colab/deep_learning_feature_selection.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "deep-learning-feature-selection.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tlatkowski/deep-learning-gene-expression/blob/master/colab/deep_learning_feature_selection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tu_OV9g9ETuO",
        "colab_type": "text"
      },
      "source": [
        "**Download the code**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpDB8zMhC0aS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "89d8cf08-2b2d-461c-9e03-ba0c5949dece"
      },
      "source": [
        "!git clone https://github.com/tlatkowski/deep-learning-gene-expression.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'deep-learning-gene-expression'...\n",
            "remote: Enumerating objects: 10, done.\u001b[K\n",
            "remote: Counting objects:  10% (1/10)\u001b[K\rremote: Counting objects:  20% (2/10)\u001b[K\rremote: Counting objects:  30% (3/10)\u001b[K\rremote: Counting objects:  40% (4/10)\u001b[K\rremote: Counting objects:  50% (5/10)\u001b[K\rremote: Counting objects:  60% (6/10)\u001b[K\rremote: Counting objects:  70% (7/10)\u001b[K\rremote: Counting objects:  80% (8/10)\u001b[K\rremote: Counting objects:  90% (9/10)\u001b[K\rremote: Counting objects: 100% (10/10)\u001b[K\rremote: Counting objects: 100% (10/10), done.\u001b[K\n",
            "remote: Compressing objects: 100% (10/10), done.\u001b[K\n",
            "remote: Total 113 (delta 3), reused 1 (delta 0), pack-reused 103\n",
            "Receiving objects: 100% (113/113), 43.37 MiB | 22.79 MiB/s, done.\n",
            "Resolving deltas: 100% (51/51), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ovF61paETDv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "baef6eb5-20f6-4e95-fc2a-ed488159429b"
      },
      "source": [
        "ls -l"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 8\n",
            "drwxr-xr-x 9 root root 4096 Sep 16 07:54 \u001b[0m\u001b[01;34mdeep-learning-gene-expression\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Aug 27 16:17 \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7628tdupNlnt",
        "colab_type": "text"
      },
      "source": [
        "**Install all mandatory requirements**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kaD0G74EY03",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f731ef1d-df7a-4b17-c3b8-11b26ecd7693"
      },
      "source": [
        "!pip install -r deep-learning-gene-expression/requirements.txt"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting numpy==1.14.2 (from -r deep-learning-gene-expression/requirements.txt (line 1))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6e/dc/92c0f670e7b986829fc92c4c0208edb9d72908149da38ecda50d816ea057/numpy-1.14.2-cp36-cp36m-manylinux1_x86_64.whl (12.2MB)\n",
            "\u001b[K     |████████████████████████████████| 12.2MB 4.9MB/s \n",
            "\u001b[?25hCollecting pandas==0.22.0 (from -r deep-learning-gene-expression/requirements.txt (line 2))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/da/c6/0936bc5814b429fddb5d6252566fe73a3e40372e6ceaf87de3dec1326f28/pandas-0.22.0-cp36-cp36m-manylinux1_x86_64.whl (26.2MB)\n",
            "\u001b[K     |████████████████████████████████| 26.3MB 1.4MB/s \n",
            "\u001b[?25hCollecting tqdm==4.11.2 (from -r deep-learning-gene-expression/requirements.txt (line 3))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/0d/174388e99e21ee2c91ea318994c3f8744e26158e43cff0ec9d045bf08a96/tqdm-4.11.2-py2.py3-none-any.whl (46kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 24.8MB/s \n",
            "\u001b[?25hCollecting matplotlib==1.5.3 (from -r deep-learning-gene-expression/requirements.txt (line 4))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/61/f6/9aeee05e614043add127f1485a8e25758bed58f2a5361bbd870aa071e87a/matplotlib-1.5.3-cp36-cp36m-manylinux1_x86_64.whl (13.7MB)\n",
            "\u001b[K     |████████████████████████████████| 13.8MB 39.6MB/s \n",
            "\u001b[?25hCollecting scikit_learn==0.19.1 (from -r deep-learning-gene-expression/requirements.txt (line 5))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3d/2d/9fbc7baa5f44bc9e88ffb7ed32721b879bfa416573e85031e16f52569bc9/scikit_learn-0.19.1-cp36-cp36m-manylinux1_x86_64.whl (12.4MB)\n",
            "\u001b[K     |████████████████████████████████| 12.4MB 41.4MB/s \n",
            "\u001b[?25hCollecting scipy==1.0.0 (from -r deep-learning-gene-expression/requirements.txt (line 6))\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d8/5e/caa01ba7be11600b6a9d39265440d7b3be3d69206da887c42bef049521f2/scipy-1.0.0-cp36-cp36m-manylinux1_x86_64.whl (50.0MB)\n",
            "\u001b[K     |████████████████████████████████| 50.0MB 1.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas==0.22.0->-r deep-learning-gene-expression/requirements.txt (line 2)) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas==0.22.0->-r deep-learning-gene-expression/requirements.txt (line 2)) (2.5.3)\n",
            "Requirement already satisfied: cycler in /usr/local/lib/python3.6/dist-packages (from matplotlib==1.5.3->-r deep-learning-gene-expression/requirements.txt (line 4)) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.0,!=2.0.4,!=2.1.2,>=1.5.6 in /usr/local/lib/python3.6/dist-packages (from matplotlib==1.5.3->-r deep-learning-gene-expression/requirements.txt (line 4)) (2.4.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2->pandas==0.22.0->-r deep-learning-gene-expression/requirements.txt (line 2)) (1.12.0)\n",
            "\u001b[31mERROR: yellowbrick 0.9.1 has requirement scikit-learn>=0.20, but you'll have scikit-learn 0.19.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow 1.14.0 has requirement numpy<2.0,>=1.14.5, but you'll have numpy 1.14.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: spacy 2.1.8 has requirement numpy>=1.15.0, but you'll have numpy 1.14.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: scikit-image 0.15.0 has requirement matplotlib!=3.0.0,>=2.0.0, but you'll have matplotlib 1.5.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: plotnine 0.5.1 has requirement matplotlib>=3.0.0, but you'll have matplotlib 1.5.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: plotnine 0.5.1 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: mizani 0.5.4 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: imgaug 0.2.9 has requirement numpy>=1.15.0, but you'll have numpy 1.14.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: imbalanced-learn 0.4.3 has requirement scikit-learn>=0.20, but you'll have scikit-learn 0.19.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=0.24.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: featuretools 0.4.1 has requirement pandas>=0.23.0, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: featuretools 0.4.1 has requirement tqdm>=4.19.2, but you'll have tqdm 4.11.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: fbprophet 0.5 has requirement matplotlib>=2.0.0, but you'll have matplotlib 1.5.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: fbprophet 0.5 has requirement pandas>=0.23.4, but you'll have pandas 0.22.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: fastai 1.0.57 has requirement numpy>=1.15, but you'll have numpy 1.14.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: cvxpy 1.0.25 has requirement numpy>=1.15, but you'll have numpy 1.14.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: cvxpy 1.0.25 has requirement scipy>=1.1.0, but you'll have scipy 1.0.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: blis 0.2.4 has requirement numpy>=1.15.0, but you'll have numpy 1.14.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: numpy, pandas, tqdm, matplotlib, scikit-learn, scipy\n",
            "  Found existing installation: numpy 1.16.5\n",
            "    Uninstalling numpy-1.16.5:\n",
            "      Successfully uninstalled numpy-1.16.5\n",
            "  Found existing installation: pandas 0.24.2\n",
            "    Uninstalling pandas-0.24.2:\n",
            "      Successfully uninstalled pandas-0.24.2\n",
            "  Found existing installation: tqdm 4.28.1\n",
            "    Uninstalling tqdm-4.28.1:\n",
            "      Successfully uninstalled tqdm-4.28.1\n",
            "  Found existing installation: matplotlib 3.0.3\n",
            "    Uninstalling matplotlib-3.0.3:\n",
            "      Successfully uninstalled matplotlib-3.0.3\n",
            "  Found existing installation: scikit-learn 0.21.3\n",
            "    Uninstalling scikit-learn-0.21.3:\n",
            "      Successfully uninstalled scikit-learn-0.21.3\n",
            "  Found existing installation: scipy 1.3.1\n",
            "    Uninstalling scipy-1.3.1:\n",
            "      Successfully uninstalled scipy-1.3.1\n",
            "Successfully installed matplotlib-1.5.3 numpy-1.14.2 pandas-0.22.0 scikit-learn-0.19.1 scipy-1.0.0 tqdm-4.11.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits",
                  "numpy",
                  "pandas",
                  "tqdm"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMa2tTwJEh9Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cb842704-deec-4537-98c0-28f5f1a8cade"
      },
      "source": [
        "!pip freeze"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "absl-py==0.8.0\n",
            "alabaster==0.7.12\n",
            "albumentations==0.1.12\n",
            "altair==3.2.0\n",
            "astor==0.8.0\n",
            "astropy==3.0.5\n",
            "atari-py==0.1.15\n",
            "atomicwrites==1.3.0\n",
            "attrs==19.1.0\n",
            "audioread==2.1.8\n",
            "autograd==1.3\n",
            "Babel==2.7.0\n",
            "backcall==0.1.0\n",
            "backports.tempfile==1.0\n",
            "backports.weakref==1.0.post1\n",
            "beautifulsoup4==4.6.3\n",
            "bleach==3.1.0\n",
            "blis==0.2.4\n",
            "bokeh==1.0.4\n",
            "boto==2.49.0\n",
            "boto3==1.9.224\n",
            "botocore==1.12.224\n",
            "Bottleneck==1.2.1\n",
            "branca==0.3.1\n",
            "bs4==0.0.1\n",
            "bz2file==0.98\n",
            "cachetools==3.1.1\n",
            "certifi==2019.6.16\n",
            "cffi==1.12.3\n",
            "chainer==5.4.0\n",
            "chardet==3.0.4\n",
            "Click==7.0\n",
            "cloudpickle==0.6.1\n",
            "cmake==3.12.0\n",
            "community==1.0.0b1\n",
            "contextlib2==0.5.5\n",
            "convertdate==2.1.3\n",
            "coverage==3.7.1\n",
            "coveralls==0.5\n",
            "crcmod==1.7\n",
            "cvxopt==1.2.3\n",
            "cvxpy==1.0.25\n",
            "cycler==0.10.0\n",
            "cymem==2.0.2\n",
            "Cython==0.29.13\n",
            "daft==0.0.4\n",
            "dask==1.1.5\n",
            "dataclasses==0.6\n",
            "datascience==0.10.6\n",
            "decorator==4.4.0\n",
            "defusedxml==0.6.0\n",
            "descartes==1.1.0\n",
            "dill==0.3.0\n",
            "distributed==1.25.3\n",
            "Django==2.2.5\n",
            "dlib==19.16.0\n",
            "dm-sonnet==1.35\n",
            "docopt==0.6.2\n",
            "docutils==0.15.2\n",
            "dopamine-rl==1.0.5\n",
            "easydict==1.9\n",
            "ecos==2.0.7.post1\n",
            "editdistance==0.5.3\n",
            "en-core-web-sm==2.1.0\n",
            "entrypoints==0.3\n",
            "ephem==3.7.7.0\n",
            "et-xmlfile==1.0.1\n",
            "fa2==0.3.5\n",
            "fancyimpute==0.4.3\n",
            "fastai==1.0.57\n",
            "fastcache==1.1.0\n",
            "fastdtw==0.3.2\n",
            "fastprogress==0.1.21\n",
            "fastrlock==0.4\n",
            "fbprophet==0.5\n",
            "feather-format==0.4.0\n",
            "featuretools==0.4.1\n",
            "filelock==3.0.12\n",
            "fix-yahoo-finance==0.0.22\n",
            "Flask==1.1.1\n",
            "folium==0.8.3\n",
            "fsspec==0.4.4\n",
            "future==0.16.0\n",
            "gast==0.2.2\n",
            "GDAL==2.2.2\n",
            "gdown==3.6.4\n",
            "gensim==3.6.0\n",
            "geographiclib==1.49\n",
            "geopy==1.17.0\n",
            "gevent==1.4.0\n",
            "gin-config==0.2.1\n",
            "glob2==0.7\n",
            "google==2.0.2\n",
            "google-api-core==1.14.2\n",
            "google-api-python-client==1.7.11\n",
            "google-auth==1.4.2\n",
            "google-auth-httplib2==0.0.3\n",
            "google-auth-oauthlib==0.4.0\n",
            "google-cloud-bigquery==1.14.0\n",
            "google-cloud-core==1.0.3\n",
            "google-cloud-datastore==1.8.0\n",
            "google-cloud-language==1.2.0\n",
            "google-cloud-storage==1.16.1\n",
            "google-cloud-translate==1.5.0\n",
            "google-colab==1.0.0\n",
            "google-pasta==0.1.7\n",
            "google-resumable-media==0.4.0\n",
            "googleapis-common-protos==1.6.0\n",
            "googledrivedownloader==0.4\n",
            "graph-nets==1.0.4\n",
            "graphviz==0.10.1\n",
            "greenlet==0.4.15\n",
            "grpcio==1.15.0\n",
            "gspread==3.0.1\n",
            "gspread-dataframe==3.0.3\n",
            "gunicorn==19.9.0\n",
            "gym==0.10.11\n",
            "h5py==2.8.0\n",
            "HeapDict==1.0.0\n",
            "holidays==0.9.11\n",
            "html5lib==1.0.1\n",
            "httpimport==0.5.16\n",
            "httplib2==0.11.3\n",
            "humanize==0.5.1\n",
            "hyperopt==0.1.2\n",
            "ideep4py==2.0.0.post3\n",
            "idna==2.8\n",
            "image==1.5.27\n",
            "imageio==2.4.1\n",
            "imagesize==1.1.0\n",
            "imbalanced-learn==0.4.3\n",
            "imblearn==0.0\n",
            "imgaug==0.2.9\n",
            "importlib-metadata==0.20\n",
            "imutils==0.5.3\n",
            "inflect==2.1.0\n",
            "intel-openmp==2019.0\n",
            "intervaltree==2.1.0\n",
            "ipykernel==4.6.1\n",
            "ipython==5.5.0\n",
            "ipython-genutils==0.2.0\n",
            "ipython-sql==0.3.9\n",
            "ipywidgets==7.5.1\n",
            "itsdangerous==1.1.0\n",
            "jax==0.1.44\n",
            "jaxlib==0.1.26\n",
            "jdcal==1.4.1\n",
            "jedi==0.15.1\n",
            "jieba==0.39\n",
            "Jinja2==2.10.1\n",
            "jmespath==0.9.4\n",
            "joblib==0.13.2\n",
            "jpeg4py==0.1.4\n",
            "jsonschema==2.6.0\n",
            "jupyter==1.0.0\n",
            "jupyter-client==5.3.1\n",
            "jupyter-console==5.2.0\n",
            "jupyter-core==4.5.0\n",
            "kaggle==1.5.5\n",
            "kapre==0.1.3.1\n",
            "Keras==2.2.5\n",
            "Keras-Applications==1.0.8\n",
            "Keras-Preprocessing==1.1.0\n",
            "keras-vis==0.4.1\n",
            "kiwisolver==1.1.0\n",
            "knnimpute==0.1.0\n",
            "librosa==0.6.3\n",
            "lightgbm==2.2.3\n",
            "llvmlite==0.29.0\n",
            "lmdb==0.97\n",
            "lucid==0.3.8\n",
            "lunardate==0.2.0\n",
            "lxml==4.2.6\n",
            "magenta==0.3.19\n",
            "Markdown==3.1.1\n",
            "MarkupSafe==1.1.1\n",
            "matplotlib==1.5.3\n",
            "matplotlib-venn==0.11.5\n",
            "mesh-tensorflow==0.0.5\n",
            "mido==1.2.6\n",
            "mir-eval==0.5\n",
            "missingno==0.4.2\n",
            "mistune==0.8.4\n",
            "mizani==0.5.4\n",
            "mkl==2019.0\n",
            "mlxtend==0.14.0\n",
            "more-itertools==7.2.0\n",
            "moviepy==0.2.3.5\n",
            "mpi4py==3.0.2\n",
            "mpmath==1.1.0\n",
            "msgpack==0.5.6\n",
            "multiprocess==0.70.8\n",
            "multitasking==0.0.9\n",
            "murmurhash==1.0.2\n",
            "music21==5.5.0\n",
            "natsort==5.5.0\n",
            "nbconvert==5.6.0\n",
            "nbformat==4.4.0\n",
            "networkx==2.3\n",
            "nibabel==2.3.3\n",
            "nltk==3.2.5\n",
            "notebook==5.2.2\n",
            "np-utils==0.5.11.1\n",
            "numba==0.40.1\n",
            "numexpr==2.7.0\n",
            "numpy==1.14.2\n",
            "nvidia-ml-py3==7.352.0\n",
            "oauth2client==4.1.3\n",
            "oauthlib==3.1.0\n",
            "okgrade==0.4.3\n",
            "olefile==0.46\n",
            "opencv-contrib-python==3.4.3.18\n",
            "opencv-python==3.4.5.20\n",
            "openpyxl==2.5.9\n",
            "opt-einsum==3.0.1\n",
            "osqp==0.6.1\n",
            "packaging==19.1\n",
            "palettable==3.2.0\n",
            "pandas==0.22.0\n",
            "pandas-datareader==0.7.4\n",
            "pandas-gbq==0.4.1\n",
            "pandas-profiling==1.4.1\n",
            "pandocfilters==1.4.2\n",
            "parso==0.5.1\n",
            "pathlib==1.0.1\n",
            "patsy==0.5.1\n",
            "pexpect==4.7.0\n",
            "pickleshare==0.7.5\n",
            "Pillow==4.3.0\n",
            "pip-tools==3.9.0\n",
            "plac==0.9.6\n",
            "plotly==4.1.1\n",
            "plotnine==0.5.1\n",
            "pluggy==0.7.1\n",
            "portpicker==1.2.0\n",
            "prefetch-generator==1.0.1\n",
            "preshed==2.0.1\n",
            "pretty-midi==0.2.8\n",
            "prettytable==0.7.2\n",
            "progressbar2==3.38.0\n",
            "prometheus-client==0.7.1\n",
            "promise==2.2.1\n",
            "prompt-toolkit==1.0.16\n",
            "protobuf==3.7.1\n",
            "psutil==5.4.8\n",
            "psycopg2==2.7.6.1\n",
            "ptyprocess==0.6.0\n",
            "py==1.8.0\n",
            "pyarrow==0.14.1\n",
            "pyasn1==0.4.7\n",
            "pyasn1-modules==0.2.6\n",
            "pycocotools==2.0.0\n",
            "pycparser==2.19\n",
            "pydot==1.3.0\n",
            "pydot-ng==2.0.0\n",
            "pydotplus==2.0.2\n",
            "PyDrive==1.3.1\n",
            "pyemd==0.5.1\n",
            "pyglet==1.4.3\n",
            "Pygments==2.1.3\n",
            "pygobject==3.26.1\n",
            "pymc3==3.7\n",
            "pymongo==3.9.0\n",
            "pymystem3==0.2.0\n",
            "PyOpenGL==3.1.0\n",
            "pyparsing==2.4.2\n",
            "pyrsistent==0.15.4\n",
            "pysndfile==1.3.7\n",
            "PySocks==1.7.0\n",
            "pystan==2.19.0.0\n",
            "pytest==3.6.4\n",
            "python-apt==1.6.4\n",
            "python-chess==0.23.11\n",
            "python-dateutil==2.5.3\n",
            "python-louvain==0.13\n",
            "python-rtmidi==1.3.0\n",
            "python-slugify==3.0.3\n",
            "python-utils==2.3.0\n",
            "pytz==2018.9\n",
            "PyWavelets==1.0.3\n",
            "PyYAML==3.13\n",
            "pyzmq==17.0.0\n",
            "qtconsole==4.5.5\n",
            "requests==2.21.0\n",
            "requests-oauthlib==1.2.0\n",
            "resampy==0.2.2\n",
            "retrying==1.3.3\n",
            "rpy2==2.9.5\n",
            "rsa==4.0\n",
            "s3fs==0.3.4\n",
            "s3transfer==0.2.1\n",
            "scikit-image==0.15.0\n",
            "scikit-learn==0.19.1\n",
            "scipy==1.0.0\n",
            "screen-resolution-extra==0.0.0\n",
            "scs==2.1.1.post2\n",
            "seaborn==0.9.0\n",
            "semantic-version==2.8.2\n",
            "Send2Trash==1.5.0\n",
            "setuptools-git==1.2\n",
            "Shapely==1.6.4.post2\n",
            "simplegeneric==0.8.1\n",
            "six==1.12.0\n",
            "sklearn==0.0\n",
            "sklearn-pandas==1.8.0\n",
            "smart-open==1.8.4\n",
            "snowballstemmer==1.9.1\n",
            "sortedcontainers==2.1.0\n",
            "spacy==2.1.8\n",
            "Sphinx==1.8.5\n",
            "sphinxcontrib-websupport==1.1.2\n",
            "SQLAlchemy==1.3.8\n",
            "sqlparse==0.3.0\n",
            "srsly==0.1.0\n",
            "stable-baselines==2.2.1\n",
            "statsmodels==0.10.1\n",
            "sympy==1.1.1\n",
            "tables==3.4.4\n",
            "tabulate==0.8.3\n",
            "tblib==1.4.0\n",
            "tensor2tensor==1.11.0\n",
            "tensorboard==1.14.0\n",
            "tensorboardcolab==0.0.22\n",
            "tensorflow==1.14.0\n",
            "tensorflow-estimator==1.14.0\n",
            "tensorflow-hub==0.6.0\n",
            "tensorflow-metadata==0.14.0\n",
            "tensorflow-probability==0.7.0\n",
            "termcolor==1.1.0\n",
            "terminado==0.8.2\n",
            "testpath==0.4.2\n",
            "text-unidecode==1.2\n",
            "textblob==0.15.3\n",
            "textgenrnn==1.4.1\n",
            "tfds-nightly==1.2.0.dev201909050105\n",
            "tflearn==0.3.2\n",
            "Theano==1.0.4\n",
            "thinc==7.0.8\n",
            "toolz==0.10.0\n",
            "torch==1.1.0\n",
            "torchsummary==1.5.1\n",
            "torchtext==0.3.1\n",
            "torchvision==0.3.0\n",
            "tornado==4.5.3\n",
            "tqdm==4.11.2\n",
            "traitlets==4.3.2\n",
            "tweepy==3.6.0\n",
            "typing==3.7.4.1\n",
            "tzlocal==1.5.1\n",
            "umap-learn==0.3.10\n",
            "uritemplate==3.0.0\n",
            "urllib3==1.24.3\n",
            "vega-datasets==0.7.0\n",
            "wasabi==0.2.2\n",
            "wcwidth==0.1.7\n",
            "webencodings==0.5.1\n",
            "Werkzeug==0.15.6\n",
            "widgetsnbextension==3.5.1\n",
            "wordcloud==1.5.0\n",
            "wrapt==1.11.2\n",
            "xarray==0.11.3\n",
            "xgboost==0.90\n",
            "xkit==0.0.0\n",
            "xlrd==1.1.0\n",
            "xlwt==1.3.0\n",
            "yellowbrick==0.9.1\n",
            "zict==1.0.0\n",
            "zipp==0.6.0\n",
            "zmq==0.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dnEsmO4GOC2E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp -r deep-learning-gene-expression/data/ ."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qk6v6G-WObQh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "7b2b11d9-a50b-4963-c38f-65923f624809"
      },
      "source": [
        "ls -l"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 12\n",
            "drwxr-xr-x 2 root root 4096 Sep 16 08:38 \u001b[0m\u001b[01;34mdata\u001b[0m/\n",
            "drwxr-xr-x 9 root root 4096 Sep 16 07:54 \u001b[01;34mdeep-learning-gene-expression\u001b[0m/\n",
            "drwxr-xr-x 1 root root 4096 Aug 27 16:17 \u001b[01;34msample_data\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWzG9VUzOtDz",
        "colab_type": "text"
      },
      "source": [
        "**Run the pipeline**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ybFpJ3_NzbW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "outputId": "5ef3b688-d323-4c61-ff45-5ff8b08793d7"
      },
      "source": [
        "!python deep-learning-gene-expression/pipeline.py"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:selection.log_decorator:Running Correlation feature selection...\n",
            "INFO:selection.log_decorator:Running Fisher feature selection...\n",
            "INFO:selection.log_decorator:Running Ttest feature selection...\n",
            "INFO:selection.log_decorator:Running Random feature selection...\n",
            "INFO:__main__:Loading data...\n",
            "INFO:__main__:Loaded data...\n",
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
            "INFO:utils.features_utils:Making feature selection with [fisher] method...\n",
            "INFO:selection.log_decorator:Read Fisher features from file : [features/Fisher.csv]...\n",
            "INFO:utils.features_utils:[fisher] feature selection last 18295 ms.\n",
            "INFO:utils.features_utils:Making feature selection with [corr] method...\n",
            "INFO:selection.log_decorator:Read Correlation features from file : [features/Correlation.csv]...\n",
            "INFO:utils.features_utils:[corr] feature selection last 21466 ms.\n",
            "INFO:utils.features_utils:Making feature selection with [ttest] method...\n",
            "INFO:selection.log_decorator:Saving Ttest features to [features/Ttest.csv]...\n",
            "INFO:selection.log_decorator:Ttest selection last [60329.95820045471]...\n",
            "INFO:utils.features_utils:[ttest] feature selection last 18217 ms.\n",
            "INFO:utils.features_utils:Making feature selection with [random] method...\n",
            "INFO:selection.log_decorator:Read Random features from file : [features/Random.csv]...\n",
            "INFO:utils.features_utils:[random] feature selection last 818 ms.\n",
            "INFO:utils.nn_utils:Training neural network for fisher selection method...\n",
            "100% 10000/10000 [00:22<00:00, 453.39it/s, accuracy=81.5, fold_id=[1/10], loss=0.421, method=fisher]\n",
            "INFO:selection.log_decorator:Read Fisher features from file : [features/Fisher.csv]...\n",
            "INFO:utils.nn_utils:Training neural network for corr selection method...\n",
            "100% 10000/10000 [00:24<00:00, 438.30it/s, accuracy=100, fold_id=[1/10], loss=0.0506, method=corr]\n",
            "INFO:selection.log_decorator:Read Correlation features from file : [features/Correlation.csv]...\n",
            "INFO:utils.nn_utils:Training neural network for ttest selection method...\n",
            " 11% 1106/10000 [00:02<00:19, 460.18it/s, accuracy=77.7, fold_id=[1/10], loss=0.749, method=ttest]"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phlxM-qtN_AW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}