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{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
 "colab": {
 "provenance": [],
 "gpuType": "T4"
 },
 "kernelspec": {
 "name": "python3",
 "display_name": "Python 3"
 },
 "language_info": {
 "name": "python"
 },
 "accelerator": "GPU"
 },
 "cells": [
 {
 "cell_type": "code",
 "execution_count": 1,
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "aogx2_DhmVv5",
 "outputId": "40088380-0a87-41aa-97c4-9674e911369d"
 },
 "outputs": [
 {
 "output_type": "stream",
 "name": "stdout",
 "text": [
 "--2025-08-05 09:43:39-- https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
 "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.191.158, 104.16.32.241, 2606:4700::6810:bf9e, ...\n",
 "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.191.158|:443... connected.\n",
 "HTTP request sent, awaiting response... 200 OK\n",
 "Length: 160039710 (153M) [application/octet-stream]\n",
 "Saving to: ‘Miniconda3-latest-Linux-x86_64.sh’\n",
 "\n",
 "Miniconda3-latest-L 100%[===================>] 152.62M 263MB/s in 0.6s \n",
 "\n",
 "2025-08-05 09:43:39 (263 MB/s) - ‘Miniconda3-latest-Linux-x86_64.sh’ saved [160039710/160039710]\n",
 "\n",
 "PREFIX=/usr/local\n",
 "Unpacking payload ...\n",
 "entry_point.py:256: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n",
 "entry_point.py:256: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n",
 "\n",
 "Installing base environment...\n",
 "\n",
 "Preparing transaction: ...working... done\n",
 "Executing transaction: ...working... done\n",
 "entry_point.py:256: DeprecationWarning: Python 3.14 will, by default, filter extracted tar archives and reject files or modify their metadata. Use the filter argument to control this behavior.\n",
 "installation finished.\n",
 "WARNING:\n",
 " You currently have a PYTHONPATH environment variable set. This may cause\n",
 " unexpected behavior when running the Python interpreter in Miniconda3.\n",
 " For best results, please verify that your PYTHONPATH only points to\n",
 " directories of packages that are compatible with the Python interpreter\n",
 " in Miniconda3: /usr/local\n",
 "accepted Terms of Service for \u001b[4;94mhttps://repo.anaconda.com/pkgs/main\u001b[0m\n",
 "accepted Terms of Service for \u001b[4;94mhttps://repo.anaconda.com/pkgs/r\u001b[0m\n"
 ]
 }
 ],
 "source": [
 "!wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
 "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
 "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
 "\n",
 "import sys\n",
 "sys.path.append('/usr/local/lib/python3.9/site-packages')\n",
 "\n",
 "!conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main\n",
 "!conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r"
 ]
 },
 {
 "cell_type": "code",
 "source": [
 "!git clone https://github.com/vkinakh/binary-diffusion-tabular.git"
 ],
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "PUNpVBjfnZkc",
 "outputId": "692f66b4-3997-4cf5-abb5-0da56f657402"
 },
 "execution_count": 3,
 "outputs": [
 {
 "output_type": "stream",
 "name": "stdout",
 "text": [
 "Cloning into 'binary-diffusion-tabular'...\n",
 "remote: Enumerating objects: 172, done.\u001b[K\n",
 "remote: Counting objects: 100% (172/172), done.\u001b[K\n",
 "remote: Compressing objects: 100% (122/122), done.\u001b[K\n",
 "remote: Total 172 (delta 86), reused 135 (delta 49), pack-reused 0 (from 0)\u001b[K\n",
 "Receiving objects: 100% (172/172), 5.80 MiB | 18.23 MiB/s, done.\n",
 "Resolving deltas: 100% (86/86), done.\n"
 ]
 }
 ]
 },
 {
 "cell_type": "code",
 "source": [
 "%cd binary-diffusion-tabular"
 ],
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "2fEySbHTng7z",
 "outputId": "463602a8-7bc7-4bf4-cf38-d65343dc2dab"
 },
 "execution_count": 4,
 "outputs": [
 {
 "output_type": "stream",
 "name": "stdout",
 "text": [
 "/content/binary-diffusion-tabular\n"
 ]
 }
 ]
 },
 {
 "cell_type": "code",
 "source": [
 "!conda env create -f environment.yml"
 ],
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "pRleBstwnmbz",
 "outputId": "d95130bd-ad88-4d0e-f30b-07e4c02b5f82"
 },
 "execution_count": 5,
 "outputs": [
 {
 "output_type": "stream",
 "name": "stdout",
 "text": [
 "\u001b[1;32m2\u001b[0m\u001b[1;32m channel Terms of Service accepted\u001b[0m\n",
 "Channels:\n",
 " - defaults\n",
 " - conda-forge\n",
 "Platform: linux-64\n",
 "Collecting package metadata (repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
 "Solving environment: | \b\bdone\n",
 "\n",
 "Downloading and Extracting Packages:\n",
 "python-3.11.10 | 32.9 MB | : 0% 0/1 [00:00<?, ?it/s]\n",
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 "setuptools-78.1.1 | 2.3 MB | : 51% 0.5128815151040852/1 [00:00<00:00, 5.12it/s]\u001b[A\u001b[A\u001b[A\n",
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 "openssl-3.0.17 | 5.2 MB | : 1% 0.011991117175954311/1 [00:00<00:08, 8.84s/it]\u001b[A\n",
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 "wheel-0.44.0 | 145 KB | : 11% 0.11043705680929655/1 [00:00<00:01, 1.56s/it]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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 "ncurses-6.5 | 1.1 MB | : 100% 1.0/1 [00:01<00:00, 6.47it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
 " \n",
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 " \u001b[A\u001b[A\n",
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 " \u001b[A\u001b[A\u001b[A\n",
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 " \u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
 "Preparing transaction: - \b\b\\ \b\bdone\n",
 "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n",
 "Executing transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
 "Installing pip dependencies: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| 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\b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ Ran pip subprocess with arguments:\n",
 "['/usr/local/envs/binary-diffusion-tabular/bin/python', '-m', 'pip', 'install', '-U', '-r', '/content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt', '--exists-action=b']\n",
 "Pip subprocess output:\n",
 "Collecting accelerate==1.1.1 (from -r /content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt (line 1))\n",
 " Downloading accelerate-1.1.1-py3-none-any.whl.metadata (19 kB)\n",
 "Collecting ema-pytorch==0.7.6 (from -r /content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt (line 2))\n",
 " Downloading ema_pytorch-0.7.6-py3-none-any.whl.metadata (689 bytes)\n",
 "Collecting numpy==2.1.3 (from -r /content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt (line 3))\n",
 " Downloading numpy-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\n",
 "Collecting pandas==2.2.3 (from -r /content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt (line 4))\n",
 " Downloading pandas-2.2.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\n",
 "Collecting pyyaml==6.0.2 (from -r /content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt (line 5))\n",
 " Downloading PyYAML-6.0.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB)\n",
 "Collecting scikit-learn==1.5.2 (from -r /content/binary-diffusion-tabular/condaenv.dk0stnxu.requirements.txt (line 6))\n",
 " Downloading scikit_learn-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (13 kB)\n",
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 "Installing collected packages: pytz, mpmath, urllib3, tzdata, typing-extensions, tqdm, threadpoolctl, sympy, smmap, six, setproctitle, safetensors, pyyaml, psutil, protobuf, platformdirs, packaging, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, joblib, idna, hf-xet, fsspec, filelock, click, charset_normalizer, certifi, annotated-types, typing-inspection, triton, sentry-sdk, scipy, requests, python-dateutil, pydantic-core, nvidia-cusparse-cu12, nvidia-cudnn-cu12, lightning-utilities, jinja2, gitdb, docker-pycreds, scikit-learn, pydantic, pandas, nvidia-cusolver-cu12, huggingface-hub, gitpython, wandb, torch, torchmetrics, ema-pytorch, accelerate\n",
 "Successfully installed MarkupSafe-3.0.2 accelerate-1.1.1 annotated-types-0.7.0 certifi-2025.8.3 charset_normalizer-3.4.2 click-8.2.1 docker-pycreds-0.4.0 ema-pytorch-0.7.6 filelock-3.18.0 fsspec-2025.7.0 gitdb-4.0.12 gitpython-3.1.45 hf-xet-1.1.5 huggingface-hub-0.34.3 idna-3.10 jinja2-3.1.6 joblib-1.5.1 lightning-utilities-0.15.1 mpmath-1.3.0 networkx-3.5 numpy-2.1.3 nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.4.127 packaging-25.0 pandas-2.2.3 platformdirs-4.3.8 protobuf-5.29.5 psutil-7.0.0 pydantic-2.11.7 pydantic-core-2.33.2 python-dateutil-2.9.0.post0 pytz-2025.2 pyyaml-6.0.2 requests-2.32.4 safetensors-0.5.3 scikit-learn-1.5.2 scipy-1.16.1 sentry-sdk-2.34.1 setproctitle-1.3.6 six-1.17.0 smmap-5.0.2 sympy-1.13.1 threadpoolctl-3.6.0 torch-2.5.0 torchmetrics-1.6.0 tqdm-4.67.1 triton-3.1.0 typing-extensions-4.14.1 typing-inspection-0.4.1 tzdata-2025.2 urllib3-2.5.0 wandb-0.19.0\n",
 "\n",
 "\b\b- \b\bdone\n",
 "#\n",
 "# To activate this environment, use\n",
 "#\n",
 "# $ conda activate binary-diffusion-tabular\n",
 "#\n",
 "# To deactivate an active environment, use\n",
 "#\n",
 "# $ conda deactivate\n",
 "\n"
 ]
 }
 ]
 },
 {
 "cell_type": "code",
 "source": [
 "import os\n",
 "os.environ['WANDB_MODE'] = 'disabled'\n",
 "\n",
 "\n",
 "#on /content/binary-diffusion-tabular/configs/housing.yaml\n",
 "#set line 42 : dataloader_workers: according to ur worker value\n",
 "#IN COLLAB : dataloader_workers: 2 GIVES BEST PERFORMANCE.\n"
 ],
 "metadata": {
 "id": "z6cEJX3Bvrv7"
 },
 "execution_count": 10,
 "outputs": []
 },
 {
 "cell_type": "code",
 "source": [
 "!source /usr/local/bin/activate binary-diffusion-tabular && python train.py -c=/content/binary-diffusion-tabular/configs/housing.yaml"
 ],
 "metadata": {
 "colab": {
 "base_uri": "https://localhost:8080/"
 },
 "id": "38F05UHVofSj",
 "outputId": "a0f05586-a079-4606-a4ed-3d5df4350d3a"
 },
 "execution_count": 12,
 "outputs": [
 {
 "output_type": "stream",
 "name": "stdout",
 "text": [
 "/content/binary-diffusion-tabular/binary_diffusion_tabular/transformation.py:235: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
 " y_trans = torch.tensor(y_trans, dtype=torch.float)\n",
 "Loss: 0.7250 | acc_target: 0.8399 | acc_mask: 0.8297: 1% 6637/500000 [01:21<1:41:02, 81.37it/s]\n",
 "Traceback (most recent call last):\n",
 " File \"/content/binary-diffusion-tabular/train.py\", line 23, in <module>\n",
 " trainer.train()\n",
 " File \"/content/binary-diffusion-tabular/binary_diffusion_tabular/trainer.py\", line 471, in train\n",
 " loss, losses, accs = self.diffusion(x=data, y=label)\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 " File \"/usr/local/envs/binary-diffusion-tabular/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n",
 " return self._call_impl(*args, **kwargs)\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 " File \"/usr/local/envs/binary-diffusion-tabular/lib/python3.11/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n",
 " return forward_call(*args, **kwargs)\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 " File \"/content/binary-diffusion-tabular/binary_diffusion_tabular/diffusion.py\", line 374, in forward\n",
 " acc_target = accuracy(self.pred_postproc(pred_target), x, task=\"binary\")\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 " File \"/usr/local/envs/binary-diffusion-tabular/lib/python3.11/site-packages/torchmetrics/functional/classification/accuracy.py\", line 419, in accuracy\n",
 " return binary_accuracy(preds, target, threshold, multidim_average, ignore_index, validate_args)\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 " File \"/usr/local/envs/binary-diffusion-tabular/lib/python3.11/site-packages/torchmetrics/functional/classification/accuracy.py\", line 162, in binary_accuracy\n",
 " tp, fp, tn, fn = _binary_stat_scores_update(preds, target, multidim_average)\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 " File \"/usr/local/envs/binary-diffusion-tabular/lib/python3.11/site-packages/torchmetrics/functional/classification/stat_scores.py\", line 131, in _binary_stat_scores_update\n",
 " tp = ((target == preds) & (target == 1)).sum(sum_dim).squeeze()\n",
 " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
 "KeyboardInterrupt\n",
 "^C\n"
 ]
 }
 ]
 }
 ]
}

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