs":[],"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","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"]},{"cell_type":"code","execution_count":413,"metadata":{"id":"nB724X33v4Qd","executionInfo":{"status":"ok","timestamp":1699431298162,"user_tz":-420,"elapsed":16120,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["model_dir_3 = f\"{model_dir_2}/{df_name}\"\n","rtf_model = rtf_model.load_from_dir(\n"," model_dir_3,\n"," config_file=\"rtf_config.json\",\n"," model_file=f\"{model_subdir}/pytorch_model.bin\"\n",")"]},{"cell_type":"code","execution_count":414,"metadata":{"id":"5S-S8SKlbaZE","executionInfo":{"status":"ok","timestamp":1699431298162,"user_tz":-420,"elapsed":5,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["text_embedding = rtf_model.model.transformer.wte\n","position_embedding = rtf_model.model.transformer.wpe"]},{"cell_type":"code","execution_count":415,"metadata":{"id":"83txVTxksE7V","executionInfo":{"status":"ok","timestamp":1699431298162,"user_tz":-420,"elapsed":5,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["model_dir_4 = f\"{model_dir_3}/{model_subdir}\""]},{"cell_type":"code","execution_count":416,"metadata":{"id":"hbIV6cI4bf5f","executionInfo":{"status":"ok","timestamp":1699431299504,"user_tz":-420,"elapsed":1346,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["import torch\n","import os\n","from copy import deepcopy\n","\n","torch.save(text_embedding, os.path.join(model_dir_4, \"text_embedding.pt\"))\n","torch.save(deepcopy(text_embedding.state_dict()), os.path.join(model_dir_4, \"text_embedding.states.pt\"))"]},{"cell_type":"code","execution_count":417,"metadata":{"id":"16Ot4B7Zrumx","executionInfo":{"status":"ok","timestamp":1699431299504,"user_tz":-420,"elapsed":6,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["import torch\n","import os\n","from copy import deepcopy\n","\n","torch.save(position_embedding, os.path.join(model_dir_4, \"position_embedding.pt\"))\n","torch.save(deepcopy(position_embedding.state_dict()), os.path.join(model_dir_4, \"position_embedding.states.pt\"))"]},{"cell_type":"code","execution_count":418,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lPJcW_9psoYR","executionInfo":{"status":"ok","timestamp":1699431299504,"user_tz":-420,"elapsed":6,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"3396842a-4b26-4927-ed43-25ebf330d34d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["768"]},"metadata":{},"execution_count":418}],"source":["text_embedding.weight.shape[-1]"]},{"cell_type":"code","execution_count":418,"metadata":{"id":"axQZ1NOWuPIE","executionInfo":{"status":"ok","timestamp":1699431299504,"user_tz":-420,"elapsed":5,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Model/RealTabFormer.ipynb b/Model/RealTabFormer.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1af95828140cff08d756c4ecbd0ece4e79e50fff --- /dev/null +++ b/Model/RealTabFormer.ipynb @@ -0,0 +1 @@ +{"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"],"text/html":["\n","

\n"," \n"," \n"," [410/410 03:21, Epoch 4/5]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
1000.933800
2000.695700
3000.675500
4000.659600

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [820/820 03:31, Epoch 9/10]\n","
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StepTraining Loss
5000.643100
6000.629500
7000.621000
8000.611200

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [1230/1230 03:25, Epoch 14/15]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
9000.602000
10000.593300
11000.584600
12000.579600

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [1640/1640 03:15, Epoch 19/20]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
13000.572900
14000.568600
15000.561000
16000.555100

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [2050/2050 03:17, Epoch 24/25]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
17000.547200
18000.544100
19000.536200
20000.527600

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [2460/2460 03:29, Epoch 29/30]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
21000.514700
22000.513500
23000.503400
24000.487400

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [2870/2870 03:25, Epoch 34/35]\n","
\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
StepTraining Loss
25000.463500
26000.461700
27000.443800
28000.425400

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00"],"text/html":["\n","

\n"," \n"," \n"," [3280/3280 03:24, Epoch 39/40]\n","
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StepTraining Loss
29000.385600
30000.386600
31000.369200
32000.350100

"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 0%| | 0/1308 [00:00=0.20.3 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (0.25.0)\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) (2.6.3)\n","Requirement already satisfied: datasets>=2.6.1 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (2.16.1)\n","Requirement already satisfied: delu==0.0.18 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (0.0.18)\n","Requirement already satisfied: dython~=0.6.4.post1 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) (0.6.8)\n","Requirement already satisfied: icecream>=2.1.2 in /usr/local/lib/python3.10/dist-packages (from ml-utility-loss==0.1.1) 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ml-utility-loss: filename=ml_utility_loss-0.1.1-py3-none-any.whl size=468064 sha256=d2e2df86cd6a7414b54114de1a5b39dfa209ec7bdf96104f8fd6e1f40359e618\n"," Stored in directory: /root/.cache/pip/wheels/fd/ae/ad/45ef81978c0a87b3b639287726b0327d7c54589a6ed68c17b6\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","Processing /content/ml-utility-loss\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=468064 sha256=d8ea3a92f99c28c8076d99af83ed02d555e2d8680b573f95c8faf0cb15eadbf6\n"," Stored in directory: /root/.cache/pip/wheels/fd/ae/ad/45ef81978c0a87b3b639287726b0327d7c54589a6ed68c17b6\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"]}]},{"cell_type":"code","source":["import os\n","datasets = [\n"," \"insurance\",\n"," \"treatment\",\n"," \"contraceptive\"\n","]\n","models = [\n"," \"lct_gan\",\n"," \"realtabformer\",\n"," \"tab_ddpm\",\n"," \"tvae\"\n","]\n","\n","data_dir = \"synthetics\"\n","model = \".\"\n","data_dir = os.path.join(data_dir, model)\n","dataset_dir = \"datasets\""],"metadata":{"id":"vKq9n0J3wHH1","executionInfo":{"status":"ok","timestamp":1704105946552,"user_tz":-420,"elapsed":301,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["for d in datasets:\n"," !ls {data_dir}/{d} | grep info"],"metadata":{"id":"5muTC284DH1D","executionInfo":{"status":"ok","timestamp":1704105947393,"user_tz":-420,"elapsed":344,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"f79c4caa-ea20-4963-9962-1dead4763fda"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["info.csv\n","info.csv\n","info.csv\n"]}]},{"cell_type":"code","source":["import json\n","import os\n","infos = {}\n","for dataset in datasets:\n"," with open(os.path.join(dataset_dir, f\"{dataset}.json\")) as f:\n"," info = json.load(f)\n"," infos[dataset] = info"],"metadata":{"id":"iMeMVZfs0f_X","executionInfo":{"status":"ok","timestamp":1704105947393,"user_tz":-420,"elapsed":7,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["import os\n","from ml_utility_loss.loss_learning.evaluation.pipeline import score_datasets\n","\n","for dataset in datasets:\n"," score_datasets(os.path.join(data_dir, dataset), list(range(5)), infos[dataset])"],"metadata":{"id":"YpS3ifzNHpE6","colab":{"base_uri":"https://localhost:8080/","height":391},"outputId":"f7a52eb2-ce57-44ef-fe32-e213faf87cf5","executionInfo":{"status":"error","timestamp":1704105948284,"user_tz":-420,"elapsed":897,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Loaded info_out 20 tab_ddpm_4\n"]},{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mscore_datasets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/content/ml-utility-loss/ml_utility_loss/loss_learning/evaluation/pipeline.py\u001b[0m in \u001b[0;36mscore_datasets\u001b[0;34m(data_dir, subfolders, info, info_out, ml_utility_params, save_info, drop_first_column, augmenter)\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0mdataset_types\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDATASET_TYPES_VAL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"{t}.csv\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataset_types\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mdf_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 39\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0maugmenter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0mdf_synth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maugmenter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maugment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 209\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 212\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 330\u001b[0m )\n\u001b[0;32m--> 331\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m 948\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 949\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 950\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 951\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 952\u001b[0m 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\u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m 1440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1441\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1442\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1443\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1444\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m 1733\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1735\u001b[0;31m self.handles = get_handle(\n\u001b[0m\u001b[1;32m 1736\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1737\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m 854\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 855\u001b[0m \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 856\u001b[0;31m handle = open(\n\u001b[0m\u001b[1;32m 857\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 858\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'synthetics/./insurance/0/train.csv'"]}]},{"cell_type":"code","source":["\n","!git pull\n","!git add .\n","!git commit -m \"a\"\n","!git push"],"metadata":{"id":"XcRHtfEqKuSl","executionInfo":{"status":"aborted","timestamp":1704105948285,"user_tz":-420,"elapsed":10,"user":{"displayName":"Rizqi 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24d50a3e7e6baee21e4801cfca8972d152cc4657\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) (0.24.0)\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) (2.14.6)\n","Requirement already satisfied: delu==0.0.18 in /usr/local/lib/python3.10/dist-packages (from 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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.git\n"," Cloning https://github.com/R-N/ml-utility-loss.git to /tmp/pip-req-build-99m4rbmk\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss.git /tmp/pip-req-build-99m4rbmk\n"," Resolved https://github.com/R-N/ml-utility-loss.git to commit 24d50a3e7e6baee21e4801cfca8972d152cc4657\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=396570 sha256=f1a733056ddab47ed8572c2cf763404def3220b9dbc2c4cb9d19326196e76825\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-ce0u6g3p/wheels/4c/bf/aa/7a48a646b041e8e425159fc8b8a571fe8fabcb1c6196eb94ad\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"]}]},{"cell_type":"code","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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qZbCP3H6zcpq","executionInfo":{"status":"ok","timestamp":1698341548860,"user_tz":-420,"elapsed":40021,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"23812d78-85a2-487b-b2be-df16446a519f"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","Cloning into 'ml-utility-loss'...\n","remote: Enumerating objects: 56195, done.\u001b[K\n","remote: Counting objects: 100% (9108/9108), done.\u001b[K\n","remote: Compressing objects: 100% (3950/3950), done.\u001b[K\n","remote: Total 56195 (delta 5193), reused 9050 (delta 5135), pack-reused 47087\u001b[K\n","Receiving objects: 100% (56195/56195), 282.12 MiB | 14.52 MiB/s, done.\n","Resolving deltas: 100% (7241/7241), done.\n","Updating files: 100% (50924/50924), done.\n","/content/ml-utility-loss\n"]}]},{"cell_type":"code","source":["import os\n","datasets = [\n"," \"insurance\",\n"," \"treatment\",\n"," \"contraceptive\"\n","]\n","\n","data_dir = \"/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/\"\n","model = \"realtabformer\"\n","data_dir = os.path.join(data_dir, model)\n","dataset_dir = \"datasets\""],"metadata":{"id":"vKq9n0J3wHH1","executionInfo":{"status":"ok","timestamp":1698341845922,"user_tz":-420,"elapsed":3,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["for d in datasets:\n"," !ls {data_dir}/{d} | grep info"],"metadata":{"id":"5muTC284DH1D","executionInfo":{"status":"ok","timestamp":1698341847255,"user_tz":-420,"elapsed":562,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["import json\n","import os\n","infos = {}\n","for dataset in datasets:\n"," with open(os.path.join(dataset_dir, f\"{dataset}.json\")) as f:\n"," info = json.load(f)\n"," infos[dataset] = info"],"metadata":{"id":"iMeMVZfs0f_X","executionInfo":{"status":"ok","timestamp":1698341847757,"user_tz":-420,"elapsed":504,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["import os\n","from ml_utility_loss.loss_learning.estimator.pipeline import score_datasets\n","\n","for dataset in datasets:\n"," score_datasets(os.path.join(data_dir, dataset), list(range(5)), infos[dataset])"],"metadata":{"id":"YpS3ifzNHpE6","colab":{"base_uri":"https://localhost:8080/","height":0,"referenced_widgets":["3945695bc81740fcb1ede9059bab0138","533d63c2846940dba53e3df30c6e63fe","d995077ff55145a5babe4300304d68ae","15bb4fb9dcd14a408bd3cd55e3e3eca9","6487ffd39d8d4be6826ae9266310d649","2e15dcde9df14a6da8a759b6c025526b","31d967962f77486fb4a1441c521b25b1","e984b333f4a24fed8a23fdc4a42468c7","dd588bbf80b245a48401c28af3129f43","00fd0b55767046969ea63559e6c7c3e3","fcb8b579ebed438bbcf76bf58a389b0c","aa330ce2cac047898db25f45c21dae2b","3a919208a2c44b22b707c24713e48847","1c750c7166e14aa18071887329c55221","138d5ef5cfa34c04a7befeb7ee2c13f5","f5af6264df2f4be88faf3e79e22d32ec","677b438df17f4e9e9af9a19e4aba2281","887a28ea3253495f8548ae7dbdb206f0","5fb506abd363435d8dbacec0b19a7c8e","847a88b78e7d47bd8054905001aa3de1","f6af4ea3867f47f5a4b80d6b72bc9677","fd82db433ed248b7bef76a2bce69cb49","ceb64ac1fb4d49f5a16d682e38598f85","a87e26896b4a48d5abdb537f25032f95","4c6a58496cc549e8b9299d2506b367ca","c0c1ff73acba412194679007a1de5d4d","3eac61bf6d0d4d1b89a340a98803a0cb","001b0286842e4e54a66f0c7520ed4140","aaa66eea7184443baa9734aeb7da90d5","d6364d0755be44a0a63678470a97b9d8"]},"outputId":"591ffa2b-559c-4db8-df99-bb4c90bcdbc6","executionInfo":{"status":"ok","timestamp":1698341862200,"user_tz":-420,"elapsed":14445,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3945695bc81740fcb1ede9059bab0138"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"533d63c2846940dba53e3df30c6e63fe"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d995077ff55145a5babe4300304d68ae"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"15bb4fb9dcd14a408bd3cd55e3e3eca9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6487ffd39d8d4be6826ae9266310d649"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2e15dcde9df14a6da8a759b6c025526b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"31d967962f77486fb4a1441c521b25b1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e984b333f4a24fed8a23fdc4a42468c7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dd588bbf80b245a48401c28af3129f43"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"00fd0b55767046969ea63559e6c7c3e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fcb8b579ebed438bbcf76bf58a389b0c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa330ce2cac047898db25f45c21dae2b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3a919208a2c44b22b707c24713e48847"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1c750c7166e14aa18071887329c55221"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"138d5ef5cfa34c04a7befeb7ee2c13f5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f5af6264df2f4be88faf3e79e22d32ec"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"677b438df17f4e9e9af9a19e4aba2281"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"887a28ea3253495f8548ae7dbdb206f0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["MetricVisualizer(layout=Layout(align_self='stretch', 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a/Model/TVAE.ipynb b/Model/TVAE.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d41b1e3d95959331985ea49edfb48ae4f9f8425e --- /dev/null +++ b/Model/TVAE.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":556,"status":"ok","timestamp":1697339009466,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"COkMuAOy2J5o"},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lqNWRhS42u4P","executionInfo":{"status":"ok","timestamp":1697339133946,"user_tz":-420,"elapsed":124486,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"3cdb97a2-db83-402c-8f66-6a2c43019d16"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: optuna in /usr/local/lib/python3.10/dist-packages (3.3.0)\n","Requirement 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https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-nzsce8__\n"," Resolved https://github.com/R-N/ml-utility-loss to commit 999fa9d4bdbb358553011a9c2171aded09814fc4\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=377794 sha256=68ecd9b641c43bd54b3164c62198ea7e02cdec2fbfa306865f59772940fd8d11\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-hcxwsw4f/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":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nrYplK5Rkgcn","executionInfo":{"status":"ok","timestamp":1697339136134,"user_tz":-420,"elapsed":2198,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"32c1323a-7501-48ac-f70e-5a7f36301f50"},"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 28.1M 0 0:00:01 0:00:01 --:--:-- 73.9M\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":4,"metadata":{"id":"MdkECEu12xpL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1697339176557,"user_tz":-420,"elapsed":40429,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"a8da7e6f-dc56-47d0-8bc9-1182196254e5"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content\n","Cloning into 'ml-utility-loss'...\n","remote: Enumerating objects: 54414, done.\u001b[K\n","remote: Counting objects: 100% (7327/7327), done.\u001b[K\n","remote: Compressing objects: 100% (3252/3252), done.\u001b[K\n","remote: Total 54414 (delta 4134), reused 7251 (delta 4059), pack-reused 47087\u001b[K\n","Receiving objects: 100% (54414/54414), 239.42 MiB | 10.85 MiB/s, done.\n","Resolving deltas: 100% (6182/6182), 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":5,"metadata":{"id":"ns5hFcVL2yvs","executionInfo":{"status":"ok","timestamp":1697339176558,"user_tz":-420,"elapsed":18,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"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":26,"metadata":{"id":"UdvXYv3c3LXy","executionInfo":{"status":"ok","timestamp":1697340246093,"user_tz":-420,"elapsed":573,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"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 = \"insurance\"\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":27,"metadata":{"id":"Vrl2QkoV3o_8","executionInfo":{"status":"ok","timestamp":1697340246097,"user_tz":-420,"elapsed":17,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"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":{"id":"a-SjylvlYl7i","executionInfo":{"status":"ok","timestamp":1697343112569,"user_tz":-420,"elapsed":365,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["from ml_utility_loss.util import mkdir\n","\n","model_name = \"tvae\"\n","df_name = \"df\"\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","model_path = os.path.join(model_dir_2, f\"model.pt\")\n","state_path = os.path.join(model_dir_2, f\"state.json\")\n","params_path = os.path.join(model_dir_2, f\"params.json\")"]},{"cell_type":"code","execution_count":59,"metadata":{"id":"-YDZUU9QUCwx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1697343113004,"user_tz":-420,"elapsed":4,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"3d0fe03c-d2ee-4955-9589-0be8e213faf1"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/tvae/insurance/df/model.pt\n"]}],"source":["print(model_path)"]},{"cell_type":"code","execution_count":60,"metadata":{"id":"NgahtU1q9uLO","executionInfo":{"status":"ok","timestamp":1697343113004,"user_tz":-420,"elapsed":3,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["\n","from ml_utility_loss.tuning import create_objective\n","import ml_utility_loss.synthesizers.tvae.params as PARAMS\n","from ml_utility_loss.util import filter_dict_2\n","\n","params = getattr(PARAMS, dataset_name).BEST\n","\n","model_params={\n"," **params,\n","}\n","\n","for x in [\"compress\", \"decompress\"]:\n"," model_params[f\"{x}_dims\"] = [\n"," model_params[f\"{x}_dims\"]\n"," for i in range(\n"," model_params.pop(f\"{x}_depth\")\n"," )\n"," ]"]},{"cell_type":"code","execution_count":61,"metadata":{"id":"wGsDQTlk8Zl1","executionInfo":{"status":"ok","timestamp":1697343399823,"user_tz":-420,"elapsed":286822,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["from ml_utility_loss.synthesizers.tvae.wrapper import TVAE\n","\n","train = dfs[df_name]\n","tvae = TVAE(**model_params)\n","tvae.fit(train, cat_features)\n","synth_df = tvae.sample(len(train))"]},{"cell_type":"code","execution_count":62,"metadata":{"id":"qUHHYJNRJdDy","executionInfo":{"status":"ok","timestamp":1697343400203,"user_tz":-420,"elapsed":403,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["import torch\n","from copy import deepcopy\n","import json\n","\n","!rm -rf params_path\n","!rm -rf model_path\n","!rm -rf g_state_path\n","!rm -rf d_state_path\n","torch.save(tvae, model_path)\n","torch.save(deepcopy(tvae.model.state_dict()), state_path)\n","with open(params_path, \"w\") as f:\n"," json.dump(model_params, f, indent=4)"]},{"cell_type":"code","execution_count":63,"metadata":{"id":"7SQgzf5-P249","colab":{"base_uri":"https://localhost:8080/","height":228},"executionInfo":{"status":"error","timestamp":1697343400203,"user_tz":-420,"elapsed":13,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"57519fd1-a4d2-4922-c834-1fe290391c16"},"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0msynth_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msynth_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mdfs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdfs_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mdfs_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf_name\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'df'"]}],"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","executionInfo":{"status":"aborted","timestamp":1697343400204,"user_tz":-420,"elapsed":10,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"1Cug9laqjkt9fyDxiylSn9Jzam9kQyDu3","authorship_tag":"ABX9TyMhtJHOKAMnUJyVHa+D8Sml"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Model/TabDDPM.ipynb b/Model/TabDDPM.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b8cd0111c9b5f0eafcc745f8ec942e63ea0a93af --- /dev/null +++ b/Model/TabDDPM.ipynb @@ -0,0 +1 @@ +{"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":107224,"status":"ok","timestamp":1697352037556,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"lqNWRhS42u4P","outputId":"19625d04-29a5-4317-df95-1ab6deec06e1"},"outputs":[{"name":"stdout","output_type":"stream","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-vpxbfeox\n"," Running command git clone --filter=blob:none --quiet https://github.com/R-N/ml-utility-loss /tmp/pip-req-build-vpxbfeox\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=b56375e2780574b57742479287c4d9caaddaa28e9087d2856957506a6c4a529b\n"," Stored in directory: /tmp/pip-ephem-wheel-cache-clfcxhqn/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":2458,"status":"ok","timestamp":1697352040008,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"nrYplK5Rkgcn","outputId":"a5b42144-304f-47a1-9b60-bef19d037084"},"outputs":[{"name":"stdout","output_type":"stream","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 22.8M 0 0:00:01 0:00:01 --:--:-- 33.8M\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":34944,"status":"ok","timestamp":1697352074946,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"MdkECEu12xpL","outputId":"d3b92014-fee1-4e61-b6f3-8eda213942fe"},"outputs":[{"name":"stdout","output_type":"stream","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 | 12.87 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":87,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1697361146939,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"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 = \"insurance\"\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":88,"metadata":{"executionInfo":{"elapsed":590,"status":"ok","timestamp":1697361147518,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"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":89,"metadata":{"executionInfo":{"elapsed":12,"status":"ok","timestamp":1697361147519,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"a-SjylvlYl7i"},"outputs":[],"source":["from ml_utility_loss.util import mkdir\n","\n","model_name = \"tab_ddpm\"\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","model_path = os.path.join(model_dir_2, f\"model.pt\")\n","diffusion_path = os.path.join(model_dir_2, f\"diffusion.pt\")\n","trainer_path = os.path.join(model_dir_2, f\"trainer.pt\")\n","model_state_path = os.path.join(model_dir_2, f\"model_state.json\")\n","diffusion_state_path = os.path.join(model_dir_2, f\"diffusion_state.json\")\n","params_path = os.path.join(model_dir_2, f\"params.json\")"]},{"cell_type":"code","execution_count":90,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11,"status":"ok","timestamp":1697361147519,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"-YDZUU9QUCwx","outputId":"50dcb58c-f0de-4f40-f97f-79a3cd23681b"},"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/rizqinur2010/Documents/ITS/Tesis/Model/tab_ddpm/insurance/4/model.pt\n"]}],"source":["print(model_path)"]},{"cell_type":"code","execution_count":91,"metadata":{"executionInfo":{"elapsed":6,"status":"ok","timestamp":1697361147931,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"},"user_tz":-420},"id":"NgahtU1q9uLO"},"outputs":[],"source":["\n","from ml_utility_loss.tuning import create_objective\n","from ml_utility_loss.synthesizers.tab_ddpm.params.default import RTDL_PARAMS\n","import ml_utility_loss.synthesizers.tab_ddpm.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","n_layers = model_params.pop(\"n_layers\")\n","d_layers_0 = model_params.pop(\"d_layers_0\")\n","d_layers_i = model_params.pop(\"d_layers_i\")\n","d_layers_n = model_params.pop(\"d_layers_n\")\n","d_layers = [\n"," d_layers_0,\n"," *[d_layers_i for _ in range(n_layers-2)],\n"," d_layers_n,\n","]\n","model_params[\"d_layers\"] = d_layers\n","\n","rtdl_params = filter_dict(model_params, RTDL_PARAMS)\n","model_params = {k: v for k, v in model_params.items() if k not in rtdl_params}\n","model_params[\"rtdl_params\"] = rtdl_params\n","model_params_ = deepcopy(model_params)"]},{"cell_type":"code","execution_count":92,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wGsDQTlk8Zl1","executionInfo":{"status":"ok","timestamp":1697361491663,"user_tz":-420,"elapsed":343737,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"996f2f4a-9065-4001-9854-bff40326818c"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/delu/_utils.py:16: DeLUDeprecationWarning: The function improve_reproducibility` is deprecated and will be removed in future releases. Instead, use `delu.random.seed` and manually set flags mentioned in the `PyTorch docs on reproducibility `_\n"," warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n"," warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["[0]\n","11\n","{'rtdl_params': {'dropout': 0.07319902815233517, 'd_layers': [512, 512, 512, 512, 512, 2048]}, 'is_y_cond': True, 'd_in': 11}\n","Step 10/42617 MLoss: 0.0 GLoss: 0.9943 Sum: 0.9943\n","Step 20/42617 MLoss: 0.0 GLoss: 0.9767 Sum: 0.9767\n","Step 30/42617 MLoss: 0.0 GLoss: 0.8696 Sum: 0.8696\n","Step 40/42617 MLoss: 0.0 GLoss: 0.736 Sum: 0.736\n","Step 50/42617 MLoss: 0.0 GLoss: 0.6526 Sum: 0.6526\n","Step 60/42617 MLoss: 0.0 GLoss: 0.6061 Sum: 0.6061\n","Step 70/42617 MLoss: 0.0 GLoss: 0.5551 Sum: 0.5551\n","Step 80/42617 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0.0666\n","Step 27880/42617 MLoss: 0.0 GLoss: 0.0651 Sum: 0.0651\n","Step 27890/42617 MLoss: 0.0 GLoss: 0.0643 Sum: 0.0643\n","Step 27900/42617 MLoss: 0.0 GLoss: 0.0657 Sum: 0.0657\n","Step 27910/42617 MLoss: 0.0 GLoss: 0.063 Sum: 0.063\n","Step 27920/42617 MLoss: 0.0 GLoss: 0.0644 Sum: 0.0644\n","Step 27930/42617 MLoss: 0.0 GLoss: 0.0614 Sum: 0.0614\n","Step 27940/42617 MLoss: 0.0 GLoss: 0.066 Sum: 0.066\n","Step 27950/42617 MLoss: 0.0 GLoss: 0.0637 Sum: 0.0637\n","Step 27960/42617 MLoss: 0.0 GLoss: 0.0662 Sum: 0.0662\n","Step 27970/42617 MLoss: 0.0 GLoss: 0.066 Sum: 0.066\n","Step 27980/42617 MLoss: 0.0 GLoss: 0.0644 Sum: 0.0644\n","Step 27990/42617 MLoss: 0.0 GLoss: 0.0637 Sum: 0.0637\n","Step 28000/42617 MLoss: 0.0 GLoss: 0.0628 Sum: 0.0628\n","Step 28010/42617 MLoss: 0.0 GLoss: 0.0653 Sum: 0.0653\n","Step 28020/42617 MLoss: 0.0 GLoss: 0.0642 Sum: 0.0642\n","Step 28030/42617 MLoss: 0.0 GLoss: 0.0657 Sum: 0.0657\n","Step 28040/42617 MLoss: 0.0 GLoss: 0.0644 Sum: 0.0644\n","Step 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Instead, use `delu.random.seed` and manually set flags mentioned in the `PyTorch docs on reproducibility `_\n"," warnings.warn(\n"]},{"output_type":"stream","name":"stdout","text":["Sample timestep 0\n"]}],"source":["from ml_utility_loss.synthesizers.tvae.wrapper import TVAE\n","from ml_utility_loss.synthesizers.tab_ddpm.pipeline import train as _train, sample as _sample\n","\n","train = dfs[df_name]\n","\n","model, diffusion, trainer = _train(\n"," train,\n"," task_type=task,\n"," target=target,\n"," cat_features=cat_features,\n"," **model_params,\n",")\n","# Create synthetic data\n","synth_df = _sample(\n"," diffusion,\n"," batch_size=model_params[\"batch_size\"],\n"," num_samples=len(train)\n",")"]},{"cell_type":"code","execution_count":93,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BR6lIxsgXcc5","executionInfo":{"status":"ok","timestamp":1697361491664,"user_tz":-420,"elapsed":57,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}},"outputId":"11e7cfe9-70f6-4840-8813-580bec66ec70"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["False"]},"metadata":{},"execution_count":93}],"source":["import types\n","isinstance(trainer, types.GeneratorType)"]},{"cell_type":"code","execution_count":94,"metadata":{"id":"qUHHYJNRJdDy","executionInfo":{"status":"ok","timestamp":1697361491665,"user_tz":-420,"elapsed":13,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":["import torch\n","from copy import deepcopy\n","import json\n","\n","torch.save(model, model_path)\n","torch.save(diffusion, diffusion_path)\n","#torch.save(trainer, trainer_path)\n","torch.save(deepcopy(model.state_dict()), model_state_path)\n","torch.save(deepcopy(diffusion.state_dict()), diffusion_state_path)\n","with open(params_path, \"w\") as f:\n"," json.dump(model_params_, f, indent=4)"]},{"cell_type":"code","execution_count":95,"metadata":{"id":"7SQgzf5-P249","executionInfo":{"status":"ok","timestamp":1697361491666,"user_tz":-420,"elapsed":13,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"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":95,"metadata":{"id":"nB724X33v4Qd","executionInfo":{"status":"ok","timestamp":1697361491666,"user_tz":-420,"elapsed":13,"user":{"displayName":"Rizqi Nur","userId":"09644007964068789560"}}},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"provenance":[],"mount_file_id":"1thrslXDsb3UiDvvPU--356QMeNaiFBdW","authorship_tag":"ABX9TyP9QeEydtXrVGW6XxyuLIx2"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/Model/desktop.ini b/Model/desktop.ini new file mode 100644 index 0000000000000000000000000000000000000000..e75f0dd6bb27f19e41f222630150ffbae6e1d34d Binary files /dev/null and b/Model/desktop.ini differ diff --git a/Model/lct_ae/contraceptive/0/desktop.ini b/Model/lct_ae/contraceptive/0/desktop.ini new file mode 100644 index 0000000000000000000000000000000000000000..e75f0dd6bb27f19e41f222630150ffbae6e1d34d Binary files /dev/null and b/Model/lct_ae/contraceptive/0/desktop.ini differ diff --git a/Model/lct_ae/contraceptive/0/model.pt b/Model/lct_ae/contraceptive/0/model.pt new file mode 100644 index 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b/Model/lct_gan/contraceptive/0/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..ca8fcce3ac3805838e0471868ba3a62847205958 --- /dev/null +++ b/Model/lct_gan/contraceptive/0/val.csv @@ -0,0 +1,295 @@ +,age,education,partner_education,n_children,islam,working,husband_occupation,living_standard,media_exposure,contraceptive +546,25,4,4,1,1,1,1,4,0,2 +658,32,2,3,5,1,0,2,3,0,3 +1203,48,1,1,8,1,1,1,1,1,1 +1019,38,2,4,3,1,1,1,3,0,1 +1233,30,4,4,3,1,1,2,1,0,2 +959,36,2,4,8,1,1,1,4,0,3 +1385,28,4,4,1,1,1,2,4,0,3 +283,41,3,3,5,1,1,3,4,0,1 +797,27,4,4,1,0,0,1,3,0,3 +225,32,4,4,4,1,0,1,3,0,1 +26,38,4,4,1,1,0,1,4,0,1 +437,33,2,2,3,1,1,2,3,0,2 +1431,27,4,4,2,0,0,2,4,0,3 +229,28,3,4,0,1,1,3,2,0,1 +37,25,3,3,4,1,1,3,2,0,1 +749,23,4,4,2,1,1,3,4,0,3 +1045,44,1,4,9,1,1,1,2,0,1 +469,42,2,4,8,1,1,1,4,0,2 +1255,21,3,4,3,1,1,1,4,0,2 +1446,45,2,3,3,0,0,2,4,0,3 +687,36,4,4,6,0,1,1,2,1,3 +1354,43,4,4,2,1,1,1,4,0,3 +639,25,2,4,3,1,1,1,4,1,2 +1271,35,1,2,9,1,0,3,1,0,2 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b/Model/lct_gan/contraceptive/3/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..b654a191c7e91dbf02357ff5d93bfd2fa703c9e2 --- /dev/null +++ b/Model/lct_gan/contraceptive/3/train.csv @@ -0,0 +1,1180 @@ +,age,education,partner_education,n_children,islam,working,husband_occupation,living_standard,media_exposure,contraceptive +852,29,3,3,4,1,1,3,4,0,3 +184,35,3,4,5,1,0,2,2,0,1 +1261,23,4,4,1,0,1,1,4,0,2 +67,30,2,3,3,1,1,3,1,0,1 +220,22,2,3,1,1,1,3,3,0,1 +494,25,4,4,2,1,1,2,4,0,2 +430,23,4,4,1,1,1,1,2,0,2 +240,19,3,4,1,1,1,2,3,0,1 +218,32,3,2,4,1,1,2,2,1,1 +49,26,4,4,0,1,0,1,4,0,1 +677,33,3,3,8,1,1,3,4,0,3 +843,28,3,4,2,1,1,3,4,0,3 +618,34,4,4,6,1,1,3,4,0,2 +361,43,3,3,4,1,1,2,4,0,1 +1426,21,3,4,3,1,1,3,1,0,3 +984,32,4,4,3,1,0,1,4,0,3 +807,25,2,3,4,1,1,3,3,0,3 +873,26,2,3,2,1,1,3,3,0,3 +271,28,3,4,1,1,1,3,4,0,1 +1199,31,3,4,2,1,1,1,4,0,1 +259,39,3,3,1,0,1,3,4,0,1 +428,30,2,4,3,1,1,3,4,0,2 +605,30,4,4,1,0,1,2,4,0,2 +277,35,2,3,0,1,1,2,4,0,1 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diff --git a/Model/realtabformer/contraceptive/0/val.csv b/Model/realtabformer/contraceptive/0/val.csv new file mode 100644 index 0000000000000000000000000000000000000000..ca8fcce3ac3805838e0471868ba3a62847205958 --- /dev/null +++ b/Model/realtabformer/contraceptive/0/val.csv @@ -0,0 +1,295 @@ +,age,education,partner_education,n_children,islam,working,husband_occupation,living_standard,media_exposure,contraceptive +546,25,4,4,1,1,1,1,4,0,2 +658,32,2,3,5,1,0,2,3,0,3 +1203,48,1,1,8,1,1,1,1,1,1 +1019,38,2,4,3,1,1,1,3,0,1 +1233,30,4,4,3,1,1,2,1,0,2 +959,36,2,4,8,1,1,1,4,0,3 +1385,28,4,4,1,1,1,2,4,0,3 +283,41,3,3,5,1,1,3,4,0,1 +797,27,4,4,1,0,0,1,3,0,3 +225,32,4,4,4,1,0,1,3,0,1 +26,38,4,4,1,1,0,1,4,0,1 +437,33,2,2,3,1,1,2,3,0,2 +1431,27,4,4,2,0,0,2,4,0,3 +229,28,3,4,0,1,1,3,2,0,1 +37,25,3,3,4,1,1,3,2,0,1 +749,23,4,4,2,1,1,3,4,0,3 +1045,44,1,4,9,1,1,1,2,0,1 +469,42,2,4,8,1,1,1,4,0,2 +1255,21,3,4,3,1,1,1,4,0,2 +1446,45,2,3,3,0,0,2,4,0,3 +687,36,4,4,6,0,1,1,2,1,3 +1354,43,4,4,2,1,1,1,4,0,3 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b/Model/realtabformer/contraceptive/3/train.csv new file mode 100644 index 0000000000000000000000000000000000000000..b654a191c7e91dbf02357ff5d93bfd2fa703c9e2 --- /dev/null +++ b/Model/realtabformer/contraceptive/3/train.csv @@ -0,0 +1,1180 @@ +,age,education,partner_education,n_children,islam,working,husband_occupation,living_standard,media_exposure,contraceptive +852,29,3,3,4,1,1,3,4,0,3 +184,35,3,4,5,1,0,2,2,0,1 +1261,23,4,4,1,0,1,1,4,0,2 +67,30,2,3,3,1,1,3,1,0,1 +220,22,2,3,1,1,1,3,3,0,1 +494,25,4,4,2,1,1,2,4,0,2 +430,23,4,4,1,1,1,1,2,0,2 +240,19,3,4,1,1,1,2,3,0,1 +218,32,3,2,4,1,1,2,2,1,1 +49,26,4,4,0,1,0,1,4,0,1 +677,33,3,3,8,1,1,3,4,0,3 +843,28,3,4,2,1,1,3,4,0,3 +618,34,4,4,6,1,1,3,4,0,2 +361,43,3,3,4,1,1,2,4,0,1 +1426,21,3,4,3,1,1,3,1,0,3 +984,32,4,4,3,1,0,1,4,0,3 +807,25,2,3,4,1,1,3,3,0,3 +873,26,2,3,2,1,1,3,3,0,3 +271,28,3,4,1,1,1,3,4,0,1 +1199,31,3,4,2,1,1,1,4,0,1 +259,39,3,3,1,0,1,3,4,0,1 +428,30,2,4,3,1,1,3,4,0,2 +605,30,4,4,1,0,1,2,4,0,2 +277,35,2,3,0,1,1,2,4,0,1 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--git a/Model/tab_ddpm/contraceptive/4/params.json b/Model/tab_ddpm/contraceptive/4/params.json new file mode 100644 index 0000000000000000000000000000000000000000..a6513b5ed176a99e8a8f571623e6f099a458b961 --- /dev/null +++ b/Model/tab_ddpm/contraceptive/4/params.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:17c04d3350e66c35b685a00d70d8ef11ba4e1ecaa0619de71161a1d1cebcb67c +size 401 diff --git a/Model/tab_ddpm/contraceptive/4/synth.csv b/Model/tab_ddpm/contraceptive/4/synth.csv new file mode 100644 index 0000000000000000000000000000000000000000..0c52f3d0d93c3cadb270a9fd2db0e36263bc74f5 --- /dev/null +++ b/Model/tab_ddpm/contraceptive/4/synth.csv @@ -0,0 +1,1180 @@ +,age,education,partner_education,n_children,islam,working,husband_occupation,living_standard,media_exposure,contraceptive +0,27,1,2,1,1,1,3,4,0,3 +1,32,3,4,4,1,0,2,4,0,1 +2,28,3,4,4,1,1,3,3,0,3 +3,29,4,4,4,1,1,3,3,0,2 +4,31,1,2,6,0,1,2,3,0,3 +5,24,2,1,2,1,0,3,3,0,2 +6,24,4,4,1,1,1,1,4,0,1 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