DeepSet.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/erezimm/ML-ML/blob/master/DeepSet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCU4DoTLIIoO",
        "colab_type": "text"
      },
      "source": [
        "# Main notebook, to be run on Google Colab"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWYU8-BTJ-XN",
        "colab_type": "text"
      },
      "source": [
        "## Import dependencies and download dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NWzkUtMrJ6Dy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# download and unzip:\n",
        "\n",
        "# !wget -O vdata.zip https://www.dropbox.com/s/lwr0b1o0vh61i80/validset.zip?dl=1\n",
        "# !unzip -o -q vdata.zip -d /content/valid\n",
        "\n",
        "# !wget -O tdata.zip https://github.com/erezimm/ML-ML/raw/master/data.zip\n",
        "# !unzip -o -q tdata.zip -d /content/train\n",
        "\n",
        "!wget -O data.zip https://www.dropbox.com/s/a418n3tytv8vfrx/data.zip?dl=1\n",
        "!unzip -o -q data.zip -d /content/data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5E9Y_nnn3m8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "outputId": "197c9004-09b2-452f-a1a2-09d74da43f8c"
      },
      "source": [
        "!pip install dgl\n",
        "\n",
        "import glob\n",
        "import os\n",
        "import shutil\n",
        "from datetime import datetime\n",
        "\n",
        "import astropy.io.ascii\n",
        "import dgl\n",
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from tqdm.notebook import tqdm\n",
        "\n",
        "if not torch.cuda.is_available():\n",
        "    raise Exception('Cuda unavailable, turn it on under Runtime>Change runtime type>GPU')\n",
        "device = torch.device('cuda')\n",
        "\n",
        "\n",
        "validation_path = '/content/valid/data'\n",
        "training_path = '/content/train/data'\n",
        "data_path = '/content/data/data'\n",
        "microlist = glob.glob(data_path+'/microlensedconst_*')\n",
        "varlist = glob.glob(data_path+'/cleanvar_*')\n",
        "for typelist in (microlist, varlist):\n",
        "    for i, f in enumerate(typelist):\n",
        "        dest = training_path if i<len(microlist)/2 else validation_path\n",
        "        shutil.move(f, os.path.join(dest, os.path.basename(f)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting dgl\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/c5/b4/84e4ebd70ef3985181ef5d2d2a366a45af0e3cd18d249fb212ac03f683cf/dgl-0.4.3.post2-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 7.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from dgl) (1.18.5)\n",
            "Requirement already satisfied: networkx>=2.1 in /usr/local/lib/python3.6/dist-packages (from dgl) (2.4)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.6/dist-packages (from dgl) (2.23.0)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from dgl) (1.4.1)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.1->dgl) (4.4.2)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->dgl) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->dgl) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->dgl) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.19.0->dgl) (2020.6.20)\n",
            "Installing collected packages: dgl\n",
            "Successfully installed dgl-0.4.3.post2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "DGL backend not selected or invalid.  Assuming PyTorch for now.\n",
            "Using backend: pytorch\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Setting the default backend to \"pytorch\". You can change it in the ~/.dgl/config.json file or export the DGLBACKEND environment variable.  Valid options are: pytorch, mxnet, tensorflow (all lowercase)\n",
            "--2020-08-18 10:39:29--  https://www.dropbox.com/s/a418n3tytv8vfrx/data.zip?dl=1\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.67.1, 2620:100:6020:1::a27d:4001\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.67.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/dl/a418n3tytv8vfrx/data.zip [following]\n",
            "--2020-08-18 10:39:29--  https://www.dropbox.com/s/dl/a418n3tytv8vfrx/data.zip\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://ucc3f65c366fc4e854f80164370e.dl.dropboxusercontent.com/cd/0/get/A9rn23zDXGL0uRr6m5I_ulkTF9ZmwNWiDKW0rOyYLnbleHECy0J9bABtduWk8fqFLNlddt9JG6-SwAXZfd16nQfvJrcOoghRmWmE5uUvQPMNSX1lyrHi1AMDAprKMtK9g-M/file?dl=1# [following]\n",
            "--2020-08-18 10:39:30--  https://ucc3f65c366fc4e854f80164370e.dl.dropboxusercontent.com/cd/0/get/A9rn23zDXGL0uRr6m5I_ulkTF9ZmwNWiDKW0rOyYLnbleHECy0J9bABtduWk8fqFLNlddt9JG6-SwAXZfd16nQfvJrcOoghRmWmE5uUvQPMNSX1lyrHi1AMDAprKMtK9g-M/file?dl=1\n",
            "Resolving ucc3f65c366fc4e854f80164370e.dl.dropboxusercontent.com (ucc3f65c366fc4e854f80164370e.dl.dropboxusercontent.com)... 162.125.67.15, 2620:100:6024:15::a27d:440f\n",
            "Connecting to ucc3f65c366fc4e854f80164370e.dl.dropboxusercontent.com (ucc3f65c366fc4e854f80164370e.dl.dropboxusercontent.com)|162.125.67.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1434472017 (1.3G) [application/binary]\n",
            "Saving to: ‘data.zip’\n",
            "\n",
            "data.zip            100%[===================>]   1.34G  17.8MB/s    in 79s     \n",
            "\n",
            "2020-08-18 10:40:49 (17.3 MB/s) - ‘data.zip’ saved [1434472017/1434472017]\n",
            "\n"
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          "name": "stdout"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ff2ASGtVn3nB",
        "colab_type": "text"
      },
      "source": [
        "## Create Datasets\n",
        "Class CustomDataset loads all files and converts them to a list of graphs.\n",
        "Each node in a graph is a data point, i.e. node features are days since start, mag, magerr"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, path):\n",
        "        filelist = glob.glob(path+'/*')\n",
        "        \n",
        "        self.graphs = []\n",
        "        self.isMicrolensed = []\n",
        "        for fname in tqdm(filelist):\n",
        "            filebasename = os.path.basename(fname)\n",
        "            if filebasename.startswith('cleanvar'):\n",
        "                microlensed = torch.tensor([0])\n",
        "            elif filebasename.startswith('microlensedconst'):\n",
        "                microlensed = torch.tensor([1])\n",
        "            else:\n",
        "                raise Exception('Filename ' + fname + ' not clean_* or microlensed_*')\n",
        "            \n",
        "            with open(fname) as f:\n",
        "                data = astropy.io.ascii.read(f.read())\n",
        "                times, mags, magerrs = data['col1'], [], data['col3']\n",
        "                for m, err in zip(data['col2'], magerrs):\n",
        "                    mags.append(round(m, len(str(err).split('.')[1])))\n",
        "                zipped = list(zip(times, mags, magerrs))\n",
        "                zipped.sort(key=lambda tup: tup[0])\n",
        "                times, mags, magerrs = zip(*zipped)\n",
        "                times, mags, magerrs =torch.tensor(times), torch.tensor(mags), torch.tensor(magerrs)\n",
        "                \n",
        "                times = times - min(times)\n",
        "                flux = 10 ** (mags/(-2.5))\n",
        "                medflux = torch.median(flux)\n",
        "                flux = flux/medflux\n",
        "                fluxerr = flux * magerrs * np.log(10) * 0.4 / medflux\n",
        "\n",
        "                n = len(times)\n",
        "                g = dgl.DGLGraph()\n",
        "                g.add_nodes(n)\n",
        "                g.ndata['time'] = torch.tensor(times).float()  # days\n",
        "                g.ndata['mag'] = torch.tensor(flux).float()\n",
        "                g.ndata['magerr'] = torch.tensor(fluxerr).float()\n",
        "\n",
        "\n",
        "                #g.add_edges([i for i in range(n)], [(i+1)%n for i in range(n)])\n",
        "                #g.add_edges([i for i in range(n)], [(i-1)%n for i in range(n)])\n",
        "                \n",
        "                self.graphs.append(g)\n",
        "                self.isMicrolensed.append(microlensed)\n",
        "        \n",
        "    def __len__(self):\n",
        "        return len(self.graphs)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        return self.graphs[idx], self.isMicrolensed[idx]\n",
        "\n",
        "\n",
        "train_ds = CustomDataset(training_path)\n",
        "validation_ds = CustomDataset(validation_path)\n",
        "\n",
        "nx.draw(dgl.to_networkx(train_ds[20][0]))\n",
        "\n",
        "print('training set size:', len(train_ds))\n",
        "print('valid set size:', len(validation_ds)"
      ],
      "execution_count": null,
      "outputs": [
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            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:35: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:36: 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",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:37: 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"
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          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnD7mWfjn3nK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def collate(samples):\n",
        "    # The input `samples` is a list, a batch of whatever comes out of your dataset object\n",
        "    graphs = [x[0] for x in samples]\n",
        "    labels = [x[1] for x in samples]\n",
        "    \n",
        "    batched_graph = dgl.batch(graphs, node_attrs=['time', 'mag', 'magerr'])\n",
        "    targets = torch.cat(labels)\n",
        "    \n",
        "    return batched_graph, targets.unsqueeze(1).float()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FGDTIcKIuGW2",
        "colab_type": "text"
      },
      "source": [
        "## The Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "soJyGlbvMErI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "layers = [10,10]  # hidden layers in the DeepSetLayer\n",
        "outputsize = 7  # length of the representation passed to the final network\n",
        "cllayers = [10,5,5]  # hidden layers in the final network (takes outputsize, returns a scalar which is the classification)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xb2gAmYhiELn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_layers(inp,out,sizes):\n",
        "    sizes = [inp]+sizes+[out]\n",
        "    layers = []\n",
        "    for i in range(len(sizes)-1):\n",
        "        layers.extend([nn.Linear(sizes[i], sizes[i+1]), nn.LeakyReLU()])\n",
        "    \n",
        "    return layers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iO90uxpOLy76",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #Deep Set\n",
        "\n",
        "class DeepSet(nn.Module):\n",
        "\n",
        "    def __init__(self, config):\n",
        "        super(DeepSet, self).__init__()\n",
        "\n",
        "        self.config = config\n",
        "\n",
        "        self.layers = nn.ModuleList()\n",
        "\n",
        "        self.layers.append(DeepSetLayer(config['inputsize'], config['layers'][0], config['inputs'], config['output'] ))\n",
        "\n",
        "        n_layers = len(config['layers'])\n",
        "\n",
        "        for i in range(n_layers - 1):\n",
        "\n",
        "            self.layers.append(DeepSetLayer(config['inputsize']+ config['layers'][i],\n",
        "                                            config['layers'][i+1], \n",
        "                                            config['inputs'] + [config['output']], \n",
        "                                            config['output']))\n",
        "            \n",
        "\n",
        "        self.layers.append(DeepSetLayer(config['inputsize']+ config['layers'][-1],\n",
        "                                            config['outputsize'], \n",
        "                                            config['inputs'] + [config['output']],\n",
        "                                            config['output']))  \n",
        "\n",
        "        layers = build_layers(config['outputsize'], 2, config['cllayers'])  # OFEK chnaged to output=2\n",
        "        self.classifier = nn.Sequential(*(layers+ [nn.Softmax()]))  # OFEK added SoftMax\n",
        "    \n",
        "    def forward(self, g):\n",
        "\n",
        "        for layer in self.layers:\n",
        "            \n",
        "            g = layer(g) \n",
        "\n",
        "        mean_nodes= dgl.mean_nodes(g, 'node_embed', weight = None)\n",
        "        output = self.classifier(mean_nodes)\n",
        "\n",
        "\n",
        "        return output \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2TODvj6AMLJP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DeepSetLayer(nn.Module):\n",
        "    def __init__(self, inputsize, outputsize, inputnames, outputname):\n",
        "        \n",
        "\n",
        "\n",
        "        super(DeepSetLayer,self).__init__()\n",
        "\n",
        "        self.inputs      = inputnames\n",
        "        self.outputname = outputname\n",
        "\n",
        "\n",
        "  \n",
        "\n",
        "        layers = build_layers(inputsize, outputsize, [int(0.5*(outputsize + inputsize)   )])\n",
        "\n",
        "        self.node_embedd = nn.Sequential(*layers)\n",
        "\n",
        "        layers = build_layers(outputsize + inputsize, outputsize,[ int( 0.5*(outputsize + inputsize)) ] )\n",
        "\n",
        "        self.node_update = nn.Sequential(*layers)\n",
        "\n",
        "\n",
        "\n",
        "    def forward(self, g):\n",
        "\n",
        "        gn = [g.ndata[inputname] for inputname in self.inputs]\n",
        "        unsqdim = max(max([len(n.shape) for n in gn]),2)\n",
        "        tocat = []\n",
        "        for n in gn:\n",
        "            t = n\n",
        "            for _ in range(unsqdim-len(n.shape)):\n",
        "                t = t.unsqueeze(-1)\n",
        "            tocat.append(t)\n",
        "            # print(t.shape)\n",
        "        node_data = torch.cat(tocat, dim=1)\n",
        "\n",
        "        g.ndata['node_embed'] = self.node_embedd(node_data)\n",
        "\n",
        "\n",
        "        node_sum = dgl.mean_nodes(g, 'node_embed', weight = None)  #Global representation of graph \n",
        "\n",
        "        g.ndata['mean_nodes'] = dgl.broadcast_nodes(g, node_sum)\n",
        "        \n",
        "        \n",
        "\n",
        "        gn = [g.ndata[inputname] for inputname in self.inputs] + [g.ndata['mean_nodes']]\n",
        "        unsqdim = max(max([len(n.shape) for n in gn]),2)\n",
        "        tocat = []\n",
        "        for n in gn:\n",
        "            t = n\n",
        "            for _ in range(unsqdim-len(n.shape)):\n",
        "                t = t.unsqueeze(-1)\n",
        "            tocat.append(t)\n",
        "            # print(t.shape)\n",
        "        node_update_input = torch.cat( tocat, dim=1  )\n",
        "\n",
        "        g.ndata[self.outputname] = self.node_update(node_update_input)\n",
        "\n",
        "\n",
        "        return g #contains global information \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EZc6nI6TkmSC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 986
        },
        "outputId": "a015f3a4-c049-4fa6-872e-4c17367e72bc"
      },
      "source": [
        "net = DeepSet({'inputsize':3 , 'outputsize':outputsize, 'inputs':['time', 'mag', 'magerr'], 'output':'finalrep', 'layers':layers, 'cllayers':cllayers})\n",
        "net.to(device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DeepSet(\n",
              "  (layers): ModuleList(\n",
              "    (0): DeepSetLayer(\n",
              "      (node_embedd): Sequential(\n",
              "        (0): Linear(in_features=3, out_features=6, bias=True)\n",
              "        (1): LeakyReLU(negative_slope=0.01)\n",
              "        (2): Linear(in_features=6, out_features=10, bias=True)\n",
              "        (3): LeakyReLU(negative_slope=0.01)\n",
              "      )\n",
              "      (node_update): Sequential(\n",
              "        (0): Linear(in_features=13, out_features=6, bias=True)\n",
              "        (1): LeakyReLU(negative_slope=0.01)\n",
              "        (2): Linear(in_features=6, out_features=10, bias=True)\n",
              "        (3): LeakyReLU(negative_slope=0.01)\n",
              "      )\n",
              "    )\n",
              "    (1): DeepSetLayer(\n",
              "      (node_embedd): Sequential(\n",
              "        (0): Linear(in_features=13, out_features=11, bias=True)\n",
              "        (1): LeakyReLU(negative_slope=0.01)\n",
              "        (2): Linear(in_features=11, out_features=10, bias=True)\n",
              "        (3): LeakyReLU(negative_slope=0.01)\n",
              "      )\n",
              "      (node_update): Sequential(\n",
              "        (0): Linear(in_features=23, out_features=11, bias=True)\n",
              "        (1): LeakyReLU(negative_slope=0.01)\n",
              "        (2): Linear(in_features=11, out_features=10, bias=True)\n",
              "        (3): LeakyReLU(negative_slope=0.01)\n",
              "      )\n",
              "    )\n",
              "    (2): DeepSetLayer(\n",
              "      (node_embedd): Sequential(\n",
              "        (0): Linear(in_features=13, out_features=10, bias=True)\n",
              "        (1): LeakyReLU(negative_slope=0.01)\n",
              "        (2): Linear(in_features=10, out_features=7, bias=True)\n",
              "        (3): LeakyReLU(negative_slope=0.01)\n",
              "      )\n",
              "      (node_update): Sequential(\n",
              "        (0): Linear(in_features=20, out_features=10, bias=True)\n",
              "        (1): LeakyReLU(negative_slope=0.01)\n",
              "        (2): Linear(in_features=10, out_features=7, bias=True)\n",
              "        (3): LeakyReLU(negative_slope=0.01)\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): Sequential(\n",
              "    (0): Linear(in_features=7, out_features=10, bias=True)\n",
              "    (1): LeakyReLU(negative_slope=0.01)\n",
              "    (2): Linear(in_features=10, out_features=5, bias=True)\n",
              "    (3): LeakyReLU(negative_slope=0.01)\n",
              "    (4): Linear(in_features=5, out_features=5, bias=True)\n",
              "    (5): LeakyReLU(negative_slope=0.01)\n",
              "    (6): Linear(in_features=5, out_features=2, bias=True)\n",
              "    (7): LeakyReLU(negative_slope=0.01)\n",
              "    (8): Softmax(dim=None)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgrXh0Jdn3nb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def evaluate(net, validation_ds):\n",
        "    test_data_loader = DataLoader(validation_ds, batch_size=1000, shuffle=True, collate_fn=collate)\n",
        "\n",
        "    net.cpu()\n",
        "    net.eval()\n",
        "\n",
        "    true_positive = 0\n",
        "    false_positive = 0\n",
        "    true_negative = 0\n",
        "    false_negative = 0\n",
        "    total_positives = 0\n",
        "    total_negatives = 0\n",
        "\n",
        "    for i, (x,y) in enumerate(test_data_loader):\n",
        "            y = y.data.numpy()\n",
        "            y = y.transpose()\n",
        "            prediction = net(x).cpu().data.numpy()\n",
        "            pred = (prediction[:,1]>prediction[:,0])\n",
        "            \n",
        "            total_positives+=len(np.where( y==1 )[0])\n",
        "            total_negatives+=len(np.where( y==0 )[0])\n",
        "            \n",
        "\n",
        "            true_positive+= len(np.where( (pred==1) & (y==1) )[0])\n",
        "            false_positive+= len(np.where( (pred==1) & (y==0) )[0])\n",
        "    # print(pred, prediction, y)\n",
        "    print('Probability of detection:', true_positive/total_positives)  # probability of recognizing a microlensing when you see it\n",
        "    print('Probability of false alarm:', false_positive/total_negatives)  # probability for misclassification of a variable star as microlensing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6x-ALDRlwBDo",
        "colab_type": "text"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBHUZcTcXPO5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "ae0e6bb15f674d5da476006cba662373",
            "3e31075f4dec47bb9e09861406f5a8fd",
            "ba98e1e0f6a943b2bda061822e1367b7",
            "f8340a70fd6a418c89e3098e4345a218",
            "066224d1ffe9493eaa18ef697d0050fe",
            "331fa49380944bfbbddf24b2b096acfb",
            "bc04a52a7af34560bbfb387235f5dd6a",
            "5a1f235f8621497cbfd1a5ba95b5170d"
          ]
        },
        "outputId": "a333acf6-a161-43e1-ff48-4473d6d013b9"
      },
      "source": [
        "# options:\n",
        "# loss_func = nn.BCEWithLogitsLoss()\n",
        "# loss_func = nn.L1Loss()\n",
        "loss_func = nn.CrossEntropyLoss()\n",
        "#optimizer = optim.Adadelta(net.parameters(), lr=1)\n",
        "#optimizer = optim.SGD(net.parameters(), lr=0.01)\n",
        "optimizer = optim.Adam(net.parameters(), lr=0.001)\n",
        "exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=20, gamma=0.5)\n",
        "\n",
        "n_epochs = 100\n",
        "\n",
        "losses = []\n",
        "\n",
        "data_loader = DataLoader(train_ds, batch_size=1000, shuffle=True, collate_fn=collate)\n",
        "for epoch in tqdm(range(n_epochs)):\n",
        "    net.train()\n",
        "    this_losses = []\n",
        "    for x,y in data_loader:\n",
        "        x, y = x.to(device), y.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = net(x)\n",
        "        # print(output)\n",
        "        # output = torch.cat((1-output, output), dim=1)\n",
        "        # print(output)\n",
        "        loss = loss_func(output.squeeze(), y.long().squeeze())\n",
        "        this_losses.append(float(loss))\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "    losses.append(np.mean(this_losses))\n",
        "    print('Epoch:', epoch, 'Loss:', round(losses[-1], 4))  # for some real-time indication (couldn't manage to get this to dynamically plot)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae0e6bb15f674d5da476006cba662373",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=200.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0 Loss: 0.7989\n",
            "Epoch: 1 Loss: 0.7508\n",
            "Epoch: 2 Loss: 0.7393\n",
            "Epoch: 3 Loss: 0.7067\n",
            "Epoch: 4 Loss: 0.6742\n",
            "Epoch: 5 Loss: 0.673\n",
            "Epoch: 6 Loss: 0.6627\n",
            "Epoch: 7 Loss: 0.6605\n",
            "Epoch: 8 Loss: 0.6608\n",
            "Epoch: 9 Loss: 0.6601\n",
            "Epoch: 10 Loss: 0.6599\n",
            "Epoch: 11 Loss: 0.6597\n",
            "Epoch: 12 Loss: 0.6594\n",
            "Epoch: 13 Loss: 0.6591\n",
            "Epoch: 14 Loss: 0.6588\n",
            "Epoch: 15 Loss: 0.6584\n",
            "Epoch: 16 Loss: 0.6578\n",
            "Epoch: 17 Loss: 0.657\n",
            "Epoch: 18 Loss: 0.6567\n",
            "Epoch: 19 Loss: 0.6556\n",
            "Epoch: 20 Loss: 0.6544\n",
            "Epoch: 21 Loss: 0.6516\n",
            "Epoch: 22 Loss: 0.6469\n",
            "Epoch: 23 Loss: 0.6418\n",
            "Epoch: 24 Loss: 0.6354\n",
            "Epoch: 25 Loss: 0.627\n",
            "Epoch: 26 Loss: 0.6122\n",
            "Epoch: 27 Loss: 0.5966\n",
            "Epoch: 28 Loss: 0.582\n",
            "Epoch: 29 Loss: 0.5678\n",
            "Epoch: 30 Loss: 0.5541\n",
            "Epoch: 31 Loss: 0.5412\n",
            "Epoch: 32 Loss: 0.5317\n",
            "Epoch: 33 Loss: 0.5134\n",
            "Epoch: 34 Loss: 0.4957\n",
            "Epoch: 35 Loss: 0.4494\n",
            "Epoch: 36 Loss: 0.4391\n",
            "Epoch: 37 Loss: 0.431\n",
            "Epoch: 38 Loss: 0.4214\n",
            "Epoch: 39 Loss: 0.4094\n",
            "Epoch: 40 Loss: 0.4313\n",
            "Epoch: 41 Loss: 0.4125\n",
            "Epoch: 42 Loss: 0.4097\n",
            "Epoch: 43 Loss: 0.4074\n",
            "Epoch: 44 Loss: 0.4052\n",
            "Epoch: 45 Loss: 0.4206\n",
            "Epoch: 46 Loss: 0.4226\n",
            "Epoch: 47 Loss: 0.4126\n",
            "Epoch: 48 Loss: 0.4143\n",
            "Epoch: 49 Loss: 0.4228\n",
            "Epoch: 50 Loss: 0.4098\n",
            "Epoch: 51 Loss: 0.4123\n",
            "Epoch: 52 Loss: 0.4117\n",
            "Epoch: 53 Loss: 0.4124\n",
            "Epoch: 54 Loss: 0.4238\n",
            "Epoch: 55 Loss: 0.4402\n",
            "Epoch: 56 Loss: 0.4269\n",
            "Epoch: 57 Loss: 0.4089\n",
            "Epoch: 58 Loss: 0.4074\n",
            "Epoch: 59 Loss: 0.4016\n",
            "Epoch: 60 Loss: 0.3995\n",
            "Epoch: 61 Loss: 0.3998\n",
            "Epoch: 62 Loss: 0.4002\n",
            "Epoch: 63 Loss: 0.4017\n",
            "Epoch: 64 Loss: 0.3985\n",
            "Epoch: 65 Loss: 0.4001\n",
            "Epoch: 66 Loss: 0.398\n",
            "Epoch: 67 Loss: 0.3968\n",
            "Epoch: 68 Loss: 0.401\n",
            "Epoch: 69 Loss: 0.3965\n",
            "Epoch: 70 Loss: 0.3942\n",
            "Epoch: 71 Loss: 0.4\n",
            "Epoch: 72 Loss: 0.3983\n",
            "Epoch: 73 Loss: 0.4085\n",
            "Epoch: 74 Loss: 0.3967\n",
            "Epoch: 75 Loss: 0.3928\n",
            "Epoch: 76 Loss: 0.3927\n",
            "Epoch: 77 Loss: 0.3997\n",
            "Epoch: 78 Loss: 0.3945\n",
            "Epoch: 79 Loss: 0.3988\n",
            "Epoch: 80 Loss: 0.4092\n",
            "Epoch: 81 Loss: 0.3985\n",
            "Epoch: 82 Loss: 0.4012\n",
            "Epoch: 83 Loss: 0.4041\n",
            "Epoch: 84 Loss: 0.3991\n",
            "Epoch: 85 Loss: 0.3954\n",
            "Epoch: 86 Loss: 0.3968\n",
            "Epoch: 87 Loss: 0.392\n",
            "Epoch: 88 Loss: 0.3942\n",
            "Epoch: 89 Loss: 0.3967\n",
            "Epoch: 90 Loss: 0.3967\n",
            "Epoch: 91 Loss: 0.4013\n",
            "Epoch: 92 Loss: 0.4012\n",
            "Epoch: 93 Loss: 0.3999\n",
            "Epoch: 94 Loss: 0.3925\n",
            "Epoch: 95 Loss: 0.4011\n",
            "Epoch: 96 Loss: 0.3941\n",
            "Epoch: 97 Loss: 0.3922\n",
            "Epoch: 98 Loss: 0.3959\n",
            "Epoch: 99 Loss: 0.3937\n",
            "Epoch: 100 Loss: 0.3984\n",
            "Epoch: 101 Loss: 0.401\n",
            "Epoch: 102 Loss: 0.4016\n",
            "Epoch: 103 Loss: 0.4043\n",
            "Epoch: 104 Loss: 0.3938\n",
            "Epoch: 105 Loss: 0.4039\n",
            "Epoch: 106 Loss: 0.4119\n",
            "Epoch: 107 Loss: 0.4187\n",
            "Epoch: 108 Loss: 0.3915\n",
            "Epoch: 109 Loss: 0.3918\n",
            "Epoch: 110 Loss: 0.3903\n",
            "Epoch: 111 Loss: 0.39\n",
            "Epoch: 112 Loss: 0.3882\n",
            "Epoch: 113 Loss: 0.3894\n",
            "Epoch: 114 Loss: 0.3919\n",
            "Epoch: 115 Loss: 0.3922\n",
            "Epoch: 116 Loss: 0.3888\n",
            "Epoch: 117 Loss: 0.3916\n",
            "Epoch: 118 Loss: 0.3998\n",
            "Epoch: 119 Loss: 0.3919\n",
            "Epoch: 120 Loss: 0.3893\n",
            "Epoch: 121 Loss: 0.3898\n",
            "Epoch: 122 Loss: 0.3945\n",
            "Epoch: 123 Loss: 0.3884\n",
            "Epoch: 124 Loss: 0.3881\n",
            "Epoch: 125 Loss: 0.3876\n",
            "Epoch: 126 Loss: 0.3954\n",
            "Epoch: 127 Loss: 0.4151\n",
            "Epoch: 128 Loss: 0.3998\n",
            "Epoch: 129 Loss: 0.395\n",
            "Epoch: 130 Loss: 0.3921\n",
            "Epoch: 131 Loss: 0.3909\n",
            "Epoch: 132 Loss: 0.388\n",
            "Epoch: 133 Loss: 0.3879\n",
            "Epoch: 134 Loss: 0.3878\n",
            "Epoch: 135 Loss: 0.3937\n",
            "Epoch: 136 Loss: 0.3905\n",
            "Epoch: 137 Loss: 0.3906\n",
            "Epoch: 138 Loss: 0.3898\n",
            "Epoch: 139 Loss: 0.3875\n",
            "Epoch: 140 Loss: 0.3981\n",
            "Epoch: 141 Loss: 0.395\n",
            "Epoch: 142 Loss: 0.3974\n",
            "Epoch: 143 Loss: 0.3921\n",
            "Epoch: 144 Loss: 0.3931\n",
            "Epoch: 145 Loss: 0.4035\n",
            "Epoch: 146 Loss: 0.397\n",
            "Epoch: 147 Loss: 0.3936\n",
            "Epoch: 148 Loss: 0.3959\n",
            "Epoch: 149 Loss: 0.3912\n",
            "Epoch: 150 Loss: 0.3891\n",
            "Epoch: 151 Loss: 0.3861\n",
            "Epoch: 152 Loss: 0.3869\n",
            "Epoch: 153 Loss: 0.3839\n",
            "Epoch: 154 Loss: 0.3913\n",
            "Epoch: 155 Loss: 0.389\n",
            "Epoch: 156 Loss: 0.3852\n",
            "Epoch: 157 Loss: 0.3871\n",
            "Epoch: 158 Loss: 0.3856\n",
            "Epoch: 159 Loss: 0.3865\n",
            "Epoch: 160 Loss: 0.4044\n",
            "Epoch: 161 Loss: 0.4136\n",
            "Epoch: 162 Loss: 0.4044\n",
            "Epoch: 163 Loss: 0.4007\n",
            "Epoch: 164 Loss: 0.3891\n",
            "Epoch: 165 Loss: 0.3897\n",
            "Epoch: 166 Loss: 0.3869\n",
            "Epoch: 167 Loss: 0.3871\n",
            "Epoch: 168 Loss: 0.3857\n",
            "Epoch: 169 Loss: 0.3868\n",
            "Epoch: 170 Loss: 0.3849\n",
            "Epoch: 171 Loss: 0.3824\n",
            "Epoch: 172 Loss: 0.3821\n",
            "Epoch: 173 Loss: 0.3841\n",
            "Epoch: 174 Loss: 0.3898\n",
            "Epoch: 175 Loss: 0.3891\n",
            "Epoch: 176 Loss: 0.386\n",
            "Epoch: 177 Loss: 0.3824\n",
            "Epoch: 178 Loss: 0.3824\n",
            "Epoch: 179 Loss: 0.3814\n",
            "Epoch: 180 Loss: 0.3816\n",
            "Epoch: 181 Loss: 0.3843\n",
            "Epoch: 182 Loss: 0.3879\n",
            "Epoch: 183 Loss: 0.388\n",
            "Epoch: 184 Loss: 0.3861\n",
            "Epoch: 185 Loss: 0.3887\n",
            "Epoch: 186 Loss: 0.3852\n",
            "Epoch: 187 Loss: 0.3854\n",
            "Epoch: 188 Loss: 0.3916\n",
            "Epoch: 189 Loss: 0.3954\n",
            "Epoch: 190 Loss: 0.382\n",
            "Epoch: 191 Loss: 0.3803\n",
            "Epoch: 192 Loss: 0.3846\n",
            "Epoch: 193 Loss: 0.3929\n",
            "Epoch: 194 Loss: 0.3887\n",
            "Epoch: 195 Loss: 0.3867\n",
            "Epoch: 196 Loss: 0.3853\n",
            "Epoch: 197 Loss: 0.3829\n",
            "Epoch: 198 Loss: 0.3833\n",
            "Epoch: 199 Loss: 0.3838\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rU3-bAMS-D-I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "outputId": "3c843885-3acf-4f80-d4c7-5a15b164c590"
      },
      "source": [
        "# plot the printed losses:\n",
        "losses = np.array(losses)\n",
        "plt.plot(losses[1:],label='training loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "evaluate(net, validation_ds)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/container.py:117: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
            "  input = module(input)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Probability of detection: 0.9509439108496172\n",
            "Probability of false alarm: 0.09345775757213216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2VfHxWg7aL98",
        "colab_type": "text"
      },
      "source": [
        "## Save\n",
        "Don't forget to download"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cVQVmottKTu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "timestamp = datetime.now().strftime(\"%d-%m-%Y_%H-%M-%S\")\n",
        "torch.save(net.state_dict(), 'model_' + timestamp + '.pt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eomLQW_E0eaY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.listdir(validation_path)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}