feu_colab_demo.ipynb

{
  "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",
      "source": [
        "! pip install git+https://github.com/cyrilakafia/feu-pipeline.git"
      ],
      "metadata": {
        "id": "6LWv5bJwQwXj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4af2b18-e3c8-4ba1-e043-ca9ccd5276dd"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/cyrilakafia/feu-pipeline.git\n",
            "  Cloning https://github.com/cyrilakafia/feu-pipeline.git to /tmp/pip-req-build-m7y91ven\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/cyrilakafia/feu-pipeline.git /tmp/pip-req-build-m7y91ven\n",
            "  Resolved https://github.com/cyrilakafia/feu-pipeline.git to commit b7158c87c05d8a8994d63d1fa16a14d916cc9f68\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->feu==0.1)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->feu==0.1)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch->feu==0.1) (2.2.0)\n",
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            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: jsonschema>=2.6.0 in /usr/local/lib/python3.10/dist-packages (from hdmf>=3.12.2->pynwb->feu==0.1) (4.19.2)\n",
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            "Collecting ruamel.yaml.clib>=0.2.7 (from ruamel-yaml>=0.16->hdmf>=3.12.2->pynwb->feu==0.1)\n",
            "  Downloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (526 kB)\n",
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            "\u001b[?25hBuilding wheels for collected packages: feu\n",
            "  Building wheel for feu (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for feu: filename=feu-0.1-py3-none-any.whl size=13437 sha256=3402720e6209acc3408d03f3d93b51f47e9c0dd3cb12da209177bf6107858b34\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-8gdwsc1f/wheels/06/c9/6a/6d7f662d76022a9bb5701da5cb4342976741d19b442b4c1597\n",
            "Successfully built feu\n",
            "Installing collected packages: ruamel.yaml.clib, 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, ruamel-yaml, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, hdmf, pynwb, feu\n",
            "Successfully installed feu-0.1 hdmf-3.13.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 pynwb-2.7.0 ruamel-yaml-0.18.6 ruamel.yaml.clib-0.2.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from feu.inf import run_inference\n",
        "from feu.prep import prep\n",
        "import numpy as np\n",
        "import os\n",
        "import torch\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "WOrigC7GeCN8"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Simulated data\n",
        "data = torch.rand(20, 1000)"
      ],
      "metadata": {
        "id": "5rijHRBmhBSm"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Preprocess the data\n",
        "# original_data = 'data.npy'\n",
        "# preprocessed_data = 'outputs/processed_data.p'\n",
        "\n",
        "# if not os.path.exists('outputs'):\n",
        "#     os.makedirs('outputs')\n",
        "\n",
        "# prep(original_data, preprocessed_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9WteveLthwFg",
        "outputId": "a21d0e60-1a01-448a-dce8-27002d5b441b"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Preprocessing done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run the inference process\n",
        "title = 'demo_run'\n",
        "device = 'cuda' # 'cuda' or 'cpu'\n",
        "num_iterations = 10\n",
        "conc = 1\n",
        "max_clusters = 20\n",
        "stimulus_timepoint = 1\n",
        "\n",
        "\n",
        "best_assigns, best_params = run_inference(\n",
        "                                        data,\n",
        "                                        title=title,\n",
        "                                        device=device,\n",
        "                                        iterations=num_iterations,\n",
        "                                        concentration=conc,\n",
        "                                        max_clusters=max_clusters,\n",
        "                                        num_trials=1,\n",
        "                                        t_stimulus=stimulus_timepoint,\n",
        "                                        seed=None\n",
        "                                        )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "crPXs8oCh_pU",
        "outputId": "e742c032-5aba-46e4-d61b-bbb5d009d40b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "demo_run, Iterations 10, Concentration 1, Max Clusters 20, Number of trials 1, Stimulus Timepoint 1, Seed None\n",
            "Device cpu\n",
            "iter: #  0 | time: 13.17 | num clusters: 8 | counts: [1 6 2 1 3 4 1 2] | accept prob: 0.375\n",
            "             params: [ 0.95  0.6   0.45  1.65 -0.23  1.31  4.49  1.95]\n",
            "                     [-10.76  -9.71 -17.81 -15.48  -9.06  -4.16 -14.66 -18.03]\n",
            "iter: #  1 | time: 6.99 | num clusters: 7 | counts: [4 4 1 3 1 6 1] | accept prob: 0.400\n",
            "             params: [ 0.6   0.38  4.73 -0.23  0.25  2.25  1.82]\n",
            "                     [ -9.71 -17.68 -13.96  -9.06 -17.41  -8.12 -18.08]\n",
            "iter: #  2 | time: 8.51 | num clusters: 7 | counts: [4 2 5 1 1 4 3] | accept prob: 0.409\n",
            "             params: [ 0.6   0.38  0.25  4.77 -0.12  2.99  2.2 ]\n",
            "                     [ -9.71 -17.68 -15.59 -14.96  -8.14  -9.36  -8.86]\n",
            "iter: #  3 | time: 10.00 | num clusters: 8 | counts: [2 2 2 5 1 1 4 3] | accept prob: 0.433\n",
            "             params: [ 0.69  0.6   0.43  0.19  4.77 -0.13  2.99  2.19]\n",
            "                     [-19.55  -9.71 -17.92 -16.57 -14.96  -8.58  -9.36  -8.72]\n",
            "iter: #  4 | time: 3.93 | num clusters: 9 | counts: [2 1 3 4 1 1 1 4 3] | accept prob: 0.385\n",
            "             params: [ 0.69  0.6   0.43  0.19  0.88  4.77 -0.02  2.99  2.19]\n",
            "                     [-19.55  -9.71 -17.92 -16.57 -16.98 -14.96  -8.89  -9.36  -8.72]\n",
            "iter: #  5 | time: 7.52 | num clusters: 9 | counts: [2 2 3 3 1 1 1 4 3] | accept prob: 0.333\n",
            "             params: [ 0.64  0.43  0.19  0.3   0.88  4.77 -0.02  2.99  2.19]\n",
            "                     [-19.01 -17.92 -16.57 -11.18 -16.98 -14.96  -8.89  -9.36  -8.72]\n",
            "iter: #  6 | time: 6.87 | num clusters: 12 | counts: [1 2 4 2 1 1 1 4 1 1 1 1] | accept prob: 0.333\n",
            "             params: [ 0.64  0.43  0.14  0.3   0.88  4.77 -0.02  3.16  0.58  1.99  1.93  2.19]\n",
            "                     [-19.01 -17.92 -16.28 -11.18 -16.98 -14.96  -8.89  -9.58 -13.59 -18.4\n",
            "  -9.14  -8.72]\n",
            "iter: #  7 | time: 7.80 | num clusters: 10 | counts: [1 1 3 3 2 1 1 1 4 3] | accept prob: 0.300\n",
            "             params: [ 0.64  0.65  0.43  0.14  0.3   0.88  4.77 -0.02  3.16  1.93]\n",
            "                     [-19.01 -13.38 -17.92 -16.28 -11.18 -16.98 -14.96  -8.89  -9.58  -9.14]\n",
            "iter: #  8 | time: 6.80 | num clusters: 10 | counts: [1 1 3 2 3 1 1 1 4 3] | accept prob: 0.287\n",
            "             params: [ 0.64  0.65  0.47  0.14  0.3   0.88  4.76 -0.02  3.16  1.93]\n",
            "                     [-19.01 -13.38 -18.07 -16.28 -11.18 -16.98 -14.12  -8.89  -9.58  -9.14]\n",
            "iter: #  9 | time: 7.61 | num clusters: 11 | counts: [1 1 2 1 4 1 1 1 1 4 3] | accept prob: 0.308\n",
            "             params: [0.64 0.75 0.39 0.32 0.14 0.3  0.88 4.76 0.06 3.16 2.05]\n",
            "                     [-19.01 -12.51 -18.07 -16.29 -16.28 -11.18 -16.98 -14.12  -8.59  -9.58\n",
            "  -9.01]\n",
            "demo_run run for 10 iterations - Inference done\n",
            "Found best parameters. Saving to file to outputs/simdemo_run_best_params.csv\n",
            "Found best assigns. Saving to file to outputs/simdemo_run_best_assigns.csv\n",
            "Saving cluster assignments heatmap to outputs/simdemo_run_assigns.png\n",
            "Saving scatter plot of cluster parameters to outputs/simdemo_run_params.png\n",
            "Pipeline done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# view results\n",
        "from PIL import Image\n",
        "assigns_image = Image.open('outputs/simdemo_run_assigns.png')\n",
        "params_image = Image.open('outputs/simdemo_run_params.png')\n",
        "\n",
        "display(assigns_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "Pp-9L4R2iRNS",
        "outputId": "9b8ce631-0887-460c-8df5-cb846ac59b8b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=3200x2400>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(params_image)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "id": "NLRI5rkNiyQP",
        "outputId": "1876b340-cae6-4677-f6e5-3b8890622b55"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=3200x2400>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# zip outputs file for download\n",
        "\n",
        "! zip -r outputs.zip outputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzZImHWI1pJF",
        "outputId": "cfa29ca4-febe-477d-e1e9-590cea45728a"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: outputs/ (stored 0%)\n",
            "  adding: outputs/simdemo_run_params.png (deflated 42%)\n",
            "  adding: outputs/simdemo_run_best_params.csv (deflated 56%)\n",
            "  adding: outputs/simdemo_run_assigns.csv (deflated 64%)\n",
            "  adding: outputs/simdemo_run_best_assigns.csv (deflated 28%)\n",
            "  adding: outputs/simdemo_run_assigns.png (deflated 41%)\n",
            "  adding: outputs/simdemo_run_params.tsv (deflated 76%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SYBbW4Kz1x4L"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}